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

"tidy"(x, conf.int = FALSE, conf.level = 0.95, exponentiate = FALSE, quick = FALSE, ...)
"tidy"(x, ...)
"augment"(x, data = stats::model.frame(x), newdata, type.predict, type.residuals, ...)
"glance"(x, ...)
"glance"(x, ...)

Arguments

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)

quick

whether to compute a smaller and faster version, containing only the term and estimate columns.

...

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

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
response: Which response column the coefficients correspond to (typically Y1, Y2, etc)
.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
.fitted: Fitted values of model
.se.fit: Standard errors of fitted values
.resid: Residuals of fitted values on the new data
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.

Code and documentation for augment.lm originated in the ggplot2 package, where it was called fortify.lm

Examples

Run this code


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()

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)

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)