Glance at a(n) clm object

Glance accepts a model object and returns a tibble::tibble() with exactly one row of model summaries. The summaries are typically goodness of fit measures, p-values for hypothesis tests on residuals, or model convergence information.

Glance never returns information from the original call to the modeling function. This includes the name of the modeling function or any arguments passed to the modeling function.

Glance does not calculate summary measures. Rather, it farms out these computations to appropriate methods and gathers the results together. Sometimes a goodness of fit measure will be undefined. In these cases the measure will be reported as NA.

Glance returns the same number of columns regardless of whether the model matrix is rank-deficient or not. If so, entries in columns that no longer have a well-defined value are filled in with an NA of the appropriate type.

# S3 method for clm
glance(x, ...)

Arguments

x

A clm object returned from ordinal::clm().

...

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. 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

tidy, ordinal::clm()

Other ordinal tidiers: augment.clm(), augment.polr(), glance.clmm(), glance.polr(), glance.svyolr(), tidy.clmm(), tidy.clm(), tidy.polr(), tidy.svyolr()

Value

A tibble::tibble() with exactly one row and columns:

AIC

Akaike's Information Criterion for the model.

BIC

Bayesian Information Criterion for the model.

df.residual

Residual degrees of freedom.

edf

The effective degrees of freedom.

logLik

The log-likelihood of the model. [stats::logLik()] may be a useful reference.

nobs

Number of observations used.

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