plot_bootstrap_multimodel {AlpsNMR} | R Documentation |
Bootstrap plot predictions
plot_bootstrap_multimodel(bp_results, dataset, y_column, plot = TRUE)
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 |
A plot of the results or a ggplot object
# 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) #plot_bootstrap_multimodel(bp_results, peak_table, "Condition")