Predictions and confidence intervals for exposure-response models

Predictions and confidence intervals for exposure-response models

Usage

erglm_predict(object, newdata = NULL, conf_level = 0.95)

Arguments

object

An erglm model, as returned by erglm_model()

newdata

Data frame containing cases to be predicted

conf_level

Confidence level for the intervals

Value

A tibble

Details

Computes intervals on the link scale and back-transforms with stats::family(object)$linkinv, so this works for any glm() family, not just binomial/logistic models. See also erglm_fun() for generating predictions at arbitrary (possibly counterfactual) parameters or data.

This is a tidy, opinionated alternative to calling base R's predict() directly on object – since object is a genuine glm object, predict() (and predict(object, se.fit = TRUE), on which this function is based) work unchanged and remain useful for quick point estimates or when a tidy data frame isn't needed. See vignette("methods", package = "erglm") for a side-by-side comparison and other inherited glm/lm methods (summary(), vcov(), AIC(), etc.).

Examples

mod <- erglm_model(ae1 ~ aucss, erglm_data, family = binomial())
prd <- erglm_predict(mod, erglm_data)
prd
#> # A tibble: 300 × 18
#>       id sex      age weight  dose treatment aucss cmaxss   ae1   ae2 ae_count
#>    <int> <fct>  <int>  <dbl> <dbl> <fct>     <dbl>  <dbl> <dbl> <dbl>    <int>
#>  1     1 Male      35     79   200 Drug       673.   97.3     0     1        1
#>  2     2 Female    22     58   200 Drug      2806.  301.      1     1        6
#>  3     3 Female    28     58     0 Placebo      0     0       0     0        1
#>  4     4 Female    18     57   100 Drug      1169.  198.      1     1        0
#>  5     5 Male      28     77   100 Drug       377.   51.4     0     0        0
#>  6     6 Female    19     76   200 Drug       327.   25.4     1     0        0
#>  7     7 Male      30     70     0 Placebo      0     0       0     0        0
#>  8     8 Female    34     60   100 Drug      1208.  133.      1     1        1
#>  9     9 Male      21     89     0 Placebo      0     0       0     0        0
#> 10    10 Female    34     56   200 Drug       254.   31.0     0     0        1
#> # ℹ 290 more rows
#> # ℹ 7 more variables: biomarker_change <dbl>, ae_duration <dbl>,
#> #   fit_link <dbl>, se_link <dbl>, fit_resp <dbl>, ci_lower <dbl>,
#> #   ci_upper <dbl>

mod_gauss <- erglm_model(biomarker_change ~ aucss, erglm_data, family = gaussian())
erglm_predict(mod_gauss, erglm_data)
#> # A tibble: 300 × 18
#>       id sex      age weight  dose treatment aucss cmaxss   ae1   ae2 ae_count
#>    <int> <fct>  <int>  <dbl> <dbl> <fct>     <dbl>  <dbl> <dbl> <dbl>    <int>
#>  1     1 Male      35     79   200 Drug       673.   97.3     0     1        1
#>  2     2 Female    22     58   200 Drug      2806.  301.      1     1        6
#>  3     3 Female    28     58     0 Placebo      0     0       0     0        1
#>  4     4 Female    18     57   100 Drug      1169.  198.      1     1        0
#>  5     5 Male      28     77   100 Drug       377.   51.4     0     0        0
#>  6     6 Female    19     76   200 Drug       327.   25.4     1     0        0
#>  7     7 Male      30     70     0 Placebo      0     0       0     0        0
#>  8     8 Female    34     60   100 Drug      1208.  133.      1     1        1
#>  9     9 Male      21     89     0 Placebo      0     0       0     0        0
#> 10    10 Female    34     56   200 Drug       254.   31.0     0     0        1
#> # ℹ 290 more rows
#> # ℹ 7 more variables: biomarker_change <dbl>, ae_duration <dbl>,
#> #   fit_link <dbl>, se_link <dbl>, fit_resp <dbl>, ci_lower <dbl>,
#> #   ci_upper <dbl>