pd_lm.fit: The work horse for fitting the probabilistic dropout model

In const-ae/proDA: Differential Abundance Analysis of Label-Free Mass Spectrometry Data

The work horse for fitting the probabilistic dropout model

Description

If there is no location and variance moderation and no missing values, the model is fitted with 'lm'.

Usage

pd_lm.fit(
  y,
  X,
  dropout_curve_position,
  dropout_curve_scale,
  location_prior_mean = NULL,
  location_prior_scale = NULL,
  variance_prior_scale = NULL,
  variance_prior_df = NULL,
  location_prior_df = 3,
  method = c("analytic_hessian", "analytic_grad", "numeric"),
  verbose = FALSE
)

Value

a list with the following entries

coefficients

a named vector with the fitted values

n_approx

the estimated "size" of the data set (n_hat - variance_prior_df)

df

the estimated degrees of freedom (n_hat - p)

s2

the estimated unbiased variance

n_obs

the number of response values that were not 'NA'

const-ae/proDA documentation built on Oct. 31, 2023, 9:39 p.m.