bp_VIP_analysis {AlpsNMR} | R Documentation

## Bootstrap and permutation over PLS-VIP

### Description

Bootstrap and permutation over PLS-VIP on AlpsNMR can be performed on both nmr_dataset_1D full spectra as well as nmr_dataset_peak_table peak tables.

### Usage

    bp_VIP_analysis(dataset, train_index, y_column, ncomp, nbootstrap = 300)

### Arguments

`dataset` | An nmr_dataset_family object
`train_index` | set of index used to generate the bootstrap datasets
`y_column` | A string with the name of the y column (present in the metadata of the dataset)
`ncomp` | number of components used in the plsda models
`nbootstrap` | number of bootstrap dataset

### Details

Use of the bootstrap and permutation methods for a more robust variable importance in the projection metric for partial least squares regression

### Value

A list with the following elements:

- `important_vips`: A list with the important vips selected

- `relevant_vips`: List of vips with some relevance

- `pls_vip`: Pls-VIPs of every bootstrap

- `pls_vip_perm`: Pls-VIPs of every bootstrap with permuted variables

- `pls_vip_means`: Pls-VIPs normaliced differences means

- `pls_vip_score_diff`: Differences of `pls_vip` and `pls_vip_perm`

- `pls_models`: pls models of the diferent bootstraps

- `pls_perm_models`: pls permuted models of the diferent bootstraps

- `classif_rate`: classification rate of the bootstrap models

- `general_model`: pls model trained with all train data

- `general_CR`: classification rate of the `general_model`

- `vips_model`: pls model trained with vips selection over all train data

- `vips_CR`: classification rate of the `vips_model`

- `error`: error spected in a t distribution

- `lower_bound`: lower bound of the confidence interval

- `upper_bound`: upper bound of the confidence interval

### 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 = 3)
    model <- nmr_data_analysis(
        peak_table,
        y_column = "Condition",
        identity_column = NULL,
        external_val = list(iterations = 1, test_size = 0.25),
        internal_val = list(iterations = 3, test_size = 0.25),
        data_analysis_method = methodology
    )
    ## Area under ROC for each outer cross-validation iteration:
    model$outer_cv_results_digested$auroc

    ## The number of components for the bootstrap models is selected
    ncomps <- model$outer_cv_results$`1`$model$ncomp
    train_index <- model$train_test_partitions$outer$`1`$outer_train

    # Bootstrap and permutation for VIP selection
    bp_VIPS <- bp_VIP_analysis(peak_table, # Data to be analized
                               train_index,
                               y_column = "Condition",
                               ncomp = ncomps,
                               nbootstrap = 10)

[Package AlpsNMR version 3.2.3 Index]