tidy.polr {broom} | R Documentation |
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 class 'polr' tidy(x, conf.int = FALSE, conf.level = 0.95, exponentiate = FALSE, quick = FALSE, ...) ## S3 method for class 'polr' glance(x, ...) ## S3 method for class 'polr' augment(x, data = stats::model.frame(x), newdata, type.predict = c("probs", "class"), ...) ## S3 method for class 'clm' tidy(x, conf.int = FALSE, conf.level = 0.95, exponentiate = FALSE, quick = FALSE, conf.type = c("profile", "Wald"), ...) ## S3 method for class 'clmm' tidy(x, conf.int = FALSE, conf.level = 0.95, exponentiate = FALSE, quick = FALSE, conf.type = c("profile", "Wald"), ...) ## S3 method for class 'clm' glance(x, ...) ## S3 method for class 'clmm' glance(x, ...) ## S3 method for class 'clm' augment(x, data = stats::model.frame(x), newdata, type.predict = c("prob", "class"), ...) ## S3 method for class 'svyolr' tidy(x, conf.int = FALSE, conf.level = 0.95, exponentiate = FALSE, quick = FALSE, ...) ## S3 method for class 'svyolr' glance(x, ...)
x |
a model of class |
conf.int |
whether to include a confidence interval |
conf.level |
confidence level of the interval, used only if
|
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
|
conf.type |
the type of confidence interval
(see |
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 |
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.
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) } 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") }