broom (version 1.0.4)

tidy.summary_emm: Tidy a(n) summary_emm 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 across 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 summary_emm
tidy(x, null.value = NULL, ...)

Value

A tibble::tibble() with columns:

conf.high

Upper bound on the confidence interval for the estimate.

conf.low

Lower bound on the confidence interval for the estimate.

contrast

Levels being compared.

den.df

Degrees of freedom of the denominator.

df

Degrees of freedom used by this term in the model.

null.value

Value to which the estimate is compared.

num.df

Degrees of freedom.

p.value

The two-sided p-value associated with the observed statistic.

std.error

The standard error of the regression term.

level1

One level of the factor being contrasted

level2

The other level of the factor being contrasted

term

Model term in joint tests

estimate

Expected marginal mean

statistic

T-ratio statistic or F-ratio statistic

Arguments

x

A summary_emm object.

null.value

Value to which estimate is compared.

...

Additional arguments passed to emmeans::summary.emmGrid() or lsmeans::summary.ref.grid(). Cautionary note: misspecified arguments may be silently ignored!

Details

Returns a data frame with one observation for each estimated marginal mean, and one column for each combination of factors. When the input is a contrast, each row will contain one estimated contrast.

There are a large number of arguments that can be passed on to emmeans::summary.emmGrid() or lsmeans::summary.ref.grid().

See Also

tidy(), emmeans::ref_grid(), emmeans::emmeans(), emmeans::contrast()

Other emmeans tidiers: tidy.emmGrid(), tidy.lsmobj(), tidy.ref.grid()

Examples

if (FALSE) { # rlang::is_installed(c("emmeans", "ggplot2"))

# load libraries for models and data
library(emmeans)

# linear model for sales of oranges per day
oranges_lm1 <- lm(sales1 ~ price1 + price2 + day + store, data = oranges)

# reference grid; see vignette("basics", package = "emmeans")
oranges_rg1 <- ref_grid(oranges_lm1)
td <- tidy(oranges_rg1)
td

# marginal averages
marginal <- emmeans(oranges_rg1, "day")
tidy(marginal)

# contrasts
tidy(contrast(marginal))
tidy(contrast(marginal, method = "pairwise"))

# plot confidence intervals
library(ggplot2)

ggplot(tidy(marginal, conf.int = TRUE), aes(day, estimate)) +
  geom_point() +
  geom_errorbar(aes(ymin = conf.low, ymax = conf.high))

# by multiple prices
by_price <- emmeans(oranges_lm1, "day",
  by = "price2",
  at = list(
    price1 = 50, price2 = c(40, 60, 80),
    day = c("2", "3", "4")
  )
)

by_price

tidy(by_price)

ggplot(tidy(by_price, conf.int = TRUE), aes(price2, estimate, color = day)) +
  geom_line() +
  geom_errorbar(aes(ymin = conf.low, ymax = conf.high))

# joint_tests
tidy(joint_tests(oranges_lm1))
}