Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies across models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.
# S3 method for Rchoice tidy(x, conf.int = FALSE, conf.level = 0.95, ...)
Logical indicating whether or not to include a confidence
interval in the tidied output. Defaults to
The confidence level to use for the confidence interval
Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
Rchoice package provides "an implementation of simulated
maximum likelihood method for the estimation of Binary (Probit and Logit),
Ordered (Probit and Logit) and Poisson models with random parameters for
cross-sectional and longitudinal data."
tibble::tibble() with columns:
Upper bound on the confidence interval for the estimate.
Lower bound on the confidence interval for the estimate.
The estimated value of the regression term.
The two-sided p-value associated with the observed statistic.
The value of a T-statistic to use in a hypothesis that the regression term is non-zero.
The standard error of the regression term.
The name of the regression term.
library(Rchoice) mod <- Rchoice(vs ~ mpg + hp + factor(cyl), data = mtcars, family = binomial("probit")) tidy(mod)#> # A tibble: 5 × 5 #> term estimate std.error statistic p.value #> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 constant 6.01 4.64 1.30 0.195 #> 2 mpg -0.0957 0.134 -0.716 0.474 #> 3 hp -0.0244 0.0214 -1.14 0.253 #> 4 factor(cyl)6 -0.980 1.23 -0.797 0.426 #> 5 factor(cyl)8 -6.39 938. -0.00682 0.995glance(mod)#> # A tibble: 1 × 5 #> logLik AIC BIC df nobs #> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 -7.30 24.6 31.9 NA 32