Parameters from ANOVAs
# S3 method for aov
model_parameters(
model,
omega_squared = NULL,
eta_squared = NULL,
epsilon_squared = NULL,
df_error = NULL,
type = NULL,
ci = NULL,
test = NULL,
power = FALSE,
keep = NULL,
drop = NULL,
parameters = keep,
table_wide = FALSE,
verbose = TRUE,
...
)
model  Object of class 

omega_squared  Compute omega squared as index of effect size. Can be

eta_squared  Compute eta squared as index of effect size. Can be

epsilon_squared  Compute epsilon squared as index of effect size. Can
be 
df_error  Denominator degrees of freedom (or degrees of freedom of the
error estimate, i.e., the residuals). This is used to compute effect sizes
for ANOVAtables from mixed models. See 'Examples'. (Ignored for

type  Numeric, type of sums of squares. May be 1, 2 or 3. If 2 or 3,
ANOVAtables using 
ci  Confidence Interval (CI) level for effect sizes

test  String, indicating the type of test for 
power  Logical, if 
keep  Character containing a regular expression pattern that
describes the parameters that should be included in the returned data frame
(for 
drop  Character containing a regular expression pattern that
describes the parameters that should be included in the returned data frame
(for 
parameters  Deprecated, alias for 
table_wide  Logical that decides whether the ANOVA table should be in
wide format, i.e. should the numerator and denominator degrees of freedom
be in the same row. Default: 
verbose  Toggle warnings and messages. 
...  Arguments passed to or from other methods. 
A data frame of indices related to the model's parameters.
For ANOVAtables from mixed models (i.e. anova(lmer())
), only
partial or adjusted effect sizes can be computed. Note that type 3 ANOVAs
with interactions involved only give sensible and informative results when
covariates are meancentred and factors are coded with orthogonal contrasts
(such as those produced by contr.sum
, contr.poly
, or
contr.helmert
, but not by the default contr.treatment
).
if (requireNamespace("effectsize", quietly = TRUE)) {
df < iris
df$Sepal.Big < ifelse(df$Sepal.Width >= 3, "Yes", "No")
model < aov(Sepal.Length ~ Sepal.Big, data = df)
model_parameters(
model,
omega_squared = "partial",
eta_squared = "partial",
epsilon_squared = "partial"
)
model_parameters(
model,
omega_squared = "partial",
eta_squared = "partial",
ci = .9
)
model < anova(lm(Sepal.Length ~ Sepal.Big, data = df))
model_parameters(model)
model_parameters(
model,
omega_squared = "partial",
eta_squared = "partial",
epsilon_squared = "partial"
)
model < aov(Sepal.Length ~ Sepal.Big + Error(Species), data = df)
model_parameters(model)
if (FALSE) {
if (require("lme4")) {
mm < lmer(Sepal.Length ~ Sepal.Big + Petal.Width + (1  Species),
data = df
)
model < anova(mm)
# simple parameters table
model_parameters(model)
# parameters table including effect sizes
model_parameters(
model,
eta_squared = "partial",
ci = .9,
df_error = dof_satterthwaite(mm)[2:3]
)
}
}
}