Tidy a(n) summary_emm 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.

Usage

# S3 method for class 'summary_emm'
tidy(x, null.value = NULL, ...)

Arguments

x: A summary_emm object.
null.value: Value to which estimate is compared.
...: 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().

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.
null.value: Value to which the estimate is compared.
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
estimate: Expected marginal mean
statistic: T-ratio statistic or F-ratio statistic

Examples


# 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
#> # A tibble: 36 × 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  
#> # ℹ 26 more rows

# marginal averages
marginal <- emmeans(oranges_rg1, "day")
tidy(marginal)
#> # A tibble: 6 × 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 × 8
#>   term  contrast null.value estimate std.error    df statistic adj.p.value
#>   <chr> <chr>         <dbl>    <dbl>     <dbl> <dbl>     <dbl>       <dbl>
#> 1 day   day1 ef…          0    -4.65      1.62    23    -2.87       0.0261
#> 2 day   day2 ef…          0    -3.72      1.58    23    -2.36       0.0547
#> 3 day   day3 ef…          0     3.45      1.60    23     2.15       0.0637
#> 4 day   day4 ef…          0    -1.47      1.59    23    -0.930      0.434 
#> 5 day   day5 ef…          0     5.22      1.64    23     3.18       0.0249
#> 6 day   day6 ef…          0     1.18      1.62    23     0.726      0.475 
tidy(contrast(marginal, method = "pairwise"))
#> # A tibble: 15 × 8
#>    term  contrast    null.value estimate std.error    df statistic
#>    <chr> <chr>            <dbl>    <dbl>     <dbl> <dbl>     <dbl>
#>  1 day   day1 - day2          0   -0.930      2.47    23    -0.377
#>  2 day   day1 - day3          0   -8.10       2.47    23    -3.29 
#>  3 day   day1 - day4          0   -3.18       2.51    23    -1.27 
#>  4 day   day1 - day5          0   -9.88       2.56    23    -3.86 
#>  5 day   day1 - day6          0   -5.83       2.52    23    -2.31 
#>  6 day   day2 - day3          0   -7.17       2.48    23    -2.89 
#>  7 day   day2 - day4          0   -2.25       2.44    23    -0.920
#>  8 day   day2 - day5          0   -8.95       2.52    23    -3.56 
#>  9 day   day2 - day6          0   -4.90       2.45    23    -2.00 
#> 10 day   day3 - day4          0    4.92       2.49    23     1.98 
#> 11 day   day3 - day5          0   -1.78       2.47    23    -0.719
#> 12 day   day3 - day6          0    2.27       2.54    23     0.894
#> 13 day   day4 - day5          0   -6.70       2.49    23    -2.69 
#> 14 day   day4 - day6          0   -2.65       2.45    23    -1.08 
#> 15 day   day5 - day6          0    4.05       2.56    23     1.58 
#> # ℹ 1 more variable: adj.p.value <dbl>

# 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 × 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 × 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

Usage

Arguments

Details

See also

Value

Examples