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, ...)

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().

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.

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

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 9 #> price1 price2 day store estimate std.error df statistic p.value #> <dbl> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 51.2 48.6 1 1 2.92 2.72 23 1.07 0.294 #> 2 51.2 48.6 2 1 3.85 2.70 23 1.42 0.168 #> 3 51.2 48.6 3 1 11.0 2.53 23 4.35 0.000237 #> 4 51.2 48.6 4 1 6.10 2.65 23 2.30 0.0309 #> 5 51.2 48.6 5 1 12.8 2.44 23 5.23 0.0000261 #> 6 51.2 48.6 6 1 8.75 2.79 23 3.14 0.00459 #> 7 51.2 48.6 1 2 4.96 2.38 23 2.09 0.0482 #> 8 51.2 48.6 2 2 5.89 2.34 23 2.52 0.0190 #> 9 51.2 48.6 3 2 13.1 2.42 23 5.41 0.0000172 #> 10 51.2 48.6 4 2 8.14 2.35 23 3.46 0.00212 #> # … with 26 more rows
# marginal averages marginal <- emmeans(oranges_rg1, "day") tidy(marginal)
#> # A tibble: 6 x 6 #> day estimate std.error df statistic p.value #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 1 5.56 1.77 23 3.15 0.00451 #> 2 2 6.49 1.73 23 3.76 0.00103 #> 3 3 13.7 1.75 23 7.80 0.0000000658 #> 4 4 8.74 1.73 23 5.04 0.0000420 #> 5 5 15.4 1.79 23 8.65 0.0000000110 #> 6 6 11.4 1.77 23 6.45 0.00000140
# contrasts tidy(contrast(marginal))
#> # A tibble: 6 x 8 #> term contrast null.value estimate std.error df statistic adj.p.value #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 day 1 effect 0 -4.65 1.62 23 -2.87 0.0261 #> 2 day 2 effect 0 -3.72 1.58 23 -2.36 0.0547 #> 3 day 3 effect 0 3.45 1.60 23 2.15 0.0637 #> 4 day 4 effect 0 -1.47 1.59 23 -0.930 0.434 #> 5 day 5 effect 0 5.22 1.64 23 3.18 0.0249 #> 6 day 6 effect 0 1.18 1.62 23 0.726 0.475
tidy(contrast(marginal, method = "pairwise"))
#> # A tibble: 15 x 8 #> term contrast null.value estimate std.error df statistic adj.p.value #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 day 1 - 2 0 -0.930 2.47 23 -0.377 0.999 #> 2 day 1 - 3 0 -8.10 2.47 23 -3.29 0.0337 #> 3 day 1 - 4 0 -3.18 2.51 23 -1.27 0.799 #> 4 day 1 - 5 0 -9.88 2.56 23 -3.86 0.00913 #> 5 day 1 - 6 0 -5.83 2.52 23 -2.31 0.229 #> 6 day 2 - 3 0 -7.17 2.48 23 -2.89 0.0777 #> 7 day 2 - 4 0 -2.25 2.44 23 -0.920 0.937 #> 8 day 2 - 5 0 -8.95 2.52 23 -3.56 0.0184 #> 9 day 2 - 6 0 -4.90 2.45 23 -2.00 0.371 #> 10 day 3 - 4 0 4.92 2.49 23 1.98 0.385 #> 11 day 3 - 5 0 -1.78 2.47 23 -0.719 0.978 #> 12 day 3 - 6 0 2.27 2.54 23 0.894 0.944 #> 13 day 4 - 5 0 -6.70 2.49 23 -2.69 0.115 #> 14 day 4 - 6 0 -2.65 2.45 23 -1.08 0.883 #> 15 day 5 - 6 0 4.05 2.56 23 1.58 0.617
# 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
#> 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 statistic p.value #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 2 40 6.24 1.89 23 3.30 0.00310 #> 2 3 40 13.4 2.12 23 6.33 0.00000187 #> 3 4 40 8.48 1.87 23 4.55 0.000145 #> 4 2 60 9.21 2.11 23 4.37 0.000225 #> 5 3 60 16.4 1.91 23 8.60 0.0000000122 #> 6 4 60 11.5 2.18 23 5.26 0.0000244 #> 7 2 80 12.2 3.65 23 3.34 0.00282 #> 8 3 80 19.4 3.27 23 5.91 0.00000502 #> 9 4 80 14.4 3.74 23 3.86 0.000788
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 x 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