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data.frame error using sem package
2 messages · Suzanne Witt, John Fox
Dear Suzanne, Take a look at your model specification:
p.ram
[,1] [,2] [,3] [1,] "LM1 -> LSMA" "LM1 -> LSMA" NA [2,] "LSMA -> RSMA" "LSMA -> RSMA" NA [3,] "RSMA -> RM1" "RSMA -> RM1" NA [4,] "LSMA <-> LSMA" "LSMA <-> LSMA" NA [5,] "RSMA <-> RSMA" "RSMA <-> RSMA" NA [6,] "RM1 <-> RM1" "RM1 <-> RM1" NA
This matrix should have three columns, the first giving the path, the second the name of the corresponding parameter, and the third the start value for the parameter (or NA if you want sem() to compute a start value). You've apparently left out the parameter names. Please see the sem examples for details and the paper at <http://socserv.socsci.mcmaster.ca/jfox/sem-package.pdf>. I hope this helps, John -------------------------------- John Fox Department of Sociology McMaster University Hamilton, Ontario Canada L8S 4M4 905-525-9140x23604 http://socserv.mcmaster.ca/jfox --------------------------------
-----Original Message-----
From: r-help-bounces at stat.math.ethz.ch
[mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Suzanne Witt
Sent: Thursday, October 06, 2005 2:55 PM
To: r-help at stat.math.ethz.ch
Subject: [R] data.frame error using sem package
I am trying to use sem to measure effective connectivity
among four brain regions. I have pasted the code that I am
trying to run since that seems easier than trying to come up
with another example.
The input data is time series data taken from SPM; they are each
1x121 columns of numbers. I get the error either when I
source the whole code, or if I enter it line by line when I
go to get the summary.
Thanks,
Suzanne
library(sem)
# Load the region timecourses.
lsma1 <- read.table("/Users/witt/parkinsons/rmrkm010905/R_files/
010905_lcomf_LSMA.dat")
rsma1 <- read.table("/Users/witt/parkinsons/rmrkm010905/R_files/
010905_lcomf_RSMA.dat")
lmc1 <- read.table("/Users/witt/parkinsons/rmrkm010905/R_files/
010905_lcomf_LM1.dat")
rmc1 <- read.table("/Users/witt/parkinsons/rmrkm010905/R_files/
010905_lcomf_RM1.dat")
# Combine all the timecourses from each session into a single
data frame and name the columns appropriately.
lcomf <- cbind(lsma1, rsma1, lmc1, rmc1)
names(lcomf) <- c("LSMA", "RSMA", "LM1", "RM1")
# Type this at the command line to see a summary of your data
str(lcomf)
# Set up the structural equation model.
p.ram <<- matrix(c( 'LM1 -> LSMA', 'LM1 -> LSMA', NA,
'LSMA -> RSMA', 'LSMA -> RSMA', NA,
'RSMA -> RM1', 'RSMA -> RM1', NA,
'LSMA <-> LSMA', 'LSMA <-> LSMA', NA,
'RSMA <-> RSMA', 'RSMA <-> RSMA', NA,
'RM1 <-> RM1', 'RM1 <-> RM1', NA),
ncol = 3, byrow = TRUE)
# Tell which variables are exogenous ('fixed').
p.fixed <- c('LM1')
# Do the fitting for session 1.
C <- cor(lcomf)
nt <- dim(lcomf)[1]
#attach(lcomf)
lcomf.results <- sem(p.ram, C, nt, obs.variables =
rownames(C), fixed.x = p.fixed)
# Check out the results using the summary function
summary(lcomf.results)
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