Skip to contents

Fit distributions to peptide abundance data

Source: R/fits.R
fit_distributions.Rd

Fits candidate distributions to each peptide's abundance values and selects the best fit by AIC. Also computes missingness statistics including dataset-level MNAR detection.

Usage

fit_distributions(data, id, group, value, distributions = "continuous")

Arguments

data

A data frame containing peptide abundance data

id

Column name for peptide identifier

group

Column name for group/condition

value

Column name for abundance values

distributions

Which distributions to fit: "continuous" (default), "counts", "all", or a character vector of distribution names

Value

A peppwr_fits object containing:

  • $data: Nested tibble with original data and fit results

  • $best: Best-fitting distribution for each peptide

  • $missingness: Per-peptide missingness statistics

  • $dataset_mnar: Dataset-level MNAR correlation and interpretation

Missingness Tracking

The returned object includes:

  • Per-peptide NA rates (in $missingness)

  • Dataset-level MNAR correlation (in $dataset_mnar)

The dataset-level MNAR metric correlates log(mean_abundance) with NA rate across peptides. A negative correlation indicates low-abundance peptides have more missing values - typical of detection-limit-driven MNAR.

Print the result to see both metrics. For small sample sizes (N < 15), the dataset-level correlation is more reliable than per-peptide scores.

See also

compute_dataset_mnar() for dataset-level MNAR details, plot_missingness() to visualize missingness patterns