To add support for you model, create a new .anova_es()
method function. This functions should generally do 3 things:
The input data frame must have these columns: -
Parameter
(char) - The name of the parameter or, more
often, the term. - Sum_Squares
(num) - The sum of squares.
- df
(num) - The degrees of freedom associated with the
Sum_Squares
. - Mean_Square_residuals
(num;
optional) - if not present, is calculated as
Sum_Squares / df
. (Any other column is ignored.)
And exactly 1 row Where Parameter
is
Residual
.
Optionally, one of the rows can have a (Intercept)
value
for Parameter
.
An example of a minimally valid data frame:
min_aov <- data.frame(
Parameter = c("(Intercept)", "A", "B", "Residuals"),
Sum_Squares = c(30, 40, 10, 100),
df = c(1, 1, 2, 50)
)
Pass the data frame to .es_aov_simple()
:
.es_aov_simple(
min_aov,
type = "eta", partial = TRUE, generalized = FALSE,
include_intercept = FALSE,
ci = 0.95, alternative = "greater",
verbose = TRUE
)
> Parameter Eta2_partial CI CI_low CI_high
> 1 A 0.286 0.95 0.12 1
> 2 B 0.091 0.95 0.00 1
The output is a data frame with the columns: Parameter
,
the effect size, and (optionally) CI
+ CI_low
+ CI_high
,
And with the following attributes: generalized
,
ci
, alternative
, anova_type
(NA
or NULL
), approximate
.
You can then set the anova_type
attribute to {1, 2, 3,
or NA
} and return the output.
(e.g., aovlist
models.)
The input data frame must have these columns:
Group
(char) - The strataParameter
(char)Sum_Squares
(num)df
(num)Mean_Square_residuals
(num; optional)And exactly 1 row per
Group
Where Parameter
is
Residual
.
Optionally, one of the rows can have a (Intercept)
value
for Parameter
.
An example of a minimally valid data frame:
min_aovlist <- data.frame(
Group = c("S", "S", "S:A", "S:A"),
Parameter = c("(Intercept)", "Residuals", "A", "Residuals"),
Sum_Squares = c(34, 21, 34, 400),
df = c(1, 12, 4, 30)
)
Pass the data frame to .es_aov_strata()
, along with a
list of predictors (including the stratifying variables) to the
DV_names
argument:
.es_aov_strata(
min_aovlist,
DV_names = c("S", "A"),
type = "omega", partial = TRUE, generalized = FALSE,
ci = 0.95, alternative = "greater",
verbose = TRUE,
include_intercept = TRUE
)
> Group Parameter Omega2_partial CI CI_low CI_high
> 1 S (Intercept) 0.57 0.95 0.21 1
> 2 S:A A 0.00 0.95 0.00 1
The output is a data frame with the columns: Group
,
Parameter
, the effect size, and (optionally)
CI
+ CI_low
+ CI_high
,
And with the following attributes: generalized
,
ci
, alternative
, approximate
.
You can then set the anova_type
attribute to {1, 2, 3,
or NA
} and return the output.
When sums of squares cannot be extracted, we can still get
approximate effect sizes based on the F_to_eta2()
family of functions.
The input data frame must have these columns:
Parameter
(char)F
(num) - The F test statistic.df
(num) - effect degrees of freedom.t
col instead, in which case
df
is set to 1, and F
is
t^2
).df_error
(num) - error degrees of freedom.Optionally, one of the rows can have (Intercept)
as the
Parameter
.
An example of a minimally valid data frame:
min_anova <- data.frame(
Parameter = c("(Intercept)", "A", "B"),
F = c(4, 7, 0.7),
df = c(1, 1, 2),
df_error = 34
)
Pass the table to .es_aov_table()
:
.es_aov_table(
min_anova,
type = "eta", partial = TRUE, generalized = FALSE,
include_intercept = FALSE,
ci = 0.95, alternative = "greater",
verbose = TRUE
)
> Parameter Eta2_partial CI CI_low CI_high
> 1 A 0.17 0.95 0.023 1
> 2 B 0.04 0.95 0.000 1
The output is a data frame with the columns: Parameter
,
the effect size, and (optionally) CI
+ CI_low
+ CI_high
,
And with the following attributes: generalized
,
ci
, alternative
, approximate
.
You can then set the anova_type
attribute to {1, 2, 3,
or NA
} and return the output, and optionally the
approximate
attribute, and return the output.
Let’s fit a simple linear model and change its class:
We now need a new .anova_es.superMODEL
function:
.anova_es.superMODEL <- function(model, ...) {
# Get ANOVA table
anov <- suppressWarnings(stats:::anova.lm(model))
anov <- as.data.frame(anov)
# Clean up
anov[["Parameter"]] <- rownames(anov)
colnames(anov)[2:1] <- c("Sum_Squares", "df")
# Pass
out <- .es_aov_simple(anov, ...)
# Set attribute
attr(out, "anova_type") <- 1
out
}
And… that’s it! Our new superMODEL
class of models is
fully supported!
> # Effect Size for ANOVA (Type I)
>
> Parameter | η² (partial) | 95% CI
> -----------------------------------------
> factor(cyl) | 0.76 | [0.61, 1.00]
> am | 0.12 | [0.00, 1.00]
>
> - One-sided CIs: upper bound fixed at [1.00].
> # Effect Size for ANOVA (Type I)
>
> Parameter | η² | 95% CI
> ---------------------------------
> factor(cyl) | 0.73 | [0.57, 1.00]
> am | 0.03 | [0.00, 1.00]
>
> - One-sided CIs: upper bound fixed at [1.00].
> # Effect Size for ANOVA (Type I)
>
> Parameter | ω² (partial) | 95% CI
> -----------------------------------------
> factor(cyl) | 0.73 | [0.56, 1.00]
> am | 0.08 | [0.00, 1.00]
>
> - One-sided CIs: upper bound fixed at [1.00].