tidy.lsmobj: Tidy a(n) lsmobj 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 lsmobj
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)

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.
df: Degrees of freedom used by this term in the model.
null.value: Value to which the estimate is compared.
p.value: The two-sided p-value associated with the observed statistic.
std.error: The standard error of the regression term.
estimate: Expected marginal mean
statistic: T-ratio statistic

Arguments

x: An lsmobj object.
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.
...: 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().

Examples

Run this code

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))
}