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Goodness of Fit

Source: R/gof.R
ssd_gof.Rd

Returns a tbl data frame with the following columns

dist

The distribution name (chr)

aic

Akaike's Information Criterion (dbl)

bic

Bayesian Information Criterion (dbl)

at_bound

Parameter(s) at boundary (lgl)

computable

All parameter have computable standard errors (lgl)

and if the data are non-censored

aicc

Akaike's Information Criterion corrected for sample size (dbl)

and if there are 8 or more samples

ad

Anderson-Darling statistic (dbl)

ks

Kolmogorov-Smirnov statistic (dbl)

cvm

Cramer-von Mises statistic (dbl)

In the case of an object of class fitdists the function also returns

delta

The Information Criterion differences (dbl)

wt

The Information Criterion weights (dbl)

where delta and wt are based on aic for censored data and aicc for non-censored data.

Usage

ssd_gof(x, ...)

# S3 method for class 'fitdists'
ssd_gof(x, ..., pvalue = FALSE, wt = FALSE)

Arguments

x

The object.

...

Unused.

pvalue

A flag specifying whether to return p-values or the statistics (default) for the various tests.

wt

A flag specifying whether to return the Akaike weight as "wt" instead of "weight".

Value

A tbl data frame of the gof statistics.

Methods (by class)

  • ssd_gof(fitdists): Goodness of Fit

See also

glance.fitdists()

Examples

fits <- ssd_fit_dists(ssddata::ccme_boron)
ssd_gof(fits)
#> Warning: ssd_gof(wt = FALSE) was deprecated in ssdtools 2.3.1.
#> ℹ Please use ssd_gof(wt = TRUE) instead.
#> ℹ Please set the `wt` argument to `ssd_gof()` to be TRUE which will rename the
#>   'weight' column to 'wt' and then update your downstream code accordingly.
#> # A tibble: 6 × 14
#>   dist    npars  nobs log_lik   aic  aicc delta weight   bic    ad     ks    cvm
#>   <chr>   <int> <int>   <dbl> <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl>  <dbl>  <dbl>
#> 1 gamma       2    28   -117.  238.  238. 0.005  0.357  240. 0.440 0.117  0.0554
#> 2 lgumbel     2    28   -120.  244.  245. 6.56   0.013  247. 0.829 0.158  0.134 
#> 3 llogis      2    28   -119.  241.  241. 3.39   0.066  244. 0.487 0.0994 0.0595
#> 4 lnorm       2    28   -118.  239.  240. 1.40   0.177  242. 0.507 0.107  0.0703
#> 5 lnorm_…     5    28   -115.  240.  243. 4.98   0.03   247. 0.320 0.116  0.0414
#> 6 weibull     2    28   -117.  238.  238. 0      0.357  240. 0.434 0.117  0.0542
#> # ℹ 2 more variables: at_bound <lgl>, computable <lgl>
ssd_gof(fits, pvalue = TRUE, wt = TRUE)
#> # A tibble: 6 × 14
#>   dist       npars  nobs log_lik   aic  aicc delta    wt   bic    ad    ks   cvm
#>   <chr>      <int> <int>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 gamma          2    28   -117.  238.  238. 0.005 0.357  240. 0.807 0.839 0.847
#> 2 lgumbel        2    28   -120.  244.  245. 6.56  0.013  247. 0.460 0.485 0.445
#> 3 llogis         2    28   -119.  241.  241. 3.39  0.066  244. 0.759 0.945 0.821
#> 4 lnorm          2    28   -118.  239.  240. 1.40  0.177  242. 0.738 0.908 0.754
#> 5 lnorm_lno…     5    28   -115.  240.  243. 4.98  0.03   247. 0.922 0.846 0.929
#> 6 weibull        2    28   -117.  238.  238. 0     0.357  240. 0.813 0.839 0.854
#> # ℹ 2 more variables: at_bound <lgl>, computable <lgl>