Dear Adam,
(1) Note that your input correlation matrix appears to be numerically
singular:
solve(R)
Error in solve.default(R) :
system is computationally singular: reciprocal condition number =
2.38183e-17
det(R)
[1] -1.753523e-25
qr(R)$rank
[1] 23
(2) In addition, you have specified what are essentially redundant
constraints on the model, fixing *both* the loading for a "reference"
indicator for each factor to 1 *and* fixing the factor variances to 1. One
would normally do one or the other.
I think that (1) rather than (2) is the essential source of the problem, and
fixing (2) doesn't make the problem go away.
Because sem() can't compute the covariance matrix of the estimated
parameters, this component is missing from the returned object, causing the
cryptic error message in summary.sem(). I'll provide a more informative
error or warning.
I hope this helps,
John
------------------------------
John Fox, Professor
Department of Sociology
McMaster University
Hamilton, Ontario, Canada
web: socserv.mcmaster.ca/jfox
-----Original Message-----
From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED]
On
Behalf Of Adam D. I. Kramer
Sent: September-18-08 1:37 PM
To: r-help@r-project.org
Subject: Re: [R] Difficulty understanding sem errors / failed confirmatory
factor analysis
No new info, but the model and correlation table are pasted at the end of
this message.
--Adam
On Thu, 18 Sep 2008, Adam D. I. Kramer wrote:
Hello,
I'm trying to fit a pretty simple confirmatory factor analysis using
the sem package. There's a CFA example in the examples, which is
helpful,
but the output for my (failing) model is hard to understand. I'd be
interested in any other ways to do a CFA in R, if this proves
troublesome.
The CFA is replicating a 5 uncorrelated-factor structure (for those
interested, it is a structure of word usage patterns in weblogs) in a
special population. The model looks like model.txt (attached as many
people
hate long emails); the correlation matrix cors.txt as well.
I'm setting no overlap between factors, no correlation between
factors, and estimating a separate variance for each observed variable
(which should be everything on the right-hand side of the -> arrows),
but
setting the factor variances equal to 1...pretty standard. I've ensured
that
everything is typed correctly to the best I am able.
The problem:
library(sem)
model.kr <- specify.model(file="model.txt") # printing it checks out ok
correl <- read.csv("cors.csv", header=TRUE) # printing it checks out ok
kr.sem <- sem(ram=model.kr,S=correl,N=3034)
...about 10 seconds pass...
Warning message:
In sem.default(ram = ram, S = S, N = N, param.names = pars, var.names =
vars,
:
Could not compute QR decomposition of Hessian.
Optimization probably did not converge.
(running qr on correl works fine; randomly-generated correl matrices
fail
in
the same way; I do not know how to further troubleshoot this)
...and then the model itself (which is produced, as the above was just a
warning):
summary(kr.sem)
Error in data.frame(object$coeff, se, z, 2 * (1 - pnorm(abs(z))),
par.code)
:
arguments imply differing number of rows: 47, 0
...both of these error messages are beyond my ability to troubleshoot.
Any
help would be greatly appreciated. Because I am unsure what exactly the
problem with this analysis is, I can't create a simpler example for
testing
purposes...but I think my model and correlation matrix are fairly
simple.
unlist(R.Version())
platform arch
"x86_64-unknown-linux-gnu" "x86_64"
os system
"linux-gnu" "x86_64, linux-gnu"
status major
"" "2"
minor year
"7.2" "2008"
month day
"08" "25"
svn rev language
"46428" "R"
version.string "R version 2.7.2 (2008-08-25)"
...sem installed via install.packages("sem") which I assume is current.
Cordially,
Adam Kramer
model.kr <- specify.model()
Melancholy -> Affect, mel.aff, NA
Melancholy -> Negemo, mel.neg, NA
Melancholy -> Sad, mel.sad, NA
Melancholy -> Physcal, mel.phys, NA
Melancholy -> Body, mel.phys, NA
Melancholy -> Eating, mel.eat, NA
Melancholy -> Groom, NA, 1
Social -> numlines, soc.nrow, NA
Social -> Leisure, soc.leis, NA
Social -> Home, soc.home, NA
Social -> Sports, soc.sports, NA
Social -> TV, soc.tv, NA
Social -> Music, soc.mus, NA
Social -> Money, NA, 1
Rant -> Swear, rant.swear, NA
Rant -> Sexual, rant.sex, NA
Rant -> Anger, rant.anger, NA
Rant -> I, NA, 1
Metaphysical -> Metaph, met.met, NA
Metaphysical -> Relig, met.relig, NA
Metaphysical -> Death, NA, 1
Work -> Occup, work.occ, NA
Work -> School, work.school, NA
Work -> Job, NA, 1
Affect <-> Affect,Affect.var,NA
Negemo <-> Negemo,Negemo.var,NA
Sad <-> Sad,Sad.var,NA
Physcal <-> Physcal,Physcal.var,NA
Body <-> Body,Body.var,NA
Eating <-> Eating,Eating.var,NA
Groom <-> Groom,Groom.var,NA
numlines <-> numlines,numlines.var,NA
Leisure <-> Leisure,Leisure.var,NA
Home <-> Home,Home.var,NA
Sports <-> Sports,Sports.var,NA
TV <-> TV,TV.var,NA
Music <-> Music,Music.var,NA
Money <-> Money,Money.var,NA
Swear <-> Swear,Swear.var,NA
Sexual <-> Sexual,Sexual.var,NA
Anger <-> Anger,Anger.var,NA
I <-> I,I.var,NA
Metaph <-> Metaph,Metaph.var,NA
Relig <-> Relig,Relig.var,NA
Death <-> Death,Death.var,NA
Occup <-> Occup,Occup.var,NA
School <-> School,School.var,NA
Job <-> Job,Job.var,NA
Melancholy <-> Melancholy, NA, 1
Social <-> Social, NA, 1
Rant <-> Rant, NA, 1
Work <-> Work, NA, 1
Metaphysical <-> Metaphysical, NA, 1
correl <- matrix(0,nrow=24,ncol=24)
correl[lower.tri(correl,diag=TRUE)] <- c(1, 0.940530496413442,
0.765560263936915, 0.705665939921134,
0.659038546655712, 0.282665099938120, 0.234892888297051,
0.0554321360979252,
0.671137592040541, 0.54382418910777, 0.463922205203901, 0.353418190097785,
0.414864918025334, 0.436177075274485, 0.401019650838241,
0.370116202378091,
0.777297151879925, 0.676782523496444, 0.384244495774914,
0.233569710080454,
0.381213315694389, 0.792870007525815, 0.50437548997674, 0.700522809676332,
1, 0.789549528191674, 0.706300687178796, 0.677210560231246,
0.260692661416488, 0.229468929161093, 0.0715008916402315,
0.575592411881254,
0.495811001574585, 0.387005398515213, 0.286945225104112,
0.332994303548624,
0.380017238311269, 0.430036391324925, 0.331754269605511,
0.841233992941869,
0.648407420058087, 0.383101710409617, 0.226088422466275, 0.38890278815819,
0.694900967907394, 0.445767105915179, 0.611121179516979, 1,
0.554261042721096, 0.527433237517158, 0.195884095023418,
0.180722683807294,
0.0233909540224985, 0.516740406669213, 0.435018065577118,
0.357120136043065,
0.256978759119895, 0.310082859183584, 0.346636599600033,
0.286753730820772,
0.260480159024979, 0.549021337624957, 0.48888735524692, 0.310472252813801,
0.162629025914762, 0.341951482059891, 0.604151525748714,
0.404455472396666,
0.513232045563814, 1, 0.926617967581845, 0.496550925591883,
0.348467198716147, 0.061642221232731, 0.520273451850585,
0.454688691566968,
0.361179853804786, 0.251729488407341, 0.276070295108034,
0.301418864648534,
0.416591776932984, 0.444945330539674, 0.559869838931842,
0.544386905545782,
0.27126759338533, 0.133247574277541, 0.310271891666118, 0.521537483862256,
0.311771883417551, 0.471080247067523, 1, 0.296375815811106,
0.245934996387214, 0.0955552805445385, 0.452337692426552,
0.363463874290988,
0.339558959480213, 0.244564602900861, 0.253767733666974,
0.271411427718931,
0.391479465009918, 0.232142608383647, 0.518262010456117,
0.491601506679358,
0.267644288407907, 0.125392653581526, 0.314025855170014,
0.481718318983948,
0.28021913467524, 0.425176692670869, 1, 0.183300375385584,
0.033752849675936, 0.251307861560496, 0.273349357055731,
0.154752778367666,
0.0726227557418435, 0.0810855443617927, 0.169413646643786,
0.14876182751722,
0.090027807464796, 0.21707073520028, 0.227245091073637,
0.0648985279708279,
0.0184155556622148, 0.0917329040389644, 0.224131495587368,
0.141524409848304, 0.225250463174961, 1, 0.0422205901067624,
0.335813173017425, 0.462314781501668, 0.137068724813020,
0.0723839601971734,
0.0727494211937804, 0.112428911506304, 0.09425456138948,
0.0599407261374292,
0.190826379314435, 0.211704707365072, 0.0599306146160989,
0.0296829331196419, 0.0682294826412284, 0.188274984783352,
0.139422045025596, 0.161353066485886, 1, 0.05257676892534,
0.0280480438748768, 0.00910257508927212, 0.0871857793285287,
0.0160729045542020, 0.0569498365312614, -0.0278792722216208,
-0.064821779008319, 0.0611534438874241, -0.0649834249949666,
0.0355509895650055, 0.0135445926170511, 0.0457566918082741,
0.0604365669560489, 0.0585041719421536, 0.0453745903935696, 1,
0.761816109289408, 0.589874357357904, 0.649504824987434,
0.658362125608313,
0.342115375404423, 0.236861363583780, 0.248095510733153,
0.443279264097818,
0.492471838105872, 0.248701650770021, 0.128552959879838,
0.276153894635528,
0.607556471316203, 0.449181155519957, 0.510375756616414, 1,
0.247939328658872, 0.239982254184636, 0.241566313660839,
0.287799155446014,
0.209294226000604, 0.159562148116037, 0.379361578341484,
0.497577130930334,
0.218564559998989, 0.09910467906514, 0.260723180575602, 0.467924257159533,
0.326606718662009, 0.418920180615036, 1, 0.279500783890266,
0.315054486693380, 0.222378322027149, 0.175009967321649,
0.171392982263514,
0.303685681481775, 0.277189930927861, 0.112262899972587,
0.0486869353393796,
0.136799339182926, 0.439080141905746, 0.335750129078362,
0.357071201278175,
1, 0.354042555500904, 0.186671191628747, 0.0988194936238798,
0.161389983251376, 0.225124570709853, 0.166070213591245,
0.121050680740651,
0.0555233084604175, 0.143574719627254, 0.314332705080321,
0.233696321192899,
0.250453706966593, 1, 0.194173439107269, 0.128637091334456,
0.188288180271912, 0.249261729284129, 0.294002924006152,
0.180881281305379,
0.130825345306321, 0.152316871694807, 0.400492580214257,
0.313557675560858,
0.314387144865789, 1, 0.184856884327958, 0.147800700051061,
0.314348653487276, 0.223052727856440, 0.160836428547767,
0.0788465159084819,
0.184166318589758, 0.464779622013576, 0.296714228371417,
0.509475424226691,
1, 0.243783177939616, 0.433294149172899,
0.385435708448960,0.165204287365281, 0.122878967887846, 0.134705298271121,
0.292692353692189,
0.175825474932413, 0.302111138660379, 1, 0.325702026715680,
0.265660847381981, 0.144919463251553, 0.105698215649586,
0.120886293524908,
0.266994210674339, 0.158210355462592, 0.256685716586081, 1,
0.50128293661388, 0.376612773737787, 0.200216055489841, 0.410973414330935,
0.544902004754666, 0.340576879662518, 0.495895604908182, 1,
0.210485715826384, 0.162841078754691, 0.163460255178148,
0.530490890488822,
0.327408998238198, 0.501112169084101, 1, 0.834421294834807,
0.697570273879324, 0.284190669063073, 0.165736914780320,
0.243184491397893,
1, 0.187175885786238, 0.178409162228837, 0.107264487137595,
0.157214632976541, 1, 0.274591668601347, 0.155954924109372,
0.229057302047291, 1, 0.824656000910955, 0.82923430112462, 1,
0.476782357161847, 1)
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
