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 lsmobj
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)
An lsmobj
object.
Logical indicating whether or not to include a confidence
interval in the tidied output. Defaults to FALSE
.
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!
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()
.
tidy()
, emmeans::ref_grid()
, emmeans::emmeans()
,
emmeans::contrast()
Other emmeans tidiers:
tidy.emmGrid()
,
tidy.ref.grid()
,
tidy.summary_emm()
A tibble::tibble()
with columns:
Upper bound on the confidence interval for the estimate.
Lower bound on the confidence interval for the estimate.
Levels being compared.
Degrees of freedom used by this term in the model.
Value to which the estimate is compared.
The two-sided p-value associated with the observed statistic.
The standard error of the regression term.
Expected marginal mean
T-ratio statistic
# feel free to ignore the following line—it allows {broom} to supply
# examples without requiring the model-supplying package to be installed.
if (requireNamespace("emmeans", quietly = TRUE)) {
# 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))
}
#> # A tibble: 4 × 5
#> term num.df den.df statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 price1 1 23 30.3 0.0000134
#> 2 price2 1 23 2.23 0.149
#> 3 day 5 23 4.88 0.00346
#> 4 store 5 23 2.52 0.0583