Create a Poisson outcome from linear predictors — add_poisson_outcome • rollout

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)
#> Error in data %>% dplyr::mutate(`:=`(!!linear_col, rowSums(dplyr::pick(all_of(dot_cols)))),     `:=`(!!rate_col, exp(.data[[linear_col]])), `:=`(!!count_col,         rpois(dplyr::n(), lambda = .data[[rate_col]]))): could not find function "%>%"