- 4.1 Time slicing -
- 4.2 Customised subsamples +
- 4.2 Customised subsets
- 4.3 Bootstraps and rarefactions
- 4.4 Disparity metrics @@ -224,11 +224,11 @@
- Discrete time subsamples (or time-binning) using
method = discrete
- - Continuous time subsamples (or time-slicing) using
method = continuous
+ - Discrete time subsets (or time-binning) using
method = discrete
+ - Continuous time subsets (or time-slicing) using
method = continuous quantilevalues for the confidence interval levels (by default, the 50 and 95 quantiles are calculated)
@@ -723,23 +723,23 @@ pairwise(default): to compare each subsample in a pairwise manner
-referential: to compare each subsample to the first subsample
-sequential: to compare each subsample to the following subsample
-all: to compare all the subsamples together (like in analysis of variance)
+pairwise(default): to compare each subset in a pairwise manner
+referential: to compare each subset to the first subset
+sequential: to compare each subset to the following subset
+all: to compare all the subsets together (like in analysis of variance)- 4 Details of specific functions
- 4.1 Time slicing -
- 4.2 Customised subsamples +
- 4.2 Customised subsets
- 4.3 Bootstraps and rarefactions
- 4.4 Disparity metrics @@ -275,26 +275,26 @@
8.2 Classic analysis
4 Details of specific functionsdata(BeckLee_tree) ; data(BeckLee_ages)
4.1 Time slicing
-The function time.subsamples allows users to divide the matrix into different time subsamples or slices given a dated phylogeny that contains all the elements (i.e. taxa) from the matrix. Each subsample generated by this function will then contain all the elements present at a specific point in time or during a specific period in time.
-Two types of time subsamples can be performed by using the method option:
+The function time.subsets allows users to divide the matrix into different time subsets or slices given a dated phylogeny that contains all the elements (i.e. taxa) from the matrix. Each subset generated by this function will then contain all the elements present at a specific point in time or during a specific period in time.
+Two types of time subsets can be performed by using the method option:
-
For the time-slicing method details see Cooper and Guillerme (in prep.). For both methods, the function takes the time argument which can be a vector of numeric values for:
@@ -238,17 +238,17 @@ 4.1 Time slicing
Otherwise, the time argument can be set as a single numeric value for automatically generating a given number of equidistant time-bins/slices. Additionally, it is also possible to input a dataframe containing the first and last occurrence data (FAD/LAD) for taxa that span over a longer time than the given tips/nodes age, so taxa can appear in more than one time bin/slice.
Here is an example for method = discrete:
## Generating three time bins containing the taxa present every 40 Ma
-time.subsamples(data = BeckLee_mat50, tree = BeckLee_tree, method = "discrete",
+time.subsets(data = BeckLee_mat50, tree = BeckLee_tree, method = "discrete",
time = c(120, 80, 40, 0))
## ---- dispRity object ----
-## 3 discrete time subsamples for 50 elements:
+## 3 discrete time subsets for 50 elements:
## 120 - 80, 80 - 40, 40 - 0.
Note that we can also generate equivalent results by just telling the function that we want three time-bins as follow:
## Automatically generate three equal length bins:
-time.subsamples(data = BeckLee_mat50, tree = BeckLee_tree, method = "discrete",
+time.subsets(data = BeckLee_mat50, tree = BeckLee_tree, method = "discrete",
time = 3)
## ---- dispRity object ----
-## 3 discrete time subsamples for 50 elements:
+## 3 discrete time subsets for 50 elements:
## 133.51104 - 89.00736, 89.00736 - 44.50368, 44.50368 - 0.
In this example, the taxa were split inside each time-bin according to their age. However, the taxa here are considered as single points in time. It is totally possible that some taxa could have had longer longevity and that they exist in multiple time bins. In this case, it is possible to include them in more than one bin by providing a table of first and last occurrence dates (FAD/LAD). This table should have the taxa names as row names and two columns for respectively the first and last occurrence age:
## Displaying the table of first and last occurrence dates for each taxa
@@ -261,10 +261,10 @@ 4.1 Time slicing
## Mimotona 61.6 59.2
## Notharctus 50.2 47.0
## Generating time bins including taxa that might span between them
-time.subsamples(data = BeckLee_mat50, tree = BeckLee_tree, method = "discrete",
+time.subsets(data = BeckLee_mat50, tree = BeckLee_tree, method = "discrete",
time = c(120, 80, 40, 0), FADLAD = BeckLee_ages)
## ---- dispRity object ----
-## 3 discrete time subsamples for 50 elements:
+## 3 discrete time subsets for 50 elements:
## 120 - 80, 80 - 40, 40 - 0.
When using this method, the oldest boundary of the first bin (or the first slice, see below) is automatically generated as the root age plus 1% of the tree length, as long as at least three elements/taxa are present at that point in time. The algorithm adds an extra 1% tree length until reaching the required minimum of three elements. It is also possible to include nodes in each bin by using inc.nodes = TRUE and providing a matrix that contains the ordinated distance among tips and nodes.
For the time-slicing method (method = continuous), the idea is fairly similar. This option, however, requires a matrix that contains the ordinated distance among taxa and nodes and an extra argument describing the assumed evolutionary model (via the model argument). This model argument is used when the time slice occurs along a branch of the tree rather than on a tip or a node, meaning that a decision must be made about what the value for the branch should be. The model can be one of the following:
@@ -280,30 +280,30 @@ 4.1 Time slicing
Note that the four first models are “fixed”: the selected data is always either the one of the descendant or the ancestor. The two last models are “probabilistic”: the data from both the descendant and the ancestor is used with an associate probability. These later models perform better when bootstrapped, effectively approximating the “intermediate” state between and the ancestor and the descendants.
## Generating four time slices every 40 million years under a model of proximity evolution
-time.subsamples(data = BeckLee_mat99, tree = BeckLee_tree,
+time.subsets(data = BeckLee_mat99, tree = BeckLee_tree,
method = "continuous", model = "proximity", time = c(120, 80, 40, 0),
FADLAD = BeckLee_ages)
## ---- dispRity object ----
-## 4 continuous (proximity) time subsamples for 99 elements:
+## 4 continuous (proximity) time subsets for 99 elements:
## 120, 80, 40, 0.
## Generating four time slices automatically
-time.subsamples(data = BeckLee_mat99, tree = BeckLee_tree,
+time.subsets(data = BeckLee_mat99, tree = BeckLee_tree,
method = "continuous", model = "proximity", time = 4, FADLAD = BeckLee_ages)
## ---- dispRity object ----
-## 4 continuous (proximity) time subsamples for 99 elements:
+## 4 continuous (proximity) time subsets for 99 elements:
## 133.51104, 89.00736, 44.50368, 0.
-
-4.2 Customised subsamples
-Another way of separating elements into different categories is to use customised subsamples as briefly explained above. This function simply takes the list of elements to put in each group (whether they are the actual element names or their position in the matrix).
+
+4.2 Customised subsets
+Another way of separating elements into different categories is to use customised subsets as briefly explained above. This function simply takes the list of elements to put in each group (whether they are the actual element names or their position in the matrix).
## Creating the two groups as a list
mammal_groups <- list("crown" = c(16, 19:41, 45:50),
"stem" = c(1:15, 17:18, 42:44))
## Separating the dataset into two different groups
-custom.subsamples(BeckLee_mat50, group = mammal_groups)
+custom.subsets(BeckLee_mat50, group = mammal_groups)
## ---- dispRity object ----
-## 2 customised subsamples for 50 elements:
+## 2 customised subsets for 50 elements:
## crown, stem.
Elements can easily be assigned to different groups if necessary!
## Creating the three groups as a list
@@ -330,7 +330,7 @@ 4.3 Bootstraps and rarefactions
This function also allows users to rarefy the data using the rarefaction argument. Rarefaction allows users to limit the number of elements to be drawn at each bootstrap replication. This is useful if, for example, one is interested in looking at the effect of reducing the number of elements on the results of an analysis.
-This can be achieved by using the rarefaction option that draws only n-x at each bootstrap replicate (where x is the number of elements not sampled). The default argument is FALSE but it can be set to TRUE to fully rarefy the data (i.e. remove x elements for the number of pseudo-replicates, where x varies from the maximum number of elements present in each subsample to a minimum of three elements). It can also be set to one or more numeric values to only rarefy to the corresponding number of elements.
+This can be achieved by using the rarefaction option that draws only n-x at each bootstrap replicate (where x is the number of elements not sampled). The default argument is FALSE but it can be set to TRUE to fully rarefy the data (i.e. remove x elements for the number of pseudo-replicates, where x varies from the maximum number of elements present in each subset to a minimum of three elements). It can also be set to one or more numeric values to only rarefy to the corresponding number of elements.
## Bootstrapping with the full rarefaction
boot.matrix(BeckLee_mat50, bootstraps = 20, rarefaction = TRUE)
## ---- dispRity object ----
@@ -352,27 +352,27 @@ 4.3 Bootstraps and rarefactions## ---- dispRity object ----
## 50 elements with 10 dimensions.
## Data was bootstrapped 100 times (method:"full").
-Of course, one could directly supply the subsamples generated above (using time.subsamples or custom.subsamples) to this function.
-## Creating subsamples of crown and stem mammals
-crown_stem <- custom.subsamples(BeckLee_mat50,
+Of course, one could directly supply the subsets generated above (using time.subsets or custom.subsets) to this function.
+## Creating subsets of crown and stem mammals
+crown_stem <- custom.subsets(BeckLee_mat50,
group = list("crown" = c(16, 19:41, 45:50),
"stem" = c(1:15, 17:18, 42:44)))
## Bootstrapping and rarefying these groups
boot.matrix(crown_stem, bootstraps = 200, rarefaction = TRUE)
## ---- dispRity object ----
-## 2 customised subsamples for 50 elements with 48 dimensions:
+## 2 customised subsets for 50 elements with 48 dimensions:
## crown, stem.
## Data was bootstrapped 200 times (method:"full") and fully rarefied.
-## Creating time slice subsamples
-time_slices <- time.subsamples(data = BeckLee_mat99, tree = BeckLee_tree,
+## Creating time slice subsets
+time_slices <- time.subsets(data = BeckLee_mat99, tree = BeckLee_tree,
method = "continuous", model = "proximity",
time = c(120, 80, 40, 0),
FADLAD = BeckLee_ages)
-## Bootstrapping the time slice subsamples
+## Bootstrapping the time slice subsets
boot.matrix(time_slices, bootstraps = 100)
## ---- dispRity object ----
-## 4 continuous (proximity) time subsamples for 99 elements with 97 dimensions:
+## 4 continuous (proximity) time subsets for 99 elements with 97 dimensions:
## 120, 80, 40, 0.
## Data was bootstrapped 100 times (method:"full").
@@ -474,18 +474,18 @@ 4.4.3 Metrics in the dispRi
## Measuring disparity as the standard deviation of all the values of the
## ordinated matrix (dimension-level 1 function).
summary(dispRity(BeckLee_mat50, metric = sd))
-## subsamples n obs
-## 1 1 50 0.201
+## subsets n obs
+## 1 1 50 0.201
## Measuring disparity as the standard deviation of the variance of each axis of
## the ordinated matrix (dimension-level 1 and 2 functions).
summary(dispRity(BeckLee_mat50, metric = c(sd, variances)))
-## subsamples n obs
-## 1 1 50 0.028
+## subsets n obs
+## 1 1 50 0.028
## Measuring disparity as the standard deviation of the variance of each axis of
## the variance covariance matrix (dimension-level 1, 2 and 3 functions).
summary(dispRity(BeckLee_mat50, metric = c(sd, variances, var)), round = 10)
-## subsamples n obs
-## 1 1 50 0.0001025857
+## subsets n obs
+## 1 1 50 0.0001025857
Note that the order of each function in the metric argument does not matter, the dispRity function will automatically detect the function dimension-levels (using make.metric) and apply them to the data in decreasing order (dimension-level 3 > 2 > 1).
## Disparity as the standard deviation of the variance of each axis of the
## variance covariance matrix:
@@ -498,8 +498,8 @@ 4.4.3 Metrics in the dispRi
## Both ways output the same disparity values:
disparity1 == disparity2
-## subsamples n obs
-## [1,] TRUE TRUE TRUE
+## subsets n obs
+## [1,] TRUE TRUE TRUE
In these examples, we considered disparity to be a single value. For example, in the previous example, we defined disparity as the standard deviation of the variances of each column of the variance/covariance matrix (metric = c(variances, sd, var)). It is, however, possible to calculate disparity as a distribution.
@@ -582,19 +582,19 @@ 4.4.6.1 Volumes and surface metri
The functions ellipse.volume, convhull.surface and convhull.volume all measure the surface or the volume of the ordinated space occupied:
## Calculating the ellipsoid volume
summary(dispRity(dummy_space, metric = ellipse.volume))
-## subsamples n obs
-## 1 1 10 257.8
+## subsets n obs
+## 1 1 10 257.8
-Because there is only one subsample (i.e. one matrix) in the dispRity object, this operation is the equivalent of ellipse.volume(dummy_space) (with rounding).
+Because there is only one subset (i.e. one matrix) in the dispRity object, this operation is the equivalent of ellipse.volume(dummy_space) (with rounding).
## Calculating the convex hull surface
summary(dispRity(dummy_space, metric = convhull.surface))
-## subsamples n obs
-## 1 1 10 11.91
+## subsets n obs
+## 1 1 10 11.91
## Calculating the convex hull volume
summary(dispRity(dummy_space, metric = convhull.volume))
-## subsamples n obs
-## 1 1 10 1.031
+## subsets n obs
+## 1 1 10 1.031
The convex hull functions make a (good) estimation of the multidimensional properties of the ordinated space.
Cautionary note: measuring volumes in a high number of dimensions can be strongly affected by the curse of dimensionality that often results in near 0 disparity values.
@@ -609,34 +609,34 @@ 4.4.6.2 Ranges, variances and dia
## [1] 2.430909 3.726481 2.908329 2.735739 1.588603
## Calculating disparity as the distribution of these ranges
summary(dispRity(dummy_space, metric = ranges))
-## subsamples n obs.median 2.5% 25% 75% 97.5%
-## 1 1 10 2.736 1.673 2.431 2.908 3.645
+## subsets n obs.median 2.5% 25% 75% 97.5%
+## 1 1 10 2.736 1.673 2.431 2.908 3.645
## Calculating disparity as the sum and the product of these ranges
summary(dispRity(dummy_space, metric = c(sum, ranges)))
-## subsamples n obs
-## 1 1 10 13.39
+## subsets n obs
+## 1 1 10 13.39
summary(dispRity(dummy_space, metric = c(prod, ranges)))
-## subsamples n obs
-## 1 1 10 114.5
+## subsets n obs
+## 1 1 10 114.5
## Calculating the variances of each dimension in the ordinated space
variances(dummy_space)
## [1] 0.6093144 1.1438620 0.9131859 0.6537768 0.3549372
## Calculating disparity as the distribution of these variances
summary(dispRity(dummy_space, metric = variances))
-## subsamples n obs.median 2.5% 25% 75% 97.5%
-## 1 1 10 0.654 0.38 0.609 0.913 1.121
+## subsets n obs.median 2.5% 25% 75% 97.5%
+## 1 1 10 0.654 0.38 0.609 0.913 1.121
## Calculating disparity as the sum and the product of these variances
summary(dispRity(dummy_space, metric = c(sum, variances)))
-## subsamples n obs
-## 1 1 10 3.675
+## subsets n obs
+## 1 1 10 3.675
summary(dispRity(dummy_space, metric = c(prod, variances)))
-## subsamples n obs
-## 1 1 10 0.148
+## subsets n obs
+## 1 1 10 0.148
The diagonal function measures the multidimensional diagonal of the whole space (i.e. in our case the longest Euclidean distance in our five dimensional space):
## Calculating the ordinated space's diagonal
summary(dispRity(dummy_space, metric = diagonal))
-## subsamples n obs
-## 1 1 10 3.659
+## subsets n obs
+## 1 1 10 3.659
This metric is only a Euclidean diagonal (mathematically valid) if the dimensions within the space are all orthogonal!
@@ -646,21 +646,21 @@ 4.4.6.3 Centroids metric
The centroids metric allows users to measure the position of the different elements compared to a fixed point in the ordinated space. By default, this function measures the distance between each element and their centroid (centre point):
## The distribution of the distances between each element and their centroid
summary(dispRity(dummy_space, metric = centroids))
-## subsamples n obs.median 2.5% 25% 75% 97.5%
-## 1 1 10 1.435 0.788 1.267 1.993 3.167
+## subsets n obs.median 2.5% 25% 75% 97.5%
+## 1 1 10 1.435 0.788 1.267 1.993 3.167
## Disparity as the median value of these distances
summary(dispRity(dummy_space, metric = c(median, centroids)))
-## subsamples n obs
-## 1 1 10 1.435
+## subsets n obs
+## 1 1 10 1.435
It is however possible to fix the coordinates of the centroid to a specific point in the ordinated space, as long as it has the correct number of dimensions:
## The distance between each element and the origin of the ordinated space
summary(dispRity(dummy_space, metric = centroids, centroid = c(0,0,0,0,0)))
-## subsamples n obs.median 2.5% 25% 75% 97.5%
-## 1 1 10 1.487 0.785 1.2 2.044 3.176
+## subsets n obs.median 2.5% 25% 75% 97.5%
+## 1 1 10 1.487 0.785 1.2 2.044 3.176
## Disparity as the distance between each element and a specific point in space
summary(dispRity(dummy_space, metric = centroids, centroid = c(0,1,2,3,4)))
-## subsamples n obs.median 2.5% 25% 75% 97.5%
-## 1 1 10 5.489 4.293 5.032 6.155 6.957
+## subsets n obs.median 2.5% 25% 75% 97.5%
+## 1 1 10 5.489 4.293 5.032 6.155 6.957
@@ -671,7 +671,7 @@ 4.5 Summarising dispRity data (pl
4.5.1 Summarising dispRity data
This function is an S3 function (summary.dispRity) allowing users to summarise the content of dispRity objects that contain disparity calculations.
## Example data from previous sections
-crown_stem <- custom.subsamples(BeckLee_mat50,
+crown_stem <- custom.subsets(BeckLee_mat50,
group = list("crown" = c(16, 19:41, 45:50),
"stem" = c(1:15, 17:18, 42:44)))
## Bootstrapping and rarefying these groups
@@ -679,40 +679,40 @@ 4.5.1 Summarising dispRity<
## Calculate disparity
disparity_crown_stem <- dispRity(boot_crown_stem, metric = c(sum, variances))
-## Creating time slice subsamples
-time_slices <- time.subsamples(data = BeckLee_mat99, tree = BeckLee_tree,
+## Creating time slice subsets
+time_slices <- time.subsets(data = BeckLee_mat99, tree = BeckLee_tree,
method = "continuous", model = "proximity", time = c(120, 80, 40, 0),
FADLAD = BeckLee_ages)
-## Bootstrapping the time slice subsamples
+## Bootstrapping the time slice subsets
boot_time_slices <- boot.matrix(time_slices, bootstraps = 100)
## Calculate disparity
disparity_time_slices <- dispRity(boot_time_slices, metric = c(sum, variances))
-## Creating time bin subsamples
-time_bins <- time.subsamples(data = BeckLee_mat99, tree = BeckLee_tree,
+## Creating time bin subsets
+time_bins <- time.subsets(data = BeckLee_mat99, tree = BeckLee_tree,
method = "discrete", time = c(120, 80, 40, 0), FADLAD = BeckLee_ages,
inc.nodes = TRUE)
-## Bootstrapping the time bin subsamples
+## Bootstrapping the time bin subsets
boot_time_bins <- boot.matrix(time_bins, bootstraps = 100)
## Calculate disparity
disparity_time_bins <- dispRity(boot_time_bins, metric = c(sum, variances))
These objects are easy to summarise as follows:
## Default summary
summary(disparity_time_slices)
-## subsamples n obs bs.median 2.5% 25% 75% 97.5%
-## 1 120 6 2.899 2.399 1.749 2.202 2.642 2.794
-## 2 80 22 3.558 3.414 3.127 3.314 3.463 3.557
-## 3 40 15 4.032 3.764 3.569 3.694 3.842 3.954
-## 4 0 10 4.055 3.661 3.282 3.564 3.771 3.927
-Information about the number of elements in each subsample and the observed (i.e. non-bootstrapped) disparity are also calculated. This is specifically handy when rarefying the data for example:
+## subsets n obs bs.median 2.5% 25% 75% 97.5%
+## 1 120 6 2.899 2.399 1.749 2.202 2.642 2.794
+## 2 80 22 3.558 3.414 3.127 3.314 3.463 3.557
+## 3 40 15 4.032 3.764 3.569 3.694 3.842 3.954
+## 4 0 10 4.055 3.661 3.282 3.564 3.771 3.927
+Information about the number of elements in each subset and the observed (i.e. non-bootstrapped) disparity are also calculated. This is specifically handy when rarefying the data for example:
head(summary(disparity_crown_stem))
-## subsamples n obs bs.median 2.5% 25% 75% 97.5%
-## 1 crown 30 1.995 1.930 1.866 1.909 1.950 1.970
-## 2 crown 29 NA 1.932 1.871 1.915 1.953 1.974
-## 3 crown 28 NA 1.926 1.852 1.903 1.942 1.978
-## 4 crown 27 NA 1.935 1.860 1.908 1.954 1.982
-## 5 crown 26 NA 1.931 1.859 1.908 1.953 1.977
-## 6 crown 25 NA 1.933 1.855 1.909 1.954 1.978
+## subsets n obs bs.median 2.5% 25% 75% 97.5%
+## 1 crown 30 1.995 1.930 1.866 1.909 1.950 1.970
+## 2 crown 29 NA 1.932 1.871 1.915 1.953 1.974
+## 3 crown 28 NA 1.926 1.852 1.903 1.942 1.978
+## 4 crown 27 NA 1.935 1.860 1.908 1.954 1.982
+## 5 crown 26 NA 1.931 1.859 1.908 1.953 1.977
+## 6 crown 25 NA 1.933 1.855 1.909 1.954 1.978
The summary functions can also take various options such as:
4.5.1 Summarising dispRity<
These options can easily be changed from the defaults as follows:
## Same as above but using the 88th quantile and the standard deviation as the summary
summary(disparity_time_slices, quantile = 88, cent.tend = sd)
-## subsamples n obs bs.sd 6% 94%
-## 1 120 6 2.899 0.293 1.917 2.762
-## 2 80 22 3.558 0.113 3.208 3.542
-## 3 40 15 4.032 0.109 3.612 3.918
-## 4 0 10 4.055 0.169 3.387 3.882
+## subsets n obs bs.sd 6% 94%
+## 1 120 6 2.899 0.293 1.917 2.762
+## 2 80 22 3.558 0.113 3.208 3.542
+## 3 40 15 4.032 0.109 3.612 3.918
+## 4 0 10 4.055 0.169 3.387 3.882
## Printing the details of the object and rounding the values to the 5th decimal place
summary(disparity_time_slices, recall = TRUE, rounding = 5)
## ---- dispRity object ----
-## 4 continuous (proximity) time subsamples for 99 elements with 97 dimensions:
+## 4 continuous (proximity) time subsets for 99 elements with 97 dimensions:
## 120, 80, 40, 0.
## Data was bootstrapped 100 times (method:"full").
## Disparity was calculated as: c(sum, variances).
-## subsamples n obs bs.median 2.5% 25% 75% 97.5%
-## 1 120 6 2.89926 2.39864 1.74855 2.20230 2.64153 2.79428
-## 2 80 22 3.55782 3.41432 3.12737 3.31353 3.46313 3.55661
-## 3 40 15 4.03191 3.76393 3.56890 3.69427 3.84171 3.95368
-## 4 0 10 4.05457 3.66131 3.28211 3.56431 3.77130 3.92706
+## subsets n obs bs.median 2.5% 25% 75% 97.5%
+## 1 120 6 2.89926 2.39864 1.74855 2.20230 2.64153 2.79428
+## 2 80 22 3.55782 3.41432 3.12737 3.31353 3.46313 3.55661
+## 3 40 15 4.03191 3.76393 3.56890 3.69427 3.84171 3.95368
+## 4 0 10 4.05457 3.66131 3.28211 3.56431 3.77130 3.92706
Note that the summary table is a data.frame, hence it is as easy to modify as any dataframe using dplyr. You can also export it in csv format using write.csv or write_csv or even directly export into LaTeX format using the following;
## Loading the xtable package
require(xtable)
@@ -755,9 +755,9 @@ 4.5.2 Plotting dispRitybox, lines, and polygons to display discrete disparity results in respectively a boxplot, confidence interval lines, and confidence interval polygons.
-This argument can be left empty. In this case, the algorithm will automatically detect the type of subsamples from the dispRity object and plot accordingly.
+This argument can be left empty. In this case, the algorithm will automatically detect the type of subsets from the dispRity object and plot accordingly.
-It is also possible to display the number of elements in each subsample (as a horizontal dotted line) using the option elements = TRUE. Additionally, when the data is rarefied, one can indicate which level of rarefaction to display (i.e. only display the results for a certain number of elements) by using the rarefaction argument.
+It is also possible to display the number of elements in each subset (as a horizontal dotted line) using the option elements = TRUE. Additionally, when the data is rarefied, one can indicate which level of rarefaction to display (i.e. only display the results for a certain number of elements) by using the rarefaction argument.
## Graphical parameters
op <- par(mfrow = c(2, 2), bty = "n")
@@ -799,7 +799,7 @@ 4.5.2 Plotting dispRityplot(disparity_time_slices, quantile = c(seq(from = 10, to = 100, by = 10)),
cent.tend = sd, type = "c", elements = TRUE, col = c("black", rainbow(10)),
- ylab = c("Disparity", "Diversity"), time.subsamples = FALSE,
+ ylab = c("Disparity", "Diversity"), time.subsets = FALSE,
xlab = "Time (in in units from past to present)", observed = TRUE,
main = "Many more options...")
@@ -836,12 +836,12 @@ 4.6 Testing disparity hypotheses<
The dispRity package allows users to apply statistical tests to the calculated disparity to test various hypotheses. The function test.dispRity works in a similar way to the dispRity function: it takes a dispRity object, a test and a comparisons argument.
The comparisons argument indicates the way the test should be applied to the data:
-
-It is also possible to input a list of pairs of numeric values or characters matching the subsample names to create personalised tests. Some other tests implemented in dispRity such as the dispRity::null.test have a specific way they are applied to the data and therefore ignore the comparisons argument.
+It is also possible to input a list of pairs of numeric values or characters matching the subset names to create personalised tests. Some other tests implemented in dispRity such as the dispRity::null.test have a specific way they are applied to the data and therefore ignore the comparisons argument.
The test argument can be any statistical or non-statistical test to apply to the disparity object. It can be a common statistical test function (e.g. stats::t.test), a function implemented in dispRity (e.g. see ?null.test) or any function defined by the user.
This function also allows users to correct for Type I error inflation (false positives) when using multiple comparisons via the correction argument. This argument can be empty (no correction applied) or can contain one of the corrections from the stats::p.adjust function (see ?p.adjust).
Note that the test.dispRity algorithm deals with some classical test outputs (h.test, lm and numeric vector) and summarises the test output. It is, however, possible to get the full detailed output by using the options details = TRUE.
@@ -911,11 +911,11 @@ 4.6 Testing disparity hypotheses<
## This will inflate Type I error!
##
## Call:
-## test(formula = data ~ subsamples, data = data)
+## test(formula = data ~ subsets, data = data)
##
## Coefficients:
-## (Intercept) subsamples120 subsamples40 subsamples80
-## 3.6517 -1.2627 0.1145 -0.2634
+## (Intercept) subsets120 subsets40 subsets80
+## 3.6517 -1.2627 0.1145 -0.2634
## The output is a regular `lm` output
class(slice_lm)
## [1] "lm"
@@ -923,18 +923,18 @@ 4.6 Testing disparity hypotheses<
summary(slice_lm)
##
## Call:
-## test(formula = data ~ subsamples, data = data)
+## test(formula = data ~ subsets, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.91282 -0.08285 0.00993 0.10745 0.56989
##
## Coefficients:
-## Estimate Std. Error t value Pr(>|t|)
-## (Intercept) 3.65169 0.01865 195.810 < 2e-16 ***
-## subsamples120 -1.26273 0.02637 -47.878 < 2e-16 ***
-## subsamples40 0.11452 0.02637 4.342 1.79e-05 ***
-## subsamples80 -0.26339 0.02637 -9.987 < 2e-16 ***
+## Estimate Std. Error t value Pr(>|t|)
+## (Intercept) 3.65169 0.01865 195.810 < 2e-16 ***
+## subsets120 -1.26273 0.02637 -47.878 < 2e-16 ***
+## subsets40 0.11452 0.02637 4.342 1.79e-05 ***
+## subsets80 -0.26339 0.02637 -9.987 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
@@ -956,18 +956,18 @@ 4.7 Disparity as a distribution## Summarising both disparity measurements:
## The distributions:
summary(disparity_centroids)
-## subsamples n obs.median bs.median 2.5% 25% 75% 97.5%
-## 1 120 6 1.536 1.375 0.864 1.187 1.611 1.886
-## 2 80 22 1.871 1.807 1.481 1.681 1.904 2.075
-## 3 40 15 1.943 1.885 1.529 1.777 1.989 2.097
-## 4 0 10 1.910 1.802 1.463 1.687 1.960 2.095
+## subsets n obs.median bs.median 2.5% 25% 75% 97.5%
+## 1 120 6 1.536 1.375 0.864 1.187 1.611 1.886
+## 2 80 22 1.871 1.807 1.481 1.681 1.904 2.075
+## 3 40 15 1.943 1.885 1.529 1.777 1.989 2.097
+## 4 0 10 1.910 1.802 1.463 1.687 1.960 2.095
## The summary of the distributions (as median)
summary(disparity_centroids_median)
-## subsamples n obs bs.median 2.5% 25% 75% 97.5%
-## 1 120 6 1.536 1.313 0.822 1.260 1.464 1.592
-## 2 80 22 1.871 1.803 1.700 1.774 1.830 1.864
-## 3 40 15 1.943 1.887 1.764 1.847 1.928 1.968
-## 4 0 10 1.910 1.814 1.593 1.773 1.844 1.917
+## subsets n obs bs.median 2.5% 25% 75% 97.5%
+## 1 120 6 1.536 1.313 0.822 1.260 1.464 1.592
+## 2 80 22 1.871 1.803 1.700 1.774 1.830 1.864
+## 3 40 15 1.943 1.887 1.764 1.847 1.928 1.968
+## 4 0 10 1.910 1.814 1.593 1.773 1.844 1.917
4.1 Time slicing
-The function time.subsamples allows users to divide the matrix into different time subsamples or slices given a dated phylogeny that contains all the elements (i.e. taxa) from the matrix. Each subsample generated by this function will then contain all the elements present at a specific point in time or during a specific period in time.
Two types of time subsamples can be performed by using the method option:
The function time.subsets allows users to divide the matrix into different time subsets or slices given a dated phylogeny that contains all the elements (i.e. taxa) from the matrix. Each subset generated by this function will then contain all the elements present at a specific point in time or during a specific period in time.
Two types of time subsets can be performed by using the method option:
-
-
For the time-slicing method details see Cooper and Guillerme (in prep.). For both methods, the function takes the time argument which can be a vector of numeric values for:
-
@@ -238,17 +238,17 @@
4.1 Time slicing
Otherwise, the time argument can be set as a single numeric value for automatically generating a given number of equidistant time-bins/slices. Additionally, it is also possible to input a dataframe containing the first and last occurrence data (FAD/LAD) for taxa that span over a longer time than the given tips/nodes age, so taxa can appear in more than one time bin/slice.
Here is an example for method = discrete:
## Generating three time bins containing the taxa present every 40 Ma
-time.subsamples(data = BeckLee_mat50, tree = BeckLee_tree, method = "discrete",
+time.subsets(data = BeckLee_mat50, tree = BeckLee_tree, method = "discrete",
time = c(120, 80, 40, 0))## ---- dispRity object ----
-## 3 discrete time subsamples for 50 elements:
+## 3 discrete time subsets for 50 elements:
## 120 - 80, 80 - 40, 40 - 0.Note that we can also generate equivalent results by just telling the function that we want three time-bins as follow:
## Automatically generate three equal length bins:
-time.subsamples(data = BeckLee_mat50, tree = BeckLee_tree, method = "discrete",
+time.subsets(data = BeckLee_mat50, tree = BeckLee_tree, method = "discrete",
time = 3)## ---- dispRity object ----
-## 3 discrete time subsamples for 50 elements:
+## 3 discrete time subsets for 50 elements:
## 133.51104 - 89.00736, 89.00736 - 44.50368, 44.50368 - 0.In this example, the taxa were split inside each time-bin according to their age. However, the taxa here are considered as single points in time. It is totally possible that some taxa could have had longer longevity and that they exist in multiple time bins. In this case, it is possible to include them in more than one bin by providing a table of first and last occurrence dates (FAD/LAD). This table should have the taxa names as row names and two columns for respectively the first and last occurrence age:
## Displaying the table of first and last occurrence dates for each taxa
@@ -261,10 +261,10 @@ 4.1 Time slicing
## Mimotona 61.6 59.2
## Notharctus 50.2 47.0## Generating time bins including taxa that might span between them
-time.subsamples(data = BeckLee_mat50, tree = BeckLee_tree, method = "discrete",
+time.subsets(data = BeckLee_mat50, tree = BeckLee_tree, method = "discrete",
time = c(120, 80, 40, 0), FADLAD = BeckLee_ages)## ---- dispRity object ----
-## 3 discrete time subsamples for 50 elements:
+## 3 discrete time subsets for 50 elements:
## 120 - 80, 80 - 40, 40 - 0.When using this method, the oldest boundary of the first bin (or the first slice, see below) is automatically generated as the root age plus 1% of the tree length, as long as at least three elements/taxa are present at that point in time. The algorithm adds an extra 1% tree length until reaching the required minimum of three elements. It is also possible to include nodes in each bin by using inc.nodes = TRUE and providing a matrix that contains the ordinated distance among tips and nodes.
For the time-slicing method (method = continuous), the idea is fairly similar. This option, however, requires a matrix that contains the ordinated distance among taxa and nodes and an extra argument describing the assumed evolutionary model (via the model argument). This model argument is used when the time slice occurs along a branch of the tree rather than on a tip or a node, meaning that a decision must be made about what the value for the branch should be. The model can be one of the following:
4.1 Time slicing
Note that the four first models are “fixed”: the selected data is always either the one of the descendant or the ancestor. The two last models are “probabilistic”: the data from both the descendant and the ancestor is used with an associate probability. These later models perform better when bootstrapped, effectively approximating the “intermediate” state between and the ancestor and the descendants.
## Generating four time slices every 40 million years under a model of proximity evolution
-time.subsamples(data = BeckLee_mat99, tree = BeckLee_tree,
+time.subsets(data = BeckLee_mat99, tree = BeckLee_tree,
method = "continuous", model = "proximity", time = c(120, 80, 40, 0),
FADLAD = BeckLee_ages)## ---- dispRity object ----
-## 4 continuous (proximity) time subsamples for 99 elements:
+## 4 continuous (proximity) time subsets for 99 elements:
## 120, 80, 40, 0.## Generating four time slices automatically
-time.subsamples(data = BeckLee_mat99, tree = BeckLee_tree,
+time.subsets(data = BeckLee_mat99, tree = BeckLee_tree,
method = "continuous", model = "proximity", time = 4, FADLAD = BeckLee_ages)## ---- dispRity object ----
-## 4 continuous (proximity) time subsamples for 99 elements:
+## 4 continuous (proximity) time subsets for 99 elements:
## 133.51104, 89.00736, 44.50368, 0.
-
@@ -671,7 +671,7 @@ 4.2 Customised subsamples
-Another way of separating elements into different categories is to use customised subsamples as briefly explained above. This function simply takes the list of elements to put in each group (whether they are the actual element names or their position in the matrix).
+
+
+custom.subsets(BeckLee_mat50, group = mammal_groups)
4.2 Customised subsets
+Another way of separating elements into different categories is to use customised subsets as briefly explained above. This function simply takes the list of elements to put in each group (whether they are the actual element names or their position in the matrix).
## Creating the two groups as a list
mammal_groups <- list("crown" = c(16, 19:41, 45:50),
"stem" = c(1:15, 17:18, 42:44))
## Separating the dataset into two different groups
-custom.subsamples(BeckLee_mat50, group = mammal_groups)## ---- dispRity object ----
-## 2 customised subsamples for 50 elements:
+## 2 customised subsets for 50 elements:
## crown, stem.Elements can easily be assigned to different groups if necessary!
## Creating the three groups as a list
@@ -330,7 +330,7 @@ 4.3 Bootstraps and rarefactions
This function also allows users to rarefy the data using the rarefaction argument. Rarefaction allows users to limit the number of elements to be drawn at each bootstrap replication. This is useful if, for example, one is interested in looking at the effect of reducing the number of elements on the results of an analysis.
This can be achieved by using the rarefaction option that draws only n-x at each bootstrap replicate (where x is the number of elements not sampled). The default argument is FALSE but it can be set to TRUE to fully rarefy the data (i.e. remove x elements for the number of pseudo-replicates, where x varies from the maximum number of elements present in each subsample to a minimum of three elements). It can also be set to one or more numeric values to only rarefy to the corresponding number of elements.
This can be achieved by using the rarefaction option that draws only n-x at each bootstrap replicate (where x is the number of elements not sampled). The default argument is FALSE but it can be set to TRUE to fully rarefy the data (i.e. remove x elements for the number of pseudo-replicates, where x varies from the maximum number of elements present in each subset to a minimum of three elements). It can also be set to one or more numeric values to only rarefy to the corresponding number of elements.
## Bootstrapping with the full rarefaction
boot.matrix(BeckLee_mat50, bootstraps = 20, rarefaction = TRUE)## ---- dispRity object ----
@@ -352,27 +352,27 @@ 4.3 Bootstraps and rarefactions## ---- dispRity object ----
## 50 elements with 10 dimensions.
## Data was bootstrapped 100 times (method:"full").
Of course, one could directly supply the subsamples generated above (using time.subsamples or custom.subsamples) to this function.
## Creating subsamples of crown and stem mammals
-crown_stem <- custom.subsamples(BeckLee_mat50,
+Of course, one could directly supply the subsets generated above (using time.subsets or custom.subsets) to this function.
+## Creating subsets of crown and stem mammals
+crown_stem <- custom.subsets(BeckLee_mat50,
group = list("crown" = c(16, 19:41, 45:50),
"stem" = c(1:15, 17:18, 42:44)))
## Bootstrapping and rarefying these groups
boot.matrix(crown_stem, bootstraps = 200, rarefaction = TRUE)
## ---- dispRity object ----
-## 2 customised subsamples for 50 elements with 48 dimensions:
+## 2 customised subsets for 50 elements with 48 dimensions:
## crown, stem.
## Data was bootstrapped 200 times (method:"full") and fully rarefied.
-## Creating time slice subsamples
-time_slices <- time.subsamples(data = BeckLee_mat99, tree = BeckLee_tree,
+## Creating time slice subsets
+time_slices <- time.subsets(data = BeckLee_mat99, tree = BeckLee_tree,
method = "continuous", model = "proximity",
time = c(120, 80, 40, 0),
FADLAD = BeckLee_ages)
-## Bootstrapping the time slice subsamples
+## Bootstrapping the time slice subsets
boot.matrix(time_slices, bootstraps = 100)
## ---- dispRity object ----
-## 4 continuous (proximity) time subsamples for 99 elements with 97 dimensions:
+## 4 continuous (proximity) time subsets for 99 elements with 97 dimensions:
## 120, 80, 40, 0.
## Data was bootstrapped 100 times (method:"full").
@@ -474,18 +474,18 @@ 4.4.3 Metrics in the dispRi
## Measuring disparity as the standard deviation of all the values of the
## ordinated matrix (dimension-level 1 function).
summary(dispRity(BeckLee_mat50, metric = sd))
-## subsamples n obs
-## 1 1 50 0.201
+## subsets n obs
+## 1 1 50 0.201
## Measuring disparity as the standard deviation of the variance of each axis of
## the ordinated matrix (dimension-level 1 and 2 functions).
summary(dispRity(BeckLee_mat50, metric = c(sd, variances)))
-## subsamples n obs
-## 1 1 50 0.028
+## subsets n obs
+## 1 1 50 0.028
## Measuring disparity as the standard deviation of the variance of each axis of
## the variance covariance matrix (dimension-level 1, 2 and 3 functions).
summary(dispRity(BeckLee_mat50, metric = c(sd, variances, var)), round = 10)
-## subsamples n obs
-## 1 1 50 0.0001025857
+## subsets n obs
+## 1 1 50 0.0001025857
Note that the order of each function in the metric argument does not matter, the dispRity function will automatically detect the function dimension-levels (using make.metric) and apply them to the data in decreasing order (dimension-level 3 > 2 > 1).
## Disparity as the standard deviation of the variance of each axis of the
## variance covariance matrix:
@@ -498,8 +498,8 @@ 4.4.3 Metrics in the dispRi
## Both ways output the same disparity values:
disparity1 == disparity2
-## subsamples n obs
-## [1,] TRUE TRUE TRUE
+## subsets n obs
+## [1,] TRUE TRUE TRUE
In these examples, we considered disparity to be a single value. For example, in the previous example, we defined disparity as the standard deviation of the variances of each column of the variance/covariance matrix (metric = c(variances, sd, var)). It is, however, possible to calculate disparity as a distribution.
@@ -582,19 +582,19 @@
-
-
-
4.4.6.1 Volumes and surface metri
The functions ellipse.volume, convhull.surface and convhull.volume all measure the surface or the volume of the ordinated space occupied:
## Calculating the ellipsoid volume
summary(dispRity(dummy_space, metric = ellipse.volume))## subsamples n obs
-## 1 1 10 257.8## subsets n obs
+## 1 1 10 257.8-Because there is only one subsample (i.e. one matrix) in the dispRity object, this operation is the equivalent of
+ellipse.volume(dummy_space)(with rounding).Because there is only one subset (i.e. one matrix) in the dispRity object, this operation is the equivalent of
ellipse.volume(dummy_space)(with rounding).
## Calculating the convex hull surface
summary(dispRity(dummy_space, metric = convhull.surface))## subsamples n obs
-## 1 1 10 11.91## subsets n obs
+## 1 1 10 11.91## Calculating the convex hull volume
summary(dispRity(dummy_space, metric = convhull.volume))## subsamples n obs
-## 1 1 10 1.031## subsets n obs
+## 1 1 10 1.031The convex hull functions make a (good) estimation of the multidimensional properties of the ordinated space.
Cautionary note: measuring volumes in a high number of dimensions can be strongly affected by the curse of dimensionality that often results in near 0 disparity values.
@@ -609,34 +609,34 @@4.4.6.2 Ranges, variances and dia
## [1] 2.430909 3.726481 2.908329 2.735739 1.588603-## Calculating disparity as the distribution of these ranges summary(dispRity(dummy_space, metric = ranges))+## subsamples n obs.median 2.5% 25% 75% 97.5% -## 1 1 10 2.736 1.673 2.431 2.908 3.645## subsets n obs.median 2.5% 25% 75% 97.5% +## 1 1 10 2.736 1.673 2.431 2.908 3.645-## Calculating disparity as the sum and the product of these ranges summary(dispRity(dummy_space, metric = c(sum, ranges)))+## subsamples n obs -## 1 1 10 13.39## subsets n obs +## 1 1 10 13.39-summary(dispRity(dummy_space, metric = c(prod, ranges)))+## subsamples n obs -## 1 1 10 114.5## subsets n obs +## 1 1 10 114.5## Calculating the variances of each dimension in the ordinated space variances(dummy_space)## [1] 0.6093144 1.1438620 0.9131859 0.6537768 0.3549372-## Calculating disparity as the distribution of these variances summary(dispRity(dummy_space, metric = variances))+## subsamples n obs.median 2.5% 25% 75% 97.5% -## 1 1 10 0.654 0.38 0.609 0.913 1.121## subsets n obs.median 2.5% 25% 75% 97.5% +## 1 1 10 0.654 0.38 0.609 0.913 1.121-## Calculating disparity as the sum and the product of these variances summary(dispRity(dummy_space, metric = c(sum, variances)))+## subsamples n obs -## 1 1 10 3.675## subsets n obs +## 1 1 10 3.675-summary(dispRity(dummy_space, metric = c(prod, variances)))+## subsamples n obs -## 1 1 10 0.148## subsets n obs +## 1 1 10 0.148The
diagonalfunction measures the multidimensional diagonal of the whole space (i.e. in our case the longest Euclidean distance in our five dimensional space):-## Calculating the ordinated space's diagonal summary(dispRity(dummy_space, metric = diagonal))+## subsamples n obs -## 1 1 10 3.659## subsets n obs +## 1 1 10 3.659@@ -646,21 +646,21 @@This metric is only a Euclidean diagonal (mathematically valid) if the dimensions within the space are all orthogonal!
4.4.6.3 Centroids metric
The
centroidsmetric allows users to measure the position of the different elements compared to a fixed point in the ordinated space. By default, this function measures the distance between each element and their centroid (centre point):-## The distribution of the distances between each element and their centroid summary(dispRity(dummy_space, metric = centroids))+## subsamples n obs.median 2.5% 25% 75% 97.5% -## 1 1 10 1.435 0.788 1.267 1.993 3.167## subsets n obs.median 2.5% 25% 75% 97.5% +## 1 1 10 1.435 0.788 1.267 1.993 3.167-## Disparity as the median value of these distances summary(dispRity(dummy_space, metric = c(median, centroids)))+## subsamples n obs -## 1 1 10 1.435## subsets n obs +## 1 1 10 1.435It is however possible to fix the coordinates of the centroid to a specific point in the ordinated space, as long as it has the correct number of dimensions:
-## The distance between each element and the origin of the ordinated space summary(dispRity(dummy_space, metric = centroids, centroid = c(0,0,0,0,0)))+## subsamples n obs.median 2.5% 25% 75% 97.5% -## 1 1 10 1.487 0.785 1.2 2.044 3.176## subsets n obs.median 2.5% 25% 75% 97.5% +## 1 1 10 1.487 0.785 1.2 2.044 3.176-## Disparity as the distance between each element and a specific point in space summary(dispRity(dummy_space, metric = centroids, centroid = c(0,1,2,3,4)))+## subsamples n obs.median 2.5% 25% 75% 97.5% -## 1 1 10 5.489 4.293 5.032 6.155 6.957## subsets n obs.median 2.5% 25% 75% 97.5% +## 1 1 10 5.489 4.293 5.032 6.155 6.957
4.5 Summarising dispRity data (pl
4.5.1 Summarising dispRity data
dispRity dataThis function is an S3 function (summary.dispRity) allowing users to summarise the content of dispRity objects that contain disparity calculations.
## Example data from previous sections
-crown_stem <- custom.subsamples(BeckLee_mat50,
+crown_stem <- custom.subsets(BeckLee_mat50,
group = list("crown" = c(16, 19:41, 45:50),
"stem" = c(1:15, 17:18, 42:44)))
## Bootstrapping and rarefying these groups
@@ -679,40 +679,40 @@ 4.5.1 Summarising dispRity<
## Calculate disparity
disparity_crown_stem <- dispRity(boot_crown_stem, metric = c(sum, variances))
-## Creating time slice subsamples
-time_slices <- time.subsamples(data = BeckLee_mat99, tree = BeckLee_tree,
+## Creating time slice subsets
+time_slices <- time.subsets(data = BeckLee_mat99, tree = BeckLee_tree,
method = "continuous", model = "proximity", time = c(120, 80, 40, 0),
FADLAD = BeckLee_ages)
-## Bootstrapping the time slice subsamples
+## Bootstrapping the time slice subsets
boot_time_slices <- boot.matrix(time_slices, bootstraps = 100)
## Calculate disparity
disparity_time_slices <- dispRity(boot_time_slices, metric = c(sum, variances))
-## Creating time bin subsamples
-time_bins <- time.subsamples(data = BeckLee_mat99, tree = BeckLee_tree,
+## Creating time bin subsets
+time_bins <- time.subsets(data = BeckLee_mat99, tree = BeckLee_tree,
method = "discrete", time = c(120, 80, 40, 0), FADLAD = BeckLee_ages,
inc.nodes = TRUE)
-## Bootstrapping the time bin subsamples
+## Bootstrapping the time bin subsets
boot_time_bins <- boot.matrix(time_bins, bootstraps = 100)
## Calculate disparity
disparity_time_bins <- dispRity(boot_time_bins, metric = c(sum, variances))
These objects are easy to summarise as follows:
## Default summary
summary(disparity_time_slices)## subsamples n obs bs.median 2.5% 25% 75% 97.5%
-## 1 120 6 2.899 2.399 1.749 2.202 2.642 2.794
-## 2 80 22 3.558 3.414 3.127 3.314 3.463 3.557
-## 3 40 15 4.032 3.764 3.569 3.694 3.842 3.954
-## 4 0 10 4.055 3.661 3.282 3.564 3.771 3.927Information about the number of elements in each subsample and the observed (i.e. non-bootstrapped) disparity are also calculated. This is specifically handy when rarefying the data for example:
+## subsets n obs bs.median 2.5% 25% 75% 97.5%
+## 1 120 6 2.899 2.399 1.749 2.202 2.642 2.794
+## 2 80 22 3.558 3.414 3.127 3.314 3.463 3.557
+## 3 40 15 4.032 3.764 3.569 3.694 3.842 3.954
+## 4 0 10 4.055 3.661 3.282 3.564 3.771 3.927Information about the number of elements in each subset and the observed (i.e. non-bootstrapped) disparity are also calculated. This is specifically handy when rarefying the data for example:
head(summary(disparity_crown_stem))## subsamples n obs bs.median 2.5% 25% 75% 97.5%
-## 1 crown 30 1.995 1.930 1.866 1.909 1.950 1.970
-## 2 crown 29 NA 1.932 1.871 1.915 1.953 1.974
-## 3 crown 28 NA 1.926 1.852 1.903 1.942 1.978
-## 4 crown 27 NA 1.935 1.860 1.908 1.954 1.982
-## 5 crown 26 NA 1.931 1.859 1.908 1.953 1.977
-## 6 crown 25 NA 1.933 1.855 1.909 1.954 1.978## subsets n obs bs.median 2.5% 25% 75% 97.5%
+## 1 crown 30 1.995 1.930 1.866 1.909 1.950 1.970
+## 2 crown 29 NA 1.932 1.871 1.915 1.953 1.974
+## 3 crown 28 NA 1.926 1.852 1.903 1.942 1.978
+## 4 crown 27 NA 1.935 1.860 1.908 1.954 1.982
+## 5 crown 26 NA 1.931 1.859 1.908 1.953 1.977
+## 6 crown 25 NA 1.933 1.855 1.909 1.954 1.978The summary functions can also take various options such as:
4.5.1 Summarising dispRity<
These options can easily be changed from the defaults as follows:
## Same as above but using the 88th quantile and the standard deviation as the summary
summary(disparity_time_slices, quantile = 88, cent.tend = sd)
-## subsamples n obs bs.sd 6% 94%
-## 1 120 6 2.899 0.293 1.917 2.762
-## 2 80 22 3.558 0.113 3.208 3.542
-## 3 40 15 4.032 0.109 3.612 3.918
-## 4 0 10 4.055 0.169 3.387 3.882
+## subsets n obs bs.sd 6% 94%
+## 1 120 6 2.899 0.293 1.917 2.762
+## 2 80 22 3.558 0.113 3.208 3.542
+## 3 40 15 4.032 0.109 3.612 3.918
+## 4 0 10 4.055 0.169 3.387 3.882
## Printing the details of the object and rounding the values to the 5th decimal place
summary(disparity_time_slices, recall = TRUE, rounding = 5)
## ---- dispRity object ----
-## 4 continuous (proximity) time subsamples for 99 elements with 97 dimensions:
+## 4 continuous (proximity) time subsets for 99 elements with 97 dimensions:
## 120, 80, 40, 0.
## Data was bootstrapped 100 times (method:"full").
## Disparity was calculated as: c(sum, variances).
-## subsamples n obs bs.median 2.5% 25% 75% 97.5%
-## 1 120 6 2.89926 2.39864 1.74855 2.20230 2.64153 2.79428
-## 2 80 22 3.55782 3.41432 3.12737 3.31353 3.46313 3.55661
-## 3 40 15 4.03191 3.76393 3.56890 3.69427 3.84171 3.95368
-## 4 0 10 4.05457 3.66131 3.28211 3.56431 3.77130 3.92706
+## subsets n obs bs.median 2.5% 25% 75% 97.5%
+## 1 120 6 2.89926 2.39864 1.74855 2.20230 2.64153 2.79428
+## 2 80 22 3.55782 3.41432 3.12737 3.31353 3.46313 3.55661
+## 3 40 15 4.03191 3.76393 3.56890 3.69427 3.84171 3.95368
+## 4 0 10 4.05457 3.66131 3.28211 3.56431 3.77130 3.92706
Note that the summary table is a data.frame, hence it is as easy to modify as any dataframe using dplyr. You can also export it in csv format using write.csv or write_csv or even directly export into LaTeX format using the following;
## Loading the xtable package
require(xtable)
@@ -755,9 +755,9 @@ 4.5.2 Plotting dispRitybox, lines, and polygons to display discrete disparity results in respectively a boxplot, confidence interval lines, and confidence interval polygons.
-This argument can be left empty. In this case, the algorithm will automatically detect the type of subsamples from the dispRity object and plot accordingly.
+This argument can be left empty. In this case, the algorithm will automatically detect the type of subsets from the dispRity object and plot accordingly.
-It is also possible to display the number of elements in each subsample (as a horizontal dotted line) using the option elements = TRUE. Additionally, when the data is rarefied, one can indicate which level of rarefaction to display (i.e. only display the results for a certain number of elements) by using the rarefaction argument.
+It is also possible to display the number of elements in each subset (as a horizontal dotted line) using the option elements = TRUE. Additionally, when the data is rarefied, one can indicate which level of rarefaction to display (i.e. only display the results for a certain number of elements) by using the rarefaction argument.
## Graphical parameters
op <- par(mfrow = c(2, 2), bty = "n")
@@ -799,7 +799,7 @@ 4.5.2 Plotting dispRityplot(disparity_time_slices, quantile = c(seq(from = 10, to = 100, by = 10)),
cent.tend = sd, type = "c", elements = TRUE, col = c("black", rainbow(10)),
- ylab = c("Disparity", "Diversity"), time.subsamples = FALSE,
+ ylab = c("Disparity", "Diversity"), time.subsets = FALSE,
xlab = "Time (in in units from past to present)", observed = TRUE,
main = "Many more options...")
@@ -836,12 +836,12 @@ 4.6 Testing disparity hypotheses<
The dispRity package allows users to apply statistical tests to the calculated disparity to test various hypotheses. The function test.dispRity works in a similar way to the dispRity function: it takes a dispRity object, a test and a comparisons argument.
The comparisons argument indicates the way the test should be applied to the data:
-
-It is also possible to input a list of pairs of numeric values or characters matching the subsample names to create personalised tests. Some other tests implemented in dispRity such as the dispRity::null.test have a specific way they are applied to the data and therefore ignore the comparisons argument.
+It is also possible to input a list of pairs of numeric values or characters matching the subset names to create personalised tests. Some other tests implemented in dispRity such as the dispRity::null.test have a specific way they are applied to the data and therefore ignore the comparisons argument.
The test argument can be any statistical or non-statistical test to apply to the disparity object. It can be a common statistical test function (e.g. stats::t.test), a function implemented in dispRity (e.g. see ?null.test) or any function defined by the user.
This function also allows users to correct for Type I error inflation (false positives) when using multiple comparisons via the correction argument. This argument can be empty (no correction applied) or can contain one of the corrections from the stats::p.adjust function (see ?p.adjust).
Note that the test.dispRity algorithm deals with some classical test outputs (h.test, lm and numeric vector) and summarises the test output. It is, however, possible to get the full detailed output by using the options details = TRUE.
@@ -911,11 +911,11 @@ 4.6 Testing disparity hypotheses<
## This will inflate Type I error!
##
## Call:
-## test(formula = data ~ subsamples, data = data)
+## test(formula = data ~ subsets, data = data)
##
## Coefficients:
-## (Intercept) subsamples120 subsamples40 subsamples80
-## 3.6517 -1.2627 0.1145 -0.2634
+## (Intercept) subsets120 subsets40 subsets80
+## 3.6517 -1.2627 0.1145 -0.2634
## The output is a regular `lm` output
class(slice_lm)
## [1] "lm"
@@ -923,18 +923,18 @@ 4.6 Testing disparity hypotheses<
summary(slice_lm)
##
## Call:
-## test(formula = data ~ subsamples, data = data)
+## test(formula = data ~ subsets, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.91282 -0.08285 0.00993 0.10745 0.56989
##
## Coefficients:
-## Estimate Std. Error t value Pr(>|t|)
-## (Intercept) 3.65169 0.01865 195.810 < 2e-16 ***
-## subsamples120 -1.26273 0.02637 -47.878 < 2e-16 ***
-## subsamples40 0.11452 0.02637 4.342 1.79e-05 ***
-## subsamples80 -0.26339 0.02637 -9.987 < 2e-16 ***
+## Estimate Std. Error t value Pr(>|t|)
+## (Intercept) 3.65169 0.01865 195.810 < 2e-16 ***
+## subsets120 -1.26273 0.02637 -47.878 < 2e-16 ***
+## subsets40 0.11452 0.02637 4.342 1.79e-05 ***
+## subsets80 -0.26339 0.02637 -9.987 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
@@ -956,18 +956,18 @@ 4.7 Disparity as a distribution## Summarising both disparity measurements:
## The distributions:
summary(disparity_centroids)
These options can easily be changed from the defaults as follows:
## Same as above but using the 88th quantile and the standard deviation as the summary
summary(disparity_time_slices, quantile = 88, cent.tend = sd)## subsamples n obs bs.sd 6% 94%
-## 1 120 6 2.899 0.293 1.917 2.762
-## 2 80 22 3.558 0.113 3.208 3.542
-## 3 40 15 4.032 0.109 3.612 3.918
-## 4 0 10 4.055 0.169 3.387 3.882## subsets n obs bs.sd 6% 94%
+## 1 120 6 2.899 0.293 1.917 2.762
+## 2 80 22 3.558 0.113 3.208 3.542
+## 3 40 15 4.032 0.109 3.612 3.918
+## 4 0 10 4.055 0.169 3.387 3.882## Printing the details of the object and rounding the values to the 5th decimal place
summary(disparity_time_slices, recall = TRUE, rounding = 5)## ---- dispRity object ----
-## 4 continuous (proximity) time subsamples for 99 elements with 97 dimensions:
+## 4 continuous (proximity) time subsets for 99 elements with 97 dimensions:
## 120, 80, 40, 0.
## Data was bootstrapped 100 times (method:"full").
## Disparity was calculated as: c(sum, variances).## subsamples n obs bs.median 2.5% 25% 75% 97.5%
-## 1 120 6 2.89926 2.39864 1.74855 2.20230 2.64153 2.79428
-## 2 80 22 3.55782 3.41432 3.12737 3.31353 3.46313 3.55661
-## 3 40 15 4.03191 3.76393 3.56890 3.69427 3.84171 3.95368
-## 4 0 10 4.05457 3.66131 3.28211 3.56431 3.77130 3.92706## subsets n obs bs.median 2.5% 25% 75% 97.5%
+## 1 120 6 2.89926 2.39864 1.74855 2.20230 2.64153 2.79428
+## 2 80 22 3.55782 3.41432 3.12737 3.31353 3.46313 3.55661
+## 3 40 15 4.03191 3.76393 3.56890 3.69427 3.84171 3.95368
+## 4 0 10 4.05457 3.66131 3.28211 3.56431 3.77130 3.92706Note that the summary table is a data.frame, hence it is as easy to modify as any dataframe using dplyr. You can also export it in csv format using write.csv or write_csv or even directly export into LaTeX format using the following;
## Loading the xtable package
require(xtable)
@@ -755,9 +755,9 @@ 4.5.2 Plotting dispRitybox, lines, and polygons to display discrete disparity results in respectively a boxplot, confidence interval lines, and confidence interval polygons.
-This argument can be left empty. In this case, the algorithm will automatically detect the type of subsamples from the dispRity object and plot accordingly.
+This argument can be left empty. In this case, the algorithm will automatically detect the type of subsets from the dispRity object and plot accordingly.
-It is also possible to display the number of elements in each subsample (as a horizontal dotted line) using the option elements = TRUE. Additionally, when the data is rarefied, one can indicate which level of rarefaction to display (i.e. only display the results for a certain number of elements) by using the rarefaction argument.
+It is also possible to display the number of elements in each subset (as a horizontal dotted line) using the option elements = TRUE. Additionally, when the data is rarefied, one can indicate which level of rarefaction to display (i.e. only display the results for a certain number of elements) by using the rarefaction argument.
## Graphical parameters
op <- par(mfrow = c(2, 2), bty = "n")
@@ -799,7 +799,7 @@ 4.5.2 Plotting dispRityplot(disparity_time_slices, quantile = c(seq(from = 10, to = 100, by = 10)),
cent.tend = sd, type = "c", elements = TRUE, col = c("black", rainbow(10)),
- ylab = c("Disparity", "Diversity"), time.subsamples = FALSE,
+ ylab = c("Disparity", "Diversity"), time.subsets = FALSE,
xlab = "Time (in in units from past to present)", observed = TRUE,
main = "Many more options...")
@@ -836,12 +836,12 @@ 4.6 Testing disparity hypotheses<
The dispRity package allows users to apply statistical tests to the calculated disparity to test various hypotheses. The function test.dispRity works in a similar way to the dispRity function: it takes a dispRity object, a test and a comparisons argument.
The comparisons argument indicates the way the test should be applied to the data:
-
-It is also possible to input a list of pairs of numeric values or characters matching the subsample names to create personalised tests. Some other tests implemented in dispRity such as the dispRity::null.test have a specific way they are applied to the data and therefore ignore the comparisons argument.
+It is also possible to input a list of pairs of numeric values or characters matching the subset names to create personalised tests. Some other tests implemented in dispRity such as the dispRity::null.test have a specific way they are applied to the data and therefore ignore the comparisons argument.
The test argument can be any statistical or non-statistical test to apply to the disparity object. It can be a common statistical test function (e.g. stats::t.test), a function implemented in dispRity (e.g. see ?null.test) or any function defined by the user.
This function also allows users to correct for Type I error inflation (false positives) when using multiple comparisons via the correction argument. This argument can be empty (no correction applied) or can contain one of the corrections from the stats::p.adjust function (see ?p.adjust).
Note that the test.dispRity algorithm deals with some classical test outputs (h.test, lm and numeric vector) and summarises the test output. It is, however, possible to get the full detailed output by using the options details = TRUE.
@@ -911,11 +911,11 @@ 4.6 Testing disparity hypotheses<
## This will inflate Type I error!
##
## Call:
-## test(formula = data ~ subsamples, data = data)
+## test(formula = data ~ subsets, data = data)
##
## Coefficients:
-## (Intercept) subsamples120 subsamples40 subsamples80
-## 3.6517 -1.2627 0.1145 -0.2634## The output is a regular `lm` output
class(slice_lm)## [1] "lm"4.6 Testing disparity hypotheses< summary(slice_lm)
##
## Call:
-## test(formula = data ~ subsamples, data = data)
+## test(formula = data ~ subsets, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.91282 -0.08285 0.00993 0.10745 0.56989
##
## Coefficients:
-## Estimate Std. Error t value Pr(>|t|)
-## (Intercept) 3.65169 0.01865 195.810 < 2e-16 ***
-## subsamples120 -1.26273 0.02637 -47.878 < 2e-16 ***
-## subsamples40 0.11452 0.02637 4.342 1.79e-05 ***
-## subsamples80 -0.26339 0.02637 -9.987 < 2e-16 ***
+## Estimate Std. Error t value Pr(>|t|)
+## (Intercept) 3.65169 0.01865 195.810 < 2e-16 ***
+## subsets120 -1.26273 0.02637 -47.878 < 2e-16 ***
+## subsets40 0.11452 0.02637 4.342 1.79e-05 ***
+## subsets80 -0.26339 0.02637 -9.987 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
@@ -956,18 +956,18 @@ 4.7 Disparity as a distribution## Summarising both disparity measurements:
## The distributions:
summary(disparity_centroids)
## subsamples n obs.median bs.median 2.5% 25% 75% 97.5%
-## 1 120 6 1.536 1.375 0.864 1.187 1.611 1.886
-## 2 80 22 1.871 1.807 1.481 1.681 1.904 2.075
-## 3 40 15 1.943 1.885 1.529 1.777 1.989 2.097
-## 4 0 10 1.910 1.802 1.463 1.687 1.960 2.095## subsets n obs.median bs.median 2.5% 25% 75% 97.5%
+## 1 120 6 1.536 1.375 0.864 1.187 1.611 1.886
+## 2 80 22 1.871 1.807 1.481 1.681 1.904 2.075
+## 3 40 15 1.943 1.885 1.529 1.777 1.989 2.097
+## 4 0 10 1.910 1.802 1.463 1.687 1.960 2.095## The summary of the distributions (as median)
summary(disparity_centroids_median)## subsamples n obs bs.median 2.5% 25% 75% 97.5%
-## 1 120 6 1.536 1.313 0.822 1.260 1.464 1.592
-## 2 80 22 1.871 1.803 1.700 1.774 1.830 1.864
-## 3 40 15 1.943 1.887 1.764 1.847 1.928 1.968
-## 4 0 10 1.910 1.814 1.593 1.773 1.844 1.917## subsets n obs bs.median 2.5% 25% 75% 97.5%
+## 1 120 6 1.536 1.313 0.822 1.260 1.464 1.592
+## 2 80 22 1.871 1.803 1.700 1.774 1.830 1.864
+## 3 40 15 1.943 1.887 1.764 1.847 1.928 1.968
+## 4 0 10 1.910 1.814 1.593 1.773 1.844 1.917We can see that the summary message for the distribution is slightly different than before. Here summary also displays the observed central tendency (i.e. the central tendency of the measured distributions). Note that, as expected, this central tendency is the same in both metrics!
Another, maybe more intuitive way, to compare both approaches for measuring disparity is to plot the distributions:
## Graphical parameters
diff --git a/inst/gitbook/_book/dispRity_manual.pdf b/inst/gitbook/_book/dispRity_manual.pdf
index ad15f06b..8ead4bcb 100644
Binary files a/inst/gitbook/_book/dispRity_manual.pdf and b/inst/gitbook/_book/dispRity_manual.pdf differ
diff --git a/inst/gitbook/_book/dispRity_manual.tex b/inst/gitbook/_book/dispRity_manual.tex
index 20147998..3075266a 100644
--- a/inst/gitbook/_book/dispRity_manual.tex
+++ b/inst/gitbook/_book/dispRity_manual.tex
@@ -262,13 +262,13 @@ \chapter{Glossary}\label{glossary}
The dimensions can be referred to as axes of variation, or principal
components, for ordinated spaces obtained from a PCA for example.
\item
- \textbf{Subsamples}. Subsamples of the multidimensional space. A
- subsample (or subsamples) contains the same number of dimensions as
- the space but may contain a smaller subset of elements. For example,
- if our space is composed of birds and mammals (the elements) and 50
- principal components of variation (the dimensions), we can create two
- subsamples containing just mammals or birds, but with the same 50
- dimensions, to compare disparity in the two clades.
+ \textbf{Subsets}. Subsets of the multidimensional space. A subset (or
+ subsets) contains the same number of dimensions as the space but may
+ contain a smaller subset of elements. For example, if our space is
+ composed of birds and mammals (the elements) and 50 principal
+ components of variation (the dimensions), we can create two subsets
+ containing just mammals or birds, but with the same 50 dimensions, to
+ compare disparity in the two clades.
\end{itemize}
\chapter{\texorpdfstring{Getting started with
@@ -350,7 +350,7 @@ \subsection{\texorpdfstring{Ordination matrices from
you can give the function a \texttt{geomorph.data.frame} object. If the
latter contains sorting information (i.e.~factors), they can be directly
used to make a customised \texttt{dispRity} object
-\protect\hyperlink{customised-subsamples}{customised \texttt{dispRity}
+\protect\hyperlink{customised-subsets}{customised \texttt{dispRity}
object}!
\begin{Shaded}
@@ -366,7 +366,7 @@ \subsection{\texorpdfstring{Ordination matrices from
\begin{verbatim}
## ---- dispRity object ----
-## 4 customised subsamples for 40 elements:
+## 4 customised subsets for 40 elements:
## species.Jord, species.Teyah, site.Allo, site.Symp.
\end{verbatim}
@@ -497,8 +497,8 @@ \section{Performing a simple dispRity
question and data}. For example, which metric should you use? How many
bootstraps do you require? What model of evolution is most appropriate
if you are time slicing? Should you rarefy the data? See
-\protect\hyperlink{time-slicing}{\texttt{time.subsamples}},
-\protect\hyperlink{customised-subsamples}{\texttt{custom.subsamples}},
+\protect\hyperlink{time-slicing}{\texttt{time.subsets}},
+\protect\hyperlink{customised-subsets}{\texttt{custom.subsets}},
\protect\hyperlink{bootstraps-and-rarefactions}{\texttt{boot.matrix}}
and \protect\hyperlink{disparity-metrics}{\texttt{dispRity.metric}} for
more details of the defaults used in each of these functions. Note that
@@ -609,7 +609,7 @@ \subsection{Disparity through time}\label{disparity-through-time}
do\textbackslash{}
\texttt{my\_tree\$root.time\ \textless{}-\ my\_age}.
\item
- The required number of time subsamples (here \texttt{time\ =\ 3})
+ The required number of time subsets (here \texttt{time\ =\ 3})
\item
Your favourite disparity metric (here the sum of variances)
\end{itemize}
@@ -639,15 +639,15 @@ \subsection{Disparity through time}\label{disparity-through-time}
\begin{verbatim}
## ---- dispRity object ----
-## 3 discrete time subsamples for 50 elements with 48 dimensions:
+## 3 discrete time subsets for 50 elements with 48 dimensions:
## 133.51104 - 89.00736, 89.00736 - 44.50368, 44.50368 - 0.
## Data was bootstrapped 100 times (method:"full").
## Disparity was calculated as: metric.
\end{verbatim}
-We asked for three subsamples (evenly spread across the age of the
-tree), the data was bootstrapped 100 times (default) and the metric used
-was the sum of variances.
+We asked for three subsets (evenly spread across the age of the tree),
+the data was bootstrapped 100 times (default) and the metric used was
+the sum of variances.
We can now summarise or plot the \texttt{disparity\_data} object, or
perform statistical tests on it (e.g.~a simple \texttt{lm}):
@@ -660,7 +660,7 @@ \subsection{Disparity through time}\label{disparity-through-time}
\end{Shaded}
\begin{verbatim}
-## subsamples n obs bs.median 2.5% 25% 75% 97.5%
+## subsets n obs bs.median 2.5% 25% 75% 97.5%
## 1 133.51104 - 89.00736 5 1.575 1.305 0.729 1.118 1.420 1.509
## 2 89.00736 - 44.50368 29 1.922 1.867 1.775 1.830 1.889 1.922
## 3 44.50368 - 0 16 1.990 1.871 1.716 1.831 1.914 1.942
@@ -696,17 +696,17 @@ \subsection{Disparity through time}\label{disparity-through-time}
\begin{verbatim}
##
## Call:
-## test(formula = data ~ subsamples, data = data)
+## test(formula = data ~ subsets, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.56623 -0.04160 0.01049 0.05507 0.31886
##
## Coefficients:
-## Estimate Std. Error t value Pr(>|t|)
-## (Intercept) 1.25647 0.01270 98.97 <2e-16 ***
-## subsamples44.50368 - 0 0.60863 0.01795 33.90 <2e-16 ***
-## subsamples89.00736 - 44.50368 0.60169 0.01795 33.51 <2e-16 ***
+## Estimate Std. Error t value Pr(>|t|)
+## (Intercept) 1.25647 0.01270 98.97 <2e-16 ***
+## subsets44.50368 - 0 0.60863 0.01795 33.90 <2e-16 ***
+## subsets89.00736 - 44.50368 0.60169 0.01795 33.51 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
@@ -775,7 +775,7 @@ \subsection{Disparity through time}\label{disparity-through-time}
\begin{verbatim}
## ---- dispRity object ----
-## 2 customised subsamples for 50 elements with 48 dimensions:
+## 2 customised subsets for 50 elements with 48 dimensions:
## crown, stem.
## Data was bootstrapped 100 times (method:"full").
## Disparity was calculated as: metric.
@@ -789,9 +789,9 @@ \subsection{Disparity through time}\label{disparity-through-time}
\end{Shaded}
\begin{verbatim}
-## subsamples n obs bs.median 2.5% 25% 75% 97.5%
-## 1 crown 30 1.995 1.936 1.866 1.914 1.947 1.971
-## 2 stem 20 1.715 1.632 1.541 1.604 1.667 1.695
+## subsets n obs bs.median 2.5% 25% 75% 97.5%
+## 1 crown 30 1.995 1.936 1.866 1.914 1.947 1.971
+## 2 stem 20 1.715 1.632 1.541 1.604 1.667 1.695
\end{verbatim}
\begin{Shaded}
@@ -850,22 +850,22 @@ \chapter{Details of specific
\hypertarget{time-slicing}{\section{Time slicing}\label{time-slicing}}
-The function \texttt{time.subsamples} allows users to divide the matrix
-into different time subsamples or slices given a dated phylogeny that
-contains all the elements (i.e.~taxa) from the matrix. Each subsample
+The function \texttt{time.subsets} allows users to divide the matrix
+into different time subsets or slices given a dated phylogeny that
+contains all the elements (i.e.~taxa) from the matrix. Each subset
generated by this function will then contain all the elements present at
a specific point in time or during a specific period in time.
-Two types of time subsamples can be performed by using the
-\texttt{method} option:
+Two types of time subsets can be performed by using the \texttt{method}
+option:
\begin{itemize}
\tightlist
\item
- Discrete time subsamples (or time-binning) using
+ Discrete time subsets (or time-binning) using
\texttt{method\ =\ discrete}
\item
- Continuous time subsamples (or time-slicing) using
+ Continuous time subsets (or time-slicing) using
\texttt{method\ =\ continuous}
\end{itemize}
@@ -894,14 +894,14 @@ \chapter{Details of specific
\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{## Generating three time bins containing the taxa present every 40 Ma}
-\KeywordTok{time.subsamples}\NormalTok{(}\DataTypeTok{data =}\NormalTok{ BeckLee_mat50, }\DataTypeTok{tree =}\NormalTok{ BeckLee_tree, }\DataTypeTok{method =} \StringTok{"discrete"}\NormalTok{,}
+\KeywordTok{time.subsets}\NormalTok{(}\DataTypeTok{data =}\NormalTok{ BeckLee_mat50, }\DataTypeTok{tree =}\NormalTok{ BeckLee_tree, }\DataTypeTok{method =} \StringTok{"discrete"}\NormalTok{,}
\DataTypeTok{time =} \KeywordTok{c}\NormalTok{(}\DecValTok{120}\NormalTok{, }\DecValTok{80}\NormalTok{, }\DecValTok{40}\NormalTok{, }\DecValTok{0}\NormalTok{))}
\end{Highlighting}
\end{Shaded}
\begin{verbatim}
## ---- dispRity object ----
-## 3 discrete time subsamples for 50 elements:
+## 3 discrete time subsets for 50 elements:
## 120 - 80, 80 - 40, 40 - 0.
\end{verbatim}
@@ -911,14 +911,14 @@ \chapter{Details of specific
\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{## Automatically generate three equal length bins:}
-\KeywordTok{time.subsamples}\NormalTok{(}\DataTypeTok{data =}\NormalTok{ BeckLee_mat50, }\DataTypeTok{tree =}\NormalTok{ BeckLee_tree, }\DataTypeTok{method =} \StringTok{"discrete"}\NormalTok{,}
+\KeywordTok{time.subsets}\NormalTok{(}\DataTypeTok{data =}\NormalTok{ BeckLee_mat50, }\DataTypeTok{tree =}\NormalTok{ BeckLee_tree, }\DataTypeTok{method =} \StringTok{"discrete"}\NormalTok{,}
\DataTypeTok{time =} \DecValTok{3}\NormalTok{)}
\end{Highlighting}
\end{Shaded}
\begin{verbatim}
## ---- dispRity object ----
-## 3 discrete time subsamples for 50 elements:
+## 3 discrete time subsets for 50 elements:
## 133.51104 - 89.00736, 89.00736 - 44.50368, 44.50368 - 0.
\end{verbatim}
@@ -951,14 +951,14 @@ \chapter{Details of specific
\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{## Generating time bins including taxa that might span between them}
-\KeywordTok{time.subsamples}\NormalTok{(}\DataTypeTok{data =}\NormalTok{ BeckLee_mat50, }\DataTypeTok{tree =}\NormalTok{ BeckLee_tree, }\DataTypeTok{method =} \StringTok{"discrete"}\NormalTok{,}
+\KeywordTok{time.subsets}\NormalTok{(}\DataTypeTok{data =}\NormalTok{ BeckLee_mat50, }\DataTypeTok{tree =}\NormalTok{ BeckLee_tree, }\DataTypeTok{method =} \StringTok{"discrete"}\NormalTok{,}
\DataTypeTok{time =} \KeywordTok{c}\NormalTok{(}\DecValTok{120}\NormalTok{, }\DecValTok{80}\NormalTok{, }\DecValTok{40}\NormalTok{, }\DecValTok{0}\NormalTok{), }\DataTypeTok{FADLAD =}\NormalTok{ BeckLee_ages)}
\end{Highlighting}
\end{Shaded}
\begin{verbatim}
## ---- dispRity object ----
-## 3 discrete time subsamples for 50 elements:
+## 3 discrete time subsets for 50 elements:
## 120 - 80, 80 - 40, 40 - 0.
\end{verbatim}
@@ -1015,7 +1015,7 @@ \chapter{Details of specific
\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{## Generating four time slices every 40 million years under a model of proximity evolution}
-\KeywordTok{time.subsamples}\NormalTok{(}\DataTypeTok{data =}\NormalTok{ BeckLee_mat99, }\DataTypeTok{tree =}\NormalTok{ BeckLee_tree, }
+\KeywordTok{time.subsets}\NormalTok{(}\DataTypeTok{data =}\NormalTok{ BeckLee_mat99, }\DataTypeTok{tree =}\NormalTok{ BeckLee_tree, }
\DataTypeTok{method =} \StringTok{"continuous"}\NormalTok{, }\DataTypeTok{model =} \StringTok{"proximity"}\NormalTok{, }\DataTypeTok{time =} \KeywordTok{c}\NormalTok{(}\DecValTok{120}\NormalTok{, }\DecValTok{80}\NormalTok{, }\DecValTok{40}\NormalTok{, }\DecValTok{0}\NormalTok{),}
\DataTypeTok{FADLAD =}\NormalTok{ BeckLee_ages)}
\end{Highlighting}
@@ -1023,29 +1023,29 @@ \chapter{Details of specific
\begin{verbatim}
## ---- dispRity object ----
-## 4 continuous (proximity) time subsamples for 99 elements:
+## 4 continuous (proximity) time subsets for 99 elements:
## 120, 80, 40, 0.
\end{verbatim}
\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{## Generating four time slices automatically}
-\KeywordTok{time.subsamples}\NormalTok{(}\DataTypeTok{data =}\NormalTok{ BeckLee_mat99, }\DataTypeTok{tree =}\NormalTok{ BeckLee_tree,}
+\KeywordTok{time.subsets}\NormalTok{(}\DataTypeTok{data =}\NormalTok{ BeckLee_mat99, }\DataTypeTok{tree =}\NormalTok{ BeckLee_tree,}
\DataTypeTok{method =} \StringTok{"continuous"}\NormalTok{, }\DataTypeTok{model =} \StringTok{"proximity"}\NormalTok{, }\DataTypeTok{time =} \DecValTok{4}\NormalTok{, }\DataTypeTok{FADLAD =}\NormalTok{ BeckLee_ages)}
\end{Highlighting}
\end{Shaded}
\begin{verbatim}
## ---- dispRity object ----
-## 4 continuous (proximity) time subsamples for 99 elements:
+## 4 continuous (proximity) time subsets for 99 elements:
## 133.51104, 89.00736, 44.50368, 0.
\end{verbatim}
-\hypertarget{customised-subsamples}{\section{Customised
-subsamples}\label{customised-subsamples}}
+\hypertarget{customised-subsets}{\section{Customised
+subsets}\label{customised-subsets}}
Another way of separating elements into different categories is to use
-customised subsamples as briefly explained
+customised subsets as briefly explained
\protect\hyperlink{disparity-among-groups}{above}. This function simply
takes the list of elements to put in each group (whether they are the
actual element names or their position in the matrix).
@@ -1057,13 +1057,13 @@ \chapter{Details of specific
\StringTok{"stem"}\NormalTok{ =}\StringTok{ }\KeywordTok{c}\NormalTok{(}\DecValTok{1}\OperatorTok{:}\DecValTok{15}\NormalTok{, }\DecValTok{17}\OperatorTok{:}\DecValTok{18}\NormalTok{, }\DecValTok{42}\OperatorTok{:}\DecValTok{44}\NormalTok{))}
\NormalTok{## Separating the dataset into two different groups}
-\KeywordTok{custom.subsamples}\NormalTok{(BeckLee_mat50, }\DataTypeTok{group =}\NormalTok{ mammal_groups)}
+\KeywordTok{custom.subsets}\NormalTok{(BeckLee_mat50, }\DataTypeTok{group =}\NormalTok{ mammal_groups)}
\end{Highlighting}
\end{Shaded}
\begin{verbatim}
## ---- dispRity object ----
-## 2 customised subsamples for 50 elements:
+## 2 customised subsets for 50 elements:
## crown, stem.
\end{verbatim}
@@ -1140,8 +1140,8 @@ \chapter{Details of specific
number of elements not sampled). The default argument is \texttt{FALSE}
but it can be set to \texttt{TRUE} to fully rarefy the data (i.e.~remove
\emph{x} elements for the number of pseudo-replicates, where \emph{x}
-varies from the maximum number of elements present in each subsample to
-a minimum of three elements). It can also be set to one or more
+varies from the maximum number of elements present in each subset to a
+minimum of three elements). It can also be set to one or more
\texttt{numeric} values to only rarefy to the corresponding number of
elements.
@@ -1201,14 +1201,13 @@ \chapter{Details of specific
## Data was bootstrapped 100 times (method:"full").
\end{verbatim}
-Of course, one could directly supply the subsamples generated above
-(using \texttt{time.subsamples} or \texttt{custom.subsamples}) to this
-function.
+Of course, one could directly supply the subsets generated above (using
+\texttt{time.subsets} or \texttt{custom.subsets}) to this function.
\begin{Shaded}
\begin{Highlighting}[]
-\NormalTok{## Creating subsamples of crown and stem mammals}
-\NormalTok{crown_stem <-}\StringTok{ }\KeywordTok{custom.subsamples}\NormalTok{(BeckLee_mat50,}
+\NormalTok{## Creating subsets of crown and stem mammals}
+\NormalTok{crown_stem <-}\StringTok{ }\KeywordTok{custom.subsets}\NormalTok{(BeckLee_mat50,}
\DataTypeTok{group =} \KeywordTok{list}\NormalTok{(}\StringTok{"crown"}\NormalTok{ =}\StringTok{ }\KeywordTok{c}\NormalTok{(}\DecValTok{16}\NormalTok{, }\DecValTok{19}\OperatorTok{:}\DecValTok{41}\NormalTok{, }\DecValTok{45}\OperatorTok{:}\DecValTok{50}\NormalTok{), }
\StringTok{"stem"}\NormalTok{ =}\StringTok{ }\KeywordTok{c}\NormalTok{(}\DecValTok{1}\OperatorTok{:}\DecValTok{15}\NormalTok{, }\DecValTok{17}\OperatorTok{:}\DecValTok{18}\NormalTok{, }\DecValTok{42}\OperatorTok{:}\DecValTok{44}\NormalTok{)))}
\NormalTok{## Bootstrapping and rarefying these groups}
@@ -1218,27 +1217,27 @@ \chapter{Details of specific
\begin{verbatim}
## ---- dispRity object ----
-## 2 customised subsamples for 50 elements with 48 dimensions:
+## 2 customised subsets for 50 elements with 48 dimensions:
## crown, stem.
## Data was bootstrapped 200 times (method:"full") and fully rarefied.
\end{verbatim}
\begin{Shaded}
\begin{Highlighting}[]
-\NormalTok{## Creating time slice subsamples}
-\NormalTok{time_slices <-}\StringTok{ }\KeywordTok{time.subsamples}\NormalTok{(}\DataTypeTok{data =}\NormalTok{ BeckLee_mat99, }\DataTypeTok{tree =}\NormalTok{ BeckLee_tree, }
+\NormalTok{## Creating time slice subsets}
+\NormalTok{time_slices <-}\StringTok{ }\KeywordTok{time.subsets}\NormalTok{(}\DataTypeTok{data =}\NormalTok{ BeckLee_mat99, }\DataTypeTok{tree =}\NormalTok{ BeckLee_tree, }
\DataTypeTok{method =} \StringTok{"continuous"}\NormalTok{, }\DataTypeTok{model =} \StringTok{"proximity"}\NormalTok{, }
\DataTypeTok{time =} \KeywordTok{c}\NormalTok{(}\DecValTok{120}\NormalTok{, }\DecValTok{80}\NormalTok{, }\DecValTok{40}\NormalTok{, }\DecValTok{0}\NormalTok{),}
\DataTypeTok{FADLAD =}\NormalTok{ BeckLee_ages)}
-\NormalTok{## Bootstrapping the time slice subsamples}
+\NormalTok{## Bootstrapping the time slice subsets}
\KeywordTok{boot.matrix}\NormalTok{(time_slices, }\DataTypeTok{bootstraps =} \DecValTok{100}\NormalTok{)}
\end{Highlighting}
\end{Shaded}
\begin{verbatim}
## ---- dispRity object ----
-## 4 continuous (proximity) time subsamples for 99 elements with 97 dimensions:
+## 4 continuous (proximity) time subsets for 99 elements with 97 dimensions:
## 120, 80, 40, 0.
## Data was bootstrapped 100 times (method:"full").
\end{verbatim}
@@ -1491,8 +1490,8 @@ \subsection{\texorpdfstring{Metrics in the \texttt{dispRity}
\end{Shaded}
\begin{verbatim}
-## subsamples n obs
-## 1 1 50 0.201
+## subsets n obs
+## 1 1 50 0.201
\end{verbatim}
\begin{Shaded}
@@ -1504,8 +1503,8 @@ \subsection{\texorpdfstring{Metrics in the \texttt{dispRity}
\end{Shaded}
\begin{verbatim}
-## subsamples n obs
-## 1 1 50 0.028
+## subsets n obs
+## 1 1 50 0.028
\end{verbatim}
\begin{Shaded}
@@ -1517,8 +1516,8 @@ \subsection{\texorpdfstring{Metrics in the \texttt{dispRity}
\end{Shaded}
\begin{verbatim}
-## subsamples n obs
-## 1 1 50 0.0001025857
+## subsets n obs
+## 1 1 50 0.0001025857
\end{verbatim}
Note that the order of each function in the metric argument does not
@@ -1544,8 +1543,8 @@ \subsection{\texorpdfstring{Metrics in the \texttt{dispRity}
\end{Shaded}
\begin{verbatim}
-## subsamples n obs
-## [1,] TRUE TRUE TRUE
+## subsets n obs
+## [1,] TRUE TRUE TRUE
\end{verbatim}
In these examples, we considered disparity to be a single value. For
@@ -1702,12 +1701,12 @@ \subsubsection{Volumes and surface
\end{Shaded}
\begin{verbatim}
-## subsamples n obs
-## 1 1 10 257.8
+## subsets n obs
+## 1 1 10 257.8
\end{verbatim}
\begin{quote}
-Because there is only one subsample (i.e.~one matrix) in the dispRity
+Because there is only one subset (i.e.~one matrix) in the dispRity
object, this operation is the equivalent of
\texttt{ellipse.volume(dummy\_space)} (with rounding).
\end{quote}
@@ -1720,8 +1719,8 @@ \subsubsection{Volumes and surface
\end{Shaded}
\begin{verbatim}
-## subsamples n obs
-## 1 1 10 11.91
+## subsets n obs
+## 1 1 10 11.91
\end{verbatim}
\begin{Shaded}
@@ -1732,8 +1731,8 @@ \subsubsection{Volumes and surface
\end{Shaded}
\begin{verbatim}
-## subsamples n obs
-## 1 1 10 1.031
+## subsets n obs
+## 1 1 10 1.031
\end{verbatim}
The convex hull functions make a (good) estimation of the
@@ -1776,8 +1775,8 @@ \subsubsection{Ranges, variances and
\end{Shaded}
\begin{verbatim}
-## subsamples n obs.median 2.5% 25% 75% 97.5%
-## 1 1 10 2.736 1.673 2.431 2.908 3.645
+## subsets n obs.median 2.5% 25% 75% 97.5%
+## 1 1 10 2.736 1.673 2.431 2.908 3.645
\end{verbatim}
\begin{Shaded}
@@ -1788,8 +1787,8 @@ \subsubsection{Ranges, variances and
\end{Shaded}
\begin{verbatim}
-## subsamples n obs
-## 1 1 10 13.39
+## subsets n obs
+## 1 1 10 13.39
\end{verbatim}
\begin{Shaded}
@@ -1799,8 +1798,8 @@ \subsubsection{Ranges, variances and
\end{Shaded}
\begin{verbatim}
-## subsamples n obs
-## 1 1 10 114.5
+## subsets n obs
+## 1 1 10 114.5
\end{verbatim}
\begin{Shaded}
@@ -1822,8 +1821,8 @@ \subsubsection{Ranges, variances and
\end{Shaded}
\begin{verbatim}
-## subsamples n obs.median 2.5% 25% 75% 97.5%
-## 1 1 10 0.654 0.38 0.609 0.913 1.121
+## subsets n obs.median 2.5% 25% 75% 97.5%
+## 1 1 10 0.654 0.38 0.609 0.913 1.121
\end{verbatim}
\begin{Shaded}
@@ -1834,8 +1833,8 @@ \subsubsection{Ranges, variances and
\end{Shaded}
\begin{verbatim}
-## subsamples n obs
-## 1 1 10 3.675
+## subsets n obs
+## 1 1 10 3.675
\end{verbatim}
\begin{Shaded}
@@ -1845,8 +1844,8 @@ \subsubsection{Ranges, variances and
\end{Shaded}
\begin{verbatim}
-## subsamples n obs
-## 1 1 10 0.148
+## subsets n obs
+## 1 1 10 0.148
\end{verbatim}
The \texttt{diagonal} function measures the multidimensional diagonal of
@@ -1861,8 +1860,8 @@ \subsubsection{Ranges, variances and
\end{Shaded}
\begin{verbatim}
-## subsamples n obs
-## 1 1 10 3.659
+## subsets n obs
+## 1 1 10 3.659
\end{verbatim}
\begin{quote}
@@ -1885,8 +1884,8 @@ \subsubsection{Centroids metric}\label{centroids-metric}
\end{Shaded}
\begin{verbatim}
-## subsamples n obs.median 2.5% 25% 75% 97.5%
-## 1 1 10 1.435 0.788 1.267 1.993 3.167
+## subsets n obs.median 2.5% 25% 75% 97.5%
+## 1 1 10 1.435 0.788 1.267 1.993 3.167
\end{verbatim}
\begin{Shaded}
@@ -1897,8 +1896,8 @@ \subsubsection{Centroids metric}\label{centroids-metric}
\end{Shaded}
\begin{verbatim}
-## subsamples n obs
-## 1 1 10 1.435
+## subsets n obs
+## 1 1 10 1.435
\end{verbatim}
It is however possible to fix the coordinates of the centroid to a
@@ -1913,8 +1912,8 @@ \subsubsection{Centroids metric}\label{centroids-metric}
\end{Shaded}
\begin{verbatim}
-## subsamples n obs.median 2.5% 25% 75% 97.5%
-## 1 1 10 1.487 0.785 1.2 2.044 3.176
+## subsets n obs.median 2.5% 25% 75% 97.5%
+## 1 1 10 1.487 0.785 1.2 2.044 3.176
\end{verbatim}
\begin{Shaded}
@@ -1925,8 +1924,8 @@ \subsubsection{Centroids metric}\label{centroids-metric}
\end{Shaded}
\begin{verbatim}
-## subsamples n obs.median 2.5% 25% 75% 97.5%
-## 1 1 10 5.489 4.293 5.032 6.155 6.957
+## subsets n obs.median 2.5% 25% 75% 97.5%
+## 1 1 10 5.489 4.293 5.032 6.155 6.957
\end{verbatim}
\section{Summarising dispRity data
@@ -1950,7 +1949,7 @@ \subsection{\texorpdfstring{Summarising \texttt{dispRity}
\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{## Example data from previous sections}
-\NormalTok{crown_stem <-}\StringTok{ }\KeywordTok{custom.subsamples}\NormalTok{(BeckLee_mat50,}
+\NormalTok{crown_stem <-}\StringTok{ }\KeywordTok{custom.subsets}\NormalTok{(BeckLee_mat50,}
\DataTypeTok{group =} \KeywordTok{list}\NormalTok{(}\StringTok{"crown"}\NormalTok{ =}\StringTok{ }\KeywordTok{c}\NormalTok{(}\DecValTok{16}\NormalTok{, }\DecValTok{19}\OperatorTok{:}\DecValTok{41}\NormalTok{, }\DecValTok{45}\OperatorTok{:}\DecValTok{50}\NormalTok{), }
\StringTok{"stem"}\NormalTok{ =}\StringTok{ }\KeywordTok{c}\NormalTok{(}\DecValTok{1}\OperatorTok{:}\DecValTok{15}\NormalTok{, }\DecValTok{17}\OperatorTok{:}\DecValTok{18}\NormalTok{, }\DecValTok{42}\OperatorTok{:}\DecValTok{44}\NormalTok{)))}
\NormalTok{## Bootstrapping and rarefying these groups}
@@ -1958,20 +1957,20 @@ \subsection{\texorpdfstring{Summarising \texttt{dispRity}
\NormalTok{## Calculate disparity}
\NormalTok{disparity_crown_stem <-}\StringTok{ }\KeywordTok{dispRity}\NormalTok{(boot_crown_stem, }\DataTypeTok{metric =} \KeywordTok{c}\NormalTok{(sum, variances))}
-\NormalTok{## Creating time slice subsamples}
-\NormalTok{time_slices <-}\StringTok{ }\KeywordTok{time.subsamples}\NormalTok{(}\DataTypeTok{data =}\NormalTok{ BeckLee_mat99, }\DataTypeTok{tree =}\NormalTok{ BeckLee_tree, }
+\NormalTok{## Creating time slice subsets}
+\NormalTok{time_slices <-}\StringTok{ }\KeywordTok{time.subsets}\NormalTok{(}\DataTypeTok{data =}\NormalTok{ BeckLee_mat99, }\DataTypeTok{tree =}\NormalTok{ BeckLee_tree, }
\DataTypeTok{method =} \StringTok{"continuous"}\NormalTok{, }\DataTypeTok{model =} \StringTok{"proximity"}\NormalTok{, }\DataTypeTok{time =} \KeywordTok{c}\NormalTok{(}\DecValTok{120}\NormalTok{, }\DecValTok{80}\NormalTok{, }\DecValTok{40}\NormalTok{, }\DecValTok{0}\NormalTok{),}
\DataTypeTok{FADLAD =}\NormalTok{ BeckLee_ages)}
-\NormalTok{## Bootstrapping the time slice subsamples}
+\NormalTok{## Bootstrapping the time slice subsets}
\NormalTok{boot_time_slices <-}\StringTok{ }\KeywordTok{boot.matrix}\NormalTok{(time_slices, }\DataTypeTok{bootstraps =} \DecValTok{100}\NormalTok{)}
\NormalTok{## Calculate disparity}
\NormalTok{disparity_time_slices <-}\StringTok{ }\KeywordTok{dispRity}\NormalTok{(boot_time_slices, }\DataTypeTok{metric =} \KeywordTok{c}\NormalTok{(sum, variances))}
-\NormalTok{## Creating time bin subsamples}
-\NormalTok{time_bins <-}\StringTok{ }\KeywordTok{time.subsamples}\NormalTok{(}\DataTypeTok{data =}\NormalTok{ BeckLee_mat99, }\DataTypeTok{tree =}\NormalTok{ BeckLee_tree, }
+\NormalTok{## Creating time bin subsets}
+\NormalTok{time_bins <-}\StringTok{ }\KeywordTok{time.subsets}\NormalTok{(}\DataTypeTok{data =}\NormalTok{ BeckLee_mat99, }\DataTypeTok{tree =}\NormalTok{ BeckLee_tree, }
\DataTypeTok{method =} \StringTok{"discrete"}\NormalTok{, }\DataTypeTok{time =} \KeywordTok{c}\NormalTok{(}\DecValTok{120}\NormalTok{, }\DecValTok{80}\NormalTok{, }\DecValTok{40}\NormalTok{, }\DecValTok{0}\NormalTok{), }\DataTypeTok{FADLAD =}\NormalTok{ BeckLee_ages,}
\DataTypeTok{inc.nodes =} \OtherTok{TRUE}\NormalTok{)}
-\NormalTok{## Bootstrapping the time bin subsamples}
+\NormalTok{## Bootstrapping the time bin subsets}
\NormalTok{boot_time_bins <-}\StringTok{ }\KeywordTok{boot.matrix}\NormalTok{(time_bins, }\DataTypeTok{bootstraps =} \DecValTok{100}\NormalTok{)}
\NormalTok{## Calculate disparity}
\NormalTok{disparity_time_bins <-}\StringTok{ }\KeywordTok{dispRity}\NormalTok{(boot_time_bins, }\DataTypeTok{metric =} \KeywordTok{c}\NormalTok{(sum, variances))}
@@ -1988,15 +1987,15 @@ \subsection{\texorpdfstring{Summarising \texttt{dispRity}
\end{Shaded}
\begin{verbatim}
-## subsamples n obs bs.median 2.5% 25% 75% 97.5%
-## 1 120 6 2.899 2.399 1.749 2.202 2.642 2.794
-## 2 80 22 3.558 3.414 3.127 3.314 3.463 3.557
-## 3 40 15 4.032 3.764 3.569 3.694 3.842 3.954
-## 4 0 10 4.055 3.661 3.282 3.564 3.771 3.927
+## subsets n obs bs.median 2.5% 25% 75% 97.5%
+## 1 120 6 2.899 2.399 1.749 2.202 2.642 2.794
+## 2 80 22 3.558 3.414 3.127 3.314 3.463 3.557
+## 3 40 15 4.032 3.764 3.569 3.694 3.842 3.954
+## 4 0 10 4.055 3.661 3.282 3.564 3.771 3.927
\end{verbatim}
-Information about the number of elements in each subsample and the
-observed (i.e.~non-bootstrapped) disparity are also calculated. This is
+Information about the number of elements in each subset and the observed
+(i.e.~non-bootstrapped) disparity are also calculated. This is
specifically handy when rarefying the data for example:
\begin{Shaded}
@@ -2006,13 +2005,13 @@ \subsection{\texorpdfstring{Summarising \texttt{dispRity}
\end{Shaded}
\begin{verbatim}
-## subsamples n obs bs.median 2.5% 25% 75% 97.5%
-## 1 crown 30 1.995 1.930 1.866 1.909 1.950 1.970
-## 2 crown 29 NA 1.932 1.871 1.915 1.953 1.974
-## 3 crown 28 NA 1.926 1.852 1.903 1.942 1.978
-## 4 crown 27 NA 1.935 1.860 1.908 1.954 1.982
-## 5 crown 26 NA 1.931 1.859 1.908 1.953 1.977
-## 6 crown 25 NA 1.933 1.855 1.909 1.954 1.978
+## subsets n obs bs.median 2.5% 25% 75% 97.5%
+## 1 crown 30 1.995 1.930 1.866 1.909 1.950 1.970
+## 2 crown 29 NA 1.932 1.871 1.915 1.953 1.974
+## 3 crown 28 NA 1.926 1.852 1.903 1.942 1.978
+## 4 crown 27 NA 1.935 1.860 1.908 1.954 1.982
+## 5 crown 26 NA 1.931 1.859 1.908 1.953 1.977
+## 6 crown 25 NA 1.933 1.855 1.909 1.954 1.978
\end{verbatim}
The summary functions can also take various options such as:
@@ -2042,11 +2041,11 @@ \subsection{\texorpdfstring{Summarising \texttt{dispRity}
\end{Shaded}
\begin{verbatim}
-## subsamples n obs bs.sd 6% 94%
-## 1 120 6 2.899 0.293 1.917 2.762
-## 2 80 22 3.558 0.113 3.208 3.542
-## 3 40 15 4.032 0.109 3.612 3.918
-## 4 0 10 4.055 0.169 3.387 3.882
+## subsets n obs bs.sd 6% 94%
+## 1 120 6 2.899 0.293 1.917 2.762
+## 2 80 22 3.558 0.113 3.208 3.542
+## 3 40 15 4.032 0.109 3.612 3.918
+## 4 0 10 4.055 0.169 3.387 3.882
\end{verbatim}
\begin{Shaded}
@@ -2058,18 +2057,18 @@ \subsection{\texorpdfstring{Summarising \texttt{dispRity}
\begin{verbatim}
## ---- dispRity object ----
-## 4 continuous (proximity) time subsamples for 99 elements with 97 dimensions:
+## 4 continuous (proximity) time subsets for 99 elements with 97 dimensions:
## 120, 80, 40, 0.
## Data was bootstrapped 100 times (method:"full").
## Disparity was calculated as: c(sum, variances).
\end{verbatim}
\begin{verbatim}
-## subsamples n obs bs.median 2.5% 25% 75% 97.5%
-## 1 120 6 2.89926 2.39864 1.74855 2.20230 2.64153 2.79428
-## 2 80 22 3.55782 3.41432 3.12737 3.31353 3.46313 3.55661
-## 3 40 15 4.03191 3.76393 3.56890 3.69427 3.84171 3.95368
-## 4 0 10 4.05457 3.66131 3.28211 3.56431 3.77130 3.92706
+## subsets n obs bs.median 2.5% 25% 75% 97.5%
+## 1 120 6 2.89926 2.39864 1.74855 2.20230 2.64153 2.79428
+## 2 80 22 3.55782 3.41432 3.12737 3.31353 3.46313 3.55661
+## 3 40 15 4.03191 3.76393 3.56890 3.69427 3.84171 3.95368
+## 4 0 10 4.05457 3.66131 3.28211 3.56431 3.77130 3.92706
\end{verbatim}
Note that the summary table is a \texttt{data.frame}, hence it is as
@@ -2110,16 +2109,15 @@ \subsection{\texorpdfstring{Plotting \texttt{dispRity}
\begin{quote}
This argument can be left empty. In this case, the algorithm will
-automatically detect the type of subsamples from the \texttt{dispRity}
+automatically detect the type of subsets from the \texttt{dispRity}
object and plot accordingly.
\end{quote}
-It is also possible to display the number of elements in each subsample
-(as a horizontal dotted line) using the option
-\texttt{elements\ =\ TRUE}. Additionally, when the data is rarefied, one
-can indicate which level of rarefaction to display (i.e.~only display
-the results for a certain number of elements) by using the
-\texttt{rarefaction} argument.
+It is also possible to display the number of elements in each subset (as
+a horizontal dotted line) using the option \texttt{elements\ =\ TRUE}.
+Additionally, when the data is rarefied, one can indicate which level of
+rarefaction to display (i.e.~only display the results for a certain
+number of elements) by using the \texttt{rarefaction} argument.
\begin{Shaded}
\begin{Highlighting}[]
@@ -2194,7 +2192,7 @@ \subsection{\texorpdfstring{Plotting \texttt{dispRity}
\NormalTok{## Plotting the results with some plot.dispRity arguments}
\KeywordTok{plot}\NormalTok{(disparity_time_slices, }\DataTypeTok{quantile =} \KeywordTok{c}\NormalTok{(}\KeywordTok{seq}\NormalTok{(}\DataTypeTok{from =} \DecValTok{10}\NormalTok{, }\DataTypeTok{to =} \DecValTok{100}\NormalTok{, }\DataTypeTok{by =} \DecValTok{10}\NormalTok{)),}
\DataTypeTok{cent.tend =}\NormalTok{ sd, }\DataTypeTok{type =} \StringTok{"c"}\NormalTok{, }\DataTypeTok{elements =} \OtherTok{TRUE}\NormalTok{, }\DataTypeTok{col =} \KeywordTok{c}\NormalTok{(}\StringTok{"black"}\NormalTok{, }\KeywordTok{rainbow}\NormalTok{(}\DecValTok{10}\NormalTok{)),}
- \DataTypeTok{ylab =} \KeywordTok{c}\NormalTok{(}\StringTok{"Disparity"}\NormalTok{, }\StringTok{"Diversity"}\NormalTok{), }\DataTypeTok{time.subsamples =} \OtherTok{FALSE}\NormalTok{,}
+ \DataTypeTok{ylab =} \KeywordTok{c}\NormalTok{(}\StringTok{"Disparity"}\NormalTok{, }\StringTok{"Diversity"}\NormalTok{), }\DataTypeTok{time.subsets =} \OtherTok{FALSE}\NormalTok{,}
\DataTypeTok{xlab =} \StringTok{"Time (in in units from past to present)"}\NormalTok{, }\DataTypeTok{observed =} \OtherTok{TRUE}\NormalTok{,}
\DataTypeTok{main =} \StringTok{"Many more options..."}\NormalTok{)}
\end{Highlighting}
@@ -2278,24 +2276,22 @@ \section{Testing disparity
\begin{itemize}
\tightlist
\item
- \texttt{pairwise} (default): to compare each subsample in a pairwise
+ \texttt{pairwise} (default): to compare each subset in a pairwise
manner
\item
- \texttt{referential}: to compare each subsample to the first subsample
+ \texttt{referential}: to compare each subset to the first subset
\item
- \texttt{sequential}: to compare each subsample to the following
- subsample
+ \texttt{sequential}: to compare each subset to the following subset
\item
- \texttt{all}: to compare all the subsamples together (like in analysis
- of variance)
+ \texttt{all}: to compare all the subsets together (like in analysis of
+ variance)
\end{itemize}
It is also possible to input a list of pairs of \texttt{numeric} values
-or \texttt{characters} matching the subsample names to create
-personalised tests. Some other tests implemented in \texttt{dispRity}
-such as the \texttt{dispRity::null.test} have a specific way they are
-applied to the data and therefore ignore the \texttt{comparisons}
-argument.
+or \texttt{characters} matching the subset names to create personalised
+tests. Some other tests implemented in \texttt{dispRity} such as the
+\texttt{dispRity::null.test} have a specific way they are applied to the
+data and therefore ignore the \texttt{comparisons} argument.
The \texttt{test} argument can be any statistical or non-statistical
test to apply to the disparity object. It can be a common statistical
@@ -2436,11 +2432,11 @@ \section{Testing disparity
\begin{verbatim}
##
## Call:
-## test(formula = data ~ subsamples, data = data)
+## test(formula = data ~ subsets, data = data)
##
## Coefficients:
-## (Intercept) subsamples120 subsamples40 subsamples80
-## 3.6517 -1.2627 0.1145 -0.2634
+## (Intercept) subsets120 subsets40 subsets80
+## 3.6517 -1.2627 0.1145 -0.2634
\end{verbatim}
\begin{Shaded}
@@ -2464,18 +2460,18 @@ \section{Testing disparity
\begin{verbatim}
##
## Call:
-## test(formula = data ~ subsamples, data = data)
+## test(formula = data ~ subsets, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.91282 -0.08285 0.00993 0.10745 0.56989
##
## Coefficients:
-## Estimate Std. Error t value Pr(>|t|)
-## (Intercept) 3.65169 0.01865 195.810 < 2e-16 ***
-## subsamples120 -1.26273 0.02637 -47.878 < 2e-16 ***
-## subsamples40 0.11452 0.02637 4.342 1.79e-05 ***
-## subsamples80 -0.26339 0.02637 -9.987 < 2e-16 ***
+## Estimate Std. Error t value Pr(>|t|)
+## (Intercept) 3.65169 0.01865 195.810 < 2e-16 ***
+## subsets120 -1.26273 0.02637 -47.878 < 2e-16 ***
+## subsets40 0.11452 0.02637 4.342 1.79e-05 ***
+## subsets80 -0.26339 0.02637 -9.987 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
@@ -2542,11 +2538,11 @@ \section{Testing disparity
\end{Shaded}
\begin{verbatim}
-## subsamples n obs.median bs.median 2.5% 25% 75% 97.5%
-## 1 120 6 1.536 1.375 0.864 1.187 1.611 1.886
-## 2 80 22 1.871 1.807 1.481 1.681 1.904 2.075
-## 3 40 15 1.943 1.885 1.529 1.777 1.989 2.097
-## 4 0 10 1.910 1.802 1.463 1.687 1.960 2.095
+## subsets n obs.median bs.median 2.5% 25% 75% 97.5%
+## 1 120 6 1.536 1.375 0.864 1.187 1.611 1.886
+## 2 80 22 1.871 1.807 1.481 1.681 1.904 2.075
+## 3 40 15 1.943 1.885 1.529 1.777 1.989 2.097
+## 4 0 10 1.910 1.802 1.463 1.687 1.960 2.095
\end{verbatim}
\begin{Shaded}
@@ -2557,11 +2553,11 @@ \section{Testing disparity
\end{Shaded}
\begin{verbatim}
-## subsamples n obs bs.median 2.5% 25% 75% 97.5%
-## 1 120 6 1.536 1.313 0.822 1.260 1.464 1.592
-## 2 80 22 1.871 1.803 1.700 1.774 1.830 1.864
-## 3 40 15 1.943 1.887 1.764 1.847 1.928 1.968
-## 4 0 10 1.910 1.814 1.593 1.773 1.844 1.917
+## subsets n obs bs.median 2.5% 25% 75% 97.5%
+## 1 120 6 1.536 1.313 0.822 1.260 1.464 1.592
+## 2 80 22 1.871 1.803 1.700 1.774 1.830 1.864
+## 3 40 15 1.943 1.887 1.764 1.847 1.928 1.968
+## 4 0 10 1.910 1.814 1.593 1.773 1.844 1.917
\end{verbatim}
We can see that the summary message for the distribution is slightly
@@ -3310,7 +3306,7 @@ \section{\texorpdfstring{Manipulating \texttt{dispRity}
\end{Shaded}
\begin{verbatim}
-## [1] "matrix" "call" "subsamples" "disparity"
+## [1] "matrix" "call" "subsets" "disparity"
\end{verbatim}
\begin{Shaded}
@@ -3322,7 +3318,7 @@ \section{\texorpdfstring{Manipulating \texttt{dispRity}
\begin{verbatim}
## ---- dispRity object ----
-## 7 continuous (acctran) time subsamples for 99 elements with 97 dimensions:
+## 7 continuous (acctran) time subsets for 99 elements with 97 dimensions:
## 90, 80, 70, 60, 50 ...
## Data was bootstrapped 100 times (method:"full") and rarefied to 20, 15, 10, 5 elements.
## Disparity was calculated as: c(median, centroids).
@@ -3441,7 +3437,7 @@ \subsubsection{\texorpdfstring{\texttt{matrix.dispRity}}{matrix.dispRity}}\label
\begin{Highlighting}[]
\NormalTok{## Extracting the 3rd bootstrapped matrix with the 2nd rarefaction level}
\NormalTok{## (15 elements) from the second group (80 Mya)}
-\KeywordTok{str}\NormalTok{(}\KeywordTok{matrix.dispRity}\NormalTok{(disparity, }\DataTypeTok{subsamples =} \DecValTok{1}\NormalTok{, }\DataTypeTok{bootstrap =} \DecValTok{3}\NormalTok{, }\DataTypeTok{rarefaction =} \DecValTok{2}\NormalTok{))}
+\KeywordTok{str}\NormalTok{(}\KeywordTok{matrix.dispRity}\NormalTok{(disparity, }\DataTypeTok{subsets =} \DecValTok{1}\NormalTok{, }\DataTypeTok{bootstrap =} \DecValTok{3}\NormalTok{, }\DataTypeTok{rarefaction =} \DecValTok{2}\NormalTok{))}
\end{Highlighting}
\end{Shaded}
@@ -3452,18 +3448,18 @@ \subsubsection{\texorpdfstring{\texttt{matrix.dispRity}}{matrix.dispRity}}\label
## ..$ : NULL
\end{verbatim}
-\subsubsection{\texorpdfstring{\texttt{get.subsamples.dispRity}}{get.subsamples.dispRity}}\label{get.subsamples.disprity}
+\subsubsection{\texorpdfstring{\texttt{get.subsets.dispRity}}{get.subsets.dispRity}}\label{get.subsets.disprity}
This function creates a dispRity object that contains only elements from
-one specific subsamples.
+one specific subsets.
\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{## Extracting all the data for the crown mammals}
-\NormalTok{(crown_mammals <-}\StringTok{ }\KeywordTok{get.subsamples.dispRity}\NormalTok{(disp_crown_stemBS, }\StringTok{"Group.crown"}\NormalTok{))}
+\NormalTok{(crown_mammals <-}\StringTok{ }\KeywordTok{get.subsets.dispRity}\NormalTok{(disp_crown_stemBS, }\StringTok{"Group.crown"}\NormalTok{))}
-\NormalTok{## The object keeps the properties of the parent object but is composed of only one subsamples}
-\KeywordTok{length}\NormalTok{(crown_mammals}\OperatorTok{$}\NormalTok{subsamples)}
+\NormalTok{## The object keeps the properties of the parent object but is composed of only one subsets}
+\KeywordTok{length}\NormalTok{(crown_mammals}\OperatorTok{$}\NormalTok{subsets)}
\end{Highlighting}
\end{Shaded}
@@ -3478,8 +3474,8 @@ \subsubsection{\texorpdfstring{\texttt{extract.dispRity}}{extract.dispRity}}\lab
\KeywordTok{extract.dispRity}\NormalTok{(disparity)}
\NormalTok{## Extracting the disparity from the bootstrapped values from the}
-\NormalTok{## 10th rarefaction level from the second subsamples (80 Mya)}
-\KeywordTok{extract.dispRity}\NormalTok{(disparity, }\DataTypeTok{observed =} \OtherTok{FALSE}\NormalTok{, }\DataTypeTok{subsamples =} \DecValTok{2}\NormalTok{, }\DataTypeTok{rarefaction =} \DecValTok{10}\NormalTok{)}
+\NormalTok{## 10th rarefaction level from the second subsets (80 Mya)}
+\KeywordTok{extract.dispRity}\NormalTok{(disparity, }\DataTypeTok{observed =} \OtherTok{FALSE}\NormalTok{, }\DataTypeTok{subsets =} \DecValTok{2}\NormalTok{, }\DataTypeTok{rarefaction =} \DecValTok{10}\NormalTok{)}
\end{Highlighting}
\end{Shaded}
@@ -3490,7 +3486,7 @@ \subsubsection{\texorpdfstring{\texttt{scale.dispRity}}{scale.dispRity}}\label{s
\begin{Shaded}
\begin{Highlighting}[]
-\NormalTok{## Getting the disparity values of the time subsamples}
+\NormalTok{## Getting the disparity values of the time subsets}
\KeywordTok{head}\NormalTok{(}\KeywordTok{summary}\NormalTok{(disparity))}
\NormalTok{## Scaling the same disparity values}
@@ -3503,12 +3499,12 @@ \subsubsection{\texorpdfstring{\texttt{scale.dispRity}}{scale.dispRity}}\label{s
\subsubsection{\texorpdfstring{\texttt{sort.dispRity}}{sort.dispRity}}\label{sort.disprity}
-This is the S3 method of \texttt{sort} for sorting the subsamples
+This is the S3 method of \texttt{sort} for sorting the subsets
alphabetically (default) or following a specific pattern.
\begin{Shaded}
\begin{Highlighting}[]
-\NormalTok{## Sorting the disparity subsamples in inverse alphabetic order}
+\NormalTok{## Sorting the disparity subsets in inverse alphabetic order}
\KeywordTok{head}\NormalTok{(}\KeywordTok{summary}\NormalTok{(}\KeywordTok{sort}\NormalTok{(disparity, }\DataTypeTok{decreasing =} \OtherTok{TRUE}\NormalTok{)))}
\NormalTok{## Customised sorting}
@@ -3534,8 +3530,8 @@ \section{\texorpdfstring{The \texttt{dispRity} object
\texttt{\$call}: an object of class \texttt{list} containing
information on the \texttt{dispRity} object content.
\item
- \texttt{\$subsamples}: an object of class \texttt{list} containing the
- subsamples of the multidimensional space.
+ \texttt{\$subsets}: an object of class \texttt{list} containing the
+ subsets of the multidimensional space.
\item
\texttt{\$disparity}: an object of class \texttt{list} containing the
disparity values.
@@ -3549,7 +3545,7 @@ \section{\texorpdfstring{The \texttt{dispRity} object
time: the metric functions are called through apply family function and
can be parallelised; and (2) a really low memory footprint: at any time,
only one matrix is present in the \texttt{R} environment rather than
-multiple copies of it for each subsample.
+multiple copies of it for each subset.
\subsection{\texorpdfstring{\texttt{\$matrix}}{\$matrix}}\label{matrix}
@@ -3568,13 +3564,13 @@ \subsection{\texorpdfstring{\texttt{\$call}}{\$call}}\label{call}
\begin{itemize}
\tightlist
\item
- \texttt{\$call\$subsamples}: a vector of \texttt{character} with
- information on the subsamples type (either \texttt{"continuous"},
+ \texttt{\$call\$subsets}: a vector of \texttt{character} with
+ information on the subsets type (either \texttt{"continuous"},
\texttt{"discrete"} or \texttt{"custom"}) and their eventual model
(\texttt{"acctran"}, \texttt{"deltran"}, \texttt{"random"},
\texttt{"proximity"}, \texttt{"punctuated"}, \texttt{"gradual"}). This
- element generated only once via \texttt{time.subsamples()} and
- \texttt{custom.subsamples()}.
+ element generated only once via \texttt{time.subsets()} and
+ \texttt{custom.subsets()}.
\item
\texttt{\$call\$dimensions}: either a single \texttt{numeric} value
indicating how many dimensions to use or a vector of \texttt{numeric}
@@ -3605,22 +3601,22 @@ \subsection{\texorpdfstring{\texttt{\$call}}{\$call}}\label{call}
the first metric(s), the second, the second metric(s), etc.).
\end{itemize}
-\subsection{\texorpdfstring{\texttt{\$subsamples}}{\$subsamples}}\label{subsamples}
+\subsection{\texorpdfstring{\texttt{\$subsets}}{\$subsets}}\label{subsets}
-This element contain the eventual subsamples of the multidimensional
-space. It is a \texttt{list} of subsample names. Each subsample name is
-in turn a \texttt{list} of at least one element called \texttt{elements}
-which is in turn a \texttt{matrix}. This \texttt{elements} matrix is the
-raw (observed) elements in the subsamples. The \texttt{elements} matrix
-is composed of \texttt{numeric} values in one column and \emph{n} rows
-(the number of elements in the subsample). Each of these values are a
+This element contain the eventual subsets of the multidimensional space.
+It is a \texttt{list} of subset names. Each subset name is in turn a
+\texttt{list} of at least one element called \texttt{elements} which is
+in turn a \texttt{matrix}. This \texttt{elements} matrix is the raw
+(observed) elements in the subsets. The \texttt{elements} matrix is
+composed of \texttt{numeric} values in one column and \emph{n} rows (the
+number of elements in the subset). Each of these values are a
``pointer'' (\texttt{C} inspired) to the element of the
\texttt{\$matrix}. For example, lets assume a \texttt{dispRity} object
-called \texttt{disparity}, composed of at least one subsamples called
+called \texttt{disparity}, composed of at least one subsets called
\texttt{sub1}:
\begin{verbatim}
- disparity$subsamples$sub1$elements
+ disparity$subsets$sub1$elements
[,1]
[1,] 5
[2,] 4
@@ -3630,13 +3626,13 @@ \subsection{\texorpdfstring{\texttt{\$subsamples}}{\$subsamples}}\label{subsampl
The values in the matrix ``point'' to the elements in \texttt{\$matrix}:
here, the multidimensional space with only the 4th, 5th, 6th and 7th
-elements. The following elements in \texttt{diparity\$subsamples\$sub1}
+elements. The following elements in \texttt{diparity\$subsets\$sub1}
will correspond to the same ``pointers'' but drawn from the bootstrap
replicates. The columns will correspond to different bootstrap
replicates. For example:
\begin{verbatim}
- disparity$subsamples$sub1[[2]]
+ disparity$subsets$sub1[[2]]
[,1] [,2] [,3] [,4]
[1,] 57 43 70 4
[2,] 43 44 4 4
@@ -3655,13 +3651,12 @@ \subsection{\texorpdfstring{\texttt{\$subsamples}}{\$subsamples}}\label{subsampl
\subsection{\texorpdfstring{\texttt{\$disparity}}{\$disparity}}\label{disparity}
-The \texttt{\$disparity} element is identical to the
-\texttt{\$subsamples} element structure (a list of list(s) containing
-matrices) but the matrices don't contain ``pointers'' to
-\texttt{\$matrix} but the disparity result of the disparity metric
-applied to the ``pointers''. For example, in our first example
-(\texttt{\$elements}) from above, if the disparity metric is of
-dimensions level 1, we would have:
+The \texttt{\$disparity} element is identical to the \texttt{\$subsets}
+element structure (a list of list(s) containing matrices) but the
+matrices don't contain ``pointers'' to \texttt{\$matrix} but the
+disparity result of the disparity metric applied to the ``pointers''.
+For example, in our first example (\texttt{\$elements}) from above, if
+the disparity metric is of dimensions level 1, we would have:
\begin{verbatim}
disparity$disparity$sub1$elements
@@ -3669,7 +3664,7 @@ \subsection{\texorpdfstring{\texttt{\$disparity}}{\$disparity}}\label{disparity}
[1,] 1.82
\end{verbatim}
-This is the observed disparity (1.82) for the subsample called
+This is the observed disparity (1.82) for the subset called
\texttt{sub1}. If the disparity metric is of dimension level 2 (say the
function \texttt{range} that outputs two values), we would have:
@@ -3808,15 +3803,15 @@ \subsection{Splitting the morphospace through
time}\label{splitting-the-morphospace-through-time}
One of the crucial steps in disparity-through-time analysis is to split
-the full morphospace into smaller time subsamples that contain the total
+the full morphospace into smaller time subsets that contain the total
number of morphologies at certain points in time (time-slicing) or
during certain periods in time (time-binning). Basically, the full
morphospace represents the total number of morphologies across all time
-and will be greater than any of the time subsamples of the morphospace.
+and will be greater than any of the time subsets of the morphospace.
-The \texttt{dispRity} package provides a \texttt{time.subsamples}
-function that allows users to split the morphospace into time slices
-(using \texttt{method\ =\ continuous}) or into time bins (using
+The \texttt{dispRity} package provides a \texttt{time.subsets} function
+that allows users to split the morphospace into time slices (using
+\texttt{method\ =\ continuous}) or into time bins (using
\texttt{method\ =\ discrete}). In this example, we are going to split
the morphospace into five equal time bins of 20 million years long from
100 million years ago to the present. We will also provide to the
@@ -3837,8 +3832,8 @@ \subsection{Splitting the morphospace through
\begin{Shaded}
\begin{Highlighting}[]
-\NormalTok{## Splitting the morphospace using the time.subsamples function}
-\NormalTok{(binned_morphospace <-}\StringTok{ }\KeywordTok{time.subsamples}\NormalTok{(}\DataTypeTok{data =}\NormalTok{ BeckLee_mat50, }\DataTypeTok{tree =}\NormalTok{ BeckLee_tree,}
+\NormalTok{## Splitting the morphospace using the time.subsets function}
+\NormalTok{(binned_morphospace <-}\StringTok{ }\KeywordTok{time.subsets}\NormalTok{(}\DataTypeTok{data =}\NormalTok{ BeckLee_mat50, }\DataTypeTok{tree =}\NormalTok{ BeckLee_tree,}
\DataTypeTok{method =} \StringTok{"discrete"}\NormalTok{, }\DataTypeTok{time =}\NormalTok{ time_bins, }\DataTypeTok{inc.nodes =} \OtherTok{FALSE}\NormalTok{,}
\DataTypeTok{FADLAD =}\NormalTok{ BeckLee_ages))}
\end{Highlighting}
@@ -3846,14 +3841,14 @@ \subsection{Splitting the morphospace through
\begin{verbatim}
## ---- dispRity object ----
-## 5 discrete time subsamples for 50 elements:
+## 5 discrete time subsets for 50 elements:
## 100 - 80, 80 - 60, 60 - 40, 40 - 20, 20 - 0.
\end{verbatim}
The output object is a \texttt{dispRity} object (see more about that
\protect\hyperlink{The-guts-of-the-dispRity-package}{here}. In brief,
however, \texttt{dispRity} objects are lists of different elements
-(i.e.~disparity results, morphospace time subsamples, morphospace
+(i.e.~disparity results, morphospace time subsets, morphospace
attributes, etc.) that display only a summary of the object when calling
the object to avoiding filling the \texttt{R} console with superfluous
output.
@@ -3878,13 +3873,13 @@ \subsection{Splitting the morphospace through
\begin{verbatim}
## List of 3
-## $ matrix : num [1:50, 1:48] -0.532 -0.409 -0.692 -0.68 -0.739 ...
+## $ matrix : num [1:50, 1:48] -0.532 -0.409 -0.692 -0.68 -0.739 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:50] "Cimolestes" "Maelestes" "Batodon" "Bulaklestes" ...
## .. ..$ : NULL
-## $ call :List of 1
-## ..$ subsamples: chr "discrete"
-## $ subsamples:List of 5
+## $ call :List of 1
+## ..$ subsets: chr "discrete"
+## $ subsets:List of 5
## ..$ 100 - 80:List of 1
## .. ..$ elements: int [1:8, 1] 5 4 6 8 43 10 11 42
## ..$ 80 - 60 :List of 1
@@ -3905,7 +3900,7 @@ \subsection{Splitting the morphospace through
\end{Shaded}
\begin{verbatim}
-## [1] "matrix" "call" "subsamples"
+## [1] "matrix" "call" "subsets"
\end{verbatim}
\begin{Shaded}
@@ -3917,7 +3912,7 @@ \subsection{Splitting the morphospace through
\begin{verbatim}
## ---- dispRity object ----
-## 5 discrete time subsamples for 50 elements:
+## 5 discrete time subsets for 50 elements:
## 100 - 80, 80 - 60, 60 - 40, 40 - 20, 20 - 0.
\end{verbatim}
@@ -3928,32 +3923,32 @@ \subsection{Splitting the morphospace through
\subsection{Bootstrapping the data}\label{bootstrapping-the-data}
-Once we obtain our different time subsamples, we can bootstrap and
-rarefy them (i.e.~pseudo-replicating the data). The bootstrapping allows
-us to make each subsample more robust to outliers and the rarefaction
-allows us to compare subsamples with the same number of taxa to remove
-sampling biases (i.e.~more taxa in one subsample than the others). The
+Once we obtain our different time subsets, we can bootstrap and rarefy
+them (i.e.~pseudo-replicating the data). The bootstrapping allows us to
+make each subset more robust to outliers and the rarefaction allows us
+to compare subsets with the same number of taxa to remove sampling
+biases (i.e.~more taxa in one subset than the others). The
\texttt{boot.matrix} function bootstraps the \texttt{dispRity} object
and the \texttt{rarefaction} option within performs rarefaction.
\begin{Shaded}
\begin{Highlighting}[]
-\NormalTok{## Bootstrapping each time subsample 100 times (default)}
+\NormalTok{## Bootstrapping each time subset 100 times (default)}
\NormalTok{(boot_bin_morphospace <-}\StringTok{ }\KeywordTok{boot.matrix}\NormalTok{(binned_morphospace))}
\end{Highlighting}
\end{Shaded}
\begin{verbatim}
## ---- dispRity object ----
-## 5 discrete time subsamples for 50 elements with 48 dimensions:
+## 5 discrete time subsets for 50 elements with 48 dimensions:
## 100 - 80, 80 - 60, 60 - 40, 40 - 20, 20 - 0.
## Data was bootstrapped 100 times (method:"full").
\end{verbatim}
\begin{Shaded}
\begin{Highlighting}[]
-\NormalTok{## Getting the minimum number of rows (i.e. taxa) in the time subsamples}
-\KeywordTok{min}\NormalTok{(}\KeywordTok{size.subsamples}\NormalTok{(boot_bin_morphospace))}
+\NormalTok{## Getting the minimum number of rows (i.e. taxa) in the time subsets}
+\KeywordTok{min}\NormalTok{(}\KeywordTok{size.subsets}\NormalTok{(boot_bin_morphospace))}
\end{Highlighting}
\end{Shaded}
@@ -3963,7 +3958,7 @@ \subsection{Bootstrapping the data}\label{bootstrapping-the-data}
\begin{Shaded}
\begin{Highlighting}[]
-\NormalTok{## Bootstrapping each time subsample 100 times and rarefying them }
+\NormalTok{## Bootstrapping each time subset 100 times and rarefying them }
\NormalTok{(rare_bin_morphospace <-}\StringTok{ }\KeywordTok{boot.matrix}\NormalTok{(binned_morphospace, }\DataTypeTok{bootstraps =} \DecValTok{100}\NormalTok{,}
\DataTypeTok{rarefaction =} \DecValTok{6}\NormalTok{))}
\end{Highlighting}
@@ -3971,27 +3966,27 @@ \subsection{Bootstrapping the data}\label{bootstrapping-the-data}
\begin{verbatim}
## ---- dispRity object ----
-## 5 discrete time subsamples for 50 elements with 48 dimensions:
+## 5 discrete time subsets for 50 elements with 48 dimensions:
## 100 - 80, 80 - 60, 60 - 40, 40 - 20, 20 - 0.
## Data was bootstrapped 100 times (method:"full") and rarefied to 6 elements.
\end{verbatim}
\subsection{Calculating disparity}\label{calculating-disparity}
-We can now calculate the disparity within each time subsamples along
-with some confidence intervals generated by the pseudoreplication step
-above (bootstraps/rarefaction). Disparity can be calculated in many ways
-and this package allows users to come up with their own disparity
-metrics. For more details, please refer to the
+We can now calculate the disparity within each time subsets along with
+some confidence intervals generated by the pseudoreplication step above
+(bootstraps/rarefaction). Disparity can be calculated in many ways and
+this package allows users to come up with their own disparity metrics.
+For more details, please refer to the
\protect\hyperlink{disparity-metrics}{\texttt{dispRity} metric section}.
In this example, we are going to calculate the spread of the data in
-each time subsample by calculating disparity as the sum of the variance
-of each dimension of the morphospace in each time subsample using the
+each time subset by calculating disparity as the sum of the variance of
+each dimension of the morphospace in each time subset using the
\texttt{dispRity} function. Thus, in this example, disparity is defined
-by the multi-dimensional variance of each time subsample (i.e.~the
-spread of the taxa within the morphospace). Note that this metric comes
-with a caveat (not solved here) since it ignores covariances among the
+by the multi-dimensional variance of each time subset (i.e.~the spread
+of the taxa within the morphospace). Note that this metric comes with a
+caveat (not solved here) since it ignores covariances among the
dimensions of the morphospace. We use this here because it is a standard
metric used in disparity-through-time analysis (Wills \emph{et al.}
1994).
@@ -4005,7 +4000,7 @@ \subsection{Calculating disparity}\label{calculating-disparity}
\begin{verbatim}
## ---- dispRity object ----
-## 5 discrete time subsamples for 50 elements with 48 dimensions:
+## 5 discrete time subsets for 50 elements with 48 dimensions:
## 100 - 80, 80 - 60, 60 - 40, 40 - 20, 20 - 0.
## Data was bootstrapped 100 times (method:"full").
## Disparity was calculated as: c(sum, variances).
@@ -4020,7 +4015,7 @@ \subsection{Calculating disparity}\label{calculating-disparity}
\begin{verbatim}
## ---- dispRity object ----
-## 5 discrete time subsamples for 50 elements with 48 dimensions:
+## 5 discrete time subsets for 50 elements with 48 dimensions:
## 100 - 80, 80 - 60, 60 - 40, 40 - 20, 20 - 0.
## Data was bootstrapped 100 times (method:"full") and rarefied to 6 elements.
## Disparity was calculated as: c(sum, variances).
@@ -4028,8 +4023,8 @@ \subsection{Calculating disparity}\label{calculating-disparity}
The \texttt{dispRity} function does not actually display the calculated
disparity values but rather only the properties of the disparity object
-(size, subsamples, metric, etc.). To display the actual calculated
-scores, we need to summarise the disparity object using the S3 method
+(size, subsets, metric, etc.). To display the actual calculated scores,
+we need to summarise the disparity object using the S3 method
\texttt{summary} that is applied to a \texttt{dispRity} object (see
\texttt{?summary.dispRity} for more details).
@@ -4044,12 +4039,12 @@ \subsection{Calculating disparity}\label{calculating-disparity}
\end{Shaded}
\begin{verbatim}
-## subsamples n obs bs.median 2.5% 25% 75% 97.5%
-## 1 100 - 80 8 1.675 1.488 1.087 1.389 1.568 1.648
-## 2 80 - 60 15 1.782 1.679 1.538 1.631 1.728 1.792
-## 3 60 - 40 13 1.913 1.772 1.607 1.734 1.826 1.886
-## 4 40 - 20 6 2.022 1.707 1.212 1.537 1.822 1.942
-## 5 20 - 0 10 1.971 1.794 1.598 1.716 1.842 1.890
+## subsets n obs bs.median 2.5% 25% 75% 97.5%
+## 1 100 - 80 8 1.675 1.488 1.087 1.389 1.568 1.648
+## 2 80 - 60 15 1.782 1.679 1.538 1.631 1.728 1.792
+## 3 60 - 40 13 1.913 1.772 1.607 1.734 1.826 1.886
+## 4 40 - 20 6 2.022 1.707 1.212 1.537 1.822 1.942
+## 5 20 - 0 10 1.971 1.794 1.598 1.716 1.842 1.890
\end{verbatim}
\begin{Shaded}
@@ -4059,16 +4054,16 @@ \subsection{Calculating disparity}\label{calculating-disparity}
\end{Shaded}
\begin{verbatim}
-## subsamples n obs bs.median 2.5% 25% 75% 97.5%
-## 1 100 - 80 8 1.675 1.484 1.194 1.400 1.547 1.636
-## 2 100 - 80 6 NA 1.477 0.993 1.361 1.569 1.698
-## 3 80 - 60 15 1.782 1.674 1.517 1.600 1.725 1.793
-## 4 80 - 60 6 NA 1.655 1.299 1.532 1.754 1.882
-## 5 60 - 40 13 1.913 1.767 1.601 1.714 1.829 1.861
-## 6 60 - 40 6 NA 1.787 1.314 1.672 1.879 1.984
-## 7 40 - 20 6 2.022 1.736 1.281 1.603 1.822 1.948
-## 8 20 - 0 10 1.971 1.807 1.595 1.729 1.856 1.917
-## 9 20 - 0 6 NA 1.790 1.435 1.718 1.873 1.995
+## subsets n obs bs.median 2.5% 25% 75% 97.5%
+## 1 100 - 80 8 1.675 1.484 1.194 1.400 1.547 1.636
+## 2 100 - 80 6 NA 1.477 0.993 1.361 1.569 1.698
+## 3 80 - 60 15 1.782 1.674 1.517 1.600 1.725 1.793
+## 4 80 - 60 6 NA 1.655 1.299 1.532 1.754 1.882
+## 5 60 - 40 13 1.913 1.767 1.601 1.714 1.829 1.861
+## 6 60 - 40 6 NA 1.787 1.314 1.672 1.879 1.984
+## 7 40 - 20 6 2.022 1.736 1.281 1.603 1.822 1.948
+## 8 20 - 0 10 1.971 1.807 1.595 1.729 1.856 1.917
+## 9 20 - 0 6 NA 1.790 1.435 1.718 1.873 1.995
\end{verbatim}
\begin{quote}
@@ -4108,8 +4103,8 @@ \section{Testing differences}\label{testing-differences}
bin \emph{n+2}, etc. Because our data is temporally autocorrelated
(i.e.~what happens in bin \emph{n+1} depends on what happened in bin
\emph{n}) and pseudoreplicated (i.e.~each bootstrap draw creates
-non-independent time subsamples because they are all based on the same
-time subsamples), we apply a non-parametric mean comparison: the
+non-independent time subsets because they are all based on the same time
+subsets), we apply a non-parametric mean comparison: the
\texttt{wilcox.test}. Also, we need to apply a p-value correction
(e.g.~Bonferroni correction) to correct for multiple testing (see
\texttt{?p.adjust} for more details).
@@ -4292,51 +4287,51 @@ \section{Classic analysis}\label{classic-analysis}
\section{\texorpdfstring{A multidimensional approach with
\texttt{dispRity}}{A multidimensional approach with dispRity}}\label{a-multidimensional-approach-with-disprity}
-The first step is to create different subsamples that represent
-subsamples of the ordinated space (i.e.~sub-regions within the
-\emph{n}-dimensional object). Each of these subsamples will contain only
-the individuals of a specific species.
+The first step is to create different subsets that represent subsets of
+the ordinated space (i.e.~sub-regions within the \emph{n}-dimensional
+object). Each of these subsets will contain only the individuals of a
+specific species.
\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{## Creating the table that contain the elements and their attributes}
-\NormalTok{petal_subsamples <-}\StringTok{ }\KeywordTok{custom.subsamples}\NormalTok{(petal_space, }\DataTypeTok{group =} \KeywordTok{list}\NormalTok{(}
+\NormalTok{petal_subsets <-}\StringTok{ }\KeywordTok{custom.subsets}\NormalTok{(petal_space, }\DataTypeTok{group =} \KeywordTok{list}\NormalTok{(}
\StringTok{"setosa"}\NormalTok{ =}\StringTok{ }\KeywordTok{which}\NormalTok{(species }\OperatorTok{==}\StringTok{ "setosa"}\NormalTok{),}
\StringTok{"versicolor"}\NormalTok{ =}\StringTok{ }\KeywordTok{which}\NormalTok{(species }\OperatorTok{==}\StringTok{ "versicolor"}\NormalTok{),}
\StringTok{"virginica"}\NormalTok{ =}\StringTok{ }\KeywordTok{which}\NormalTok{(species }\OperatorTok{==}\StringTok{ "virginica"}\NormalTok{)))}
\NormalTok{## Visualising the dispRity object content}
-\NormalTok{petal_subsamples}
+\NormalTok{petal_subsets}
\end{Highlighting}
\end{Shaded}
\begin{verbatim}
## ---- dispRity object ----
-## 3 customised subsamples for 150 elements:
+## 3 customised subsets for 150 elements:
## setosa, versicolor, virginica.
\end{verbatim}
This created a \texttt{dispRity} object (more about that
-\protect\hyperlink{guts}{here}) with three subsamples corresponding to
-each subspecies.
+\protect\hyperlink{guts}{here}) with three subsets corresponding to each
+subspecies.
\subsection{Bootstrapping the data}\label{bootstrapping-the-data-1}
-We can the bootstrap the subsamples to be able test the robustness of
-the measured disparity to outliers. We can do that using the default
-options of \texttt{boot.matrix} (more about that
+We can the bootstrap the subsets to be able test the robustness of the
+measured disparity to outliers. We can do that using the default options
+of \texttt{boot.matrix} (more about that
\protect\hyperlink{bootstraps-and-rarefactions}{here}):
\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{## Bootstrapping the data}
-\NormalTok{(petal_bootstrapped <-}\StringTok{ }\KeywordTok{boot.matrix}\NormalTok{(petal_subsamples))}
+\NormalTok{(petal_bootstrapped <-}\StringTok{ }\KeywordTok{boot.matrix}\NormalTok{(petal_subsets))}
\end{Highlighting}
\end{Shaded}
\begin{verbatim}
## ---- dispRity object ----
-## 3 customised subsamples for 150 elements with 4 dimensions:
+## 3 customised subsets for 150 elements with 4 dimensions:
## setosa, versicolor, virginica.
## Data was bootstrapped 100 times (method:"full").
\end{verbatim}
@@ -4368,7 +4363,7 @@ \subsection{Calculating disparity}\label{calculating-disparity-1}
\begin{verbatim}
## ---- dispRity object ----
-## 3 customised subsamples for 150 elements with 4 dimensions:
+## 3 customised subsets for 150 elements with 4 dimensions:
## setosa, versicolor, virginica.
## Data was bootstrapped 100 times (method:"full").
## Disparity was calculated as: c(median, centroids).
@@ -4377,7 +4372,7 @@ \subsection{Calculating disparity}\label{calculating-disparity-1}
\subsection{Summarising the results
(plot)}\label{summarising-the-results-plot}
-Similarly to the \texttt{custom.subsamples} and \texttt{boot.matrix}
+Similarly to the \texttt{custom.subsets} and \texttt{boot.matrix}
function, \texttt{dispRity} displays a \texttt{dispRity} object. But we
are definitely more interested in actually look at the calculated
values.
@@ -4393,7 +4388,7 @@ \subsection{Summarising the results
\end{Shaded}
\begin{verbatim}
-## subsamples n obs bs.median 2.5% 25% 75% 97.5%
+## subsets n obs bs.median 2.5% 25% 75% 97.5%
## 1 setosa 50 0.421 0.419 0.342 0.401 0.445 0.503
## 2 versicolor 50 0.693 0.657 0.530 0.622 0.692 0.733
## 3 virginica 50 0.785 0.745 0.577 0.690 0.797 0.880
@@ -4445,7 +4440,7 @@ \subsection{Testing hypothesis}\label{testing-hypothesis}
\begin{verbatim}
## Df Sum Sq Mean Sq F value Pr(>F)
-## subsamples 2 5.387 2.6935 705.5 <2e-16 ***
+## subsets 2 5.387 2.6935 705.5 <2e-16 ***
## Residuals 297 1.134 0.0038
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
@@ -4536,7 +4531,7 @@ \section{More modularity}\label{more-modularity}
I am equally slowly developing functions to allow more of the options in
the package to be modular (in the same way as the \texttt{metric}
argument in \texttt{dispRity}). The next arguments to benefit this
-increased modularity will be \texttt{time.subsamples}'s \texttt{model}
+increased modularity will be \texttt{time.subsets}'s \texttt{model}
argument and \texttt{boot.matrix}'s \texttt{type} argument.
\chapter{References}\label{references}
diff --git a/inst/gitbook/_book/dispRity_manual_files/figure-html/unnamed-chunk-100-1.png b/inst/gitbook/_book/dispRity_manual_files/figure-html/unnamed-chunk-100-1.png
index 1758e4d2..c74a1bce 100644
Binary files a/inst/gitbook/_book/dispRity_manual_files/figure-html/unnamed-chunk-100-1.png and b/inst/gitbook/_book/dispRity_manual_files/figure-html/unnamed-chunk-100-1.png differ
diff --git a/inst/gitbook/_book/dispRity_manual_files/figure-html/unnamed-chunk-99-1.png b/inst/gitbook/_book/dispRity_manual_files/figure-html/unnamed-chunk-99-1.png
index a947f1e2..e321344d 100644
Binary files a/inst/gitbook/_book/dispRity_manual_files/figure-html/unnamed-chunk-99-1.png and b/inst/gitbook/_book/dispRity_manual_files/figure-html/unnamed-chunk-99-1.png differ
diff --git a/inst/gitbook/_book/ecology-demo.html b/inst/gitbook/_book/ecology-demo.html
index 8611368a..b555bb05 100644
--- a/inst/gitbook/_book/ecology-demo.html
+++ b/inst/gitbook/_book/ecology-demo.html
@@ -127,7 +127,7 @@
8.3 A multidimensional approach with dispRity
-The first step is to create different subsamples that represent subsamples of the ordinated space (i.e. sub-regions within the n-dimensional object). Each of these subsamples will contain only the individuals of a specific species.
+The first step is to create different subsets that represent subsets of the ordinated space (i.e. sub-regions within the n-dimensional object). Each of these subsets will contain only the individuals of a specific species.
## Creating the table that contain the elements and their attributes
-petal_subsamples <- custom.subsamples(petal_space, group = list(
+petal_subsets <- custom.subsets(petal_space, group = list(
"setosa" = which(species == "setosa"),
"versicolor" = which(species == "versicolor"),
"virginica" = which(species == "virginica")))
## Visualising the dispRity object content
-petal_subsamples## ---- dispRity object ----
-## 3 customised subsamples for 150 elements:
+## 3 customised subsets for 150 elements:
## setosa, versicolor, virginica.This created a dispRity object (more about that here) with three subsamples corresponding to each subspecies.
This created a dispRity object (more about that here) with three subsets corresponding to each subspecies.
8.3.1 Bootstrapping the data
-We can the bootstrap the subsamples to be able test the robustness of the measured disparity to outliers. We can do that using the default options of boot.matrix (more about that here):
We can the bootstrap the subsets to be able test the robustness of the measured disparity to outliers. We can do that using the default options of boot.matrix (more about that here):
## Bootstrapping the data
-(petal_bootstrapped <- boot.matrix(petal_subsamples))## ---- dispRity object ----
-## 3 customised subsamples for 150 elements with 4 dimensions:
+## 3 customised subsets for 150 elements with 4 dimensions:
## setosa, versicolor, virginica.
## Data was bootstrapped 100 times (method:"full").8.3.2 Calculating disparity
## the centroid of the petal-space (petal_disparity <- dispRity(petal_bootstrapped, metric = c(median, centroids)))## ---- dispRity object ----
-## 3 customised subsamples for 150 elements with 4 dimensions:
+## 3 customised subsets for 150 elements with 4 dimensions:
## setosa, versicolor, virginica.
## Data was bootstrapped 100 times (method:"full").
## Disparity was calculated as: c(median, centroids).8.3.3 Summarising the results (plot)
-Similarly to the custom.subsamples and boot.matrix function, dispRity displays a dispRity object. But we are definitely more interested in actually look at the calculated values.
Similarly to the custom.subsets and boot.matrix function, dispRity displays a dispRity object. But we are definitely more interested in actually look at the calculated values.
First we can summarise the data in a table by simply using summary:
## Displaying the summary of the calculated disparity
summary(petal_disparity)## subsamples n obs bs.median 2.5% 25% 75% 97.5%
+## subsets n obs bs.median 2.5% 25% 75% 97.5%
## 1 setosa 50 0.421 0.419 0.342 0.401 0.445 0.503
## 2 versicolor 50 0.693 0.657 0.530 0.622 0.692 0.733
## 3 virginica 50 0.785 0.745 0.577 0.690 0.797 0.880
@@ -340,7 +340,7 @@ 8.3.4 Testing hypothesis
## Testing whether there is a difference in disparity between the different species
summary(aov(disparity_lm))
## Df Sum Sq Mean Sq F value Pr(>F)
-## subsamples 2 5.387 2.6935 705.5 <2e-16 ***
+## subsets 2 5.387 2.6935 705.5 <2e-16 ***
## Residuals 297 1.134 0.0038
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
diff --git a/inst/gitbook/_book/future-directions.html b/inst/gitbook/_book/future-directions.html
index 9252de27..8325deed 100644
--- a/inst/gitbook/_book/future-directions.html
+++ b/inst/gitbook/_book/future-directions.html
@@ -127,7 +127,7 @@
4 Details of specific functions
- 4.1 Time slicing
-- 4.2 Customised subsamples
+- 4.2 Customised subsets
- 4.3 Bootstraps and rarefactions
- 4.4 Disparity metrics
@@ -227,7 +227,7 @@ 9.2 Faster disparity calculations
9.3 More modularity
-I am equally slowly developing functions to allow more of the options in the package to be modular (in the same way as the metric argument in dispRity). The next arguments to benefit this increased modularity will be time.subsamples’s model argument and boot.matrix’s type argument.
I am equally slowly developing functions to allow more of the options in the package to be modular (in the same way as the metric argument in dispRity). The next arguments to benefit this increased modularity will be time.subsets’s model argument and boot.matrix’s type argument.
