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(). Note that weights, subset, and offset don't work reliably here – see Details below.

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.

weights, subset, and offset can't currently be passed through ... to stats::glm(): glm() resolves these non-standard-evaluation arguments via match.call(), which breaks once they've been forwarded through another function's ... rather than named directly in the call glm() itself sees. This reproduces with a trivial wrapper (function(formula, data, family, ...) glm(formula, data, family, ...)) and is a limitation of glm()/lm()'s NSE, not something specific to erglm's family generalisation – see e.g. the "Note" in ?lm about wrapping lm(). Attempting it currently fails with a low-level error ("..1 used in an incorrect context, no ... to look in"). Two workarounds: fold an offset into the formula itself (e.g. y ~ x + offset(z)) rather than passing offset =, and pre-filter data yourself rather than passing subset =. There's no similar formula-level workaround for weights; call stats::glm(formula, data, family, weights = ...) directly instead, then (if you want the erglm_model class for consistency, e.g. for simulate()'s S3 dispatch) run class(mod) <- c("erglm_model", class(mod)) on the result – every other erglm function works on a plain glm object just as well, since none of them require the class specifically.

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