Dear Adam, > -----Original Message----- > From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On > Behalf Of Adam D. I. Kramer > Sent: September-18-08 2:33 PM > To: John Fox > Cc: r-help@r-project.org > Subject: Re: [R] Difficulty understanding sem errors / failed confirmatory > factor analysis > > Hi John, > > Thanks very much for your response. It does appear now that my > correlation matrix and not your software is the problem. I apologize for > wasting your time! I do think that a more informative error message may have > prompted me to consider this possibility more fully.
Agreed. I've put a more informative error message into summary.sem() in the development version of the package on R-Forge. I think that I'll also add a check for singularity of the input covariance matrix. Regards, John > > --Adam > > On Thu, 18 Sep 2008, John Fox wrote: > > > 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. ______________________________________________ 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.
