plot_plsda_samples {AlpsNMR} | R Documentation |
Plot PLSDA predictions
plot_plsda_samples(model, newdata = NULL, plot = TRUE)
model |
A plsda model |
newdata |
newdata to predict, if not included model$X_test will be used |
plot |
A boolean that indicate if results are plotted or not |
A plot of the samples or a ggplot object
#' # Data analysis for a table of integrated peaks ## Generate an artificial nmr_dataset_peak_table: ### Generate artificial metadata: num_samples <- 32 # use an even number in this example num_peaks <- 20 metadata <- data.frame( NMRExperiment = as.character(1:num_samples), Condition = rep(c("A", "B"), times = num_samples/2), stringsAsFactors = FALSE ) ### The matrix with peaks peak_means <- runif(n = num_peaks, min = 300, max = 600) peak_sd <- runif(n = num_peaks, min = 30, max = 60) peak_matrix <- mapply(function(mu, sd) rnorm(num_samples, mu, sd), mu = peak_means, sd = peak_sd) colnames(peak_matrix) <- paste0("Peak", 1:num_peaks) ## Artificial differences depending on the condition: peak_matrix[metadata$Condition == "A", "Peak2"] <- peak_matrix[metadata$Condition == "A", "Peak2"] + 70 peak_matrix[metadata$Condition == "A", "Peak6"] <- peak_matrix[metadata$Condition == "A", "Peak6"] - 60 ### The nmr_dataset_peak_table peak_table <- new_nmr_dataset_peak_table( peak_table = peak_matrix, metadata = list(external = metadata) ) ## We will use a double cross validation, splitting the samples with random ## subsampling both in the external and internal validation. ## The classification model will be a PLSDA, exploring at maximum 3 latent ## variables. ## The best model will be selected based on the area under the ROC curve methodology <- plsda_auroc_vip_method(ncomp = 1) model <- nmr_data_analysis( peak_table, y_column = "Condition", identity_column = NULL, external_val = list(iterations = 1, test_size = 0.25), internal_val = list(iterations = 1, test_size = 0.25), data_analysis_method = methodology ) #plot_plsda_samples(model$outer_cv_results[[1]]$model)