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Provides estimation tools for exposure-response models based on
glm(). It is mostly intended as a convenience package: the
core tools are wrappers around glm(), tested and supported
for binomial, poisson, gaussian, and gamma families. For plotting
exposure-response models (including those fitted with erglm), see the
companion package erplots,
which supplies a model-agnostic mini-language for building
exposure-response plots. For an analogous approach to time-to-event
models see ertte, and for Emax
regression models see emaxnls.
You can install the latest CRAN release of erglm with this:
install.packages("erglm")Alternatively you can install the development version of erglm like so:
pak::pak("djnavarro/erglm")library(erglm)
head(erglm_data)
#> id sex age weight dose treatment aucss cmaxss ae1 ae2 ae_count
#> 1 1 Male 35 79 200 Drug 673.09 97.33 0 1 1
#> 2 2 Female 22 58 200 Drug 2806.12 300.62 1 1 6
#> 3 3 Female 28 58 0 Placebo 0.00 0.00 0 0 1
#> 4 4 Female 18 57 100 Drug 1169.04 197.78 1 1 0
#> 5 5 Male 28 77 100 Drug 377.29 51.43 0 0 0
#> 6 6 Female 19 76 200 Drug 327.08 25.37 1 0 0
#> biomarker_change ae_duration
#> 1 1.22 12.40
#> 2 4.87 13.70
#> 3 -1.83 5.26
#> 4 1.90 6.70
#> 5 -1.01 6.15
#> 6 -4.97 12.83
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: 197.4mod1 <- erglm_model(ae1 ~ aucss + sex + dose, erglm_data, family = binomial())
mod2 <- erglm_scm_backward(mod1, candidates = c("sex", "dose"))
erglm_scm_history(mod2)
#> iteration attempt step criterion action term_tested
#> 1 0 0 base model <NA> <NA> <NA>
#> 2 1 1 backward p-value remove ~dose
#> 3 1 2 backward p-value remove ~sex
#> 4 2 3 backward p-value remove ~sex
#> model_tested model_converged term_p_value model_aic model_bic
#> 1 ae1 ~ aucss + sex + dose TRUE NA 200.5606 215.3758
#> 2 ae1 ~ aucss + sex TRUE 0.7405585 198.6703 209.7816
#> 3 ae1 ~ aucss + dose TRUE 0.4025456 199.2613 210.3727
#> 4 ae1 ~ aucss TRUE 0.3906289 197.4073 204.8148
#> model_updated
#> 1 NA
#> 2 1
#> 3 0
#> 4 1mod <- erglm_model(ae1 ~ aucss + sex, erglm_data, family = binomial())
sim <- simulate(mod, nsim = 5, seed = 1234)
head(sim)
#> dat_id sim_id mu val coef_(Intercept) coef_aucss coef_sexMale aucss
#> 1 1 1 0.8940479 1 -2.092198 0.006010498 0.1793639 673.09
#> 2 2 1 0.9999996 1 -2.092198 0.006010498 0.1793639 2806.12
#> 3 3 1 0.1098574 0 -2.092198 0.006010498 0.1793639 0.00
#> 4 4 1 0.9928560 1 -2.092198 0.006010498 0.1793639 1169.04
#> 5 5 1 0.5877972 1 -2.092198 0.006010498 0.1793639 377.29
#> 6 6 1 0.4684708 1 -2.092198 0.006010498 0.1793639 327.08
#> sex
#> 1 Male
#> 2 Female
#> 3 Female
#> 4 Female
#> 5 Male
#> 6 FemaleTo visualise the simulations against the observed data (e.g. as a
VPC-style plot) see erplots’ er_vpc() mini-grammar –
specifically erplots::er_vpc_add_simulated(), which can
build its own simulated replicates directly from a fitted
erglm_model
(er_vpc_add_simulated(model = mod)).
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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