Bootstrap plot predictions

Usage

plot_bootstrap_multimodel(bp_results, dataset, y_column, plot = TRUE)

Arguments

bp_results: bp_kfold_VIP_analysis results
dataset: An nmr_dataset_family object
y_column: A string with the name of the y column (present in the metadata of the dataset)
plot: A boolean that indicate if results are plotted or not

Value

A plot of the results or a ggplot object

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)
)

### 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)

# plot_bootstrap_multimodel(bp_results, peak_table, "Condition")