Skip to content

Tidy a(n) fitdistr object

Source: R/mass-fitdistr-tidiers.R
tidy.fitdistr.Rd

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.

Usage

# S3 method for fitdistr
tidy(x, ...)

Arguments

x

A fitdistr object returned by MASS::fitdistr().

...

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:

  • tidy() methods will warn when supplied an exponentiate argument if it will be ignored.

  • augment() methods will warn when supplied a newdata argument if it will be ignored.

See also

tidy(), MASS::fitdistr()

Other fitdistr tidiers: glance.fitdistr()

Value

A tibble::tibble() with columns:

estimate

The estimated value of the regression term.

std.error

The standard error of the regression term.

term

The name of the regression term.

Examples


# load libraries for models and data
library(MASS)

# generate data
set.seed(2015)
x <- rnorm(100, 5, 2)

#  fit models
fit <- fitdistr(x, dnorm, list(mean = 3, sd = 1))
#> Warning: NaNs produced
#> Warning: NaNs produced
#> Warning: NaNs produced
#> Warning: NaNs produced
#> Warning: NaNs produced
#> Warning: NaNs produced
#> Warning: NaNs produced
#> Warning: NaNs produced
#> Warning: NaNs produced

# summarize model fit with tidiers
tidy(fit)
#> # A tibble: 2 × 3
#>   term  estimate std.error
#>   <chr>    <dbl>     <dbl>
#> 1 mean      4.90     0.201
#> 2 sd        2.01     0.142
glance(fit)
#> # A tibble: 1 × 4
#>   logLik      AIC   BIC  nobs
#>   <logLik>  <dbl> <dbl> <int>
#> 1 -211.6533  427.  433.   100