tidy.lm {broom} | R Documentation |
Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies cross models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.
## S3 method for class 'lm' tidy(x, conf.int = FALSE, conf.level = 0.95, exponentiate = FALSE, quick = FALSE, ...) ## S3 method for class 'summary.lm' tidy(x, ...)
x |
An |
conf.int |
Logical indicating whether or not to include a confidence
interval in the tidied output. Defaults to |
conf.level |
The confidence level to use for the confidence interval
if |
exponentiate |
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 |
quick |
Logical indiciating if the only the |
... |
Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in |
If you have missing values in your model data, you may need to refit
the model with na.action = na.exclude
.
A tibble::tibble()
with one row for each term in the
regression. The tibble has columns:
term |
The name of the regression term. |
estimate |
The estimated value of the regression term. |
std.error |
The standard error of the regression term. |
statistic |
The value of a statistic, almost always a T-statistic, to use in a hypothesis that the regression term is non-zero. |
p.value |
The two-sided p-value associated with the observed statistic. |
conf.low |
The low end of a confidence interval for the regression
term. Included only if |
conf.high |
The high end of a confidence interval for the regression
term. Included only if |
If the linear model is an mlm
object (multiple linear model),
there is an additional column:
response |
Which response column the coefficients correspond to (typically Y1, Y2, etc) |
Other lm tidiers: augment.glm
,
augment.lm
, glance.glm
,
glance.lm
, tidy.glm
library(ggplot2) library(dplyr) mod <- lm(mpg ~ wt + qsec, data = mtcars) tidy(mod) glance(mod) # coefficient plot d <- tidy(mod) %>% mutate( low = estimate - std.error, high = estimate + std.error ) ggplot(d, aes(estimate, term, xmin = low, xmax = high, height = 0)) + geom_point() + geom_vline(xintercept = 0) + geom_errorbarh() augment(mod) augment(mod, mtcars) # predict on new data newdata <- mtcars %>% head(6) %>% mutate(wt = wt + 1) augment(mod, newdata = newdata) au <- augment(mod, data = mtcars) ggplot(au, aes(.hat, .std.resid)) + geom_vline(size = 2, colour = "white", xintercept = 0) + geom_hline(size = 2, colour = "white", yintercept = 0) + geom_point() + geom_smooth(se = FALSE) plot(mod, which = 6) ggplot(au, aes(.hat, .cooksd)) + geom_vline(xintercept = 0, colour = NA) + geom_abline(slope = seq(0, 3, by = 0.5), colour = "white") + geom_smooth(se = FALSE) + geom_point() # column-wise models a <- matrix(rnorm(20), nrow = 10) b <- a + rnorm(length(a)) result <- lm(b ~ a) tidy(result)