diff --git a/R/simulate_LFQ_data.R b/R/simulate_LFQ_data.R
index 3a55f65c..3038c75c 100644
--- a/R/simulate_LFQ_data.R
+++ b/R/simulate_LFQ_data.R
@@ -266,24 +266,37 @@ sim_lfq_data_2Factor_config <- function(Nprot = 10,
 #' @export
 #' @keywords internal
 #' @examples
-#' mod <- sim_build_models_lm(model = "interaction", weight_missing = 1)
+#' modi <- sim_build_models_lm(model = "interaction", weight_missing = 1)
 #' stopifnot(dim(mod$modelDF) == c(10,9))
+#' mod2 <- sim_build_models_lm(model = "parallel2", weight_missing = 1)
+#' mod$modelDF$linear_model[[1]]
+#' mod3 <- sim_build_models_lm(model = "parallel3", weight_missing = 1)
+#' modf <- sim_build_models_lm(model = "factors", weight_missing = 1)
 #'
-sim_build_models_lm <- function(model = c("factors", "interaction"),
+sim_build_models_lm <- function(model = c("parallel2","parallel3","factors", "interaction"),
                          Nprot = 10,
                          with_missing = TRUE,
                          weight_missing = 1) {
   model <- match.arg(model)
-  model <- if (model == "factors") {
-    "~ Treatment + Background"
-  } else {
-    "~ Treatment * Background"
-  }
+  if (model != "parallel3") {
   istar <- prolfqua::sim_lfq_data_2Factor_config(
     Nprot = Nprot,
     with_missing = with_missing,
     weight_missing = weight_missing)
+  } else {
+    istar <- prolfqua::sim_lfq_data_protein_config()
+  }
   istar <- prolfqua::LFQData$new(istar$data,istar$config)
+
+  model <- if (model == "factors") {
+    "~ Treatment + Background"
+  } else if (model == "interaction") {
+    "~ Treatment * Background"
+  } else if (model == "parallel2") {
+    "~ Treatment"
+  } else if (model == "parallel3") {
+    "~ group_"
+  } else {NULL}
   modelFunction <- strategy_lm(paste0(istar$response(), model))
   mod <- build_model(
     istar,
@@ -297,25 +310,40 @@ sim_build_models_lm <- function(model = c("factors", "interaction"),
 #' @keywords internal
 #' @examples
 #' undebug(sim_build_models_lmer)
-#' mod <- sim_build_models_lmer(model = "interaction", weight_missing = 1)
-#' stopifnot(dim(mod$modelDF) == c(10,9))
+#' modi <- sim_build_models_lmer(model = "interaction", weight_missing = 1)
+#' stopifnot(sum(modi$modelDF$exists_lmer) == 6)
+#' mod2 <- sim_build_models_lmer(model = "parallel2", weight_missing = 1)
+#' stopifnot(sum(mod2$modelDF$exists_lmer) == 6)
+#' mod4 <- sim_build_models_lmer(model = "parallel3", weight_missing = 1)
+#' stopifnot(sum(mod4$modelDF$exists_lmer) == 6)
+#' modf <- sim_build_models_lmer(model = "factors", weight_missing = 1)
+#' stopifnot(sum(modf$modelDF$exists_lmer) == 6)
 #'
-sim_build_models_lmer <- function(model = c("factors", "interaction"),
+sim_build_models_lmer <- function(model = c("parallel2", "parallel3","factors", "interaction"),
                             Nprot = 10,
                             with_missing = TRUE,
                             weight_missing = 1) {
   model <- match.arg(model)
+  if (model != "parallel3") {
+     istar <- prolfqua::sim_lfq_data_2Factor_config(
+       Nprot = Nprot,
+       with_missing = with_missing,
+       PEPTIDE = TRUE,
+       weight_missing = weight_missing)
+   } else {
+    istar <- prolfqua::sim_lfq_data_peptide_config()
+  }
+  istar <- prolfqua::LFQData$new(istar$data,istar$config)
+
   model <- if (model == "factors") {
     "~ Treatment + Background + (1|peptide_Id) + (1|sampleName)"
-  } else {
+  } else if (model == "interaction") {
     "~ Treatment * Background + (1|peptide_Id) + (1|sampleName)"
-  }
-  istar <- prolfqua::sim_lfq_data_2Factor_config(
-    Nprot = Nprot,
-    with_missing = with_missing,
-    PEPTIDE = TRUE,
-    weight_missing = weight_missing)
-  istar <- prolfqua::LFQData$new(istar$data,istar$config)
+  } else if (model == "parallel2") {
+    "~ Treatment + (1|peptide_Id) + (1|sampleName)"
+  } else if (model == "parallel3") {
+    "~ group_ + (1|peptide_Id) + (1|sampleName)"
+  } else {NULL}
   modelFunction <- strategy_lmer(paste0(istar$response(), model))
   mod <- build_model(
     istar,
@@ -330,9 +358,16 @@ sim_build_models_lmer <- function(model = c("factors", "interaction"),
 #' @keywords internal
 #' @examples
 #' m <- sim_make_model_lm()
-#' m <- sim_make_model_lm("interaction")
-#'
-sim_make_model_lm <- function(model = c("factors", "interaction")){
+#' mi <- sim_make_model_lm("interaction")
+#' length(coef(mi)) == 4
+#' mf <- sim_make_model_lm("factors")
+#' length(coef(mf)) = 3
+#' m2 <- sim_make_model_lm("parallel2")
+#' length(coef(m2)) == 2
+#' m3 <- sim_make_model_lm("parallel3")
+#' length(coef(m3)) == 3
+sim_make_model_lm <- function(model = c("parallel2", "parallel3","factors", "interaction")){
+  model <- match.arg(model)
   mod <- sim_build_models_lm(model = model, Nprot = 1, with_missing = FALSE)
   return(mod$modelDF$linear_model[[1]])
 }
@@ -347,7 +382,9 @@ sim_make_model_lm <- function(model = c("factors", "interaction")){
 #' mf <- sim_make_model_lmer("factors")
 #' mf <- sim_make_model_lmer("interaction")
 #'
-sim_make_model_lmer <- function(model = c("factors", "interaction"), singular = FALSE){
+sim_make_model_lmer <- function(model = c("parallel2", "parallel3","factors", "interaction"),
+                                singular = FALSE){
+  model <- match.arg(model)
   mod <- sim_build_models_lmer(model = model, Nprot = 10, with_missing = FALSE)
   m <- mod$modelDF |> dplyr::filter(isSingular == isSingular) |> dplyr::pull(linear_model)
   return(m[[1]])
diff --git a/R/tidyMS_R6_Modelling.R b/R/tidyMS_R6_Modelling.R
index 16486b5f..06166d92 100644
--- a/R/tidyMS_R6_Modelling.R
+++ b/R/tidyMS_R6_Modelling.R
@@ -384,7 +384,14 @@ plot_lmer_peptide_predictions <- function(m, intensity = "abundance"){
 
 # Generate linear functions -----
 
-# get matrix of indicator coefficients for each interaction
+#' get matrix of indicator coefficients for each interaction
+#' @examples
+#' m <- sim_make_model_lm("interaction")
+#' coef(m)
+#' prolfqua:::.lmer4_coeff_matrix(m)
+#' m <- sim_make_model_lm("factors")
+#' m
+#' prolfqua:::.lmer4_coeff_matrix(m)
 .lmer4_coeff_matrix <- function(m){
   data <- NULL
   if ("lm" %in% class(m)) {
@@ -443,21 +450,22 @@ plot_lmer_peptide_predictions <- function(m, intensity = "abundance"){
 #' @keywords internal
 #' @examples
 #'
-#' m <- prolfqua_data('data_basicModel_p1807')
-#' # debug(linfct_from_model)
-#' linfct <- linfct_from_model(m)
+#' m <- sim_make_model_lm()
+#' linfct <- linfct_from_model(m, as_list = TRUE)
 #'
 #' linfct$linfct_factors
 #' linfct$linfct_interactions
-#'
-#' m <- prolfqua_data('data_interactionModel_p1807')
-#' # debug(.coeff_weights_factor_levels)
+#' lf <- matrix(
+#' c(1, 1, 1, 1, 0.5, 0.5, 0, 1, 0, 1, 0.5, 0.5),
+#' nrow = 4,
+#' byrow = FALSE,
+#' dimnames = list(c("BackgroundX", "BackgroundZ", "TreatmentA", "TreatmentB"),
+#'                 c("(Intercept)", "TreatmentB", "BackgroundZ"))
+#' )
+#' stopifnot(lf == linfct$linfct_factors)
+#' m <- sim_make_model_lm("interaction")
 #' linfct <- linfct_from_model(m)
 #'
-#' all.equal(linfct$linfct_factors["CelltypeCMP/MEP",] ,
-#'  apply(linfct$linfct_interactions[grep("CelltypeCMP/MEP", rownames(linfct$linfct_interactions)),],2, mean))
-#' linfct$linfct_interactions
-#'
 #' m <- lm(Petal.Width ~ Species, data = iris)
 #' linfct_from_model(m)
 #' xx <- data.frame( Y = 1:10 , Condition = c(rep("a",5), rep("b",5)) )
@@ -471,7 +479,6 @@ plot_lmer_peptide_predictions <- function(m, intensity = "abundance"){
 #' linfct_from_model(m)
 #'
 linfct_from_model <- function(m, as_list = TRUE){
-
   cm <- .lmer4_coeff_matrix(m)
   cm_mm <- cm$mm[order(rownames(cm$mm)),]
 
diff --git a/man/dot-lmer4_coeff_matrix.Rd b/man/dot-lmer4_coeff_matrix.Rd
new file mode 100644
index 00000000..7d64be38
--- /dev/null
+++ b/man/dot-lmer4_coeff_matrix.Rd
@@ -0,0 +1,19 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/tidyMS_R6_Modelling.R
+\name{.lmer4_coeff_matrix}
+\alias{.lmer4_coeff_matrix}
+\title{get matrix of indicator coefficients for each interaction}
+\usage{
+.lmer4_coeff_matrix(m)
+}
+\description{
+get matrix of indicator coefficients for each interaction
+}
+\examples{
+m <- sim_make_model_lm("interaction")
+coef(m)
+prolfqua:::.lmer4_coeff_matrix(m)
+m <- sim_make_model_lm("factors")
+m
+prolfqua:::.lmer4_coeff_matrix(m)
+}
diff --git a/man/linfct_from_model.Rd b/man/linfct_from_model.Rd
index 375a98a3..e58bd31d 100644
--- a/man/linfct_from_model.Rd
+++ b/man/linfct_from_model.Rd
@@ -14,21 +14,22 @@ get linfct from model
 }
 \examples{
 
-m <- prolfqua_data('data_basicModel_p1807')
-# debug(linfct_from_model)
-linfct <- linfct_from_model(m)
+m <- sim_make_model_lm()
+linfct <- linfct_from_model(m, as_list = TRUE)
 
 linfct$linfct_factors
 linfct$linfct_interactions
-
-m <- prolfqua_data('data_interactionModel_p1807')
-# debug(.coeff_weights_factor_levels)
+lf <- matrix(
+c(1, 1, 1, 1, 0.5, 0.5, 0, 1, 0, 1, 0.5, 0.5),
+nrow = 4,
+byrow = FALSE,
+dimnames = list(c("BackgroundX", "BackgroundZ", "TreatmentA", "TreatmentB"),
+                c("(Intercept)", "TreatmentB", "BackgroundZ"))
+)
+stopifnot(lf == linfct$linfct_factors)
+m <- sim_make_model_lm("interaction")
 linfct <- linfct_from_model(m)
 
-all.equal(linfct$linfct_factors["CelltypeCMP/MEP",] ,
- apply(linfct$linfct_interactions[grep("CelltypeCMP/MEP", rownames(linfct$linfct_interactions)),],2, mean))
-linfct$linfct_interactions
-
 m <- lm(Petal.Width ~ Species, data = iris)
 linfct_from_model(m)
 xx <- data.frame( Y = 1:10 , Condition = c(rep("a",5), rep("b",5)) )
diff --git a/man/sim_build_models_lm.Rd b/man/sim_build_models_lm.Rd
index 9b4b1ab5..530c3cf9 100644
--- a/man/sim_build_models_lm.Rd
+++ b/man/sim_build_models_lm.Rd
@@ -5,7 +5,7 @@
 \title{build dataframe with models for testing}
 \usage{
 sim_build_models_lm(
-  model = c("factors", "interaction"),
+  model = c("parallel2", "parallel3", "factors", "interaction"),
   Nprot = 10,
   with_missing = TRUE,
   weight_missing = 1
@@ -15,8 +15,12 @@ sim_build_models_lm(
 build dataframe with models for testing
 }
 \examples{
-mod <- sim_build_models_lm(model = "interaction", weight_missing = 1)
+modi <- sim_build_models_lm(model = "interaction", weight_missing = 1)
 stopifnot(dim(mod$modelDF) == c(10,9))
+mod2 <- sim_build_models_lm(model = "parallel2", weight_missing = 1)
+mod$modelDF$linear_model[[1]]
+mod3 <- sim_build_models_lm(model = "parallel3", weight_missing = 1)
+modf <- sim_build_models_lm(model = "factors", weight_missing = 1)
 
 }
 \seealso{
diff --git a/man/sim_build_models_lmer.Rd b/man/sim_build_models_lmer.Rd
index e5e62d5d..bb474e21 100644
--- a/man/sim_build_models_lmer.Rd
+++ b/man/sim_build_models_lmer.Rd
@@ -5,7 +5,7 @@
 \title{build lmer model from simulated data}
 \usage{
 sim_build_models_lmer(
-  model = c("factors", "interaction"),
+  model = c("parallel2", "parallel3", "factors", "interaction"),
   Nprot = 10,
   with_missing = TRUE,
   weight_missing = 1
@@ -16,8 +16,14 @@ build lmer model from simulated data
 }
 \examples{
 undebug(sim_build_models_lmer)
-mod <- sim_build_models_lmer(model = "interaction", weight_missing = 1)
-stopifnot(dim(mod$modelDF) == c(10,9))
+modi <- sim_build_models_lmer(model = "interaction", weight_missing = 1)
+stopifnot(sum(modi$modelDF$exists_lmer) == 6)
+mod2 <- sim_build_models_lmer(model = "parallel2", weight_missing = 1)
+stopifnot(sum(mod2$modelDF$exists_lmer) == 6)
+mod4 <- sim_build_models_lmer(model = "parallel3", weight_missing = 1)
+stopifnot(sum(mod4$modelDF$exists_lmer) == 6)
+modf <- sim_build_models_lmer(model = "factors", weight_missing = 1)
+stopifnot(sum(modf$modelDF$exists_lmer) == 6)
 
 }
 \seealso{
diff --git a/man/sim_make_model_lm.Rd b/man/sim_make_model_lm.Rd
index deeedebf..6cdea7fa 100644
--- a/man/sim_make_model_lm.Rd
+++ b/man/sim_make_model_lm.Rd
@@ -4,15 +4,23 @@
 \alias{sim_make_model_lm}
 \title{make interaction model for examples}
 \usage{
-sim_make_model_lm(model = c("factors", "interaction"))
+sim_make_model_lm(
+  model = c("parallel2", "parallel3", "factors", "interaction")
+)
 }
 \description{
 make interaction model for examples
 }
 \examples{
 m <- sim_make_model_lm()
-m <- sim_make_model_lm("interaction")
-
+mi <- sim_make_model_lm("interaction")
+length(coef(mi)) == 4
+mf <- sim_make_model_lm("factors")
+length(coef(mf)) = 3
+m2 <- sim_make_model_lm("parallel2")
+length(coef(m2)) == 2
+m3 <- sim_make_model_lm("parallel3")
+length(coef(m3)) == 3
 }
 \seealso{
 Other modelling: 
diff --git a/man/sim_make_model_lmer.Rd b/man/sim_make_model_lmer.Rd
index 688c3611..18de2967 100644
--- a/man/sim_make_model_lmer.Rd
+++ b/man/sim_make_model_lmer.Rd
@@ -4,7 +4,10 @@
 \alias{sim_make_model_lmer}
 \title{make interaction model for examples}
 \usage{
-sim_make_model_lmer(model = c("factors", "interaction"), singular = FALSE)
+sim_make_model_lmer(
+  model = c("parallel2", "parallel3", "factors", "interaction"),
+  singular = FALSE
+)
 }
 \description{
 make interaction model for examples