Hi.
Does anyone know whether the following error is a result of a bug or a feature? I can eliminate the error by making ML=F, but I would like to see the values of the cut-points and their variance. Is there anything that I can do? tmp.vec<-c(0, 0, 0 , 0 ,0 , 1, 0, 2, 0 , 0, 5 ,5 ,3 ,1, 0 , 1, 5, 10, 27, 20, 9, 0, 1, 1, 12, 29, 57, 34, 0, 0, 1, 2, 11, 31, 32) tmp.mat<-matrix(tmp.vec, nrow=7) rownames(tmp.mat)<-1:7 colnames(tmp.mat)<-3:7 tmp.pcc<-polychor(tmp.mat, ML=T, std.err=T) Error in checkmvArgs(lower = lower, upper = upper, mean = mean, corr = corr, : at least one element of ‘lower’ is larger than ‘upper’ Thanks, Janet -------------------- This email message is for the sole use of the intended recip...{{dropped}} ______________________________________________ [hidden email] 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. |
Dear Janet,
Performing a traceback after the error gives a hint: > tmp.pcc<-polychor(tmp.mat, ML=T, std.err=T) > traceback() 8: stop("at least one element of ", sQuote("lower"), " is larger than ", sQuote("upper")) 7: checkmvArgs(lower = lower, upper = upper, mean = mean, corr = corr, sigma = sigma) 6: pmvnorm(lower = c(row.cuts[i], col.cuts[j]), upper = c(row.cuts[i + 1], col.cuts[j + 1]), corr = R) 5: binBvn(rho, row.cuts, col.cuts) 4: fn(par, ...) 3: function (par) fn(par, ...)(c(0.422816748044139, -2.20343446037221, -2.2163627792244, -1.79075055316993, -1.11077161663679, -0.323037731981826, 0.664036943094355, -2.71305188847272, -1.58338678422633, -0.534011853182102, 0.65365619155084 )) 2: optim(c(optimise(f, interval = c(-1, 1))$minimum, rc, cc), f, control = control, hessian = std.err) 1: polychor(tmp.mat, ML = T, std.err = T) The parameters are in the order correlation, row thresholds (of which there are 6), column thresholds (of which there are 4), and at this point in the maximization of the likelihood the first row threshold (-2.20343446037221) is *above* the second threshold (-2.2163627792244), causing pmvnorm() to complain. This can happen when the problem is ill-conditioned, since optim() doesn't constrain the order of the thresholds. So, why is the problem ill-conditioned? Here is your contingency table: > tmp.mat 3 4 5 6 7 1 0 2 0 0 0 2 0 0 1 1 0 3 0 0 5 1 1 4 0 5 10 12 2 5 0 5 27 29 11 6 1 3 20 57 31 7 0 1 9 34 32 There are only two observations in the first row, two in the second row, and one in the first column. You're expecting a lot out of ML to get estimates of the first couple of thresholds for rows and the first for columns. One approach would be to eliminate one or more sparse rows or columns; e.g., > polychor(tmp.mat[-1,], ML = TRUE, std.err = TRUE) Polychoric Correlation, ML est. = 0.3932 (0.05719) Test of bivariate normality: Chisquare = 14.21, df = 19, p = 0.7712 Row Thresholds Threshold Std.Err. 1 -2.4410 0.24270 2 -1.8730 0.14280 3 -1.1440 0.09236 4 -0.3353 0.07408 5 0.6636 0.07857 Column Thresholds Threshold Std.Err. 1 -2.6500 0.30910 2 -1.6420 0.12100 3 -0.5494 0.07672 4 0.6508 0.07833 > polychor(tmp.mat[,-1], ML = TRUE, std.err = TRUE) Polychoric Correlation, ML est. = 0.4364 (0.05504) Test of bivariate normality: Chisquare = 14.85, df = 17, p = 0.6062 Row Thresholds Threshold Std.Err. 1 -2.4940 0.26020 2 -2.2080 0.19160 3 -1.7850 0.13400 4 -1.1090 0.09113 5 -0.3154 0.07371 6 0.6625 0.07821 Column Thresholds Threshold Std.Err. 1 -1.6160 0.11970 2 -0.5341 0.07639 3 0.6507 0.07800 > A more defensible alternative would be to collapse sparse rows or columns. BTW, you *can* get estimated standard-errors from polychor() for your original table for the two-step estimator: > polychor(tmp.mat, ML = FALSE, std.err = TRUE) Polychoric Correlation, 2-step est. = 0.4228 (0.05298) Test of bivariate normality: Chisquare = 19.22, df = 23, p = 0.6883 That's because the two-step estimator estimates the thresholds from the marginal distributions of the variables rather than from their joint distribution. So, is this a bug or a feature? I suppose that it's a bug to allow the thresholds to get out of order, though to constrain the optimization to prevent that from happening is probably not worth the effort and could cause some strange results. On the other hand, the error tells you something about the data, so maybe it's a feature. I noticed that you posted another version of this question two days before this one. I apologize for the slow response -- I wasn't able to read my email for a few days and it's taken me most of today to catch up. Regards, John ---- original message ------- Janet Rosenbaum jrosenba at rand.org Thu Aug 24 00:41:16 CEST 2006 Hi. Does anyone know whether the following error is a result of a bug or a feature? I can eliminate the error by making ML=F, but I would like to see the values of the cut-points and their variance. Is there anything that I can do? tmp.vec<-c(0, 0, 0 , 0 ,0 , 1, 0, 2, 0 , 0, 5 ,5 ,3 ,1, 0 , 1, 5, 10, 27, 20, 9, 0, 1, 1, 12, 29, 57, 34, 0, 0, 1, 2, 11, 31, 32) tmp.mat<-matrix(tmp.vec, nrow=7) rownames(tmp.mat)<-1:7 colnames(tmp.mat)<-3:7 tmp.pcc<-polychor(tmp.mat, ML=T, std.err=T) Error in checkmvArgs(lower = lower, upper = upper, mean = mean, corr = corr, : at least one element of 'lower' is larger than 'upper' Thanks, Janet -------------------------------- John Fox Department of Sociology McMaster University Hamilton, Ontario Canada L8S 4M4 905-525-9140x23604 http://socserv.mcmaster.ca/jfox ______________________________________________ [hidden email] 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. |
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