broom (version 0.4.3)

speedlm_tidiers: Tidying methods for a speedlm model

Description

These methods tidy the coefficients of a "speedlm" object into a summary, augment the original data with information on the fitted values and residuals, and construct a one-row glance of the model's statistics.

Usage

# S3 method for speedlm
tidy(x, conf.int = FALSE, conf.level = 0.95,
  exponentiate = FALSE, quick = FALSE, ...)
# S3 method for speedlm
glance(x, ...)
# S3 method for speedlm
augment(x, data = stats::model.frame(x), newdata = data,
  ...)

Arguments

x

speedlm object

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 logistic regression)

quick

whether to compute a smaller and faster version, containing only the term and estimate columns.

...

extra arguments (not used)

data

data frame to augment

newdata

new data to use for predictions, optional

Value

All tidying methods return a data.frame without rownames, whose structure depends on the method chosen.

tidy.speedlm returns the tidied output of the lm with one row for each term in the formula. The columns match those in lm_tidiers.

glance.speedlm returns a one-row data.frame with the columns

r.squared

The percent of variance explained by the model

adj.r.squared

r.squared adjusted based on the degrees of freedom

statistic

F-statistic

p.value

p-value from the F test, describing whether the full regression is significant

df

Degrees of freedom used by the coefficients

logLik

the data's log-likelihood under the model

AIC

the Akaike Information Criterion

BIC

the Bayesian Information Criterion

deviance

deviance

df.residual

residual degrees of freedom

augment.speedlm returns one row for each observation, with just one column added:

.fitted

Fitted values of model

See Also

lm_tidiers, biglm_tidiers

Examples

# NOT RUN {
if (require("speedglm", quietly = TRUE)) {
    mod <- speedglm::speedlm(mpg ~ wt + qsec, data = mtcars)
    tidy(mod)
    glance(mod)
    augment(mod)
}

# }