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, ...)



An mle2 object created by a call to bbmle::mle2().


Logical indicating whether or not to include a confidence interval in the tidied output. Defaults to FALSE.


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.level = 0.9, all computation will proceed using conf.level = 0.95. Additionally, if you pass newdata = my_tibble to an augment() method that does not accept a newdata argument, it will use the default value for the data argument.

See also


A 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.


#> Loading required package: stats4
#> #> Attaching package: ‘bbmle’
#> The following object is masked from ‘package:ordinal’: #> #> slice
#> The following object is masked from ‘package:dplyr’: #> #> slice
x <- 0:10 y <- c(26, 17, 13, 12, 20, 5, 9, 8, 5, 4, 8) d <- data.frame(x, y) fit <- mle2(y ~ dpois(lambda = ymean), start = list(ymean = mean(y)), data = d ) tidy(fit)
#> # A tibble: 1 x 5 #> term estimate std.error statistic p.value #> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 ymean 11.5 1.02 11.3 1.86e-29