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(dp0
S'Layer'
p1
VAn vector layer containing the relocations of one or more animals.
p2
sS'Home_ranges'
p3
VThe home ranges of the animals calculated according to the selected "Percentage" parameter.
p4
sS'ALG_CREATOR'
p5
VThis algorithm was written by Filipe S. Dias using the functions written by Clement Calenge, creator of the package "adehabitatHR".
p6
sS'ALG_DESC'
p7
VThis tool computes the home range of one or more animals with the Minimum Convex Polygon estimator.\u000a\u000aR depencies: library "adehabitatHR"
p8
sS'Field'
p9
VA field containing a unique identifier for each animal (type "string").\u000a\u000a\u000a
p10
sS'ALG_HELP_CREATOR'
p11
VFilipe S. Dias
p12
sS'Output'
p13
VThe ouput is a shapefile containing the home range of each animal.
p14
sS'Percentage'
p15
V100 minus the proportion of outliers to be excluded from the computation. E.g. Percentage = 95 means that 5% of the outlier locations will be excluded from the calculations.\u000a\u000a\u000a
p16
sNV
p17
s.
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##Point pattern analysis=group
##Layer=vector
##Simulations=number 100
##Optional_plot_name=string
##showplots
library(spatstat)
library(maptools)
sp <- as(Layer, "SpatialPoints")
sp <- as(sp, "ppp")
e <- envelope(sp, Kest, nsim = Simulations)
>e
plot(e, main = Optional_plot_name)
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(dp0
S'ALG_DESC'
p1
VThis is a Monte Carlo test for the point patterns. It is based on simulations from the null hypothesis. It generates randomly dinstributed points within the study region and uses K-function for each set of generated points and compares them to the K-function for the original set of points. Detailed description can be found in correspondin section of this book: http://www.csiro.au/resources/pf16h.\u000a\u000aThis script will provide both graphical ('R plots') and verbose ('R console output') output.\u000a\u000aR dependencies: library "maptools" and "spatstat"
p2
sS'R_CONSOLE_OUTPUT'
p3
VDescription of the test results.
p4
sS'Layer'
p5
VPoint layer to be tested.
p6
sS'ALG_CREATOR'
p7
VYury Ryabov\u000ariabovv at gmail dot com\u000a2013\u000aGPLv3
p8
sS'RPLOTS'
p9
VGraph showing test results.
p10
sS'ALG_HELP_CREATOR'
p11
VYury Ryabov\u000ariabovv at gmail dot com\u000a2013\u000aCC-0
p12
sS'Simulations'
p13
VNumber of simulations for random points distributions. Positive integer must be provided here.
p14
sS'Optional_plot_name'
p15
VAn Optional name for the graph. It is Ok to leave this field blank.
p16
s.
8 changes: 4 additions & 4 deletions python/plugins/processing/r/scripts/Quadrat_analysis.rsx
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@@ -1,10 +1,10 @@
##[Example scripts]=group
##points=vector
##Point pattern analysis=group
##Layer=vector
##showplots
library("maptools")
library("spatstat")
ppp=as(as(points, "SpatialPoints"),"ppp")
ppp=as(as(Layer, "SpatialPoints"),"ppp")
qc=quadratcount(ppp)
plot(points)
plot(Layer)
plot(qc, add=TRUE)
>quadrat.test(ppp);
26 changes: 26 additions & 0 deletions python/plugins/processing/r/scripts/Quadrat_analysis.rsx.help
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(dp0
S'ALG_DESC'
p1
VThe script divides the window into quadrats and counts the numbers of points in each quadrat. Then it performs a test of Complete Spatial Randomness based on the quadrat counts.\u000a\u000aR dependencies: library "maptools" and "spatstat"
p2
sS'R_CONSOLE_OUTPUT'
p3
VThe results of the Chi-squared test of complete spatial randomness (CSR) using quadrat counts.\u000a
p4
sS'ALG_CREATOR'
p5
VVictor Olaya, volayaf(at)gmail.com
p6
sS'Layer'
p7
VA vector containg a point pattern.
p8
sS'RPLOTS'
p9
VA display containing the number of points per quadrat.
p10
sS'ALG_HELP_CREATOR'
p11
VFilipe S. Dias, filipesdias(at)gmail.com
p12
s.
6 changes: 6 additions & 0 deletions python/plugins/processing/r/scripts/Random_sampling_grid.rsx
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##Point pattern analysis=group
##Layer=vector
##Size=number 10
##Output= output vector
pts=spsample(Layer,Size,type="random")
Output=SpatialPointsDataFrame(pts, as.data.frame(pts))
26 changes: 26 additions & 0 deletions python/plugins/processing/r/scripts/Random_sampling_grid.rsx.help
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(dp0
S'ALG_DESC'
p1
VThis scripts samples point location within a given polygon(s) using a random sampling method. The used methods assume that the geometry used is not spherical, so objects should be in planar coordinates.\u000a\u000a
p2
sS'ALG_CREATOR'
p3
VVictor Olaya
p4
sS'Layer'
p5
VA vector layer containing polygons.
p6
sS'ALG_HELP_CREATOR'
p7
VFilipe Dias
p8
sS'Output'
p9
VRandomly generated random points.
p10
sS'Size'
p11
VNumber of sample points to be randomly generated
p12
s.
6 changes: 3 additions & 3 deletions python/plugins/processing/r/scripts/Raster_histogram.rsx
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@@ -1,4 +1,4 @@
##[Example scripts]=group
##layer = raster
##Raster processing=group
##Layer = raster
##showplots
hist(as.matrix(layer))
hist(as.matrix(Layer),main="Histogram",xlab="Layer")
22 changes: 22 additions & 0 deletions python/plugins/processing/r/scripts/Raster_histogram.rsx.help
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(dp0
S'ALG_CREATOR'
p1
VVictor Olaya, volayaf(at)gmail.com
p2
sS'ALG_DESC'
p3
VThe script creates a raster histogram.
p4
sS'Layer '
p5
VAn input raster.
p6
sS'RPLOTS'
p7
VHistogram
p8
sS'ALG_HELP_CREATOR'
p9
VFilipe S. Dias, filipesdias(at)gmail.co
p10
s.
6 changes: 6 additions & 0 deletions python/plugins/processing/r/scripts/Regular_sampling_grid.rsx
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##Point pattern analysis=group
##Layer=vector
##Size=number 10
##Output= output vector
pts=spsample(Layer,Size,type="regular")
Output=SpatialPointsDataFrame(pts, as.data.frame(pts))
18 changes: 18 additions & 0 deletions python/plugins/processing/r/scripts/Regular_sampling_grid.rsx.help
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(dp0
S'Output'
p1
VSampled points
p2
sS'ALG_DESC'
p3
VThis scripts samples point location within a given polygon(s) using a regular (systematically aligned) sampling method. The methods used assume that the geometry used is not spherical, so objects should be in planar coordinates.
p4
sS'Layer'
p5
VA vector layer containing polygons.\u000a
p6
sS'Size'
p7
VNumber of sample points to be generated.\u000a
p8
s.
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(dp0
S'ALG_DESC'
p1
VThis algorithm creates a graph that demonstrates the dependency of the intensity of the point process on the value of covariate. In this algorithm the covariate is the distance to the certain objects. The functionality is based on 'rhohat' function of the 'spatstat' package. \u000a\u000aR dependencies: library "maptools", "spatstat" and "rpanel".
p2
sS'ALG_CREATOR'
p3
VYury Ryabov\u000a2013\u000ariabovvv@gmail.com
p4
sS'Layer'
p5
VThe point process which distribution will be investigated.
p6
sS'Covariate_name'
p7
VThis field is mandatory. Enter the name of the covariate. It will appear at the graph.
p8
sS'Legend_position'
p9
VThis field defines the position of the legend at the graph. 'float' means that the legend will be placed at the position that would not overlap the graph itself (or will try at least). Other options are: 'topleft', 'topright', 'bottomleft', 'bottomright'.
p10
sS'x_label'
p11
VOptional label for the X axis. Note that units at the X axis will be the same as in the input layers.
p12
sS'RPLOTS'
p13
VThe empirical graph of the dependency of the intensity of the point process on the distance to the given objects.
p14
sS'ALG_HELP_CREATOR'
p15
VYury Ryabov\u000a2013\u000ariabovvv@gmail.com
p16
sS'Plot_name'
p17
VOptional plot name.
p18
sS'Covariate'
p19
VThe set of objects the distance from which will be calculated and used as a spatial covariate to the point process.
p20
s.
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@@ -0,0 +1,42 @@
(dp0
S'ALG_DESC'
p1
VThis algorithm creates a graph that demonstrates the dependency of the intensity of the point process on the value of covariate. In this algorithm the covariate is the distance to the certain objects. The functionality is based on 'rhohat' function of the 'spatstat' package. \u000a\u000aR dependencies: library "maptools", "spatstat" and "rpanel".
p2
sS'ALG_CREATOR'
p3
VYury Ryabov\u000a2013\u000ariabovvv@gmail.com
p4
sS'Layer'
p5
VThe point process which distribution will be investigated.
p6
sS'Covariate_name'
p7
VThis field is mandatory. Enter the name of the covariate. It will appear at the graph.
p8
sS'Legend_position'
p9
VThis field defines the position of the legend at the graph. 'float' means that the legend will be placed at the position that would not overlap the graph itself (or will try at least). Other options are: 'topleft', 'topright', 'bottomleft', 'bottomright'.
p10
sS'x_label'
p11
VOptional label for the X axis. Note that units at the X axis will be the same as in the input layers.
p12
sS'RPLOTS'
p13
VThe empirical graph of the dependency of the intensity of the point process on the distance to the given objects.
p14
sS'ALG_HELP_CREATOR'
p15
VYury Ryabov\u000a2013\u000ariabovvv@gmail.com
p16
sS'Plot_name'
p17
VOptional plot name.
p18
sS'Covariate'
p19
VThe set of objects the distance from which will be calculated and used as a spatial covariate to the point process.
p20
s.
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##Point pattern analysis=group
##points=vector
##covariate=raster
##covariate_name=string mandatory_covariate_name_(no_spaces)
##x_label=string
##plot_name=string
##legend_position=string float
##showplots
library(geostatsp)
library(maptools)
library(rpanel)
if (covariate_name == "") {
rp.messagebox('"covariate name" must not be empty!', title = 'oops!')
}
else {
S <- points
SP <- as(S, "SpatialPoints")
P <- as(SP, "ppp")
covariate <- raster(covariate, layer = 1)
covariate <- as.im(covariate)
library(spatstat)
S <- points
SP <- as(S, "SpatialPoints")
P <- as(SP, "ppp")
plot(rhohat(P, covariate, covname=covariate_name), xlab= x_label,
legendpos = legend_position,
legendargs=list(bg="transparent"),
main = plot_name)
}
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(dp0
S'ALG_DESC'
p1
VThis algorithm creates a graph that demonstrates the dependency of the intensity of the point process on the value of the given covariate. In this algorithm the covariate must be represented as a raster. The functionality is based on 'rhohat' function of the 'spatstat' package. \u000a\u000aR dependencies: library "geostatsp", "maptools", "rpanel", "spatstat"
p2
sS'legend_position'
p3
VThis field defines the position of the legend at the graph. 'float' means that the legend will be placed at the position that would not overlap the graph itself (or will try at least). Other options are: 'topleft', 'topright', 'bottomleft', 'bottomright'.
p4
sS'ALG_CREATOR'
p5
VYury Ryabov\u000a2013\u000ariabovvv@gmail.com
p6
sS'covariate_name'
p7
VThis field is mandatory. Enter the name of the covariate. It will appear at the graph.
p8
sS'x_label'
p9
VOptional label for the X axis. Note that units at the X axis will be the same as in the input layers.
p10
sS'points'
p11
VThe point process which distribution will be investigated.
p12
sS'RPLOTS'
p13
VThe empirical graph of the dependency of the intensity of the point process on the distance to the given objects.
p14
sS'ALG_HELP_CREATOR'
p15
VYury Ryabov\u000a2013\u000ariabovvv@gmail.com
p16
sS'plot_name'
p17
VOptional plot name.
p18
sS'covariate'
p19
VThe spatial covariate to the point process. The raster must not have discrete values, i.e. it may not be classified. Only rasters that represent continious phenomena (e.g. DEM, distance maps, etc.) are allowed. Though classified rasters will be processed if supplied, but the results will be meaningless.
p20
s.
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##Point pattern analysis=group
##Layer=vector
##Output=output vector
library("spatstat")
library("maptools")
proj4string(Layer)->crs
spatpoints = as(Layer,"SpatialPoints")
ripras=ripras(as(spatpoints,"ppp"))
polyg=as(ripras,"SpatialPolygons")
Output1= SpatialPolygonsDataFrame(polyg, data.frame(1))
proj4string(Output1)<-crs
Output<-Output1
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(dp0
S'ALG_CREATOR'
p1
VVictor Olaya, volayaf(at)gmail.com
p2
sS'Output'
p3
VRiplay-Rasson estimate of the spatial domain.
p4
sS'ALG_DESC'
p5
VThis script computes the Ripley-Rasson estimate of the spatial domain from which an observed pattern of points came.\u000a\u000aR dependencies: library "maptools" and "spatstat"
p6
sS'Layer'
p7
VA vector layer contain a point pattern.
p8
sS'ALG_HELP_CREATOR'
p9
VFilipe S. Dias, filipesdias(at)gmail.com
p10
s.
Original file line number Diff line number Diff line change
@@ -0,0 +1,12 @@
##Home Range Analysis=group
##showplots
##Layer=vector
##Field=Field Layer
##Percentage=number 10
##Home_ranges=Output vector
library(adehabitatHR)
uu<-clusthr(Layer[,Field])
Home_ranges<-getverticeshr(uu,percent=Percentage)
ii <- MCHu2hrsize(uu, percent=seq(50, 100, by=5))
par(mar=c(2,2,2,2))
plot(ii)
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@@ -0,0 +1,34 @@
(dp0
S'ALG_DESC'
p1
VThis script estimates the home range of one or more animals by single-linkage cluster analysis.\u000a\u000aR depencies: library "adehabitatHR"
p2
sS'Home_ranges'
p3
VA multipart vector layer containing the home ranges of each animal, according to the selected "Percentage" value.
p4
sS'ALG_CREATOR'
p5
VFilipe S. Dias, filipesdias(at)gmail.com
p6
sS'Layer'
p7
VA vector layer containing the relocations of one or more animals.
p8
sS'Field'
p9
VThe field that contains the unique identifier (type "string") for each animal.
p10
sS'RPLOTS'
p11
VA plot showing the relationship between the home range size and the percentage of relocations included in the home range.\u000a
p12
sS'ALG_HELP_CREATOR'
p13
VFilipe S. Dias, filipesdias(at)gmail.com
p14
sS'Percentage'
p15
VPercentage of the relocations used in the calculations.
p16
s.
14 changes: 14 additions & 0 deletions python/plugins/processing/r/scripts/Summary_statistics.rsx
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@@ -0,0 +1,14 @@
##Basic statistics=group
##Layer=vector
##Field=Field Layer
Summary_statistics<-data.frame(rbind(sum(Layer[[Field]]),
length(Layer[[Field]]),
length(unique(Layer[[Field]])),
min(Layer[[Field]]),
max(Layer[[Field]]),
max(Layer[[Field]])-min(Layer[[Field]]),
mean(Layer[[Field]]),
median(Layer[[Field]]),
sd(Layer[[Field]])),row.names=c("Sum:","Count:","Unique values:","Minimum value:","Maximum value:","Range:","Mean value:","Median value:","Standard deviation:"))
colnames(Summary_statistics)<-c(Field)
>Summary_statistics
26 changes: 26 additions & 0 deletions python/plugins/processing/r/scripts/Summary_statistics.rsx.help
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@@ -0,0 +1,26 @@
(dp0
S'ALG_DESC'
p1
VThis tool calculates the following summary statistics for a numeric field: (1) Sum, (2) Count, (3) Unique values, (4) Minimum value, (5) Maximum value, (6) Range, (7) Mean, (8) Median and (9) Standard deviation.\u000a\u000a
p2
sS'R_CONSOLE_OUTPUT'
p3
VSummary statistics table
p4
sS'ALG_CREATOR'
p5
VFilipe S. Dias, filipesdias(at)gmail.com
p6
sS'Layer'
p7
VInput vector with at least one numeric field
p8
sS'Field'
p9
VNumeric field
p10
sS'ALG_HELP_CREATOR'
p11
VFilipe S. Dias, filipesdias(at)gmail.com
p12
s.