Fit an exposure-response model based on glm()

Fit an exposure-response model based on glm()

Usage

erglm_model(formula, data, family = stats::gaussian(), ...)

Arguments

formula

Model formula

data

Data set

family

The error distribution and link function to use, as for stats::glm(). Defaults to stats::gaussian(), matching stats::glm()'s own default. Tested and officially supported for binomial(), poisson(), gaussian(), and Gamma(); other glm() families should work through the same generic mechanisms but are untested.

...

Other arguments passed to glm()

Value

A glm object

Details

The returned object has class c("erglm_model", "glm", "lm"): it is a glm object, with a little extra metadata attached. This means all of the usual glm/lm methods work unchanged, without needing an erglm-specific equivalent – e.g. summary(), coef(), vcov(), confint(), predict(), AIC(), BIC(), logLik(), and anova() for comparing nested models. See vignette("methods", package = "erglm") for worked examples of these. erglm_predict() is a separate, erglm-specific alternative to predict() that returns confidence intervals on the response scale in a tidy data frame; the two are complementary, not competing.

Examples

mod <- erglm_model(ae1 ~ aucss, erglm_data, family = binomial())
mod
#> 
#> Call:  stats::glm(formula = formula, family = family, data = data)
#> 
#> Coefficients:
#> (Intercept)        aucss  
#>   -1.791383     0.005497  
#> 
#> Degrees of Freedom: 299 Total (i.e. Null);  298 Residual
#> Null Deviance:	    402.1 
#> Residual Deviance: 193.4 	AIC: 197.4

# other glm() families are also supported
mod_pois <- erglm_model(ae_count ~ aucss, erglm_data, family = poisson())
mod_pois
#> 
#> Call:  stats::glm(formula = formula, family = family, data = data)
#> 
#> Coefficients:
#> (Intercept)        aucss  
#>   -1.003955     0.001044  
#> 
#> Degrees of Freedom: 299 Total (i.e. Null);  298 Residual
#> Null Deviance:	    868.8 
#> Residual Deviance: 275.6 	AIC: 713.8