Tidy a(n) mle2 objectSource:
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 mle2 tidy(x, conf.int = FALSE, conf.level = 0.95, ...)
mle2object created by a call to
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 if
conf.int = TRUE. Must be strictly greater than 0 and less than 1. Defaults to 0.95, which corresponds to a 95 percent confidence interval.
Additional arguments. Not used. Needed to match generic signature only. Cautionary note: Misspelled arguments will be absorbed in
..., where they will be ignored. If the misspelled argument has a default value, the default value will be used. For example, if you pass
conf.lvel = 0.9, all computation will proceed using
conf.level = 0.95. Two exceptions here are:
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.
# load libraries for models and data library(bbmle) #> Loading required package: stats4 #> #> Attaching package: ‘bbmle’ #> The following object is masked from ‘package:dfidx’: #> #> slice #> The following object is masked from ‘package:ordinal’: #> #> slice #> The following object is masked from ‘package:dplyr’: #> #> slice # generate data x <- 0:10 y <- c(26, 17, 13, 12, 20, 5, 9, 8, 5, 4, 8) d <- data.frame(x, y) # fit model fit <- mle2(y ~ dpois(lambda = ymean), start = list(ymean = mean(y)), data = d ) # summarize model fit with tidiers tidy(fit) #> # A tibble: 1 × 5 #> term estimate std.error statistic p.value #> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 ymean 11.5 1.02 11.3 1.86e-29