Create a rich-formatted table from an anova object

Create a rich-formatted table from an anova object

# S3 method for class 'anova'
tabularise_default(
  data,
  header = TRUE,
  title = header,
  auto.labs = TRUE,
  origdata = NULL,
  labs = NULL,
  lang = getOption("SciViews_lang", "en"),
  show.signif.stars = getOption("show.signif.stars", TRUE),
  ...,
  kind = "ft"
)

Arguments

data

An anova object

header

If TRUE (by default), add a header to the table

title

If TRUE, add a title to the table header. Default to the same value than header, except outside of a chunk where it is FALSE if a table caption is detected (tbl-cap YAML entry).

auto.labs

If TRUE (by default), use labels (and units) automatically (from origdata=)

origdata

The original data set used for the ANOVA. By default it is NULL. Used to extract labels that are lost in the anova object.

labs

Labels to change the default names in the term column of the table. By default it is NULL and nothing is changed.

lang

The natural language to use. The default value is set with, e.g., options(SciViews_lang = "fr") for French.

show.signif.stars

If TRUE, add the significance stars to the table. The default is taken from getOption("show.signif.stars").

...

Additional arguments (not used for now)

kind

The kind of table to produce: "tt" for tinytable, or "ft" for flextable (default).

Value

A flextable object you can print in different form or rearrange with the {flextable} functions.

Examples

is <- data.io::read("iris", package = "datasets")
#> Registered S3 method overwritten by 'tsibble':
#>   method               from 
#>   as_tibble.grouped_df dplyr

is_lm1 <- lm(data = is, petal_length ~ species)

library(tabularise)

anova(is_lm1) |> tabularise_default()
Analysis of variance
Response: petal_length
Term
Df
Sum of squares
Mean squares
Fobs. value
p value
species
2
437.1
218.551
1180
< 2·10-16
***
Residuals
147
 27.2
  0.185
0 <= '***' < 0.001 < '**' < 0.01 < '*' < 0.05
# identical
anova(is_lm1) |> tabularise()
Analysis of variance
Response: petal_length
Term
Df
Sum of squares
Mean squares
Fobs. value
p value
species
2
437.1
218.551
1180
< 2·10-16
***
Residuals
147
 27.2
  0.185
0 <= '***' < 0.001 < '**' < 0.01 < '*' < 0.05
# Use labels
anova(is_lm1) |> tabularise(origdata = is)
Analysis of variance
Response: Length of the petals  [cm]
Term
Df
Sum of squares
Mean squares
Fobs. value
p value
Iris species
2
437.1
218.551
1180
< 2·10-16
***
Residuals
147
 27.2
  0.185
0 <= '***' < 0.001 < '**' < 0.01 < '*' < 0.05

# alternative with anova_() in {modelit} package
anova_(is_lm1) |> tabularise()
Analysis of variance
Response: Length of the petals  [cm]
Term
Df
Sum of squares
Mean squares
Fobs. value
p value
Iris species
2
437.1
218.551
1180
< 2·10-16
***
Residuals
147
 27.2
  0.185
0 <= '***' < 0.001 < '**' < 0.01 < '*' < 0.05

is_lm2 <- lm(data = is, petal_length ~ sepal_length + species)

anova(is_lm1, is_lm2) |> tabularise(origdata = is)
Analysis of variance
Model 1: Length of the petals  [cm] ~ Iris species
Model 2: Length of the petals  [cm] ~ Length of the sepals  [cm] + Iris species
Model
Residuals Df
Residual sum of squares
Df
Sum of squares
Fobs. value
p value
Model 1
147
27.2
Model 2
146
11.7
 1
15.6
195
< 2·10-16
***
0 <= '***' < 0.001 < '**' < 0.01 < '*' < 0.05
anova_(is_lm1, is_lm2) |> tabularise()
Analysis of variance
Model 1: Length of the petals  [cm] ~ Iris species
Model 2: Length of the petals  [cm] ~ sepal_length + Iris species
Model
Residuals Df
Residual sum of squares
Df
Sum of squares
Fobs. value
p value
Model 1
147
27.2
Model 2
146
11.7
 1
15.6
195
< 2·10-16
***
0 <= '***' < 0.001 < '**' < 0.01 < '*' < 0.05