mrPD_bootstrap: Bootstrap Partial Dependence plots

In nfj1380/mrIML: Multi Response Interpretable Machine Learning

Bootstrap Partial Dependence plots

Description

This function bootstraps model predictions and generates partial dependence plots for each response variable. It also creates a combined plot for the top variables of interest.

Usage

mrPD_bootstrap(mrBootstrap_obj, vi_obj, X, Y, target, global_top_var = 2)

Arguments

mrBootstrap_obj	A list of model bootstraps generated using mrBootstrap function.
vi_obj	Variable Importance data.
X	The predictor data.
Y	The response data.
target	The target variable for generating plots.
global_top_var	The number of top variables to consider (default: 2).

Value

A list containing the partial dependence plots for each response variable and a combined plot.

Examples

## Not run: 
#' # Example usage:
#set up analysis
Y <- dplyr::select(Bird.parasites, -scale.prop.zos)%>% 
dplyr::select(sort(names(.)))#response variables eg. SNPs, pathogens, species....
X <- dplyr::select(Bird.parasites, scale.prop.zos) # feature set
X1 <- Y %>%
dplyr::select(sort(names(.)))
model_rf <- 
rand_forest(trees = 100, mode = "classification", mtry = tune(), min_n = tune()) %>% #100 trees are set for brevity. Aim to start with 1000
set_engine("randomForest")
yhats_rf <- mrIMLpredicts(X=X, Y=Y,
X1=X1,'Model=model_rf , 
balance_data='no',mode='classification',
tune_grid_size=5,seed = sample.int(1e8, 1),'morans=F,
prop=0.7, k=5, racing=T) #
bs_analysis <- mrBootstrap(yhats=yhats_rf,Y=Y, num_bootstrap = 5)
pds <- mrPD_bootstrap(mrBootstrap_obj=bs_malaria, vi_obj=bs_impVIa, X, Y,
target='Plas', global_top_var=5)
pd_list <- pds[[1]] #data
pds[[2]]#plot 
## End(Not run)

nfj1380/mrIML documentation built on May 17, 2024, 7:41 a.m.