I think something like table(Preference, Sex, data=table) will get you
started. With 3+ variables, you are probably looking for a MCA analysis
or simple CA using the stacked approach.
Your SAS table statement,
table Preference, Sex Age Time;
treats Preference vs. all combinations of Sex, Age & Time. This
corresponds to a loglinear model asserting Preference is jointly
independent of the other three.
See the vignette for the vcdExtra package for this kind of thing more
generally.
install.packages("vcdExtra")
browseVignettes("vcdExtra")
See my book, Discrete Data Analysis with R, http://ddar.datavis.ca/
best,
-Michael
On 3/29/2019 9:35 AM, Alfredo wrote:
Hi, I am very new to r and need help from you to do a correspondence
analysis because I don't know how to structure the following data:
Thank you.
Alfredo
library(ca,lib.loc=folder)
table <- read.csv(file="C:\\Temp\\Survey_Data.csv", header=TRUE, sep=",")
head (table, n=20)
Preference Sex Age Time
1 News/Info/Talk M 25-30 06-09
2 Classical F >35 09-12
3 Rock and Top 40 F 21-25 12-13
4 Jazz M >35 13-16
5 News/Info/Talk F 25-30 16-18
6 Don't listen F 30-35 18-20
...
19 Rock and Top 40 M 25-30 16-18
20 Easy Listening F >35 18-20
In SAS I would simply do this:
proc corresp data=table dim=2 outc=_coord;
table Preference, Sex Age Time;
run;
I don't know how convert in R a data frame to a frequency table to execute
properly this function:
ca <- ca(<frequency table>, graph=FALSE)
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--
Michael Friendly Email: friendly AT yorku DOT ca
Professor, Psychology Dept. & Chair, ASA Statistical Graphics Section
York University Voice: 416 736-2100 x66249 Fax: 416 736-5814
4700 Keele Street Web: http://www.datavis.ca | @datavisFriendly
Toronto, ONT M3J 1P3 CANADA
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