All high-level functions use standard R model
formulas:
response ~ factorA + factorB + factorC
not
need to write A:B or A*B.response
(left of ~) must be numeric
(e.g., a Likert score coded as 1..5 stored as numeric).Examples below use the included dataset mimicry.
library(factorH)
data(mimicry, package = "factorH")
str(mimicry)
Predictors should be factors. If not, functions will coerce them.
What is allowed?
# One factor (KW-style):
liking ~ condition
# Two factors (SRH-style):
liking ~ gender + condition
# Three or more factors (k-way):
liking ~ gender + condition + age_cat
You do not
need to write gender:condition or
gender*condition. The package will build all needed interactions
internally when relevant.
The response must be numeric
. For Likert-type items
(e.g., 1 = strongly disagree … 5 = strongly agree), keep them numeric;
rank-based tests are robust for such ordinal-like
data.
If your Likert is accidentally a factor
or
character
, coerce safely:
# if stored as character "1","2",...:
mimicry$liking <- as.numeric(mimicry$liking)
# if stored as factor with labels "1","2",...:
mimicry$liking <- as.numeric(as.character(mimicry$liking))
C:8WGoPl2c1c40c845ef-syntax.R