Create a Poisson outcome from linear predictors

Generates a Poisson-distributed count outcome by summing effects, exponentiating to obtain rates, and drawing counts.

Usage

add_poisson_outcome(
  data,
  linear_col = "y_linear",
  rate_col = "y_rate",
  count_col = "y_count"
)

Arguments

data

A data frame containing effect columns prefixed with ".".

linear_col

Name of the column to store the summed linear predictor (default "y_linear").

rate_col

Name of the column to store Poisson rates (default "y_rate").

count_col

Name of the column to store Poisson counts (default "y_count").

Value

A tibble with added linear predictor, rate, and count columns.

Examples

df <- tibble::tibble(.beta = 0.5, .u = rnorm(5), .error = rnorm(5))
add_poisson_outcome(df)
#> # A tibble: 5 × 6
#>   .beta      .u .error y_linear y_rate y_count
#>   <dbl>   <dbl>  <dbl>    <dbl>  <dbl>   <int>
#> 1   0.5 -0.872  -2.05    -2.42  0.0885       1
#> 2   0.5  0.107   0.151    0.757 2.13         2
#> 3   0.5 -0.587  -0.293   -0.380 0.684        0
#> 4   0.5 -0.328   0.255    0.427 1.53         2
#> 5   0.5 -0.0854 -0.553   -0.139 0.871        0