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

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

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'

proDA documentation built on Nov. 8, 2020, 5:01 p.m.