models_stability_plot_bootstrap: Models stability plot

In AlpsNMR: Automated spectraL Processing System for NMR

Description

Plot stability among models of the external cross validation

Usage

models_stability_plot_bootstrap(bp_results)

Arguments

bp_results

bp_kfold_VIP_analysis results

Value

A plot of models stability

Examples

# Data analysis for a table of integrated peaks

## Generate an artificial nmr_dataset_peak_table:
### Generate artificial metadata:
num_samples <- 64 # use an even number in this example
num_peaks <- 20
metadata <- data.frame(
    NMRExperiment = as.character(1:num_samples),
    Condition = rep(c("A", "B"), times = num_samples/2),
    stringsAsFactors = FALSE
)

### The matrix with peaks
peak_means <- runif(n = num_peaks, min = 300, max = 600)
peak_sd <- runif(n = num_peaks, min = 30, max = 60)
peak_matrix <- mapply(function(mu, sd) rnorm(num_samples, mu, sd),
                                            mu = peak_means, sd = peak_sd)
colnames(peak_matrix) <- paste0("Peak", 1:num_peaks)

## Artificial differences depending on the condition:
peak_matrix[metadata$Condition == "A", "Peak2"] <- 
    peak_matrix[metadata$Condition == "A", "Peak2"] + 70

peak_matrix[metadata$Condition == "A", "Peak6"] <- 
    peak_matrix[metadata$Condition == "A", "Peak6"] - 60
    
### The nmr_dataset_peak_table
peak_table <- new_nmr_dataset_peak_table(
    peak_table = peak_matrix,
    metadata = list(external = metadata)
)

## We will use bootstrap and permutation method for VIPs selection 
## in a a k-fold cross validation 
#bp_results <- bp_kfold_VIP_analysis(peak_table, # Data to be analized
#                           y_column = "Condition", # Label
#                           k = 3,
#                           nbootstrap = 10)

#message("Selected VIPs are: ", bp_results$importarn_vips)

#models_stability_plot_bootstrap(bp_results)

AlpsNMR documentation built on April 1, 2021, 6:02 p.m.