Tidying methods for ordinal logistic regression models

These methods tidy the coefficients of ordinal logistic regression models generated by ordinal::clm() or ordinal::clmm() of the ordinal package, MASS::polr() of the MASS packge, or survey::svyolr() of the survey package.

# S3 method for polr
tidy(x, conf.int = FALSE, conf.level = 0.95,
  exponentiate = FALSE, quick = FALSE, ...)

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

# S3 method for polr
augment(x, data = stats::model.frame(x), newdata,
  type.predict = c("probs", "class"), ...)

# S3 method for clm
tidy(x, conf.int = FALSE, conf.level = 0.95,
  exponentiate = FALSE, quick = FALSE, conf.type = c("profile",
  "Wald"), ...)

# S3 method for clmm
tidy(x, conf.int = FALSE, conf.level = 0.95,
  exponentiate = FALSE, quick = FALSE, conf.type = c("profile",
  "Wald"), ...)

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

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

# S3 method for clm
augment(x, data = stats::model.frame(x), newdata,
  type.predict = c("prob", "class"), ...)

# S3 method for svyolr
tidy(x, conf.int = FALSE, conf.level = 0.95,
  exponentiate = FALSE, quick = FALSE, ...)

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

Arguments

x	a model of class clm, clmm, polr or svyolr
conf.int	whether to include a confidence interval
conf.level	confidence level of the interval, used only if conf.int=TRUE
exponentiate	whether to exponentiate the coefficient estimates and confidence intervals (typical for ordinal logistic regression)
quick	whether to compute a smaller and faster version, containing only the term, estimate and coefficient_type columns
...	extra arguments
data	original data, defaults to the extracting it from the model
newdata	if provided, performs predictions on the new data
type.predict	type of prediction to compute for a CLM; passed on to ordinal::predict.clm() or predict.polr
conf.type	the type of confidence interval (see ordinal::confint.clm())

Value

tidy.clm, tidy.clmm, tidy.polr and tidy.svyolr return one row for each coefficient at each level of the response variable, with six columns:

term

term in the model

estimate

estimated coefficient

std.error

standard error

statistic

z-statistic

p.value

two-sided p-value

coefficient_type

type of coefficient, see ordinal::clm()

If conf.int=TRUE, it also includes columns for conf.low and glance.clm, glance.clmm, glance.polr and glance.svyolr return a one-row data.frame with the columns:

edf

the effective degrees of freedom

logLik

the data's log-likelihood under the model

AIC

the Akaike Information Criterion

BIC

the Bayesian Information Criterion

df.residual

residual degrees of freedom

augment.clm and augment.polr returns one row for each observation, with additional columns added to the original data:

.fitted

fitted values of model

.se.fit

standard errors of fitted values

augment is not supportted for ordinal::clmm() and survey::svyolr() models. All tidying methods return a data.frame without rownames. The structure depends on the method chosen.

Examples

if (require(ordinal)){
  clm_mod <- clm(rating ~ temp * contact, data = wine)
  tidy(clm_mod)
  tidy(clm_mod, conf.int = TRUE)
  tidy(clm_mod, conf.int = TRUE, conf.type = "Wald", exponentiate = TRUE)
  glance(clm_mod)
  augment(clm_mod)

  clm_mod2 <- clm(rating ~ temp, nominal = ~ contact, data = wine)
  tidy(clm_mod2)

  clmm_mod <- clmm(rating ~ temp + contact + (1 | judge), data = wine)
  tidy(clmm_mod)
  glance(clmm_mod)
}
#> Loading required package: ordinal
#> Warning: there is no package called ‘ordinal’
if (require(MASS)) {
  polr_mod <- polr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing)
  tidy(polr_mod, exponentiate = TRUE, conf.int = TRUE)
  glance(polr_mod)
  augment(polr_mod, type.predict = "class")
}
#> # A tibble: 72 x 6
#>    Sat    Infl   Type      Cont  X.weights. .fitted
#>    <ord>  <fct>  <fct>     <fct>      <int>   <dbl>
#>  1 Low    Low    Tower     Low           21       1
#>  2 Medium Low    Tower     Low           21       1
#>  3 High   Low    Tower     Low           28       1
#>  4 Low    Medium Tower     Low           34       3
#>  5 Medium Medium Tower     Low           22       3
#>  6 High   Medium Tower     Low           36       3
#>  7 Low    High   Tower     Low           10       3
#>  8 Medium High   Tower     Low           11       3
#>  9 High   High   Tower     Low           36       3
#> 10 Low    Low    Apartment Low           61       1
#> # … with 62 more rows