This method wraps tidy.lm()
.
# S3 method for lmRob
tidy(x, ...)
A lmRob
object returned from robust::lmRob()
.
Arguments passed on to tidy.lm
An lm
object created by stats::lm()
.
Logical indicating whether or not to include a confidence
interval in the tidied output. Defaults to FALSE
.
The confidence level to use for the confidence interval
if conf.int = TRUE
. Must be strictly greater than 0 and less than 1.
Defaults to 0.95, which corresponds to a 95 percent confidence interval.
Logical indiciating if the only the term
and estimate
columns should be returned. Often useful to avoid time consuming
covariance and standard error calculations. Defaults to FALSE
.
Logical indicating whether or not to exponentiate the
the coefficient estimates. This is typical for logistic and multinomial
regressions, but a bad idea if there is no log or logit link. Defaults
to FALSE
.
A tibble::tibble()
with one row for each term in the
regression. The tibble has columns:
The name of the regression term.
The estimated value of the regression term.
The standard error of the regression term.
The value of a statistic, almost always a T-statistic, to use in a hypothesis that the regression term is non-zero.
The two-sided p-value associated with the observed statistic.
The low end of a confidence interval for the regression
term. Included only if conf.int = TRUE
.
The high end of a confidence interval for the regression
term. Included only if conf.int = TRUE
.
If the linear model is an mlm object (multiple linear model), there is an additional column:
Which response column the coefficients correspond to (typically Y1, Y2, etc)
For tidiers for robust models from the MASS package see
tidy.rlm()
.
Other robust tidiers: augment.glmRob
,
augment.lmRob
, glance.glmRob
,
glance.lmRob
, tidy.glmRob
# NOT RUN {
library(robust)
m <- lmRob(mpg ~ wt, data = mtcars)
tidy(m)
augment(m)
glance(m)
gm <- glmRob(am ~ wt, data = mtcars, family = "binomial")
glance(gm)
# }
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