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. ______________________________________________ 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.
