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 cross 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 summary_emm
tidy(x, ...)

Arguments

x

An summary_emm object.

...

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 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

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.

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

statistic

T-ratio statistic or F-ratio statistic

estimate

Estimated least-squares mean.

Examples

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
#> # A tibble: 36 x 7 #> price1 price2 day store estimate std.error df #> <dbl> <dbl> <fct> <fct> <dbl> <dbl> <dbl> #> 1 51.2 48.6 1 1 2.92 2.72 23 #> 2 51.2 48.6 2 1 3.85 2.70 23 #> 3 51.2 48.6 3 1 11.0 2.53 23 #> 4 51.2 48.6 4 1 6.10 2.65 23 #> 5 51.2 48.6 5 1 12.8 2.44 23 #> 6 51.2 48.6 6 1 8.75 2.79 23 #> 7 51.2 48.6 1 2 4.96 2.38 23 #> 8 51.2 48.6 2 2 5.89 2.34 23 #> 9 51.2 48.6 3 2 13.1 2.42 23 #> 10 51.2 48.6 4 2 8.14 2.35 23 #> # … with 26 more rows
# marginal averages marginal <- emmeans(oranges_rg1, "day") tidy(marginal)
#> # A tibble: 6 x 6 #> day estimate std.error df conf.low conf.high #> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 1 5.56 1.77 23 1.91 9.22 #> 2 2 6.49 1.73 23 2.92 10.1 #> 3 3 13.7 1.75 23 10.0 17.3 #> 4 4 8.74 1.73 23 5.16 12.3 #> 5 5 15.4 1.79 23 11.7 19.1 #> 6 6 11.4 1.77 23 7.74 15.0
# contrasts tidy(contrast(marginal))
#> # A tibble: 6 x 6 #> contrast estimate std.error df statistic p.value #> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 1 effect -4.65 1.62 23 -2.87 0.0261 #> 2 2 effect -3.72 1.58 23 -2.36 0.0547 #> 3 3 effect 3.45 1.60 23 2.15 0.0637 #> 4 4 effect -1.47 1.59 23 -0.930 0.434 #> 5 5 effect 5.22 1.64 23 3.18 0.0249 #> 6 6 effect 1.18 1.62 23 0.726 0.475
tidy(contrast(marginal, method = "pairwise"))
#> # A tibble: 15 x 7 #> level1 level2 estimate std.error df statistic p.value #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 1 2 -0.930 2.47 23 -0.377 0.999 #> 2 1 3 -8.10 2.47 23 -3.29 0.0337 #> 3 1 4 -3.18 2.51 23 -1.27 0.799 #> 4 1 5 -9.88 2.56 23 -3.86 0.00913 #> 5 1 6 -5.83 2.52 23 -2.31 0.229 #> 6 2 3 -7.17 2.48 23 -2.89 0.0777 #> 7 2 4 -2.25 2.44 23 -0.920 0.937 #> 8 2 5 -8.95 2.52 23 -3.56 0.0184 #> 9 2 6 -4.90 2.45 23 -2.00 0.371 #> 10 3 4 4.92 2.49 23 1.98 0.385 #> 11 3 5 -1.78 2.47 23 -0.719 0.978 #> 12 3 6 2.27 2.54 23 0.894 0.944 #> 13 4 5 -6.70 2.49 23 -2.69 0.115 #> 14 4 6 -2.65 2.45 23 -1.08 0.883 #> 15 5 6 4.05 2.56 23 1.58 0.617
# plot confidence intervals library(ggplot2) ggplot(tidy(marginal), 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
#> price2 = 40: #> day emmean SE df lower.CL upper.CL #> 2 6.24 1.89 23 2.33 10.1 #> 3 13.41 2.12 23 9.02 17.8 #> 4 8.48 1.87 23 4.62 12.3 #> #> price2 = 60: #> day emmean SE df lower.CL upper.CL #> 2 9.21 2.11 23 4.85 13.6 #> 3 16.38 1.91 23 12.44 20.3 #> 4 11.46 2.18 23 6.96 16.0 #> #> price2 = 80: #> day emmean SE df lower.CL upper.CL #> 2 12.19 3.65 23 4.65 19.7 #> 3 19.36 3.27 23 12.59 26.1 #> 4 14.44 3.74 23 6.71 22.2 #> #> Results are averaged over the levels of: store #> Confidence level used: 0.95
tidy(by_price)
#> # A tibble: 9 x 7 #> day price2 estimate std.error df conf.low conf.high #> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 2 40 6.24 1.89 23 2.33 10.1 #> 2 3 40 13.4 2.12 23 9.02 17.8 #> 3 4 40 8.48 1.87 23 4.62 12.3 #> 4 2 60 9.21 2.11 23 4.85 13.6 #> 5 3 60 16.4 1.91 23 12.4 20.3 #> 6 4 60 11.5 2.18 23 6.96 16.0 #> 7 2 80 12.2 3.65 23 4.65 19.7 #> 8 3 80 19.4 3.27 23 12.6 26.1 #> 9 4 80 14.4 3.74 23 6.71 22.2
ggplot(tidy(by_price), 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: 2 x 5 #> term num.df den.df statistic p.value #> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 day 5 23 4.88 0.00346 #> 2 store 5 23 2.52 0.0583