broom (version 0.3.7)

lm_tidiers: Tidying methods for a linear model

Description

These methods tidy the coefficients of a linear model 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 class 'lm':
tidy(x, conf.int = FALSE, conf.level = 0.95,
  exponentiate = FALSE, ...)
## S3 method for class 'lm':
augment(x, data = model.frame(x), newdata, type.predict,
  type.residuals, ...)
## S3 method for class 'lm':
glance(x, ...)

Arguments

x

lm 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)

...

extra arguments (not used)

data

Original data, defaults to the extracting it from the model

newdata

If provided, performs predictions on the new data

type.predict

Type of prediction to compute for a GLM; passed on to predict.glm

type.residuals

Type of residuals to compute for a GLM; passed on to residuals.glm

Value

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

  tidy.lm returns one row for each coefficient, with five 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
• statistic
  t-statistic
• p.value
  two-sided p-value
• 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)
• If conf.int=TRUE, it also includes columns for conf.low and conf.high, computed with confint.

  When newdata is not supplied augment.lm returns one row for each observation, with seven columns added to the original data:

• .hat
  Diagonal of the hat matrix
• .sigma
  Estimate of residual standard deviation when corresponding observation is dropped from model
• .cooksd
  Cooks distance, cooks.distance
• .fitted
  Fitted values of model
• .se.fit
  Standard errors of fitted values
• .resid
  Residuals
• .std.resid
  Standardised residuals
• (Some unusual "lm" objects, such as "rlm" from MASS, may omit .cooksd and .std.resid)

  When newdata is supplied, augment.lm returns one row for each observation, with three columns added to the new data:

• .fitted
  Fitted values of model
• .se.fit
  Standard errors of fitted values
• .resid
  Residuals of fitted values on the new data
• glance.lm 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
• sigma
  The square root of the estimated residual variance
• 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

Details

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

If conf.int=TRUE, the confidence interval is computed with the confint function.

While tidy is supported for "mlm" objects, augment and glance are not.

When the modeling was performed with na.action = "na.omit" (as is the typical default), rows with NA in the initial data are omitted entirely from the augmented data frame. When the modeling was performed with na.action = "na.exclude", one should provide the original data as a second argument, at which point the augmented data will contain those rows (typically with NAs in place of the new columns). If the original data is not provided to augment and na.action = "na.exclude", a warning is raised and the incomplete rows are dropped.

See Also

summary.lm

na.action

Examples

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() + geom_errorbarh()

head(augment(mod))
head(augment(mod, mtcars))

# predict on new data
newdata <- mtcars %>% head(6) %>% mutate(wt = wt + 1)
augment(mod, newdata = newdata)

au <- augment(mod, data = mtcars)

plot(mod, which = 1)
qplot(.fitted, .resid, data = au) +
  geom_hline(yintercept = 0) +
  geom_smooth(se = FALSE)
qplot(.fitted, .std.resid, data = au) +
  geom_hline(yintercept = 0) +
  geom_smooth(se = FALSE)
qplot(.fitted, .std.resid, data = au,
  colour = factor(cyl))
qplot(mpg, .std.resid, data = au, colour = factor(cyl))

plot(mod, which = 2)
qplot(sample =.std.resid, data = au, stat = "qq") +
    geom_abline()

plot(mod, which = 3)
qplot(.fitted, sqrt(abs(.std.resid)), data = au) + geom_smooth(se = FALSE)

plot(mod, which = 4)
qplot(seq_along(.cooksd), .cooksd, data = au, geom = "bar",
 stat="identity")

plot(mod, which = 5)
qplot(.hat, .std.resid, data = au) + geom_smooth(se = FALSE)
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)

qplot(.hat, .std.resid, data = au, size = .cooksd) +
  geom_smooth(se = FALSE, size = 0.5)

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()
qplot(.hat, .cooksd, size = .cooksd / .hat, data = au) + scale_size_area()

# column-wise models
a <- matrix(rnorm(20), nrow = 10)
b <- a + rnorm(length(a))
result <- lm(b ~ a)
tidy(result)