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README.Rmd
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README.Rmd
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---
output:
md_document:
variant: gfm
---
<!-- README.md is generated from README.Rmd. Please edit that file
library(badger)
rmarkdown::render("README.rmd")
-->
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "README-"
)
version <- as.vector(read.dcf('DESCRIPTION')[, 'Version'])
version <- gsub('-', '.', version)
```
[](https://travis-ci.org/nicebread/RSA)
[)`-yellowgreen.svg)](/commits/master)
[](https://cran.r-project.org/package=RSA)
[](commits/master)
[](https://codecov.io/gh/nicebread/RSA)
[](https://archive.softwareheritage.org/swh:1:dir:dc97c04afda45644b8afed6571094713e4eccc5e;origin=https://github.com/nicebread/RSA;visit=swh:1:snp:8797aa5094b472ecdefca080784bc65b4b1a5a53;anchor=swh:1:rev:c92a7209cbcedca083ee50f4c8931af4ae0f135e)
RSA
===
An R package for Response Surface Analysis
## Installation
The stable CRAN version can be installed by:
install.packages("RSA")
Note that on macos, the use of X11 (including tcltk) requires [XQuartz](https://www.xquartz.org) to be installed manually since it is no longer part of OS X. Always re-install XQuartz when upgrading your macos to a new major version.
The current development version can be installed by:
install.packages(c("devtools", "lavaan", "plyr", "ggplot2", "lattice", "tkrplot", "RColorBrewer", "rgl", "gridExtra", "aplpack", "fields", "qgraph"))
devtools::install_github("nicebread/RSA")
## Questions? Go to our Google mailing list
* An email list for asking questions related to the RSA-package has been created at Google groups: https://groups.google.com/forum/?fromgroups&hl=en#!forum/rsa-in-r
## Demo script (with built-in data set)
# if not already done:
# install the RSA package
# (only has to be done once)
# install.packages("RSA")
# load the RSA package for
# the active session
library(RSA)
# open the help page
?RSA
## Motive congruency example
# load the built-in data set
data(motcon)
# Compute the RSA and save the result
# into the new variable r1
r1 <- RSA(postVA ~ ePow * iPow, data=motcon)
# Show summary of the RSA
print(r1)
# Compare all models
compare(r1, plot=TRUE)
aictab(r1, plot=TRUE)
# Show all RSA parameters with parametric
# SEs, p values, and CIs
getPar(r1, "coef", model="RR")
# Standard CIs
c1 <- confint(r1, model="RR")
c1
# Get bootstrapped confidence intervals
# (5000 bootstrap replications),
# only from the RR model
c2 <- confint(r1, model="RR", method="boot", R=5000)
c2
# Plot the final model
plot(r1, model="RR",
xlab="Explicit power motive",
ylab="Implicit power motive",
zlab="Affective valence")
## Additional functions
# contour plot
plot(r1, model="RR", type="contour")
# interactive, rotatable 3d plot
plot(r1, model="RR", type="interactive")
# open an interactive widget with control
# sliders for regression weights
demoRSA()
# Interactive plotting of polynomial surfaces
Not part of this package, but related: you can create polynomial surface plots with this [interactive app](http://shinyapps.org/showapp.php?app=https://tellmi.psy.lmu.de/felix/polySurface):
# Maintainers
[Felix Schönbrodt](https://www.nicebread.de/)
[Sarah Humberg](https://www.uni-muenster.de/PsyIFP/AEBack/members/Sarah_Humberg.html)