tidy.nbboot {evinf} | R Documentation |
Tidy function for nbboot
Description
Tidy function for nbboot
Usage
## S3 method for class 'nbboot'
tidy(
x,
coef_type = c("original", "bootstrap_mean", "bootstrap_median"),
standard_error = TRUE,
p_value = c("bootstrapped", "approx", "none"),
confint = c("none", "bootstrapped", "approx"),
conf_level = 0.95,
approx_t_value = TRUE,
symmetric_bootstrap_p = TRUE,
include_ylev = FALSE,
...
)
Arguments
x |
A fitted bootstrapped zero-inflated model |
coef_type |
What type of coefficient should be reported, original, bootstrapped mean, or bootstrapped median |
standard_error |
Should bootstrapped standard errors be reported? |
p_value |
What type of p-value should be reported? Bootstrapped p_values, approximate p-values, or none? |
confint |
What type of confidence intervals should be reported? Bootstrapped p_values, approximate p-values, or none? |
conf_level |
Confidence level for confidence intervals |
approx_t_value |
Should approximate t_values be reported |
symmetric_bootstrap_p |
Should bootstrap p-values be computed as symmetric (leaving alpha/2 percent in each tail)? FALSE gives non-symmetric, but narrower, intervals. TRUE corresponds most closely to conventional p-values. |
include_ylev |
Logical. Should y.lev be included in the tidy output? Makes for nicer tables when using modelsummary |
... |
Other arguments to be passed to tidy |
Value
A tidy function for a bootstrapped nb model
Examples
data(genevzinb2)
model <- evzinb(y~x1+x2+x3,data=genevzinb2, n_bootstraps = 10, multicore = TRUE, ncores = 2)
zinb_comp <- compare_models(model)
tidy(zinb_comp$nb)