Create custom lmer model

The strategy contains functions to fit the model but also compute the contrasts etc.

strategy_lmer(
  modelstr,
  model_name = "Model",
  report_columns = c("statistic", "p.value", "p.value.adjusted", "moderated.p.value",
    "moderated.p.value.adjusted")
)

strategy_lm(
  modelstr,
  model_name = "Model",
  report_columns = c("statistic", "p.value", "p.value.adjusted", "moderated.p.value",
    "moderated.p.value.adjusted")
)

strategy_rlm(
  modelstr,
  model_name = "Model",
  report_columns = c("statistic", "p.value", "p.value.adjusted", "moderated.p.value",
    "moderated.p.value.adjusted")
)

strategy_glm(
  modelstr,
  model_name = "Model",
  test = "Chisq",
  family = stats::binomial,
  multiplier = 1,
  offset = 1,
  report_columns = c("statistic", "p.value", "p.value.adjusted", "moderated.p.value",
    "moderated.p.value.adjusted")
)

Arguments

modelstr: model formula
model_name: name of model
report_columns: columns to report
family: either binomial or quasibinomial
multiplier: for tuning default is 1.

Value

list with model function, contrast computation function etc.

Examples


istar <- prolfqua::sim_lfq_data_peptide_config(Nprot = 10, with_missing = FALSE)
#> creating sampleName from fileName column
#> completing cases
#> completing cases done
#> setup done
istar <- prolfqua::LFQData$new(istar$data,istar$config)
istar$data <- istar$data |> dplyr::group_by(protein_Id) |>
dplyr::mutate(abundanceC = abundance - mean(abundance)) |> dplyr::ungroup()
istar$factors()
#> # A tibble: 12 × 3
#>    sample  sampleName group_
#>    <chr>   <chr>      <chr> 
#>  1 A_V1    A_V1       A     
#>  2 A_V2    A_V2       A     
#>  3 A_V3    A_V3       A     
#>  4 A_V4    A_V4       A     
#>  5 B_V1    B_V1       B     
#>  6 B_V2    B_V2       B     
#>  7 B_V3    B_V3       B     
#>  8 B_V4    B_V4       B     
#>  9 Ctrl_V1 Ctrl_V1    Ctrl  
#> 10 Ctrl_V2 Ctrl_V2    Ctrl  
#> 11 Ctrl_V3 Ctrl_V3    Ctrl  
#> 12 Ctrl_V4 Ctrl_V4    Ctrl  
modelFunction <- strategy_lmer("abundanceC ~ group_ + (1|peptide_Id) ", model_name = "random_example")
mod <- build_model(
 istar,
 modelFunction)
#> Warning: There were 4 warnings in `dplyr::mutate()`.
#> The first warning was:
#> ℹ In argument: `linear_model = purrr::map(data, model_strategy$model_fun, pb =
#>   pb)`.
#> ℹ In group 2: `protein_Id = "7cbcrd~5725"`.
#> Caused by warning in `value[[3L]]()`:
#> ! WARN :Error: grouping factors must have > 1 sampled level
#> ℹ Run `dplyr::last_dplyr_warnings()` to see the 3 remaining warnings.
#> Joining with `by = join_by(protein_Id)`
sum(mod$modelDF$exists_lmer)
#> [1] 6
sum(mod$modelDF$isSingular, na.rm=TRUE)
#> [1] 0



tmp <- strategy_lm("Intensity ~ condition", model_name = "parallel design")
tmp$model_fun(get_formula = TRUE)
#> Intensity ~ condition
#> <environment: 0x1557850d0>
tmp$isSingular
#> function(m){
#>   anyNA <- any(is.na(coefficients(m)))
#>   if (anyNA) {
#>     return(TRUE)
#>   } else {
#>     if (df.residual(m) >= 2) {
#>       return(FALSE)
#>     }
#>     return(TRUE)
#>   }
#> }
#> <environment: namespace:prolfqua>
tmp <- strategy_rlm("Intensity ~ condition", model_name = "parallel design")
tmp$model_fun(get_formula = TRUE)
#> Intensity ~ condition
#> <environment: 0x31170a1e8>
tmp$isSingular
#> function(m){
#>   anyNA <- any(is.na(coefficients(m)))
#>   if (anyNA) {
#>     return(TRUE)
#>   } else {
#>     if (df.residual(m) >= 2) {
#>       return(FALSE)
#>     }
#>     return(TRUE)
#>   }
#> }
#> <environment: namespace:prolfqua>
tmp <- strategy_glm("Intensity ~ condition", model_name = "parallel design")
tmp$model_fun(get_formula = TRUE)
#> Intensity ~ condition
#> <environment: 0x3110e7698>
tmp$isSingular
#> function(m){
#>   anyNA <- any(is.na(coefficients(m)))
#>   if (anyNA) {
#>     return(TRUE)
#>   } else {
#>     if (df.residual(m) >= 2) {
#>       return(FALSE)
#>     }
#>     return(TRUE)
#>   }
#> }
#> <environment: namespace:prolfqua>

Arguments

Value

See also

Examples