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R Package: Cross-validate one or multiple gaussian or binomial regression models at once. Perform repeated cross-validation. Returns results in a tibble for easy comparison, reporting and further analysis.
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README.md

cvms

Cross-Validation for Model Selection
Authors: Ludvig R. Olsen ( r-pkgs@ludvigolsen.dk ), Hugh Benjamin Zachariae
License: MIT
Started: October 2016

CRAN_Status_Badge metacran downloads minimal R version Codecov test coverage Travis build status AppVeyor build status DOI

Overview

R package: Cross-validate one or multiple regression models and get relevant evaluation metrics in a tidy format. Validate the best model on a test set and compare it to a baseline evaluation. Alternatively, evaluate predictions from an external model. Currently supports linear regression, logistic regression and (some functions only) multiclass classification.

Main functions:

  • cross_validate()
  • validate()
  • evaluate()
  • baseline()
  • combine_predictors()
  • cv_plot()
  • select_metrics()
  • reconstruct_formulas()

Important News

  • Adds 'multinomial' family to baseline() and evaluate().

  • evaluate() is added. Evaluate your model’s predictions with the same metrics as used in cross_validate().

  • AUC calculation has changed. Now explicitly sets the direction in pROC::roc. (27th of May 2019)

  • Argument "positive" now defaults to 2. If a dependent variable has the values 0 and 1, 1 is now the default positive class, as that’s the second smallest value. If the dependent variable is of type character, it’s in alphabetical order.

  • Results now contain a count of singular fit messages. See ?lme4::isSingular for more information.

Installation

CRAN:

install.packages(“cvms”)

Development version:

install.packages(“devtools”)

devtools::install_github(“LudvigOlsen/groupdata2”)

devtools::install_github(“LudvigOlsen/cvms”)

Examples

Attach packages

library(cvms)
library(groupdata2) # fold() partition()
library(knitr) # kable()
library(dplyr) # %>% arrange()
library(ggplot2)

Load data

The dataset participant.scores comes with cvms.

data <- participant.scores

Fold data

Create a grouping factor for subsetting of folds using groupdata2::fold(). Order the dataset by the folds.

# Set seed for reproducibility
set.seed(7)

# Fold data 
data <- fold(data, k = 4,
             cat_col = 'diagnosis',
             id_col = 'participant') %>% 
  arrange(.folds)

# Show first 15 rows of data
data %>% head(15) %>% kable()
participant age diagnosis score session .folds
9 34 0 33 1 1
9 34 0 53 2 1
9 34 0 66 3 1
8 21 1 16 1 1
8 21 1 32 2 1
8 21 1 44 3 1
2 23 0 24 1 2
2 23 0 40 2 2
2 23 0 67 3 2
1 20 1 10 1 2
1 20 1 24 2 2
1 20 1 45 3 2
6 31 1 14 1 2
6 31 1 25 2 2
6 31 1 30 3 2

Cross-validate a single model

Gaussian

CV1 <- cross_validate(data, "score~diagnosis",
                      fold_cols = '.folds',
                      family = 'gaussian',
                      REML = FALSE)

# Show results
CV1
#> # A tibble: 1 x 18
#>    RMSE   MAE   r2m   r2c   AIC  AICc   BIC Predictions Results
#>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <list>      <list> 
#> 1  16.4  13.8 0.271 0.271  195.  196.  198. <tibble [3… <tibbl…
#> # … with 9 more variables: Coefficients <list>, Folds <int>, `Fold
#> #   Columns` <int>, `Convergence Warnings` <dbl>, `Singular Fit
#> #   Messages` <int>, Family <chr>, Link <chr>, Dependent <chr>,
#> #   Fixed <chr>

# Let's take a closer look at the different parts of the output 

# Results metrics
CV1 %>% select_metrics() %>% kable()
RMSE MAE r2m r2c AIC AICc BIC Dependent Fixed
16.35261 13.75772 0.270991 0.270991 194.6218 195.9276 197.9556 score diagnosis
# Nested predictions 
# Note that [[1]] picks predictions for the first row
CV1$Predictions[[1]] %>% head() %>% kable()
Fold Column Fold Target Prediction
.folds 1 33 51.00000
.folds 1 53 51.00000
.folds 1 66 51.00000
.folds 1 16 30.66667
.folds 1 32 30.66667
.folds 1 44 30.66667
# Nested results from the different folds
CV1$Results[[1]] %>% kable()
Fold Column Fold RMSE MAE r2m r2c AIC AICc BIC
.folds 1 12.56760 10.72222 0.2439198 0.2439198 209.9622 211.1622 213.4963
.folds 2 16.60767 14.77778 0.2525524 0.2525524 182.8739 184.2857 186.0075
.folds 3 15.97355 12.87037 0.2306104 0.2306104 207.9074 209.1074 211.4416
.folds 4 20.26162 16.66049 0.3568816 0.3568816 177.7436 179.1554 180.8772
# Nested model coefficients
# Note that you have the full p-values, 
# but kable() only shows a certain number of digits
CV1$Coefficients[[1]] %>% kable()
term estimate std.error statistic p.value Fold Fold Column
(Intercept) 51.00000 5.901264 8.642216 0.0000000 1 .folds
diagnosis -20.33333 7.464574 -2.723978 0.0123925 1 .folds
(Intercept) 53.33333 5.718886 9.325826 0.0000000 2 .folds
diagnosis -19.66667 7.565375 -2.599563 0.0176016 2 .folds
(Intercept) 49.77778 5.653977 8.804030 0.0000000 3 .folds
diagnosis -18.77778 7.151778 -2.625610 0.0154426 3 .folds
(Intercept) 49.55556 5.061304 9.791065 0.0000000 4 .folds
diagnosis -22.30556 6.695476 -3.331437 0.0035077 4 .folds
# Additional information about the model
# and the training process
CV1 %>% select(11:17) %>% kable()
Folds Fold Columns Convergence Warnings Singular Fit Messages Family Link Dependent
4 1 0 0 gaussian identity score

Binomial

CV2 <- cross_validate(data, "diagnosis~score",
                      fold_cols = '.folds',
                      family = 'binomial')

# Show results
CV2
#> # A tibble: 1 x 26
#>   `Balanced Accur…    F1 Sensitivity Specificity `Pos Pred Value`
#>              <dbl> <dbl>       <dbl>       <dbl>            <dbl>
#> 1            0.736 0.821       0.889       0.583            0.762
#> # … with 21 more variables: `Neg Pred Value` <dbl>, AUC <dbl>, `Lower
#> #   CI` <dbl>, `Upper CI` <dbl>, Kappa <dbl>, MCC <dbl>, `Detection
#> #   Rate` <dbl>, `Detection Prevalence` <dbl>, Prevalence <dbl>,
#> #   Predictions <list>, ROC <list>, `Confusion Matrix` <list>,
#> #   Coefficients <list>, Folds <int>, `Fold Columns` <int>, `Convergence
#> #   Warnings` <dbl>, `Singular Fit Messages` <int>, Family <chr>,
#> #   Link <chr>, Dependent <chr>, Fixed <chr>

# Let's take a closer look at the different parts of the output 
# We won't repeat the parts too similar to those in Gaussian

# Results metrics
CV2 %>% select(1:9) %>% kable()
Balanced Accuracy F1 Sensitivity Specificity Pos Pred Value Neg Pred Value AUC Lower CI Upper CI
0.7361111 0.8205128 0.8888889 0.5833333 0.7619048 0.7777778 0.7685185 0.5962701 0.9407669
CV2 %>% select(10:14) %>% kable()
Kappa MCC Detection Rate Detection Prevalence Prevalence
0.4927536 0.5048268 0.5333333 0.7 0.6
# ROC curve info
CV2$ROC[[1]] %>% head() %>% kable()
Sensitivities Specificities
1.0000000 0.0000000
1.0000000 0.0833333
0.9444444 0.0833333
0.9444444 0.1666667
0.9444444 0.2500000
0.8888889 0.2500000
# Confusion matrix
CV2$`Confusion Matrix`[[1]] %>% kable()
Fold Column Prediction Target Pos_0 Pos_1 N
.folds 0 0 TP TN 7
.folds 1 0 FN FP 5
.folds 0 1 FP FN 2
.folds 1 1 TN TP 16

Cross-validate multiple models

Create model formulas

models <- c("score~diagnosis", "score~age")
mixed_models <- c("score~diagnosis+(1|session)", "score~age+(1|session)")

Cross-validate fixed effects models

CV3 <- cross_validate(data, models,
                      fold_cols = '.folds',
                      family = 'gaussian',
                      REML = FALSE)

# Show results
CV3
#> # A tibble: 2 x 18
#>    RMSE   MAE    r2m    r2c   AIC  AICc   BIC Predictions Results
#>   <dbl> <dbl>  <dbl>  <dbl> <dbl> <dbl> <dbl> <list>      <list> 
#> 1  16.4  13.8 0.271  0.271   195.  196.  198. <tibble [3… <tibbl…
#> 2  22.4  18.9 0.0338 0.0338  201.  202.  204. <tibble [3… <tibbl…
#> # … with 9 more variables: Coefficients <list>, Folds <int>, `Fold
#> #   Columns` <int>, `Convergence Warnings` <dbl>, `Singular Fit
#> #   Messages` <int>, Family <chr>, Link <chr>, Dependent <chr>,
#> #   Fixed <chr>

Cross-validate mixed effects models

CV4 <- cross_validate(data, mixed_models,
                      fold_cols = '.folds',
                      family = 'gaussian',
                      REML = FALSE)

# Show results
CV4
#> # A tibble: 2 x 19
#>    RMSE   MAE    r2m   r2c   AIC  AICc   BIC Predictions Results
#>   <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl> <list>      <list> 
#> 1  7.95  6.41 0.290  0.811  176.  178.  180. <tibble [3… <tibbl…
#> 2 17.5  16.2  0.0366 0.526  194.  196.  198. <tibble [3… <tibbl…
#> # … with 10 more variables: Coefficients <list>, Folds <int>, `Fold
#> #   Columns` <int>, `Convergence Warnings` <dbl>, `Singular Fit
#> #   Messages` <int>, Family <chr>, Link <chr>, Dependent <chr>,
#> #   Fixed <chr>, Random <chr>

Repeated cross-validation

Let’s first add some extra fold columns. We will use the num_fold_cols argument to add 3 unique fold columns. We tell fold() to keep the existing fold column and simply add three extra columns. We could also choose to remove the existing fold column, if for instance we were changing the number of folds (k). Note, that the original fold column will be renamed to “.folds_1”.

# Set seed for reproducibility
set.seed(2)

# Fold data 
data <- fold(data, k = 4,
             cat_col = 'diagnosis',
             id_col = 'participant',
             num_fold_cols = 3,
             handle_existing_fold_cols = "keep")

# Show first 15 rows of data
data %>% head(10) %>% kable()
participant age diagnosis score session .folds_1 .folds_2 .folds_3 .folds_4
10 32 0 29 1 4 4 3 1
10 32 0 55 2 4 4 3 1
10 32 0 81 3 4 4 3 1
2 23 0 24 1 2 3 1 2
2 23 0 40 2 2 3 1 2
2 23 0 67 3 2 3 1 2
4 21 0 35 1 3 2 4 4
4 21 0 50 2 3 2 4 4
4 21 0 78 3 3 2 4 4
9 34 0 33 1 1 1 2 3
CV5 <- cross_validate(data, "diagnosis ~ score",
                      fold_cols = paste0(".folds_", 1:4),
                      family = 'binomial',
                      REML = FALSE)

# Show results
CV5
#> # A tibble: 1 x 27
#>   `Balanced Accur…    F1 Sensitivity Specificity `Pos Pred Value`
#>              <dbl> <dbl>       <dbl>       <dbl>            <dbl>
#> 1            0.729 0.813       0.875       0.583            0.759
#> # … with 22 more variables: `Neg Pred Value` <dbl>, AUC <dbl>, `Lower
#> #   CI` <dbl>, `Upper CI` <dbl>, Kappa <dbl>, MCC <dbl>, `Detection
#> #   Rate` <dbl>, `Detection Prevalence` <dbl>, Prevalence <dbl>,
#> #   Predictions <list>, ROC <list>, Results <list>, `Confusion
#> #   Matrix` <list>, Coefficients <list>, Folds <int>, `Fold
#> #   Columns` <int>, `Convergence Warnings` <dbl>, `Singular Fit
#> #   Messages` <int>, Family <chr>, Link <chr>, Dependent <chr>,
#> #   Fixed <chr>

# The binomial output now has a nested 'Results' tibble
# Let's see a subset of the columns
CV5$Results[[1]] %>% select(1:8) %>%  kable()
Fold Column Balanced Accuracy F1 Sensitivity Specificity Pos Pred Value Neg Pred Value AUC
.folds_1 0.7361111 0.8205128 0.8888889 0.5833333 0.7619048 0.7777778 0.7685185
.folds_2 0.7361111 0.8205128 0.8888889 0.5833333 0.7619048 0.7777778 0.7777778
.folds_3 0.7083333 0.7894737 0.8333333 0.5833333 0.7500000 0.7000000 0.7476852
.folds_4 0.7361111 0.8205128 0.8888889 0.5833333 0.7619048 0.7777778 0.7662037

Evaluating predictions

Evaluate predictions from a model trained outside cvms. Works with linear regression (gaussian), logistic regression (binomial), and multiclass classification (multinomial). The following is an example of multinomial evaluation.

Multinomial

Create a dataset with 3 predictors and a target column. Partition it with groupdata2::partition() to create a training set and a validation set. multiclass_probability_tibble() is a simple helper function for generating random tibbles.

# Set seed
set.seed(1)

# Create class names
class_names <- paste0("class_", 1:4)

# Create random dataset with 100 observations 
# Partition into training set (75%) and test set (25%)
multiclass_partitions <- multiclass_probability_tibble(
  num_classes = 3, # Here, number of predictors
  num_observations = 100,
  apply_softmax = FALSE,
  FUN = rnorm,
  class_name = "predictor_") %>%
  dplyr::mutate(class = sample(
    class_names,
    size = 100,
    replace = TRUE)) %>%
  partition(p = 0.75,
            cat_col = "class")

# Extract partitions
multiclass_train_set <- multiclass_partitions[[1]]
multiclass_test_set <- multiclass_partitions[[2]]

multiclass_test_set
#> # A tibble: 26 x 4
#>    predictor_1 predictor_2 predictor_3 class  
#>          <dbl>       <dbl>       <dbl> <chr>  
#>  1      1.60         0.158     -0.331  class_1
#>  2     -1.99        -0.180     -0.341  class_1
#>  3      0.418       -0.324      0.263  class_1
#>  4      0.398        0.450      0.136  class_1
#>  5      0.0743       1.03      -1.32   class_1
#>  6      0.738        0.910      0.541  class_2
#>  7      0.576        0.384     -0.0134 class_2
#>  8     -0.305        1.68       0.510  class_2
#>  9     -0.0449      -0.393      1.52   class_2
#> 10      0.557       -0.464     -0.879  class_2
#> # … with 16 more rows

Train multinomial model using the nnet package and get the predicted probabilities.

# Train multinomial model
multiclass_model <- nnet::multinom(
   "class ~ predictor_1 + predictor_2 + predictor_3",
   data = multiclass_train_set)
#> # weights:  20 (12 variable)
#> initial  value 102.585783 
#> iter  10 value 98.124010
#> final  value 98.114250 
#> converged

# Predict the targets in the test set
predictions <- predict(multiclass_model, 
                       multiclass_test_set,
                       type = "probs") %>%
  dplyr::as_tibble()

# Add the targets
predictions[["target"]] <- multiclass_test_set[["class"]]

head(predictions, 10)
#> # A tibble: 10 x 5
#>    class_1 class_2 class_3 class_4 target 
#>      <dbl>   <dbl>   <dbl>   <dbl> <chr>  
#>  1   0.243   0.214   0.304   0.239 class_1
#>  2   0.136   0.371   0.234   0.259 class_1
#>  3   0.230   0.276   0.264   0.230 class_1
#>  4   0.194   0.218   0.262   0.326 class_1
#>  5   0.144   0.215   0.302   0.339 class_1
#>  6   0.186   0.166   0.241   0.407 class_2
#>  7   0.201   0.222   0.272   0.305 class_2
#>  8   0.117   0.131   0.195   0.557 class_2
#>  9   0.237   0.264   0.215   0.284 class_2
#> 10   0.216   0.310   0.303   0.171 class_2

Perform the evaluation. This will create one-vs-all binomial evaluations and summarize the results.

# Evaluate predictions
evaluate(data = predictions,
         target_col = "target",
         prediction_cols = class_names,
         type = "multinomial")
#> $Results
#> # A tibble: 1 x 17
#>   `Overall Accura… `Balanced Accur…    F1 Sensitivity Specificity
#>              <dbl>            <dbl> <dbl>       <dbl>       <dbl>
#> 1            0.154            0.427   NaN       0.143       0.712
#> # … with 12 more variables: `Pos Pred Value` <dbl>, `Neg Pred
#> #   Value` <dbl>, AUC <dbl>, `Lower CI` <dbl>, `Upper CI` <dbl>,
#> #   Kappa <dbl>, MCC <dbl>, `Detection Rate` <dbl>, `Detection
#> #   Prevalence` <dbl>, Prevalence <dbl>, Predictions <list>, `Confusion
#> #   Matrix` <list>
#> 
#> $`Class Level Results`
#> # A tibble: 4 x 18
#>   Class `Balanced Accur…      F1 Sensitivity Specificity `Pos Pred Value`
#>   <chr>            <dbl>   <dbl>       <dbl>       <dbl>            <dbl>
#> 1 clas…            0.476 NaN           0           0.952            0    
#> 2 clas…            0.380   0.211       0.286       0.474            0.167
#> 3 clas…            0.474 NaN           0           0.947            0    
#> 4 clas…            0.380   0.211       0.286       0.474            0.167
#> # … with 12 more variables: `Neg Pred Value` <dbl>, AUC <dbl>, `Lower
#> #   CI` <dbl>, `Upper CI` <dbl>, Kappa <dbl>, MCC <dbl>, `Detection
#> #   Rate` <dbl>, `Detection Prevalence` <dbl>, Prevalence <dbl>,
#> #   Support <int>, ROC <list>, `Confusion Matrix` <list>

Baseline evaluations

Create baseline evaluations of a test set.

Gaussian

Approach: The baseline model (y ~ 1), where 1 is simply the intercept (i.e. mean of y), is fitted on n random subsets of the training set and evaluated on the test set. We also perform an evaluation of the model fitted on the entire training set.

Start by partitioning the dataset.

# Set seed for reproducibility
set.seed(1)

# Partition the dataset 
partitions <- groupdata2::partition(participant.scores,
                                    p = 0.7,
                                    cat_col = 'diagnosis',
                                    id_col = 'participant',
                                    list_out = TRUE)
train_set <- partitions[[1]]
test_set <- partitions[[2]]

Create the baseline evaluations:

baseline(test_data = test_set, train_data = train_set,
         n = 100, dependent_col = "score", family = "gaussian")
#> $summarized_metrics
#> # A tibble: 9 x 9
#>   Measure   RMSE    MAE   r2m   r2c   AIC  AICc   BIC `Training Rows`
#>   <chr>    <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>           <dbl>
#> 1 Mean     19.7  15.8       0     0  87.0  89.5  87.4            9.63
#> 2 Median   19.2  15.5       0     0  83.3  85.3  83.7            9   
#> 3 SD        1.05  0.759     0     0  28.9  27.6  29.6            3.22
#> 4 IQR       1.16  0.264     0     0  45.9  44.3  47.0            5   
#> 5 Max      24.1  19.4       0     0 137.  138.  138.            15   
#> 6 Min      18.9  15.5       0     0  42.0  48.0  41.2            5   
#> 7 NAs       0     0         0     0   0     0     0              0   
#> 8 INFs      0     0         0     0   0     0     0              0   
#> 9 All_rows 19.1  15.5       0     0 161.  162.  163.            18   
#> 
#> $random_evaluations
#> # A tibble: 100 x 13
#>     RMSE   MAE   r2m   r2c   AIC  AICc   BIC Predictions Coefficients
#>    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <list>      <list>      
#>  1  20.0  16.3     0     0  72.5  74.9  72.7 <tibble [1… <tibble [1 …
#>  2  19.0  15.5     0     0 137.  138.  138.  <tibble [1… <tibble [1 …
#>  3  20.2  15.7     0     0  61.3  64.3  61.2 <tibble [1… <tibble [1 …
#>  4  20.0  15.7     0     0  97.7  99.2  98.5 <tibble [1… <tibble [1 …
#>  5  19.3  15.6     0     0  73.3  75.7  73.5 <tibble [1… <tibble [1 …
#>  6  20.4  15.9     0     0  44.4  50.4  43.6 <tibble [1… <tibble [1 …
#>  7  19.0  15.5     0     0 118.  120.  119.  <tibble [1… <tibble [1 …
#>  8  19.4  15.5     0     0  93.3  95.1  94.0 <tibble [1… <tibble [1 …
#>  9  20.7  16.2     0     0  71.2  73.6  71.3 <tibble [1… <tibble [1 …
#> 10  20.8  17.1     0     0  43.7  49.7  42.9 <tibble [1… <tibble [1 …
#> # … with 90 more rows, and 4 more variables: `Training Rows` <int>,
#> #   Family <chr>, Dependent <chr>, Fixed <chr>

Binomial

Approach: n random sets of predictions are evaluated against the dependent variable in the test set. We also evaluate a set of all 0s and a set of all 1s.

Create the baseline evaluations:

baseline(test_data = test_set, n = 100, 
         dependent_col = "diagnosis", family = "binomial")
#> $summarized_metrics
#> # A tibble: 10 x 15
#>    Measure `Balanced Accur…     F1 Sensitivity Specificity `Pos Pred Value`
#>    <chr>              <dbl>  <dbl>       <dbl>       <dbl>            <dbl>
#>  1 Mean               0.502  0.495       0.478       0.525            0.498
#>  2 Median             0.5    0.5         0.5         0.5              0.500
#>  3 SD                 0.147  0.159       0.215       0.210            0.194
#>  4 IQR                0.167  0.252       0.333       0.333            0.200
#>  5 Max                0.833  0.833       0.833       1                1    
#>  6 Min                0.167  0.182       0           0                0    
#>  7 NAs                0      4           0           0                0    
#>  8 INFs               0      0           0           0                0    
#>  9 All_0              0.5   NA           0           1              NaN    
#> 10 All_1              0.5    0.667       1           0                0.5  
#> # … with 9 more variables: `Neg Pred Value` <dbl>, AUC <dbl>, `Lower
#> #   CI` <dbl>, `Upper CI` <dbl>, Kappa <dbl>, MCC <dbl>, `Detection
#> #   Rate` <dbl>, `Detection Prevalence` <dbl>, Prevalence <dbl>
#> 
#> $random_evaluations
#> # A tibble: 100 x 19
#>    `Balanced Accur…    F1 Sensitivity Specificity `Pos Pred Value`
#>               <dbl> <dbl>       <dbl>       <dbl>            <dbl>
#>  1            0.417 0.364       0.333       0.5              0.4  
#>  2            0.5   0.5         0.5         0.5              0.5  
#>  3            0.417 0.364       0.333       0.5              0.4  
#>  4            0.667 0.6         0.5         0.833            0.75 
#>  5            0.583 0.667       0.833       0.333            0.556
#>  6            0.667 0.6         0.5         0.833            0.75 
#>  7            0.25  0.308       0.333       0.167            0.286
#>  8            0.5   0.4         0.333       0.667            0.500
#>  9            0.25  0.182       0.167       0.333            0.20 
#> 10            0.417 0.222       0.167       0.667            0.333
#> # … with 90 more rows, and 14 more variables: `Neg Pred Value` <dbl>,
#> #   AUC <dbl>, `Lower CI` <dbl>, `Upper CI` <dbl>, Kappa <dbl>, MCC <dbl>,
#> #   `Detection Rate` <dbl>, `Detection Prevalence` <dbl>,
#> #   Prevalence <dbl>, Predictions <list>, ROC <list>, `Confusion
#> #   Matrix` <list>, Family <chr>, Dependent <chr>

Multinomial

Approach: Creates one-vs-all (binomial) baseline evaluations for n sets of random predictions against the dependent variable, along with sets of “all class x,y,z,…” predictions.

Create the baseline evaluations:

multiclass_baseline <- baseline(
  test_data = multiclass_test_set, n = 100,
  dependent_col = "class", family = "multinomial")

# Summarized metrics
multiclass_baseline$summarized_metrics
#> # A tibble: 12 x 16
#>    Measure `Overall Accura… `Balanced Accur…      F1 Sensitivity
#>    <chr>              <dbl>            <dbl>   <dbl>       <dbl>
#>  1 Mean              0.250            0.501   0.283       0.252 
#>  2 Median            0.231            0.494   0.280       0.243 
#>  3 SD                0.0841           0.0567  0.0737      0.0853
#>  4 IQR               0.115            0.0795  0.0920      0.121 
#>  5 Max               0.538            0.786   0.667       1     
#>  6 Min               0.0769           0.262   0.111       0     
#>  7 NAs              NA                0      61           0     
#>  8 INFs             NA                0       0           0     
#>  9 All_cl…           0.192            0.5    NA           0.25  
#> 10 All_cl…           0.269            0.5    NA           0.25  
#> 11 All_cl…           0.269            0.5    NA           0.25  
#> 12 All_cl…           0.269            0.5    NA           0.25  
#> # … with 11 more variables: Specificity <dbl>, `Pos Pred Value` <dbl>,
#> #   `Neg Pred Value` <dbl>, AUC <dbl>, `Lower CI` <dbl>, `Upper CI` <dbl>,
#> #   Kappa <dbl>, MCC <dbl>, `Detection Rate` <dbl>, `Detection
#> #   Prevalence` <dbl>, Prevalence <dbl>

# Summarized class level results for class 1
multiclass_baseline$summarized_class_level_results %>% 
  dplyr::filter(Class == "class_1") %>%
  tidyr::unnest(Results)
#> # A tibble: 10 x 16
#>    Class Measure `Balanced Accur…     F1 Sensitivity Specificity
#>    <chr> <chr>              <dbl>  <dbl>       <dbl>       <dbl>
#>  1 clas… Mean               0.514  0.284       0.28       0.748 
#>  2 clas… Median             0.529  0.286       0.2        0.762 
#>  3 clas… SD                 0.102  0.106       0.191      0.0979
#>  4 clas… IQR                0.124  0.182       0.2        0.0952
#>  5 clas… Max                0.786  0.526       1          0.952 
#>  6 clas… Min                0.262  0.125       0          0.524 
#>  7 clas… NAs                0     18           0          0     
#>  8 clas… INFs               0      0           0          0     
#>  9 clas… All_0              0.5   NA           0          1     
#> 10 clas… All_1              0.5    0.323       1          0     
#> # … with 10 more variables: `Pos Pred Value` <dbl>, `Neg Pred
#> #   Value` <dbl>, AUC <dbl>, `Lower CI` <dbl>, `Upper CI` <dbl>,
#> #   Kappa <dbl>, MCC <dbl>, `Detection Rate` <dbl>, `Detection
#> #   Prevalence` <dbl>, Prevalence <dbl>

# Random evaluations
# Note, that the class level results for each repetition
# is available as well
multiclass_baseline$random_evaluations
#> # A tibble: 100 x 21
#>    Repetition `Overall Accura… `Balanced Accur…      F1 Sensitivity
#>         <dbl>            <dbl>            <dbl>   <dbl>       <dbl>
#>  1          1            0.154            0.445 NaN           0.171
#>  2          2            0.269            0.518 NaN           0.279
#>  3          3            0.192            0.460   0.195       0.193
#>  4          4            0.385            0.591   0.380       0.386
#>  5          5            0.154            0.430 NaN           0.143
#>  6          6            0.154            0.438 NaN           0.157
#>  7          7            0.154            0.445 NaN           0.171
#>  8          8            0.346            0.574   0.341       0.364
#>  9          9            0.308            0.541   0.315       0.314
#> 10         10            0.308            0.536   0.322       0.3  
#> # … with 90 more rows, and 16 more variables: Specificity <dbl>, `Pos Pred
#> #   Value` <dbl>, `Neg Pred Value` <dbl>, AUC <dbl>, `Lower CI` <dbl>,
#> #   `Upper CI` <dbl>, Kappa <dbl>, MCC <dbl>, `Detection Rate` <dbl>,
#> #   `Detection Prevalence` <dbl>, Prevalence <dbl>, Predictions <list>,
#> #   `Confusion Matrix` <list>, `Class Level Results` <list>, Family <chr>,
#> #   Dependent <chr>

Plot results

There are currently a small set of plots for quick visualization of the results. It is supposed to be easy to extract the needed information to create your own plots. If you lack access to any information or have other requests or ideas, feel free to open an issue.

Gaussian

cv_plot(CV1, type = "RMSE") +
  theme_bw()

cv_plot(CV1, type = "r2") +
  theme_bw()

cv_plot(CV1, type = "IC") +
  theme_bw()

cv_plot(CV1, type = "coefficients") +
  theme_bw()

Binomial

cv_plot(CV2, type = "ROC") +
  theme_bw()

Generate model formulas

Instead of manually typing all possible model formulas for a set of fixed effects (including the possible interactions), combine_predictors() can do it for you (with some constraints).

When including interactions, >200k formulas have been precomputed for up to 8 fixed effects, with a maximum interaction size of 3, and a maximum of 5 fixed effects per formula. It’s possible to further limit the generated formulas.

We can also append a random effects structure to the generated formulas.

combine_predictors(dependent = "y",
                   fixed_effects = c("a","b","c"),
                   random_effects = "(1|d)")
#>  [1] "y ~ a + (1|d)"                    
#>  [2] "y ~ b + (1|d)"                    
#>  [3] "y ~ c + (1|d)"                    
#>  [4] "y ~ a * b + (1|d)"                
#>  [5] "y ~ a * c + (1|d)"                
#>  [6] "y ~ a + b + (1|d)"                
#>  [7] "y ~ a + c + (1|d)"                
#>  [8] "y ~ b * c + (1|d)"                
#>  [9] "y ~ b + c + (1|d)"                
#> [10] "y ~ a * b * c + (1|d)"            
#> [11] "y ~ a * b + c + (1|d)"            
#> [12] "y ~ a * c + b + (1|d)"            
#> [13] "y ~ a + b * c + (1|d)"            
#> [14] "y ~ a + b + c + (1|d)"            
#> [15] "y ~ a * b + a * c + (1|d)"        
#> [16] "y ~ a * b + b * c + (1|d)"        
#> [17] "y ~ a * c + b * c + (1|d)"        
#> [18] "y ~ a * b + a * c + b * c + (1|d)"

If two or more fixed effects should not be in the same formula, like an effect and its log-transformed version, we can provide them as sublists.

combine_predictors(dependent = "y",
                   fixed_effects = list("a", list("b","log_b")),
                   random_effects = "(1|d)")
#> [1] "y ~ a + (1|d)"         "y ~ b + (1|d)"         "y ~ log_b + (1|d)"    
#> [4] "y ~ a * b + (1|d)"     "y ~ a * log_b + (1|d)" "y ~ a + b + (1|d)"    
#> [7] "y ~ a + log_b + (1|d)"
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