Tidy a(n) emmGrid object

Source: R/emmeans-tidiers.R

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 emmGrid
tidy(x, ...)

Arguments

x

An emmGrid 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

tidy(), emmeans::ref_grid(), emmeans::emmeans(), emmeans::contrast()

Other emmeans tidiers: tidy.lsmobj(), tidy.ref.grid(), tidy.summary_emm()

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.

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

statistic

T-ratio statistic

estimate

Estimated least-squares mean.

Examples


library(emmeans)
#> Welcome to emmeans.
#> NOTE -- Important change from versions <= 1.41:
#>     Indicator predictors are now treated as 2-level factors by default.
#>     To revert to old behavior, use emm_options(cov.keep = character(0))
# 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