anova_test: Anova Test

In rstatix: Pipe-Friendly Framework for Basic Statistical Tests

Anova Test

Description

Provides a pipe-friendly framework to perform different types of ANOVA tests, including:

• Independent measures ANOVA: between-Subjects designs,

• Repeated measures ANOVA: within-Subjects designs

• Mixed ANOVA: Mixed within within- and between-Subjects designs, also known as split-plot ANOVA and

• ANCOVA: Analysis of Covariance.

The function is an easy to use wrapper around Anova() and aov(). It makes ANOVA computation handy in R and It's highly flexible: can support model and formula as input. Variables can be also specified as character vector using the arguments dv, wid, between, within, covariate.

The results include ANOVA table, generalized effect size and some assumption checks.

Usage

anova_test(
  data,
  formula,
  dv,
  wid,
  between,
  within,
  covariate,
  type = NULL,
  effect.size = "ges",
  error = NULL,
  white.adjust = FALSE,
  observed = NULL,
  detailed = FALSE
)

get_anova_table(x, correction = c("auto", "GG", "HF", "none"))

## S3 method for class 'anova_test'
print(x, ...)

## S3 method for class 'anova_test'
plot(x, ...)

Arguments

data	a data.frame or a model to be analyzed.
formula	a formula specifying the ANOVA model similar to aov. Can be of the form y ~ group where y is a numeric variable giving the data values and group is a factor with one or multiple levels giving the corresponding groups. For example, formula = TP53 ~ cancer_group. Examples of supported formula include: Between-Ss ANOVA (independent measures ANOVA): y ~ b1*b2 Within-Ss ANOVA (repeated measures ANOVA): y ~ w1*w2 + Error(id/(w1*w2)) Mixed ANOVA: y ~ b1*b2*w1 + Error(id/w1) If the formula doesn't contain any within vars, a linear model is directly fitted and passed to the ANOVA function. For repeated designs, the ANOVA variables are parsed from the formula.
dv	(numeric) dependent variable name.
wid	(factor) column name containing individuals/subjects identifier. Should be unique per individual.
between	(optional) between-subject factor variables.
within	(optional) within-subjects factor variables
covariate	(optional) covariate names (for ANCOVA)
type	the type of sums of squares for ANOVA. Allowed values are either 1, 2 or 3. type = 2 is the default because this will yield identical ANOVA results as type = 1 when data are balanced but type = 2 will additionally yield various assumption tests where appropriate. When the data are unbalanced the type = 3 is used by popular commercial softwares including SPSS.
effect.size	the effect size to compute and to show in the ANOVA results. Allowed values can be either "ges" (generalized eta squared) or "pes" (partial eta squared) or both. Default is "ges".
error	(optional) for a linear model, an lm model object from which the overall error sum of squares and degrees of freedom are to be calculated. Read more in Anova() documentation.
white.adjust	Default is FALSE. If TRUE, heteroscedasticity correction is applied to the coefficient of covariance matrix. Used only for independent measures ANOVA.
observed	Variables that are observed (i.e, measured) as compared to experimentally manipulated. The default effect size reported (generalized eta-squared) requires correct specification of the observed variables.
detailed	If TRUE, returns extra information (sums of squares columns, intercept row, etc.) in the ANOVA table.
x	an object of class anova_test
correction	character. Used only in repeated measures ANOVA test to specify which correction of the degrees of freedom should be reported for the within-subject factors. Possible values are: "GG": applies Greenhouse-Geisser correction to all within-subjects factors even if the assumption of sphericity is met (i.e., Mauchly's test is not significant, p > 0.05). "HF": applies Hyunh-Feldt correction to all within-subjects factors even if the assumption of sphericity is met, "none": returns the ANOVA table without any correction and "auto": apply automatically GG correction to only within-subjects factors violating the sphericity assumption (i.e., Mauchly's test p-value is significant, p <= 0.05).
...	additional arguments

Details

The setting in anova_test() is done in such a way that it gives the same results as SPSS, one of the most used commercial software. By default, R uses treatment contrasts, where each of the levels is compared to the first level used as baseline. The default contrast can be checked using options('contrasts'). In the function anova_test(), the following setting is used options(contrasts=c('contr.sum','contr.poly')), which gives orthogonal contrasts where you compare every level to the overall mean. This setting gives the same output as the most commonly used commercial softwares, like SPSS. If you want to obtain the same result with the function car::Anova() as the one obtained with rstatix::anova_test(), then don't forget to set options(contrasts=c('contr.sum','contr.poly')).

Value

return an object of class anova_test a data frame containing the ANOVA table for independent measures ANOVA.

However, for repeated/mixed measures ANOVA, a list containing the following components are returned: ANOVA table, Mauchly's Test for Sphericity, Sphericity Corrections. These table are described more in the documentation of the function anova_summary().

The returned object has an attribute called args, which is a list holding the arguments used to fit the ANOVA model, including: data, dv, within, between, type, model, etc.

Functions

• anova_test(): perform anova test

• get_anova_table(): extract anova table from an object of class anova_test. When within-subject factors are present, either sphericity corrected or uncorrected degrees of freedom can be reported.

Author(s)

Alboukadel Kassambara, [e-mail]

See Also

anova_summary(), factorial_design()

Examples

# Load data
#:::::::::::::::::::::::::::::::::::::::
data("ToothGrowth")
df <- ToothGrowth

# One-way ANOVA test
#:::::::::::::::::::::::::::::::::::::::::
df %>% anova_test(len ~ dose)

# Grouped One-way ANOVA test
#:::::::::::::::::::::::::::::::::::::::::
df %>%
  group_by(supp) %>%
  anova_test(len ~ dose)

# Two-way ANOVA test
#:::::::::::::::::::::::::::::::::::::::::
df %>% anova_test(len ~ supp*dose)

# Two-way repeated measures ANOVA
#:::::::::::::::::::::::::::::::::::::::::
df$id <- rep(1:10, 6) # Add individuals id
# Use formula

df %>% anova_test(len ~ supp*dose + Error(id/(supp*dose)))



# or use character vector
df %>% anova_test(dv = len, wid = id, within = c(supp, dose))

# Extract ANOVA table and apply correction
#:::::::::::::::::::::::::::::::::::::::::
res.aov <- df %>% anova_test(dv = len, wid = id, within = c(supp, dose))
get_anova_table(res.aov, correction = "GG")


# Use model as arguments
#:::::::::::::::::::::::::::::::::::::::::
.my.model <- lm(yield ~ block + N*P*K, npk)
anova_test(.my.model)

rstatix documentation built on Feb. 16, 2023, 6:10 p.m.