| biglm_tidiers {broom} | R Documentation |
Tidiers for biglm object from the "biglm" package, which contains a linear model
object that is limited in memory usage. Generally the behavior is as similar
to the lm_tidiers as is possible. Currently no augment
is defined.
## S3 method for class 'biglm' tidy(x, conf.int = FALSE, conf.level = 0.95, exponentiate = FALSE, quick = FALSE, ...) ## S3 method for class 'biglm' glance(x, ...)
x |
a "biglm" object |
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 logistic regression) |
quick |
whether to compute a smaller and faster version, containing
only the |
... |
extra arguments (not used) |
All tidying methods return a data.frame without rownames, whose structure depends on the method chosen.
tidy.biglm returns one row for each coefficient, with columns
term |
The term in the linear model being estimated and tested |
estimate |
The estimated coefficient |
std.error |
The standard error from the linear model |
p.value |
two-sided p-value |
If conf.int=TRUE, it also includes columns for conf.low and
conf.high, computed with confint.
glance.biglm returns a one-row data frame, with columns
r.squared |
The percent of variance explained by the model |
AIC |
the Akaike Information Criterion |
deviance |
deviance |
df.residual |
residual degrees of freedom |
if (require("biglm", quietly = TRUE)) {
bfit <- biglm(mpg ~ wt + disp, mtcars)
tidy(bfit)
tidy(bfit, conf.int = TRUE)
tidy(bfit, conf.int = TRUE, conf.level = .9)
glance(bfit)
# bigglm: logistic regression
bgfit <- bigglm(am ~ mpg, mtcars, family = binomial())
tidy(bgfit)
tidy(bgfit, exponentiate = TRUE)
tidy(bgfit, conf.int = TRUE)
tidy(bgfit, conf.int = TRUE, conf.level = .9)
tidy(bgfit, conf.int = TRUE, conf.level = .9, exponentiate = TRUE)
glance(bgfit)
}