plsda_auroc_vip_compare {AlpsNMR} | R Documentation

## Compare PLSDA auroc VIP results

### Description

Compare PLSDA auroc VIP results

### Usage

    plsda_auroc_vip_compare(...)

### Arguments

`...` | Results of nmr_data_analysis to be combined. Give each result a name.

### Value

A plot of the AUC for each method

### Examples

    # Data analysis for a table of integrated peaks

    ## Generate an artificial nmr_dataset_peak_table:
    ### Generate artificial metadata:
    num_samples <- 32 # 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 a double cross validation, splitting the samples with random
    ## subsampling both in the external and internal validation.
    ## The classification model will be a PLSDA, exploring at maximum 3 latent
    ## variables.
    ## The best model will be selected based on the area under the ROC curve
    methodology <- plsda_auroc_vip_method(ncomp = 1)
    model1 <- nmr_data_analysis(
        peak_table,
        y_column = "Condition",
        identity_column = NULL,
        external_val = list(iterations = 1, test_size = 0.25),
        internal_val = list(iterations = 1, test_size = 0.25),
        data_analysis_method = methodology
    )

    methodology2 <- plsda_auroc_vip_method(ncomp = 2)
    model2 <- nmr_data_analysis(
        peak_table,
        y_column = "Condition",
        identity_column = NULL,
        external_val = list(iterations = 1, test_size = 0.25),
        internal_val = list(iterations = 1, test_size = 0.25),
        data_analysis_method = methodology2
    )

    plsda_auroc_vip_compare(model1 = model1, model2 = model2)

[Package AlpsNMR version 3.0.6 Index]