Create reports for ANOVA models.
Usage
# S3 method for class 'aov'
report(x, ...)
# S3 method for class 'aov'
report_effectsize(x, ...)
# S3 method for class 'aov'
report_table(x, ...)
# S3 method for class 'aov'
report_statistics(x, table = NULL, ...)
# S3 method for class 'aov'
report_parameters(x, ...)
# S3 method for class 'aov'
report_model(x, table = NULL, ...)
# S3 method for class 'aov'
report_info(x, effectsize = NULL, ...)
# S3 method for class 'aov'
report_text(x, table = NULL, ...)Arguments
- x
Object of class
aov,anovaoraovlist.- ...
Arguments passed to or from other methods.
- table
Provide the output of
report_table()to avoid its re-computation.- effectsize
Provide the output of
report_effectsize()to avoid its re-computation.
Value
An object of class report().
See also
Specific components of reports (especially for stats models):
Other types of reports:
Methods:
Template file for supporting new models:
Examples
data <- iris
data$Cat1 <- rep(c("A", "B"), length.out = nrow(data))
model <- aov(Sepal.Length ~ Species * Cat1, data = data)
r <- report(model)
r
#> The ANOVA (formula: Sepal.Length ~ Species * Cat1) suggests that:
#>
#> - The main effect of Species is statistically significant and large (F(2, 144)
#> = 118.43, p < .001; Eta2 (partial) = 0.62, 95% CI [0.54, 1.00])
#> - The main effect of Cat1 is statistically not significant and very small (F(1,
#> 144) = 6.25e-03, p = 0.937; Eta2 (partial) = 4.34e-05, 95% CI [0.00, 1.00])
#> - The interaction between Species and Cat1 is statistically not significant and
#> small (F(2, 144) = 0.98, p = 0.377; Eta2 (partial) = 0.01, 95% CI [0.00, 1.00])
#>
#> Effect sizes were labelled following Field's (2013) recommendations.
summary(r)
#> The ANOVA suggests that:
#>
#> - The main effect of Species is statistically significant and large (F(2, 144)
#> = 118.43, p < .001, Eta2 (partial) = 0.62)
#> - The main effect of Cat1 is statistically not significant and very small (F(1,
#> 144) = 6.25e-03, p = 0.937, Eta2 (partial) = 4.34e-05)
#> - The interaction between Species and Cat1 is statistically not significant and
#> small (F(2, 144) = 0.98, p = 0.377, Eta2 (partial) = 0.01)
as.data.frame(r)
#> Parameter | Sum_Squares | df | Mean_Square | F | p
#> ------------------------------------------------------------------
#> Species | 63.21 | 2 | 31.61 | 118.43 | < .001
#> Cat1 | 1.67e-03 | 1 | 1.67e-03 | 6.25e-03 | 0.937
#> Species:Cat1 | 0.52 | 2 | 0.26 | 0.98 | 0.377
#> Residuals | 38.43 | 144 | 0.27 | |
#>
#> Parameter | Eta2 (partial) | Eta2_partial 95% CI
#> ---------------------------------------------------
#> Species | 0.62 | [0.54, 1.00]
#> Cat1 | 4.34e-05 | [0.00, 1.00]
#> Species:Cat1 | 0.01 | [0.00, 1.00]
#> Residuals | |
summary(as.data.frame(r))
#> Parameter | Sum_Squares | df | Mean_Square | F | p | Eta2 (partial)
#> -----------------------------------------------------------------------------------
#> Species | 63.21 | 2 | 31.61 | 118.43 | < .001 | 0.62
#> Cat1 | 1.67e-03 | 1 | 1.67e-03 | 6.25e-03 | 0.937 | 4.34e-05
#> Species:Cat1 | 0.52 | 2 | 0.26 | 0.98 | 0.377 | 0.01
#> Residuals | 38.43 | 144 | 0.27 | | |