Tidy a(n) lsmobj object
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.
## S3 method for class 'lsmobj' tidy(x, conf.int = FALSE, conf.level = 0.95, ...)
x |
An |
conf.int |
Logical indicating whether or not to include a confidence
interval in the tidied output. Defaults to |
conf.level |
The confidence level to use for the confidence interval
if |
... |
Additional arguments passed to |
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().
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 |
Other emmeans tidiers:
tidy.emmGrid(),
tidy.ref.grid(),
tidy.summary_emm()
if (requireNamespace("emmeans", quietly = TRUE)) {
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))
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