models_stability_plot_bootstrap {AlpsNMR} | R Documentation

## Models stability plot

### 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)

[Package AlpsNMR version 3.0.6 Index]