pd_lm {proDA} | R Documentation

## Fit a single linear probabilistic dropout model

### Description

The function works similar to the classical `lm` but with special handling of `NA`'s. Whereas `lm` usually just ignores response value that are missing, `pd_lm` applies a probabilistic dropout model, that assumes that missing values occur because of the dropout curve. The dropout curve describes for each position the chance that that a value is missed. A negative `dropout_curve_scale` means that the lower the intensity was, the more likely it is to miss the value.

### Usage

    pd_lm(formula, data = NULL, subset = NULL, 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)

### Arguments

`formula` | a formula that specifies a linear model
`data` | an optional data.frame whose columns can be used to specify the `formula`
`subset` | an optional selection vector for data to subset it
`dropout_curve_position` | the value where the chance to observe a value is 50%. Can either be a single value that is repeated for each row or a vector with one element for each row. Not optional.
`dropout_curve_scale` | the width of the dropout curve. Smaller values mean that the sigmoidal curve is steeper. Can either be a single value that is repeated for each row or a vector with one element for each row. Not optional.
`location_prior_mean, location_prior_scale` | the optional mean and variance of the prior around which the predictions are supposed to scatter. If no value is provided no location regularization is applied.
`variance_prior_scale, variance_prior_df` | the optional scale and degrees of freedom of the variance prior. If no value is provided no variance regularization is applied.
`location_prior_df` | The degrees of freedom for the t-distribution of the location prior. If it is large (> 30) the prior is approximately Normal. Default: 3
`method` | one of 'analytic_hessian', 'analytic_gradient', or 'numeric'. If 'analytic_hessian' the `nlminb` optimization routine is used, with the hand derived first and second derivative. Otherwise, `optim` either with or without the first derivative is used.
`verbose` | boolean that signals if the method prints informative messages. Default: `FALSE`.

### Value

a list with the following entries

coefficients

  a named vector with the fitted values

coef_variance_matrix

  a `p*p` matrix with the variance associated with each coefficient estimate

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'

### Examples

      # Without missing values
      y <- rnorm(5, mean=20)
      lm(y ~ 1)
      pd_lm(y ~ 1,
            dropout_curve_position = NA,
            dropout_curve_scale = NA)

      # With some missing values
      y <- c(23, 21.4, NA)
      lm(y ~ 1)
      pd_lm(y ~ 1,
            dropout_curve_position = 19,
            dropout_curve_scale = -1)

      # With only missing values
      y <- c(NA, NA, NA)
      # lm(y ~ 1)  # Fails
      pd_lm(y ~ 1,
            dropout_curve_position = 19,
            dropout_curve_scale = -1,
            location_prior_mean = 21,
            location_prior_scale = 3,
            variance_prior_scale = 0.1,
            variance_prior_df = 2)

[Package proDA version 1.0.0 Index]