diff --git a/R/AnalysisConfiguration.R b/R/AnalysisConfiguration.R
index 7a5cbdbb3..ce0f18eba 100644
--- a/R/AnalysisConfiguration.R
+++ b/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())
diff --git a/R/ContrastsSimpleImpute.R b/R/ContrastsSimpleImpute.R
index eebf83522..4c296c400 100644
--- a/R/ContrastsSimpleImpute.R
+++ b/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") ) )
           }
 
         }
diff --git a/R/LFQDataStats.R b/R/LFQDataStats.R
index 847e941ed..0253edb7f 100644
--- a/R/LFQDataStats.R
+++ b/R/LFQDataStats.R
@@ -35,7 +35,7 @@
 #' lfqstats$stats_wide()
 #' lfqstats$violin()
 #' runallfuncs(lfqstats)
-#' x<-lfqstats
+#' x <- lfqstats
 #'
 #' #study variance of normalized data
 #'
diff --git a/R/simulate_LFQ_data.R b/R/simulate_LFQ_data.R
index e785dc25a..4a4b6e44d 100644
--- a/R/simulate_LFQ_data.R
+++ b/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$qValue <- 0
diff --git a/R/tidyMS_R6Model.R b/R/tidyMS_R6Model.R
index 608d79ba5..86643c107 100644
--- a/R/tidyMS_R6Model.R
+++ b/R/tidyMS_R6Model.R
@@ -87,35 +87,30 @@ LR_test <- function(modelProteinF,
 #'
 #' @export
 #' @examples
-#' # library(tidyverse)
-#' D <- prolfqua_data('data_ionstar')$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$subject_Id()} else {"protein_Id"},
diff --git a/R/tidyMS_aggregation.R b/R/tidyMS_aggregation.R
index 876c7c2ea..94fb3a4b6 100644
--- a/R/tidyMS_aggregation.R
+++ b/R/tidyMS_aggregation.R
@@ -137,22 +137,18 @@ plot_hierarchies_line <- function(res,
 #' config$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
diff --git a/R/tidyMS_missigness.R b/R/tidyMS_missigness.R
index 2771c40e7..53b6321c6 100644
--- a/R/tidyMS_missigness.R
+++ b/R/tidyMS_missigness.R
@@ -20,23 +20,24 @@
 #'    config)
 #' xx <- complete_cases(xx, config)
 #' x <- interaction_missing_stats(xx, config)$data |> dplyr::arrange(desc(nrNAs))
-#'
+#' nrow(x)
 #' tmp <- interaction_missing_stats(xx, config,
 #'  factors= character(),
 #'   hierarchy = config$table$hierarchy_keys()[1])$data
-#'
+#' stopifnot(nrow(tmp) == 10)
 #' tmp <- interaction_missing_stats(xx, config,
 #'   hierarchy = config$table$hierarchy_keys()[1])$data
+#' stopifnot(nrow(tmp) == length(unique(xx$protein_Id))* length(unique(xx$group_)))
 #' stopifnot(sum(is.na(tmp$nrMeasured))==0)
 #'
 #' tmp <- interaction_missing_stats(xx, config, factors = NULL)
-#'
 interaction_missing_stats <- function(pdata,
                                       config,
                                       factors = config$table$factor_keys_depth(),
                                       hierarchy = config$table$hierarchy_keys(),
                                       workIntensity = config$table$get_response())
 {
+  warning(">>>> deprecated! <<<<")
   pdata <- complete_cases(pdata, config)
   table <- config$table
   missingPrec <- pdata |> group_by_at(c(factors,
@@ -70,28 +71,23 @@ interaction_missing_stats <- function(pdata,
 #' @return function
 #' @examples
 #'
-#' istar <- sim_lfq_data_peptide_config()
+#' istar <- sim_lfq_data_peptide_config(Nprot = 20,weight_missing = 2)
 #' config <- istar$config
 #' analysis <- istar$data
-#' config$parameter$qVal_individual_threshold <- 0.01
-#'
-#' xx <- prolfqua::remove_large_QValues(analysis, config)
-#' xx <- complete_cases(xx, config)
+#' xx <- complete_cases(analysis, config)
 #' nrPepTimesDilution <- length(unique(paste0(xx$protein_Id, xx$peptide_Id))) *
-#'     length(unique(xx$dilution.))
-#' tmp <- interaction_missing_stats(xx, config)
-#' fun <- .missigness_impute_interactions(xx, config)
-#'
-#' long <- fun("long")
-#' alldata <- fun("all")
+#'     length(unique(xx$group_))
+#' funx <- .missigness_impute_interactions(xx, config)
+#' long <- funx("long")
+#' alldata <- funx("all")
 #' stopifnot(length(names(alldata)) == 5)
 #'
-#' imputed <- fun("imputed")
+#' imputed <- funx("imputed")
 #' stopifnot(nrow(imputed) == length(unique(paste0(xx$protein_Id, xx$peptide_Id))))
-#' missing <- fun("nrMeasured")
+#' missing <- funx("nrMeasured")
 #' stopifnot(nrow(missing) == length(unique(paste0(xx$protein_Id, xx$peptide_Id))))
 #'
-#'  meanAbundance <- fun("mean")
+#'  meanAbundance <- funx("mean")
 #' stopifnot(nrow(meanAbundance) == length(unique(paste0(xx$protein_Id, xx$peptide_Id))))
 #'  stopifnot(sum(is.na(imputed$mean.imp.group_A))==0)
 #'
@@ -105,7 +101,6 @@ interaction_missing_stats <- function(pdata,
   mstats <- mstats$data
   mstats <- make_interaction_column(mstats, factors, sep = ":")
 
-
   lowerMean <- function(meanAbundance, probs = probs){
     meanAbundanceNotNA <- na.omit(meanAbundance)
     small10 <- meanAbundanceNotNA[meanAbundanceNotNA < quantile(meanAbundanceNotNA, probs = probs)]
@@ -120,7 +115,6 @@ interaction_missing_stats <- function(pdata,
   }else{
     mstats <- mstats |>
       dplyr::mutate(imputed = lowerMean(.data$meanAbundance,probs = probs))
-
   }
 
   res_fun <- function(value = c("long",
@@ -146,19 +140,19 @@ interaction_missing_stats <- function(pdata,
       nrReplicates <- mstats |>
         dplyr::select( -one_of(c(base::setdiff(x_summaries,"nrReplicates"),"imputed") )) |>
         tidyr::spread(interaction, nrReplicates, sep = ".nrReplicates.") |>
-        arrange(!!!syms(pid)) |>
+        dplyr::arrange(!!!syms(pid)) |>
         dplyr::ungroup()
       nrMeasured <- mstats |> dplyr::select(-one_of(c(base::setdiff(x_summaries,"nrMeasured"),"imputed" ) )) |>
         tidyr::spread(interaction, nrMeasured, sep = ".nrMeasured.") |>
-        arrange(!!!syms(pid)) |> dplyr::ungroup()
+        dplyr::arrange(!!!syms(pid)) |> dplyr::ungroup()
 
       meanAbundance <- mstats |> dplyr::select(-one_of(c(base::setdiff(x_summaries,"meanAbundance"),"imputed" ) )) |>
         tidyr::spread(interaction, meanAbundance, sep = ".meanAbundance.") |>
-        arrange(!!!syms(pid)) |> dplyr::ungroup()
+        dplyr::arrange(!!!syms(pid)) |> dplyr::ungroup()
 
       meanAbundanceImputed <- mstats |> dplyr::select(-one_of(base::setdiff(x_summaries,"imputed" ) )) |>
         tidyr::spread(interaction, .data$imputed, sep = ".imputed.") |>
-        arrange(!!!syms(pid)) |> dplyr::ungroup()
+        dplyr::arrange(!!!syms(pid)) |> dplyr::ungroup()
 
       allTables <- list(meanAbundance = meanAbundance,
                         nrMeasured = nrMeasured,
@@ -214,59 +208,60 @@ interaction_missing_stats <- function(pdata,
 #' @family imputation
 #' @examples
 #'
-#' istar <- sim_lfq_data_peptide_config()
+#' istar <- sim_lfq_data_peptide_config(weight_missing = 2)
 #' config <- istar$config
 #' analysis <- istar$data
 #'
 #' xx <- complete_cases(analysis, config)
-#'
 #' res <- missigness_impute_factors_interactions(xx, config)
 #' res <- missigness_impute_factors_interactions(xx, config, value = "imputed")
 #' res <- missigness_impute_factors_interactions(xx, config, value = "nrMeasured")
+#' #debug(missigness_impute_factors_interactions)
 #' long <- missigness_impute_factors_interactions(xx, config, value = "long")
-#'
-missigness_impute_factors_interactions <-
-  function(pdata,
-           config,
-           probs = 0.03,
-           value = c("long", "nrReplicates", "nrMeasured", "meanAbundance", "imputed"),
-           add.prefix = FALSE,
-           global = TRUE)
-  {
-    value <- match.arg(value)
-    fac_fun <- list()
-    fac_fun[["interaction"]] <- .missigness_impute_interactions(
-      pdata,
-      config,
-      probs = probs,
-      global = global)
-    if (config$table$factorDepth > 1 ) { # if 1 only then done
-      for (factor in config$table$factor_keys_depth()) {
-        fac_fun[[factor]] <- .missigness_impute_interactions(
-          pdata,
-          config,
-          factors = factor,
-          probs = probs,
-          global = global)
-      }
+#' head(long)
+#' plot(long$meanAbundance, long$imputed)
+missigness_impute_factors_interactions <- function(
+    pdata,
+    config,
+    probs = 0.03,
+    value = c("long", "nrReplicates", "nrMeasured", "meanAbundance", "imputed"),
+    add.prefix = FALSE,
+    global = TRUE)
+{
+  value <- match.arg(value)
+  fac_fun <- list()
+  fac_fun[["interaction"]] <- .missigness_impute_interactions(
+    pdata,
+    config,
+    probs = probs,
+    global = global)
+  if (config$table$factorDepth > 1 ) { # if 1 only then done
+    for (factor in config$table$factor_keys_depth()) {
+      fac_fun[[factor]] <- .missigness_impute_interactions(
+        pdata,
+        config,
+        factors = factor,
+        probs = probs,
+        global = global)
     }
+  }
 
-    fac_res <- vector(mode = "list", length = length(fac_fun))
-    names(fac_res) <- names(fac_fun)
-    for (fun_name in names(fac_fun)) {
-      fac_res[[fun_name]] <- fac_fun[[fun_name]](value, add.prefix = add.prefix)
-    }
-    if (value == "long") {
-      intfact <- dplyr::bind_rows(fac_res)
-    } else {
-      intfact <- purrr::reduce(fac_res,
-                               dplyr::inner_join,
-                               by = c(config$table$hierarchy_keys(),
-                                      config$table$isotopeLabel, "value"))
+  fac_res <- vector(mode = "list", length = length(fac_fun))
+  names(fac_res) <- names(fac_fun)
+  for (fun_name in names(fac_fun)) {
+    fac_res[[fun_name]] <- fac_fun[[fun_name]](value, add.prefix = add.prefix)
+  }
+  if (value == "long") {
+    intfact <- dplyr::bind_rows(fac_res)
+  } else {
+    intfact <- purrr::reduce(fac_res,
+                             dplyr::inner_join,
+                             by = c(config$table$hierarchy_keys(),
+                                    config$table$isotopeLabel, "value"))
 
-    }
-    return(dplyr::ungroup(intfact))
   }
+  return(dplyr::ungroup(intfact))
+}
 
 
 
@@ -364,8 +359,6 @@ get_contrast <- function(data,
                          hierarchy_keys,
                          contrasts)
 {
-
-
   for (i in seq_along(contrasts)) {
     message(names(contrasts)[i], "=", contrasts[i],"\n")
     data <- dplyr::mutate(data, !!names(contrasts)[i] := !!rlang::parse_expr(contrasts[i]))
@@ -386,7 +379,6 @@ get_contrast <- function(data,
     res[[names(contrasts)[i]]] <- df
   }
   res <- dplyr::bind_rows(res)
-
   return(dplyr::ungroup(res))
 }
 
@@ -432,6 +424,7 @@ get_imputed_contrasts <- function(pepIntensity,
     stop("At least 1 observation in interaction to infer LOD.")
   }
   long <- missigness_impute_factors_interactions(pepIntensity, config, value = "long" )
+  # determine limit of detection
   LOD <- long |> filter(nrNAs == nrReplicates - present) |> pull(meanAbundance) |> median(na.rm=TRUE)
 
   long <- tidyr::complete(long, tidyr::nesting(!!!syms(config$table$hierarchy_keys())), interaction)
@@ -492,7 +485,7 @@ missigness_histogram <- function(x,
     if (config$table$is_response_transformed) {
       missingPrec <- missingPrec |>
         dplyr::mutate(meanAbundance = ifelse(is.na(.data$meanAbundance), min(.data$meanAbundance, na.rm = TRUE) - 1,
-                                        .data$meanAbundance))
+                                             .data$meanAbundance))
     }else{
       missingPrec <- missingPrec |>
         dplyr::mutate(meanAbundance = ifelse(is.na(.data$meanAbundance),min(.data$meanAbundance, na.rm = TRUE) - 20,.data$meanAbundance))
diff --git a/R/tidyMS_stats.R b/R/tidyMS_stats.R
index 7ce609dc3..a94c60cbd 100644
--- a/R/tidyMS_stats.R
+++ b/R/tidyMS_stats.R
@@ -4,8 +4,8 @@
 #' @return data.frame
 #' @examples
 #'
-#' x <- data.frame(not_na =c(1,2,2), var = c(3,4,4), mean = c(3,3,3))
-#' x <- data.frame(not_na =c(1,2,1,1), var = c(NA, 0.0370, NA, NA), mean = c(-1.94,-1.46,-1.87,-1.45) )
+#' x <- data.frame(nrMeasured =c(1,2,2), var = c(3,4,4), meanAbundance = c(3,3,3))
+#' x <- data.frame(nrMeasured =c(1,2,1,1), var = c(NA, 0.0370, NA, NA), meanAbundance = c(-1.94,-1.46,-1.87,-1.45) )
 #' prolfqua:::pooled_V2(na.omit(x))
 #' prolfqua:::pooled_V1(na.omit(x))
 #' x <- x[1,, drop=FALSE]
@@ -14,9 +14,9 @@
 #' prolfqua:::pooled_V2(na.omit(x))
 pooled_V2 <- function(x){
 
-  n <- x$not_na
+  n <- x$nrMeasured
   sample.var <- x$var
-  sample.mean <- x$mean
+  sample.mean <- x$meanAbundance
   pool.n <- sum(n)
 
   pool.mean <- sum(n * sample.mean)/pool.n
@@ -44,9 +44,9 @@ pooled_V2 <- function(x){
 #' @rdname pooled_var
 #' @param x data.frame
 pooled_V1 <- function(x){
-  n <- x$not_na
+  n <- x$nrMeasured
   sample.var <- x$var
-  sample.mean <- x$mean
+  sample.mean <- x$meanAbundance
   pool.n <- sum(n)
 
   n.groups <- length(sample.var)
@@ -83,34 +83,34 @@ pooled_V1 <- function(x){
 #' @family stats
 #'
 #' @examples
-#' x <- data.frame(not_na =c(1,2,2), var = c(3,4,4), mean = c(3,3,3))
-#' x <- data.frame(not_na =c(1,2,1,1), var = c(NA, 0.0370, NA, NA), mean = c(-1.94,-1.46,-1.87,-1.45) )
+#' x <- data.frame(nrMeasured =c(1,2,2), var = c(3,4,4), meanAbundance = c(3,3,3))
+#' x <- data.frame(nrMeasured =c(1,2,1,1), var = c(NA, 0.0370, NA, NA), meanAbundance = c(-1.94,-1.46,-1.87,-1.45) )
 #' compute_pooled(x)
 #' compute_pooled(x, method = "V2")
 #' #debug(compute_pooled)
 #' y <- data.frame(dilution.=c("a","b","c"),
-#'      n = c(4,4,4), not_na = c(0,0,1), sd =c(NA,NA,NA),
-#'      var = c(NA,NA,NA),mean = c(NaN,NaN,NaN))
+#'      nrReplicates = c(4,4,4), nrMeasured = c(0,0,1), sd =c(NA,NA,NA),
+#'      var = c(NA,NA,NA),meanAbundance = c(NaN,NaN,NaN))
 #' compute_pooled(y)
-#' yb <- y |> dplyr::filter(not_na > 1)
+#' yb <- y |> dplyr::filter(nrMeasured > 1)
 compute_pooled <- function(x, method = c("V1","V2")){
   method <- match.arg(method)
-  xm <- x |> dplyr::filter(.data$not_na > 0)
-  meanAll <- sum(xm$mean * xm$not_na)/sum(xm$not_na)
-  not_na  = sum(xm$not_na)
+  xm <- x |> dplyr::filter(.data$nrMeasured > 0)
+  meanAll <- sum(xm$meanAbundance * xm$nrMeasured)/sum(xm$nrMeasured)
+  nrMeasured  = sum(xm$nrMeasured)
 
   func <- pooled_V1
   if (method == "V2") {
     func <- pooled_V2
   }
-  x <- x |> dplyr::filter(.data$not_na > 1)
+  x <- x |> dplyr::filter(.data$nrMeasured > 1)
 
   res <- func(x)
   if (is.na(res$mean)) {
     res$mean <- meanAll
   }
   res$meanAll <- meanAll
-  res$not_na <- not_na
+  res$nrMeasured <- nrMeasured
   return(res)
 }
 
@@ -121,14 +121,13 @@ compute_pooled <- function(x, method = c("V1","V2")){
 #' @family stats
 #' @examples
 #'
-#'
-#' bb <- prolfqua_data('data_ionstar')$normalized()
-#' config <- old2new(bb$config)
+#' bb <- prolfqua::sim_lfq_data_peptide_config()
+#' config <- bb$config
 #' data <- bb$data
 #'
 #' res1 <- summarize_stats(data, config)
 #' pv <- poolvar(res1, config)
-#' stopifnot(nrow(pv) == nrow(res1)/5)
+#' stopifnot(nrow(pv) == nrow(res1)/3)
 #'
 poolvar <- function(res1, config,  method = c("V1","V2")){
   method <- match.arg(method)
@@ -157,31 +156,30 @@ poolvar <- function(res1, config,  method = c("V1","V2")){
 #' @examples
 #'
 #'
-#' bb <- prolfqua_data('data_ionstar')$normalized()
-#' config <- old2new(bb$config)
+#' bb <- prolfqua::sim_lfq_data_protein_config()
+#' config <- bb$config
 #' data <- bb$data
 #'
 #' res1 <- summarize_stats(data, config)
-#' d <- res1 |> dplyr::filter(protein_Id == "CON__P01030~9~NA" & peptide_Id  == "AELADQAASWLTR")
-#' d <- res1 |> dplyr::filter(protein_Id == "CON__Q3SZR3~50~NA" & peptide_Id  == "EHFVDLLLSK")
-#' #CON__P02769~18~NA VHKECCHGDLLECADDR
-#' d <- res1 |> dplyr::filter(protein_Id == "CON__P02769~18~NA" & peptide_Id  == "VHKECCHGDLLECADDR")
 #'
 summarize_stats <- function(pdata, config){
   pdata <- complete_cases(pdata, config)
   intsym <- sym(config$table$get_response())
   hierarchyFactor <- pdata |>
     dplyr::group_by(!!!syms( c(config$table$hierarchy_keys(), config$table$factor_keys_depth()) )) |>
-    dplyr::summarize(n = dplyr::n(),
-                     not_na = sum(!is.na(!!intsym)),
+    dplyr::summarize(nrReplicates = dplyr::n(),
+                     nrMeasured = sum(!is.na(!!intsym)),
+                     nrNAs = sum(is.na(!!intsym)),
                      sd = stats::sd(!!intsym, na.rm = TRUE),
                      var = stats::var(!!intsym, na.rm = TRUE),
-                     mean = mean(!!intsym, na.rm = TRUE),.groups = "drop_last") |>  dplyr::ungroup()
+                     meanAbundance = mean(!!intsym, na.rm = TRUE),
+                     medianAbundance = median(!!intsym, na.rm = TRUE),
+                     .groups = "drop_last") |>  dplyr::ungroup()
 
   hierarchyFactor <- hierarchyFactor |>
     dplyr::mutate(dplyr::across(config$table$factor_keys_depth(), as.character))
   if (config$table$is_response_transformed == FALSE) {
-    hierarchyFactor |> dplyr::mutate(CV = sd/mean * 100) -> hierarchyFactor
+    hierarchyFactor |> dplyr::mutate(CV = sd/meanAbundance * 100) -> hierarchyFactor
   }
   return(ungroup(hierarchyFactor))
 }
@@ -198,32 +196,29 @@ summarize_stats <- function(pdata, config){
 #' @examples
 #'
 #'
-#' bb <- prolfqua_data('data_ionstar')$normalized()
-#' config <- old2new(bb$config)
-#' data <- bb$data
+#' bb <- prolfqua::sim_lfq_data_protein_config()
 #'
-#' res1 <- summarize_stats_all(data, config)
-#' d <- res1 |> dplyr::filter(protein_Id == "CON__P01030~9~NA" & peptide_Id  == "AELADQAASWLTR")
-#' d <- res1 |> dplyr::filter(protein_Id == "CON__Q3SZR3~50~NA" & peptide_Id  == "EHFVDLLLSK")
-#' #CON__P02769~18~NA VHKECCHGDLLECADDR
-#' d <- res1 |> dplyr::filter(protein_Id == "CON__P02769~18~NA" & peptide_Id  == "VHKECCHGDLLECADDR")
-#' res1 |> dplyr::filter(dilution. == "pooled")
+#' res1 <- summarize_stats_all(bb$data, bb$config)
+#'
+#' stopifnot((res1 |> dplyr::filter(group_ == "All") |> nrow()) == (res1 |> nrow()))
 #'
 summarize_stats_all <- function(pdata, config){
   pdata <- complete_cases(pdata, config)
   intsym <- sym(config$table$get_response())
   hierarchy <- pdata |>
     dplyr::group_by(!!!syms( config$table$hierarchy_keys() )) |>
-    dplyr::summarize(n = dplyr::n(),
-                     not_na = sum(!is.na(!!intsym)),
+    dplyr::summarize(nrReplicates = dplyr::n(),
+                     nrMeasured = sum(!is.na(!!intsym)),
                      sd = sd(!!intsym,na.rm = TRUE),
                      var = sd(!!intsym,na.rm = TRUE),
-                     mean = mean(!!intsym,na.rm = TRUE))
+                     meanAbundance = mean(!!intsym,na.rm = TRUE),
+                     medianAbundance = median(!!intsym, na.rm = TRUE),
+                     .groups = "drop_last") |> dplyr::ungroup()
 
   hierarchy <- dplyr::mutate(hierarchy, !!config$table$factor_keys()[1] := "All")
   hierarchyFactor <- hierarchy
   if (config$table$is_response_transformed == FALSE) {
-    hierarchyFactor |> dplyr::mutate(CV = sd/mean * 100) -> hierarchyFactor
+    hierarchyFactor |> dplyr::mutate(CV = sd/meanAbundance * 100) -> hierarchyFactor
   }
   return(ungroup(hierarchyFactor))
 }
@@ -239,24 +234,23 @@ summarize_stats_all <- function(pdata, config){
 #' @family stats
 #' @examples
 #' library(ggplot2)
-#' bb1 <- prolfqua_data('data_ionstar')$filtered()
-#' config <- old2new(bb1$config$clone( deep = TRUE))
+#' bb1 <- prolfqua::sim_lfq_data_peptide_config()
+#' config <- bb1$config
 #' data <- bb1$data
 #' stats_res <- summarize_stats(data, config)
-#' summarize_stats_quantiles(stats_res, config)
-#' summarize_stats_quantiles(stats_res, config, stats = "CV")
-#'stats_res
-#' bb <- prolfqua_data('data_ionstar')$normalized()
-#' config <- old2new(bb$config$clone(deep = TRUE))
+#' sq <- summarize_stats_quantiles(stats_res, config)
+#' sq <- summarize_stats_quantiles(stats_res, config, stats = "CV")
+#' bb <- prolfqua::sim_lfq_data_peptide_config()
+#' config <- bb$config
 #' data <- bb$data
 #' config$table$get_response()
 #' stats_res <- summarize_stats(data, config)
-#' summarize_stats_quantiles(stats_res, config)
-#' summarize_stats_quantiles(stats_res, config, stats = "sd")
+#' sq <- summarize_stats_quantiles(stats_res, config)
+#' sq <- summarize_stats_quantiles(stats_res, config, stats = "sd")
 #'
 #' stats_res <- summarize_stats(data, config)
 #' xx <- summarize_stats_quantiles(stats_res, config, probs = seq(0,1,by = 0.1))
-#' ggplot2::ggplot(xx$long, aes(x = probs, y = quantiles, color = dilution.)) + geom_line() + geom_point()
+#' ggplot2::ggplot(xx$long, aes(x = probs, y = quantiles, color = group_)) + geom_line() + geom_point()
 #'
 #'
 summarize_stats_quantiles <- function(stats_res,
@@ -324,10 +318,10 @@ summarize_stats_quantiles <- function(stats_res,
 #' @examples
 #'
 #'
-#' #library(ggplot2)
 #'
-#' bb1 <- prolfqua_data('data_ionstar')$normalized()
-#' config <- old2new(bb1$config$clone( deep = TRUE))
+#'
+#' bb1 <- prolfqua::sim_lfq_data_peptide_config()
+#' config <- bb1$config
 #' data2 <- bb1$data
 #' stats_res <- summarize_stats(data2, config)
 #' xx <- summarize_stats_quantiles(stats_res, config, probs = c(0.5,0.8))
@@ -336,7 +330,7 @@ summarize_stats_quantiles <- function(stats_res,
 #' summary <- bbb |>
 #'  dplyr::select( -N_exact, -quantiles, -sdtrimmed ) |>
 #'  tidyr::spread(delta, N, sep = "=")
-#' summary
+#'
 lfq_power_t_test_quantiles_V2 <-
   function(quantile_sd,
            delta = c(0.59,1,2),
@@ -371,14 +365,13 @@ lfq_power_t_test_quantiles_V2 <-
 #' @family stats
 #' @examples
 #'
-#' bb1 <- prolfqua_data('data_ionstar')$normalized()
-#' config <- old2new(bb1$config$clone( deep = TRUE))
+#' bb1 <- prolfqua::sim_lfq_data_peptide_config()
+#' config <- bb1$config
 #' data2 <- bb1$data
 #'
 #' res <- lfq_power_t_test_quantiles(data2, config)
 #' res$summary
 #' stats_res <- summarize_stats(data2, config)
-#' unique(stats_res$dilution.)
 #' res <- lfq_power_t_test_quantiles(data2, config, delta = 2)
 #' res <- lfq_power_t_test_quantiles(data2, config, delta = c(0.5,1,2))
 #'
@@ -434,13 +427,10 @@ lfq_power_t_test_quantiles <- function(pdata,
 #' @family stats
 #' @examples
 #'
-#' bb1 <- prolfqua::prolfqua_data('data_IonstarProtein_subsetNorm')
-#'
+#' bb1 <- prolfqua::sim_lfq_data_peptide_config()
 #'
-#' ldata <- LFQData$new(bb1$data, old2new(bb1$config))
-#' ldata <- ldata$get_sample(20)
+#' ldata <- LFQData$new(bb1$data, bb1$config)
 #' stats_res <- summarize_stats(ldata$data, ldata$config)
-#'
 #' bb <- lfq_power_t_test_proteins(stats_res)
 #'
 lfq_power_t_test_proteins <- function(stats_res,
@@ -478,16 +468,16 @@ lfq_power_t_test_proteins <- function(stats_res,
 #' @examples
 #'
 #'
-#' bb1 <-prolfqua_data('data_ionstar')$filtered()
-#' config <-  old2new(bb1$config$clone( deep = TRUE))
+#' bb1 <- prolfqua::sim_lfq_data_peptide_config()
+#' config <-  bb1$config
 #' data <- bb1$data
 #' res <- summarize_stats(data, config)
-#' plot_stat_density(res, config, stat = "mean")
+#' plot_stat_density(res, config, stat = "meanAbundance")
 #' plot_stat_density(res, config, stat = "sd")
 #' plot_stat_density(res, config, stat = "CV")
 plot_stat_density <- function(pdata,
                               config,
-                              stat = c("CV","mean","sd"),
+                              stat = c("CV","meanAbundance","sd"),
                               ggstat = c("density", "ecdf")){
   stat <- match.arg(stat)
   ggstat <- match.arg(ggstat)
@@ -508,18 +498,22 @@ plot_stat_density <- function(pdata,
 #'
 #'
 #'
-#' bb1 <- prolfqua_data('data_ionstar')$filtered()
-#' config <- old2new(bb1$config$clone( deep = TRUE))
+#' bb1 <- prolfqua::sim_lfq_data_peptide_config()
+#' config <- bb1$config
 #' data2 <- bb1$data
 #' res <- summarize_stats(data2, config)
-#' plot_stat_density_median(res, config,"CV")
-#' plot_stat_density_median(res, config,"mean")
-#' plot_stat_density_median(res, config,"sd")
-plot_stat_density_median <- function(pdata, config, stat = c("CV","mean","sd"), ggstat = c("density", "ecdf")){
+#' plot_stat_density_median(res, config, "CV")
+#' plot_stat_density_median(res, config, "meanAbundance")
+#' plot_stat_density_median(res, config, "sd")
+plot_stat_density_median <- function(
+    pdata,
+    config,
+    stat = c("CV","meanAbundance","sd"),
+    ggstat = c("density", "ecdf")){
   stat <- match.arg(stat)
   ggstat <- match.arg(ggstat)
   pdata <- pdata |> dplyr::filter(!is.na(!!sym(stat)))
-  res <- pdata |> dplyr::mutate(top = ifelse(mean > median(mean, na.rm = TRUE),"top 50","bottom 50")) -> top50
+  res <- pdata |> dplyr::mutate(top = ifelse(meanAbundance > median(meanAbundance, na.rm = TRUE),"top 50","bottom 50")) -> top50
   p <- ggplot(top50, aes_string(x = stat, colour = "top")) +
     geom_line(stat = ggstat) + facet_wrap(config$table$factor_keys()[1])
   return(p)
@@ -536,16 +530,16 @@ plot_stat_density_median <- function(pdata, config, stat = c("CV","mean","sd"),
 #' @examples
 #'
 #'
-#' bb1 <- prolfqua_data('data_ionstar')$filtered()
-#' config <- old2new(bb1$config$clone( deep = TRUE))
+#' bb1 <- prolfqua::sim_lfq_data_peptide_config()
+#' config <- bb1$config
 #' data <- bb1$data
 #' res <- summarize_stats(data, config)
 #' res <- summarize_stats(data, config)
-#' plot_stat_violin(res, config, stat = "mean")
+#' plot_stat_violin(res, config, stat = "meanAbundance")
 #' plot_stat_violin(res, config, stat = "sd")
 #' plot_stat_violin(res, config, stat = "CV")
 #'
-plot_stat_violin <- function(pdata, config, stat = c("CV", "mean", "sd")){
+plot_stat_violin <- function(pdata, config, stat = c("CV", "meanAbundance", "sd")){
   stat <- match.arg(stat)
   pdata <- pdata |> tidyr::unite("groups", config$table$factor_keys_depth())
   p <- ggplot(pdata, aes_string(x = "groups", y = stat  )) +
@@ -565,12 +559,12 @@ plot_stat_violin <- function(pdata, config, stat = c("CV", "mean", "sd")){
 #' @examples
 #'
 #'
-#' bb1 <- prolfqua_data('data_ionstar')$normalized()
-#' config <- old2new(bb1$config$clone( deep = TRUE))
+#' bb1 <- prolfqua::sim_lfq_data_peptide_config()
+#' config <- bb1$config
 #' data <- bb1$data
 #' res <- summarize_stats(data, config)
-#' plot_stat_violin_median(res, config, stat = "mean")
-plot_stat_violin_median <- function(pdata, config , stat = c("CV", "mean", "sd")){
+#' plot_stat_violin_median(res, config, stat = "meanAbundance")
+plot_stat_violin_median <- function(pdata, config , stat = c("CV", "meanAbundance", "sd")){
   median.quartile <- function(x){
     out <- quantile(x, probs = c(0.25,0.5,0.75))
     names(out) <- c("ymin","y","ymax")
@@ -579,7 +573,7 @@ plot_stat_violin_median <- function(pdata, config , stat = c("CV", "mean", "sd")
   pdata <- pdata |> dplyr::filter(!is.na(!!sym(stat)))
 
   res <- pdata |>
-    dplyr::mutate(top = ifelse(mean > median(mean, na.rm = TRUE),"top 50","bottom 50")) ->
+    dplyr::mutate(top = ifelse(meanAbundance > median(meanAbundance, na.rm = TRUE),"top 50","bottom 50")) ->
     top50
 
   p <- ggplot(top50, aes_string(x = config$table$factor_keys()[1], y = stat)) +
@@ -602,8 +596,8 @@ plot_stat_violin_median <- function(pdata, config , stat = c("CV", "mean", "sd")
 #'
 #'
 #'
-#' bb1 <- prolfqua_data('data_ionstar')$filtered()
-#' config <- old2new(bb1$config$clone( deep = TRUE))
+#' bb1 <- prolfqua::sim_lfq_data_peptide_config()
+#' config <- bb1$config
 #' data <- bb1$data
 #' res <- summarize_stats(data, config)
 #'
@@ -624,11 +618,11 @@ plot_stdv_vs_mean <- function(pdata, config, size=2000){
   size <- min(size, min(summary$n))
 
   pdata <- pdata |>
-    group_by_at(config$table$factor_keys_depth()) |>
-    sample_n(size = size) |>
-    ungroup()
+    dplyr::group_by_at(config$table$factor_keys_depth()) |>
+    dplyr::sample_n(size = size) |>
+    dplyr::ungroup()
 
-  p <- ggplot(pdata, aes(x = mean, y = sd)) +
+  p <- ggplot(pdata, aes(x = meanAbundance, y = sd)) +
     geom_point() +
     geom_smooth(method = "loess") +
     facet_wrap(config$table$factor_keys_depth(), nrow = 1) +
diff --git a/man/ContrastsMissing.Rd b/man/ContrastsMissing.Rd
index 53a306823..4f68b79a3 100644
--- a/man/ContrastsMissing.Rd
+++ b/man/ContrastsMissing.Rd
@@ -28,6 +28,8 @@ lProt$rename_response("transformedIntensity")
 
 Contr <- c("dil.b_vs_a" = "group_A - group_Ctrl")
 csi <- ContrastsMissing$new(lProt, contrasts = Contr)
+csi$get_contrast_sides()
+
 res <- csi$get_contrasts()
 
 }
diff --git a/man/LFQDataStats.Rd b/man/LFQDataStats.Rd
index d57c069f3..d9a58f441 100644
--- a/man/LFQDataStats.Rd
+++ b/man/LFQDataStats.Rd
@@ -40,7 +40,7 @@ lfqstats <- lfqdata$get_Stats()
 lfqstats$stats_wide()
 lfqstats$violin()
 runallfuncs(lfqstats)
-x<-lfqstats
+x <- lfqstats
 
 #study variance of normalized data
 
diff --git a/man/build_model.Rd b/man/build_model.Rd
index 7b02feae9..dccac8459 100644
--- a/man/build_model.Rd
+++ b/man/build_model.Rd
@@ -32,35 +32,30 @@ a object of class \code{\link{Model}}
 Build protein models from data
 }
 \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)
 
+
 }
 \seealso{
 \code{\link{model_analyse}}, \code{\link{strategy_lmer}} \code{\link{strategy_lm}}
diff --git a/man/dot-missigness_impute_interactions.Rd b/man/dot-missigness_impute_interactions.Rd
index 34f874c32..00c0543db 100644
--- a/man/dot-missigness_impute_interactions.Rd
+++ b/man/dot-missigness_impute_interactions.Rd
@@ -35,28 +35,23 @@ used in Acetylation project p2916
 }
 \examples{
 
-istar <- sim_lfq_data_peptide_config()
+istar <- sim_lfq_data_peptide_config(Nprot = 20,weight_missing = 2)
 config <- istar$config
 analysis <- istar$data
-config$parameter$qVal_individual_threshold <- 0.01
-
-xx <- prolfqua::remove_large_QValues(analysis, config)
-xx <- complete_cases(xx, config)
+xx <- complete_cases(analysis, config)
 nrPepTimesDilution <- length(unique(paste0(xx$protein_Id, xx$peptide_Id))) *
-    length(unique(xx$dilution.))
-tmp <- interaction_missing_stats(xx, config)
-fun <- .missigness_impute_interactions(xx, config)
-
-long <- fun("long")
-alldata <- fun("all")
+    length(unique(xx$group_))
+funx <- .missigness_impute_interactions(xx, config)
+long <- funx("long")
+alldata <- funx("all")
 stopifnot(length(names(alldata)) == 5)
 
-imputed <- fun("imputed")
+imputed <- funx("imputed")
 stopifnot(nrow(imputed) == length(unique(paste0(xx$protein_Id, xx$peptide_Id))))
-missing <- fun("nrMeasured")
+missing <- funx("nrMeasured")
 stopifnot(nrow(missing) == length(unique(paste0(xx$protein_Id, xx$peptide_Id))))
 
- meanAbundance <- fun("mean")
+ meanAbundance <- funx("mean")
 stopifnot(nrow(meanAbundance) == length(unique(paste0(xx$protein_Id, xx$peptide_Id))))
  stopifnot(sum(is.na(imputed$mean.imp.group_A))==0)
 
diff --git a/man/interaction_missing_stats.Rd b/man/interaction_missing_stats.Rd
index 7afa1e2d5..d510d8726 100644
--- a/man/interaction_missing_stats.Rd
+++ b/man/interaction_missing_stats.Rd
@@ -38,16 +38,16 @@ xx <- prolfqua::remove_large_QValues(analysis,
    config)
 xx <- complete_cases(xx, config)
 x <- interaction_missing_stats(xx, config)$data |> dplyr::arrange(desc(nrNAs))
-
+nrow(x)
 tmp <- interaction_missing_stats(xx, config,
  factors= character(),
   hierarchy = config$table$hierarchy_keys()[1])$data
-
+stopifnot(nrow(tmp) == 10)
 tmp <- interaction_missing_stats(xx, config,
   hierarchy = config$table$hierarchy_keys()[1])$data
+stopifnot(nrow(tmp) == length(unique(xx$protein_Id))* length(unique(xx$group_)))
 stopifnot(sum(is.na(tmp$nrMeasured))==0)
 
 tmp <- interaction_missing_stats(xx, config, factors = NULL)
-
 }
 \keyword{internal}
diff --git a/man/lfq_power_t_test_proteins.Rd b/man/lfq_power_t_test_proteins.Rd
index 03149b944..8624c398f 100644
--- a/man/lfq_power_t_test_proteins.Rd
+++ b/man/lfq_power_t_test_proteins.Rd
@@ -28,13 +28,10 @@ Compute theoretical sample sizes from factor level standard deviations
 }
 \examples{
 
-bb1 <- prolfqua::prolfqua_data('data_IonstarProtein_subsetNorm')
+bb1 <- prolfqua::sim_lfq_data_peptide_config()
 
-
-ldata <- LFQData$new(bb1$data, old2new(bb1$config))
-ldata <- ldata$get_sample(20)
+ldata <- LFQData$new(bb1$data, bb1$config)
 stats_res <- summarize_stats(ldata$data, ldata$config)
-
 bb <- lfq_power_t_test_proteins(stats_res)
 
 }
diff --git a/man/lfq_power_t_test_quantiles.Rd b/man/lfq_power_t_test_quantiles.Rd
index da427401a..82f2037cc 100644
--- a/man/lfq_power_t_test_quantiles.Rd
+++ b/man/lfq_power_t_test_quantiles.Rd
@@ -29,14 +29,13 @@ Compute theoretical sample sizes from factor level standard deviations
 }
 \examples{
 
-bb1 <- prolfqua_data('data_ionstar')$normalized()
-config <- old2new(bb1$config$clone( deep = TRUE))
+bb1 <- prolfqua::sim_lfq_data_peptide_config()
+config <- bb1$config
 data2 <- bb1$data
 
 res <- lfq_power_t_test_quantiles(data2, config)
 res$summary
 stats_res <- summarize_stats(data2, config)
-unique(stats_res$dilution.)
 res <- lfq_power_t_test_quantiles(data2, config, delta = 2)
 res <- lfq_power_t_test_quantiles(data2, config, delta = c(0.5,1,2))
 
diff --git a/man/lfq_power_t_test_quantiles_V2.Rd b/man/lfq_power_t_test_quantiles_V2.Rd
index 99bd5d257..cedd9d164 100644
--- a/man/lfq_power_t_test_quantiles_V2.Rd
+++ b/man/lfq_power_t_test_quantiles_V2.Rd
@@ -29,10 +29,10 @@ estimate sample sizes
 \examples{
 
 
-#library(ggplot2)
 
-bb1 <- prolfqua_data('data_ionstar')$normalized()
-config <- old2new(bb1$config$clone( deep = TRUE))
+
+bb1 <- prolfqua::sim_lfq_data_peptide_config()
+config <- bb1$config
 data2 <- bb1$data
 stats_res <- summarize_stats(data2, config)
 xx <- summarize_stats_quantiles(stats_res, config, probs = c(0.5,0.8))
@@ -41,7 +41,7 @@ bbb <- dplyr::bind_rows(bbb)
 summary <- bbb |>
  dplyr::select( -N_exact, -quantiles, -sdtrimmed ) |>
  tidyr::spread(delta, N, sep = "=")
-summary
+
 }
 \seealso{
 Other stats: 
diff --git a/man/make_interaction_column.Rd b/man/make_interaction_column.Rd
index 18810fcbc..e7c32b4ed 100644
--- a/man/make_interaction_column.Rd
+++ b/man/make_interaction_column.Rd
@@ -14,13 +14,10 @@ xx <- data.frame(A = c("a","a","a"), B = c("d","d","e"))
 # debug(make_interaction_column)
 x <- make_interaction_column(xx, c("B","A"))
 x <- make_interaction_column(xx, c("A"))
-bb <- prolfqua_data('data_ionstar')$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())
diff --git a/man/missigness_impute_factors_interactions.Rd b/man/missigness_impute_factors_interactions.Rd
index f09db6fc2..1e3c88913 100644
--- a/man/missigness_impute_factors_interactions.Rd
+++ b/man/missigness_impute_factors_interactions.Rd
@@ -31,17 +31,18 @@ should generalize at some stage
 }
 \examples{
 
-istar <- sim_lfq_data_peptide_config()
+istar <- sim_lfq_data_peptide_config(weight_missing = 2)
 config <- istar$config
 analysis <- istar$data
 
 xx <- complete_cases(analysis, config)
-
 res <- missigness_impute_factors_interactions(xx, config)
 res <- missigness_impute_factors_interactions(xx, config, value = "imputed")
 res <- missigness_impute_factors_interactions(xx, config, value = "nrMeasured")
+#debug(missigness_impute_factors_interactions)
 long <- missigness_impute_factors_interactions(xx, config, value = "long")
-
+head(long)
+plot(long$meanAbundance, long$imputed)
 }
 \seealso{
 Other imputation: 
diff --git a/man/plot_hierarchies_line_df.Rd b/man/plot_hierarchies_line_df.Rd
index a3ecaad2d..b32236300 100644
--- a/man/plot_hierarchies_line_df.Rd
+++ b/man/plot_hierarchies_line_df.Rd
@@ -25,22 +25,18 @@ config <-  old2new(istar$config)
 config$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
diff --git a/man/plot_stat_density.Rd b/man/plot_stat_density.Rd
index be6541d20..33a415c41 100644
--- a/man/plot_stat_density.Rd
+++ b/man/plot_stat_density.Rd
@@ -7,7 +7,7 @@
 plot_stat_density(
   pdata,
   config,
-  stat = c("CV", "mean", "sd"),
+  stat = c("CV", "meanAbundance", "sd"),
   ggstat = c("density", "ecdf")
 )
 }
@@ -26,11 +26,11 @@ plot density distribution or ecdf of sd, mean or CV
 \examples{
 
 
-bb1 <-prolfqua_data('data_ionstar')$filtered()
-config <-  old2new(bb1$config$clone( deep = TRUE))
+bb1 <- prolfqua::sim_lfq_data_peptide_config()
+config <-  bb1$config
 data <- bb1$data
 res <- summarize_stats(data, config)
-plot_stat_density(res, config, stat = "mean")
+plot_stat_density(res, config, stat = "meanAbundance")
 plot_stat_density(res, config, stat = "sd")
 plot_stat_density(res, config, stat = "CV")
 }
diff --git a/man/plot_stat_density_median.Rd b/man/plot_stat_density_median.Rd
index 466acf766..b71ce6bb0 100644
--- a/man/plot_stat_density_median.Rd
+++ b/man/plot_stat_density_median.Rd
@@ -7,7 +7,7 @@
 plot_stat_density_median(
   pdata,
   config,
-  stat = c("CV", "mean", "sd"),
+  stat = c("CV", "meanAbundance", "sd"),
   ggstat = c("density", "ecdf")
 )
 }
@@ -27,13 +27,13 @@ plot density distribution or ecdf of sd, mean or cv given intensity below and ab
 
 
 
-bb1 <- prolfqua_data('data_ionstar')$filtered()
-config <- old2new(bb1$config$clone( deep = TRUE))
+bb1 <- prolfqua::sim_lfq_data_peptide_config()
+config <- bb1$config
 data2 <- bb1$data
 res <- summarize_stats(data2, config)
-plot_stat_density_median(res, config,"CV")
-plot_stat_density_median(res, config,"mean")
-plot_stat_density_median(res, config,"sd")
+plot_stat_density_median(res, config, "CV")
+plot_stat_density_median(res, config, "meanAbundance")
+plot_stat_density_median(res, config, "sd")
 }
 \seealso{
 Other stats: 
diff --git a/man/plot_stat_violin.Rd b/man/plot_stat_violin.Rd
index c18e3b686..c02ebb279 100644
--- a/man/plot_stat_violin.Rd
+++ b/man/plot_stat_violin.Rd
@@ -4,7 +4,7 @@
 \alias{plot_stat_violin}
 \title{plot Violin plot of sd CV or mean}
 \usage{
-plot_stat_violin(pdata, config, stat = c("CV", "mean", "sd"))
+plot_stat_violin(pdata, config, stat = c("CV", "meanAbundance", "sd"))
 }
 \arguments{
 \item{pdata}{data.frame}
@@ -19,12 +19,12 @@ plot Violin plot of sd CV or mean
 \examples{
 
 
-bb1 <- prolfqua_data('data_ionstar')$filtered()
-config <- old2new(bb1$config$clone( deep = TRUE))
+bb1 <- prolfqua::sim_lfq_data_peptide_config()
+config <- bb1$config
 data <- bb1$data
 res <- summarize_stats(data, config)
 res <- summarize_stats(data, config)
-plot_stat_violin(res, config, stat = "mean")
+plot_stat_violin(res, config, stat = "meanAbundance")
 plot_stat_violin(res, config, stat = "sd")
 plot_stat_violin(res, config, stat = "CV")
 
diff --git a/man/plot_stat_violin_median.Rd b/man/plot_stat_violin_median.Rd
index 0f03fefd4..095a8bab5 100644
--- a/man/plot_stat_violin_median.Rd
+++ b/man/plot_stat_violin_median.Rd
@@ -4,7 +4,7 @@
 \alias{plot_stat_violin_median}
 \title{plot Violin plot of sd CV or mean given intensity lower or above median}
 \usage{
-plot_stat_violin_median(pdata, config, stat = c("CV", "mean", "sd"))
+plot_stat_violin_median(pdata, config, stat = c("CV", "meanAbundance", "sd"))
 }
 \arguments{
 \item{pdata}{data.frame}
@@ -19,11 +19,11 @@ plot Violin plot of sd CV or mean given intensity lower or above median
 \examples{
 
 
-bb1 <- prolfqua_data('data_ionstar')$normalized()
-config <- old2new(bb1$config$clone( deep = TRUE))
+bb1 <- prolfqua::sim_lfq_data_peptide_config()
+config <- bb1$config
 data <- bb1$data
 res <- summarize_stats(data, config)
-plot_stat_violin_median(res, config, stat = "mean")
+plot_stat_violin_median(res, config, stat = "meanAbundance")
 }
 \seealso{
 Other stats: 
diff --git a/man/plot_stdv_vs_mean.Rd b/man/plot_stdv_vs_mean.Rd
index 3161ad4f1..e14edffd5 100644
--- a/man/plot_stdv_vs_mean.Rd
+++ b/man/plot_stdv_vs_mean.Rd
@@ -20,8 +20,8 @@ plot stddev vs mean to asses stability of variance
 
 
 
-bb1 <- prolfqua_data('data_ionstar')$filtered()
-config <- old2new(bb1$config$clone( deep = TRUE))
+bb1 <- prolfqua::sim_lfq_data_peptide_config()
+config <- bb1$config
 data <- bb1$data
 res <- summarize_stats(data, config)
 
diff --git a/man/pooled_var.Rd b/man/pooled_var.Rd
index e30f4a6a8..ec18f8df9 100644
--- a/man/pooled_var.Rd
+++ b/man/pooled_var.Rd
@@ -27,33 +27,32 @@ https://online.stat.psu.edu/stat500/lesson/7/7.3/7.3.1/7.3.1.1
 }
 \examples{
 
-x <- data.frame(not_na =c(1,2,2), var = c(3,4,4), mean = c(3,3,3))
-x <- data.frame(not_na =c(1,2,1,1), var = c(NA, 0.0370, NA, NA), mean = c(-1.94,-1.46,-1.87,-1.45) )
+x <- data.frame(nrMeasured =c(1,2,2), var = c(3,4,4), meanAbundance = c(3,3,3))
+x <- data.frame(nrMeasured =c(1,2,1,1), var = c(NA, 0.0370, NA, NA), meanAbundance = c(-1.94,-1.46,-1.87,-1.45) )
 prolfqua:::pooled_V2(na.omit(x))
 prolfqua:::pooled_V1(na.omit(x))
 x <- x[1,, drop=FALSE]
 x
 na.omit(x)
 prolfqua:::pooled_V2(na.omit(x))
-x <- data.frame(not_na =c(1,2,2), var = c(3,4,4), mean = c(3,3,3))
-x <- data.frame(not_na =c(1,2,1,1), var = c(NA, 0.0370, NA, NA), mean = c(-1.94,-1.46,-1.87,-1.45) )
+x <- data.frame(nrMeasured =c(1,2,2), var = c(3,4,4), meanAbundance = c(3,3,3))
+x <- data.frame(nrMeasured =c(1,2,1,1), var = c(NA, 0.0370, NA, NA), meanAbundance = c(-1.94,-1.46,-1.87,-1.45) )
 compute_pooled(x)
 compute_pooled(x, method = "V2")
 #debug(compute_pooled)
 y <- data.frame(dilution.=c("a","b","c"),
-     n = c(4,4,4), not_na = c(0,0,1), sd =c(NA,NA,NA),
-     var = c(NA,NA,NA),mean = c(NaN,NaN,NaN))
+     nrReplicates = c(4,4,4), nrMeasured = c(0,0,1), sd =c(NA,NA,NA),
+     var = c(NA,NA,NA),meanAbundance = c(NaN,NaN,NaN))
 compute_pooled(y)
-yb <- y |> dplyr::filter(not_na > 1)
+yb <- y |> dplyr::filter(nrMeasured > 1)
 
-
-bb <- prolfqua_data('data_ionstar')$normalized()
-config <- old2new(bb$config)
+bb <- prolfqua::sim_lfq_data_peptide_config()
+config <- bb$config
 data <- bb$data
 
 res1 <- summarize_stats(data, config)
 pv <- poolvar(res1, config)
-stopifnot(nrow(pv) == nrow(res1)/5)
+stopifnot(nrow(pv) == nrow(res1)/3)
 
 }
 \seealso{
diff --git a/man/sim_lfq_data_peptide_config.Rd b/man/sim_lfq_data_peptide_config.Rd
index f9d8abf3e..11512039c 100644
--- a/man/sim_lfq_data_peptide_config.Rd
+++ b/man/sim_lfq_data_peptide_config.Rd
@@ -4,7 +4,12 @@
 \alias{sim_lfq_data_peptide_config}
 \title{Simulate data, protein and peptide, with config}
 \usage{
-sim_lfq_data_peptide_config(Nprot = 10, with_missing = TRUE, seed = 1234)
+sim_lfq_data_peptide_config(
+  Nprot = 10,
+  with_missing = TRUE,
+  weight_missing = 0.2,
+  seed = 1234
+)
 }
 \arguments{
 \item{with_missing}{add missing values, default TRUE}
diff --git a/man/sim_lfq_data_protein_config.Rd b/man/sim_lfq_data_protein_config.Rd
index 721acf620..a12b6a310 100644
--- a/man/sim_lfq_data_protein_config.Rd
+++ b/man/sim_lfq_data_protein_config.Rd
@@ -4,7 +4,12 @@
 \alias{sim_lfq_data_protein_config}
 \title{Simulate data, protein, with config}
 \usage{
-sim_lfq_data_protein_config(Nprot = 10, with_missing = TRUE, seed = 1234)
+sim_lfq_data_protein_config(
+  Nprot = 10,
+  with_missing = TRUE,
+  weight_missing = 0.2,
+  seed = 1234
+)
 }
 \arguments{
 \item{with_missing}{add missing values, default TRUE}
diff --git a/man/summarize_stats.Rd b/man/summarize_stats.Rd
index e63e396e0..06bd61f58 100644
--- a/man/summarize_stats.Rd
+++ b/man/summarize_stats.Rd
@@ -40,48 +40,38 @@ summarize stats output (compute quantiles)
 \examples{
 
 
-bb <- prolfqua_data('data_ionstar')$normalized()
-config <- old2new(bb$config)
+bb <- prolfqua::sim_lfq_data_protein_config()
+config <- bb$config
 data <- bb$data
 
 res1 <- summarize_stats(data, config)
-d <- res1 |> dplyr::filter(protein_Id == "CON__P01030~9~NA" & peptide_Id  == "AELADQAASWLTR")
-d <- res1 |> dplyr::filter(protein_Id == "CON__Q3SZR3~50~NA" & peptide_Id  == "EHFVDLLLSK")
-#CON__P02769~18~NA VHKECCHGDLLECADDR
-d <- res1 |> dplyr::filter(protein_Id == "CON__P02769~18~NA" & peptide_Id  == "VHKECCHGDLLECADDR")
 
 
 
-bb <- prolfqua_data('data_ionstar')$normalized()
-config <- old2new(bb$config)
-data <- bb$data
+bb <- prolfqua::sim_lfq_data_protein_config()
+
+res1 <- summarize_stats_all(bb$data, bb$config)
 
-res1 <- summarize_stats_all(data, config)
-d <- res1 |> dplyr::filter(protein_Id == "CON__P01030~9~NA" & peptide_Id  == "AELADQAASWLTR")
-d <- res1 |> dplyr::filter(protein_Id == "CON__Q3SZR3~50~NA" & peptide_Id  == "EHFVDLLLSK")
-#CON__P02769~18~NA VHKECCHGDLLECADDR
-d <- res1 |> dplyr::filter(protein_Id == "CON__P02769~18~NA" & peptide_Id  == "VHKECCHGDLLECADDR")
-res1 |> dplyr::filter(dilution. == "pooled")
+stopifnot((res1 |> dplyr::filter(group_ == "All") |> nrow()) == (res1 |> nrow()))
 
 library(ggplot2)
-bb1 <- prolfqua_data('data_ionstar')$filtered()
-config <- old2new(bb1$config$clone( deep = TRUE))
+bb1 <- prolfqua::sim_lfq_data_peptide_config()
+config <- bb1$config
 data <- bb1$data
 stats_res <- summarize_stats(data, config)
-summarize_stats_quantiles(stats_res, config)
-summarize_stats_quantiles(stats_res, config, stats = "CV")
-stats_res
-bb <- prolfqua_data('data_ionstar')$normalized()
-config <- old2new(bb$config$clone(deep = TRUE))
+sq <- summarize_stats_quantiles(stats_res, config)
+sq <- summarize_stats_quantiles(stats_res, config, stats = "CV")
+bb <- prolfqua::sim_lfq_data_peptide_config()
+config <- bb$config
 data <- bb$data
 config$table$get_response()
 stats_res <- summarize_stats(data, config)
-summarize_stats_quantiles(stats_res, config)
-summarize_stats_quantiles(stats_res, config, stats = "sd")
+sq <- summarize_stats_quantiles(stats_res, config)
+sq <- summarize_stats_quantiles(stats_res, config, stats = "sd")
 
 stats_res <- summarize_stats(data, config)
 xx <- summarize_stats_quantiles(stats_res, config, probs = seq(0,1,by = 0.1))
-ggplot2::ggplot(xx$long, aes(x = probs, y = quantiles, color = dilution.)) + geom_line() + geom_point()
+ggplot2::ggplot(xx$long, aes(x = probs, y = quantiles, color = group_)) + geom_line() + geom_point()
 
 
 }
diff --git a/man/which_missing.Rd b/man/which_missing.Rd
index 24b1053e6..9175dc9a3 100644
--- a/man/which_missing.Rd
+++ b/man/which_missing.Rd
@@ -4,10 +4,12 @@
 \alias{which_missing}
 \title{add missing values to x vector based on the values of x}
 \usage{
-which_missing(x)
+which_missing(x, weight_missing = 0.2)
 }
 \arguments{
 \item{x}{vector of intensities}
+
+\item{weight_missing}{greater weight more missing}
 }
 \description{
 add missing values to x vector based on the values of x