The work horse for fitting the probabilistic dropout model

Source: R/pd_lm.R

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

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`