Tidy a(n) clm object

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 clm
tidy(
  x,
  conf.int = FALSE,
  conf.level = 0.95,
  conf.type = c("profile", "Wald"),
  exponentiate = FALSE,
  ...
)

Arguments

x	A `clm` object returned from `ordinal::clm()`.
conf.int	Logical indicating whether or not to include a confidence interval in the tidied output. Defaults to `FALSE`.
conf.level	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.
conf.type	Whether to use `"profile"` or `"Wald"` confidence intervals, passed to the `type` argument of `ordinal::confint.clm()`. Defaults to `"profile"`.
exponentiate	Logical indicating whether or not to exponentiate the the coefficient estimates. This is typical for logistic and multinomial regressions, but a bad idea if there is no log or logit link. Defaults to `FALSE`.
...	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.

Details

In broom 0.7.0 the coefficient_type column was renamed to coef.type, and the contents were changed as well.

Note that intercept type coefficients correspond to alpha parameters, location type coefficients correspond to beta parameters, and scale type coefficients correspond to zeta parameters.

Value

A tibble::tibble() with columns:

conf.high

Upper bound on the confidence interval for the estimate.

conf.low

Lower bound on the confidence interval for the estimate.

estimate

The estimated value of the regression term.

p.value

The two-sided p-value associated with the observed statistic.

statistic

The value of a T-statistic to use in a hypothesis that the regression term is non-zero.

std.error

The standard error of the regression term.

term

The name of the regression term.

Examples


library(ordinal)

fit <- clm(rating ~ temp * contact, data = wine)

tidy(fit)
#> # A tibble: 7 x 6
#>   term                estimate std.error statistic  p.value coef.type
#>   <chr>                  <dbl>     <dbl>     <dbl>    <dbl> <chr>    
#> 1 1|2                   -1.41      0.545    -2.59  9.66e- 3 intercept
#> 2 2|3                    1.14      0.510     2.24  2.48e- 2 intercept
#> 3 3|4                    3.38      0.638     5.29  1.21e- 7 intercept
#> 4 4|5                    4.94      0.751     6.58  4.66e-11 intercept
#> 5 tempwarm               2.32      0.701     3.31  9.28e- 4 location 
#> 6 contactyes             1.35      0.660     2.04  4.13e- 2 location 
#> 7 tempwarm:contactyes    0.360     0.924     0.389 6.97e- 1 location 
tidy(fit, conf.int = TRUE, conf.level = 0.9)
#> # A tibble: 7 x 8
#>   term        estimate std.error statistic  p.value conf.low conf.high coef.type
#>   <chr>          <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl> <chr>    
#> 1 1|2           -1.41      0.545    -2.59  9.66e- 3   NA         NA    intercept
#> 2 2|3            1.14      0.510     2.24  2.48e- 2   NA         NA    intercept
#> 3 3|4            3.38      0.638     5.29  1.21e- 7   NA         NA    intercept
#> 4 4|5            4.94      0.751     6.58  4.66e-11   NA         NA    intercept
#> 5 tempwarm       2.32      0.701     3.31  9.28e- 4    1.20       3.52 location 
#> 6 contactyes     1.35      0.660     2.04  4.13e- 2    0.284      2.47 location 
#> 7 tempwarm:c…    0.360     0.924     0.389 6.97e- 1   -1.17       1.89 location 
tidy(fit, conf.int = TRUE, conf.type = "Wald", exponentiate = TRUE)
#> # A tibble: 7 x 8
#>   term        estimate std.error statistic  p.value conf.low conf.high coef.type
#>   <chr>          <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl> <chr>    
#> 1 1|2            0.244     0.545    -2.59  9.66e- 3   0.0837     0.710 intercept
#> 2 2|3            3.14      0.510     2.24  2.48e- 2   1.16       8.52  intercept
#> 3 3|4           29.3       0.638     5.29  1.21e- 7   8.38     102.    intercept
#> 4 4|5          140.        0.751     6.58  4.66e-11  32.1      610.    intercept
#> 5 tempwarm      10.2       0.701     3.31  9.28e- 4   2.58      40.2   location 
#> 6 contactyes     3.85      0.660     2.04  4.13e- 2   1.05      14.0   location 
#> 7 tempwarm:c…    1.43      0.924     0.389 6.97e- 1   0.234      8.76  location 

glance(fit)
#> # A tibble: 1 x 6
#>     edf   AIC   BIC logLik   df.residual  nobs
#>   <int> <dbl> <dbl> <logLik>       <dbl> <dbl>
#> 1     7  187.  203. -86.4162          65    72
augment(fit, type.predict = "prob")
#> # A tibble: 72 x 4
#>    rating temp  contact .fitted
#>    <ord>  <fct> <fct>     <dbl>
#>  1 2      cold  no       0.562 
#>  2 3      cold  no       0.209 
#>  3 3      cold  yes      0.435 
#>  4 4      cold  yes      0.0894
#>  5 4      warm  no       0.190 
#>  6 4      warm  no       0.190 
#>  7 5      warm  yes      0.286 
#>  8 5      warm  yes      0.286 
#>  9 1      cold  no       0.196 
#> 10 2      cold  no       0.562 
#> # … with 62 more rows
augment(fit, type.predict = "class")
#> # A tibble: 72 x 4
#>    rating temp  contact .fitted
#>    <ord>  <fct> <fct>   <fct>  
#>  1 2      cold  no      2      
#>  2 3      cold  no      2      
#>  3 3      cold  yes     3      
#>  4 4      cold  yes     3      
#>  5 4      warm  no      3      
#>  6 4      warm  no      3      
#>  7 5      warm  yes     4      
#>  8 5      warm  yes     4      
#>  9 1      cold  no      2      
#> 10 2      cold  no      2      
#> # … with 62 more rows

fit2 <- clm(rating ~ temp, nominal = ~contact, data = wine)
tidy(fit2)
#> # A tibble: 9 x 6
#>   term            estimate std.error statistic      p.value coef.type
#>   <chr>              <dbl>     <dbl>     <dbl>        <dbl> <chr>    
#> 1 1|2.(Intercept)    -1.32     0.562     -2.35 0.0186       intercept
#> 2 2|3.(Intercept)     1.25     0.475      2.63 0.00866      intercept
#> 3 3|4.(Intercept)     3.55     0.656      5.41 0.0000000625 intercept
#> 4 4|5.(Intercept)     4.66     0.860      5.42 0.0000000608 intercept
#> 5 1|2.contactyes     -1.62     1.16      -1.39 0.164        intercept
#> 6 2|3.contactyes     -1.51     0.591     -2.56 0.0105       intercept
#> 7 3|4.contactyes     -1.67     0.649     -2.58 0.00985      intercept
#> 8 4|5.contactyes     -1.05     0.897     -1.17 0.241        intercept
#> 9 tempwarm            2.52     0.535      4.71 0.00000250   location 
glance(fit2)
#> # A tibble: 1 x 6
#>     edf   AIC   BIC logLik    df.residual  nobs
#>   <int> <dbl> <dbl> <logLik>        <dbl> <dbl>
#> 1     9  190.  211. -86.20855          63    72

Arguments

Details

See also

Value

Examples