The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
All high-level functions use standard R model formulas:
response ~ factorA + factorB + factorC
+ lists the main effects.A:B or A*B.~) must be numeric (e.g., a
Likert score coded as 1 to 5 and stored as numeric).Examples below use the included dataset mimicry.
library(factorH)
data(mimicry, package = "factorH")
str(mimicry)Predictors should be factors. If they are not, the functions will coerce them to factors internally.
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_catYou do not need to write gender:condition or
gender*condition. The package constructs the required
interaction terms internally when needed.
The response must be numeric. For Likert-type responses (e.g., 1 = strongly disagree, …, 5 = strongly agree), keep the variable numeric. Rank-based procedures can be used with such ordinal-like data.
If a Likert variable has been imported as a factor or character, coerce it 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:0u34e45b057346-syntax.R
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.