using simulated data in examples

R/AnalysisConfiguration.R

@@ -62,13 +62,10 @@ make_reduced_hierarchy_config <- function(config, workIntensity , hierarchy ){
 #' # debug(make_interaction_column)
 #' x <- make_interaction_column(xx, c("B","A"))
 #' x <- make_interaction_column(xx, c("A"))
-#' bb <- prolfqua_data('data_ionstar')$filtered()
-#' bb$config <- old2new(bb$config)
-#' stopifnot(nrow(bb$data) == 25780)
-#' config <- bb$config$clone(deep=TRUE)
+#' bb <- prolfqua::sim_lfq_data_protein_config()
+#' config <- bb$config
 #' analysis <- bb$data
 #'
-#' config$table$factor_keys()
 #' config$table$factorDepth <- 1
 #' make_interaction_column(analysis,
 #'    config$table$factor_keys_depth())

R/ContrastsSimpleImpute.R

@@ -22,6 +22,8 @@
 #'
 #' Contr <- c("dil.b_vs_a" = "group_A - group_Ctrl")
 #' csi <- ContrastsMissing$new(lProt, contrasts = Contr)
+#' csi$get_contrast_sides()
+#'
 #' res <- csi$get_contrasts()
 #'
 ContrastsMissing <- R6::R6Class(
@@ -120,8 +122,8 @@ ContrastsMissing <- R6::R6Class(
 
           result <- dplyr::inner_join(result, pooled, by = self$lfqdata$config$table$hierarchy_keys_depth())
 
-          result_sd_zero <- result[result$n == 0, ]
-          resultnot_zero <- result[result$n > 0,]
+          result_sd_zero <- result[result$nrMeasured == 0, ]
+          resultnot_zero <- result[result$nrMeasured > 0,]
           meandf <- resultnot_zero |> summarize(n = 1, df = 1, sd = mean(sd, na.rm = TRUE), sdT = mean(sdT, na.rm = TRUE))
 
           meandf$sd <-  ifelse(meandf$sd > 0, meandf$sd, self$minsd)
@@ -144,7 +146,7 @@ ContrastsMissing <- R6::R6Class(
           result <- self$p.adjust(result, column = "p.value", group_by_col = "contrast", newname = "FDR")
 
           if (!all) {
-            result <- select(result, -all_of( c("isSingular", "not_na" , "mean" ,"n.groups", "n", "meanAll") ) )
+            result <- select(result, -all_of( c("isSingular", "nrMeasured" , "mean" ,"n.groups", "n", "meanAll") ) )
           }
 
         }

R/LFQDataStats.R

@@ -35,7 +35,7 @@
 #' lfqstats$stats_wide()
 #' lfqstats$violin()
 #' runallfuncs(lfqstats)
-#' x<-lfqstats
+#' x <- lfqstats
 #'
 #' #study variance of normalized data
 #'

R/simulate_LFQ_data.R

@@ -123,13 +123,13 @@ sim_lfq_data <- function(
 #' add missing values to x vector based on the values of x
 #' @export
 #' @param x vector of intensities
+#' @param weight_missing greater weight more missing
 #'
-#'
-which_missing <- function(x){
+which_missing <- function(x, weight_missing = 0.2){
   missing_prop <- pnorm(x, mean = mean(x), sd = sd(x))
   # sample TRUE or FALSE with propability in missing_prop
   samplemiss <- function(missing_prop) {
-    mp <- c((1 - missing_prop)*0.2, missing_prop*3)
+    mp <- c((1 - missing_prop)*weight_missing, missing_prop*3)
     mp <- mp / sum(mp)
     sample(c(TRUE, FALSE), size = 1, replace = TRUE, prob = mp)
   }
@@ -151,13 +151,13 @@ which_missing <- function(x){
 #' x <- sim_lfq_data_peptide_config()
 #' stopifnot("data.frame" %in% class(x$data))
 #' stopifnot("AnalysisConfiguration" %in% class(x$config))
-sim_lfq_data_peptide_config <- function(Nprot = 10, with_missing = TRUE, seed = 1234){
+sim_lfq_data_peptide_config <- function(Nprot = 10, with_missing = TRUE, weight_missing = 0.2, seed = 1234){
   if (!is.null(seed)) {
     set.seed(seed)
   }
   data <- sim_lfq_data(Nprot = Nprot, PEPTIDE = TRUE)
   if (with_missing) {
-    not_missing <- !which_missing(data$abundance)
+    not_missing <- !which_missing(data$abundance, weight_missing = weight_missing)
     data <- data[not_missing,]
   }
   data$isotopeLabel <- "light"
@@ -185,13 +185,13 @@ sim_lfq_data_peptide_config <- function(Nprot = 10, with_missing = TRUE, seed =
 #' stopifnot("data.frame" %in% class(x$data))
 #' stopifnot("AnalysisConfiguration" %in% class(x$config))
 #'
-sim_lfq_data_protein_config <- function(Nprot = 10, with_missing = TRUE, seed = 1234){
+sim_lfq_data_protein_config <- function(Nprot = 10, with_missing = TRUE, weight_missing = 0.2, seed = 1234){
   if (!is.null(seed)) {
     set.seed(seed)
   }
   data <- sim_lfq_data(Nprot = Nprot, PEPTIDE = FALSE)
   if (with_missing) {
-    data <- data[!which_missing(data$abundance),]
+    data <- data[!which_missing(data$abundance,weight_missing = weight_missing),]
   }
   data$isotopeLabel <- "light"
   data$qValue <- 0

R/tidyMS_R6Model.R

@@ -87,35 +87,30 @@ LR_test <- function(modelProteinF,
 #'
 #' @export
 #' @examples
-#' # library(tidyverse)
-#' D <- prolfqua_data('data_ionstar')$normalized()
-#' D$config <- old2new(D$config)
-#' D$data <- dplyr::filter(D$data ,protein_Id %in% sample(protein_Id, 100))
-#'
+#' D <- prolfqua::sim_lfq_data_peptide_config(Nprot = 20, weight_missing = 0.1)
+#' D$data$abundance |> is.na() |> sum()
+#' D <- prolfqua::sim_lfq_data_peptide_config(Nprot = 20, weight_missing = 0.1, seed =3)
+#' D$data$abundance |> is.na() |> sum()
 #' modelName <- "f_condtion_r_peptide"
 #' formula_randomPeptide <-
-#'   strategy_lmer("transformedIntensity  ~ dilution. + (1 | peptide_Id) + (1 | sampleName)",
+#'   strategy_lmer("abundance  ~ group_ + (1 | peptide_Id) + (1 | sampleName)",
 #'    model_name = modelName)
 #'
 #'
-#' pepIntensity <- D$data
-#' config <- D$config
-#'
-#'
-#'
-#' mod <- prolfqua:::build_model(
-#'  pepIntensity,
+#' mod <- prolfqua::build_model(
+#'  D$data,
 #'  formula_randomPeptide,
 #'  modelName = modelName,
-#'  subject_Id = config$table$hierarchy_keys_depth())
-#' mod$get_anova()
+#'  subject_Id = D$config$table$hierarchy_keys_depth())
+#' aovtable <- mod$get_anova()
 #'
-#' mod <- prolfqua:::build_model(
-#'  LFQData$new(pepIntensity, config),
+#' mod <- prolfqua::build_model(
+#'  LFQData$new(D$data, D$config),
 #'  formula_randomPeptide,
 #'  modelName = modelName)
 #' model_summary(mod)
 #'
+#'
 build_model <- function(data,
                         model_strategy,
                         subject_Id = if ("LFQData" %in% class(data)) {data$subject_Id()} else {"protein_Id"},

R/tidyMS_aggregation.R

@@ -137,22 +137,18 @@ plot_hierarchies_line <- function(res,
 #' config$table$is_response_transformed <- FALSE
 #' #debug(plot_hierarchies_line_df)
 #' res <- plot_hierarchies_line_df(istar_data, config)
-#' res[[1]]
 #'
 #' config$table$is_response_transformed <- TRUE
 #' res <- plot_hierarchies_line_df(istar_data, config)
-#' res[[1]]
 #'
 #' istar <- prolfqua_data('data_ionstar')$filtered()
 #' istar_data <- istar$data |> dplyr::filter(protein_Id %in% sample(protein_Id, 20))
 #' config <-  old2new(istar$config)
 #' res <- plot_hierarchies_line_df(istar_data, config)
 #' config$table$is_response_transformed
-#' res[[1]]
 #' config$table$is_response_transformed <- TRUE
 #' res <- plot_hierarchies_line_df(istar_data, config)
 #' config$table$is_response_transformed
-#' res[[1]]
 #'
 #' #TODO make it work for other hiearachy levels.
 #' config$table$hierarchyDepth = 2