broom (version 0.5.4)

tidy.lm: Tidy a(n) lm object

Description

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.

Usage

# S3 method for lm
tidy(
  x,
  conf.int = FALSE,
  conf.level = 0.95,
  exponentiate = FALSE,
  quick = FALSE,
  ...
)
# S3 method for summary.lm
tidy(x, ...)

Arguments

x

An lm object created by stats::lm().

conf.int

Logical indicating whether or not to include a confidence interval in the tidied output. Defaults to FALSE.

conf.level

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.

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 FALSE.

quick

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.

...

Additional arguments. Not used. Needed to match generic signature only. Cautionary note: Misspelled arguments will be absorbed in ..., where they will be ignored. If the misspelled argument has a default value, the default value will be used. For example, if you pass conf.lvel = 0.9, all computation will proceed using conf.level = 0.95. Additionally, if you pass newdata = my_tibble to an augment() method that does not accept a newdata argument, it will use the default value for the data argument.

Value

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.int = TRUE.

conf.high

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:

response

Which response column the coefficients correspond to (typically Y1, Y2, etc)

Details

If you have missing values in your model data, you may need to refit the model with na.action = na.exclude.

See Also

tidy(), stats::summary.lm()

Other lm tidiers: augment.glm(), augment.lm(), glance.glm(), glance.lm(), tidy.glm()

Examples

# NOT RUN {
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)

# }