fit_nonlinear: Fit a non-linear trend (currently optimized for LOESS)
In proBatch: Tools for Diagnostics and Corrections of Batch Effects in Proteomics

Description Usage Arguments Value Examples

Fit a non-linear trend (currently optimized for LOESS)

fit_nonlinear(
  df_feature_batch,
  measure_col = "Intensity",
  order_col = "order",
  feature_id = NULL,
  batch_id = NULL,
  fit_func = "loess_regression",
  optimize_span = FALSE,
  no_fit_imputed = TRUE,
  qual_col = "m_score",
  qual_value = 2,
  min_measurements = 8,
  ...
)

`df_feature_batch`	data frame containing response variable e.g. samples in order and explanatory variable e.g. measurement for a specific feature (peptide) in a specific batch
`measure_col`	if `df_long` is among the parameters, it is the column with expression/abundance/intensity; otherwise, it is used internally for consistency.
`order_col`	column in `sample_annotation` that determines sample order. It is used for in initial assessment plots (plot_sample_mean_or_boxplot) and feature-level diagnostics (feature_level_diagnostics). Can be 'NULL' if sample order is irrelevant (e.g. in genomic experiments). For more details, order definition/inference, see define_sample_order and date_to_sample_order
`feature_id`	the name of the feature, required for warnings
`batch_id`	the name of the batch, required for warnings
`fit_func`	function to use for the fit, e.g. `loess_regression`
`optimize_span`	logical, whether to specify span or optimize it (specific entirely for LOESS regression)
`no_fit_imputed`	(logical) whether to fit the imputed (requant) values
`qual_col`	column to color point by certain value denoted by `color_by_qual_value`. Design with inferred/requant values in OpenSWATH output data, which means argument value has to be set to `m_score`.
`qual_value`	value in `qual_col` to color. For OpenSWATH data, this argument value has to be set to `2` (this is an `m_score` value for imputed values (requant values).
`min_measurements`	the absolute threshold to filter
`...`	additional parameters to be passed to the fitting function

vector of fitted response values

test_peptide = example_proteome$peptide_group_label[1] 
selected_peptide = example_proteome$peptide_group_label == test_peptide
df_selected = example_proteome[selected_peptide,]
selected_batch = example_sample_annotation$MS_batch == 'Batch_1'
batch_selected_df = example_sample_annotation[selected_batch,]
df_for_test = merge(df_selected, batch_selected_df, by = 'FullRunName')
fit_values = fit_nonlinear(df_for_test)

#for the case where are two many missing values, no curve is fit
selected_batch = example_sample_annotation$MS_batch == 'Batch_2'
batch_selected_df = example_sample_annotation[selected_batch,]
df_for_test = merge(df_selected, batch_selected_df, by = 'FullRunName')
fit_values = fit_nonlinear(df_for_test)
missing_values = df_for_test[['m_score']] == 2
all(fit_values[!is.na(fit_values)] == df_for_test[['Intensity']][!missing_values])