Glance at a(n) polr 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.

Usage

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

Arguments

x

A polr object returned from MASS::polr().

...

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::polr()

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

deviance

Deviance of 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

# load libraries for models and data
library(MASS)

# fit model
fit <- polr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing)

# summarize model fit with tidiers
tidy(fit, exponentiate = TRUE, conf.int = TRUE)
#> 
#> Re-fitting to get Hessian
#> # A tibble: 8 × 7
#>   term          estimate std.error statistic conf.low conf.high coef.type 
#>   <chr>            <dbl>     <dbl>     <dbl>    <dbl>     <dbl> <chr>     
#> 1 InflMedium       1.76     0.105       5.41    1.44      2.16  coefficie…
#> 2 InflHigh         3.63     0.127      10.1     2.83      4.66  coefficie…
#> 3 TypeApartment    0.564    0.119      -4.80    0.446     0.712 coefficie…
#> 4 TypeAtrium       0.693    0.155      -2.36    0.511     0.940 coefficie…
#> 5 TypeTerrace      0.336    0.151      -7.20    0.249     0.451 coefficie…
#> 6 ContHigh         1.43     0.0955      3.77    1.19      1.73  coefficie…
#> 7 Low|Medium       0.609    0.125      -3.97   NA        NA     scale     
#> 8 Medium|High      2.00     0.125       5.50   NA        NA     scale     

glance(fit)
#> # A tibble: 1 × 7
#>     edf logLik   AIC   BIC deviance df.residual  nobs
#>   <int>  <dbl> <dbl> <dbl>    <dbl>       <int> <int>
#> 1     8 -1740. 3495. 3539.    3479.        1673  1681
augment(fit, type.predict = "class")
#> # A tibble: 72 × 6
#>    Sat    Infl   Type      Cont  `(weights)` .fitted
#>    <ord>  <fct>  <fct>     <fct>       <int> <fct>  
#>  1 Low    Low    Tower     Low            21 Low    
#>  2 Medium Low    Tower     Low            21 Low    
#>  3 High   Low    Tower     Low            28 Low    
#>  4 Low    Medium Tower     Low            34 High   
#>  5 Medium Medium Tower     Low            22 High   
#>  6 High   Medium Tower     Low            36 High   
#>  7 Low    High   Tower     Low            10 High   
#>  8 Medium High   Tower     Low            11 High   
#>  9 High   High   Tower     Low            36 High   
#> 10 Low    Low    Apartment Low            61 Low    
#> # ℹ 62 more rows

fit2 <- polr(factor(gear) ~ am + mpg + qsec, data = mtcars)

tidy(fit, p.values = TRUE)
#> 
#> Re-fitting to get Hessian
#> p-values can presently only be returned for models that contain
#>               no categorical variables with more than two levels
#> # A tibble: 8 × 6
#>   term          estimate std.error statistic p.value coef.type  
#>   <chr>            <dbl>     <dbl>     <dbl> <lgl>   <chr>      
#> 1 InflMedium       0.566    0.105       5.41 NA      coefficient
#> 2 InflHigh         1.29     0.127      10.1  NA      coefficient
#> 3 TypeApartment   -0.572    0.119      -4.80 NA      coefficient
#> 4 TypeAtrium      -0.366    0.155      -2.36 NA      coefficient
#> 5 TypeTerrace     -1.09     0.151      -7.20 NA      coefficient
#> 6 ContHigh         0.360    0.0955      3.77 NA      coefficient
#> 7 Low|Medium      -0.496    0.125      -3.97 NA      scale      
#> 8 Medium|High      0.691    0.125       5.50 NA      scale