Hi Michael et al,
I solved by myself simply running the code below.
Thanks anyway for the answers
Alfredo
t <- read.csv(file="C:\\Temp\\radio_survey.csv", header=TRUE, sep=",")
t1 <- table(t$Preference, t$Sex)
t2 <- table(t$Preference, t$Age)
t3 <- table(t$Preference, t$Time)
ct
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 &
Hi Alfredo,
I have not used SAS nor done a correspondence analysis in many years
but to give R-help readers an idea of what you are doing, we probably
need a short statement of the substantive problem that would lead to
the SAS program:
proc corresp data=table dim=2 outc=_coord;
table Preferen
I am not familiar with SAS, so what did you want your output to look like.
There is the 'table' function that might do the job and then there is
always 'dplyr' which can do the hard stuff. So we need more information on
what you want.
Jim Holtman
*Data Munger Guru*
*What is the problem that you
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)
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