There appears to be a problem in both regressions, as a singularity is also reported in the second regression analysis as well. It appears that the litrate variable is considered a factor in the first analysis and continuous in the second. There also appears to be collinearity between the litrate variable and the Africa variable. Look at the package lm.influence for regression diagnostics.
asdir <dirkroettg...@gmail.com> Sent by: r-help-boun...@r-project.org 08/12/2010 10:35 AM To r-help@r-project.org cc Subject [R] Regression Error: Otherwise good variable causes singularity. Why? This command cdmoutcome<- glm(log(value)~factor(year) > +log(gdppcpppconst)+log(gdppcpppconstAII) > +log(co2eemisspc)+log(co2eemisspcAII) > +log(dist) > +fdiboth > +odapartnertohost > +corrupt > +log(infraindex) > +litrate > +africa > +imr > , data=cdmdata2, subset=zero==1, gaussian(link = > "identity")) results in this table Coefficients: (1 not defined because of singularities) > Estimate Std. Error t value Pr(>|t|) > (Intercept) 1.216e+01 5.771e+01 0.211 0.8332 > factor(year)2006 -1.403e+00 5.777e-01 -2.429 0.0157 * > factor(year)2007 -2.799e-01 7.901e-01 -0.354 0.7234 > log(gdppcpppconst) 2.762e-01 5.517e+00 0.050 0.9601 > log(gdppcpppconstAII) -1.344e-01 9.025e-01 -0.149 0.8817 > log(co2eemisspc) 5.655e+00 2.903e+00 1.948 0.0523 . > log(co2eemisspcAII) -1.411e-01 4.245e-01 -0.332 0.7399 > log(dist) -2.938e-01 4.023e-01 -0.730 0.4658 > fdiboth 1.326e-04 1.133e-04 1.171 0.2425 > odapartnertohost 2.319e-03 1.437e-03 1.613 0.1078 > corrupt 1.875e+00 3.313e+00 0.566 0.5718 > log(infraindex) 4.783e+00 1.091e+01 0.438 0.6615 > litrate0.47 -2.485e+01 3.190e+01 -0.779 0.4365 > litrate0.499 -1.657e+01 2.591e+01 -0.639 0.5230 > litrate0.523 -2.440e+01 3.427e+01 -0.712 0.4769 > litrate0.528 -9.184e+00 1.379e+01 -0.666 0.5060 > litrate0.595 -2.309e+01 2.776e+01 -0.832 0.4062 > litrate0.66 -1.451e+01 2.734e+01 -0.531 0.5961 > litrate0.675 -1.707e+01 2.813e+01 -0.607 0.5444 > litrate0.68 -6.346e+00 1.063e+01 -0.597 0.5509 > litrate0.699 2.717e+00 3.541e+00 0.768 0.4434 > litrate0.706 -1.960e+01 2.933e+01 -0.668 0.5046 > litrate0.714 -2.586e+01 4.002e+01 -0.646 0.5186 > litrate0.736 5.641e+00 1.561e+01 0.361 0.7181 > litrate0.743 -2.692e+01 4.253e+01 -0.633 0.5273 > litrate0.762 -2.208e+01 3.100e+01 -0.712 0.4767 > litrate0.802 -2.325e+01 3.766e+01 -0.617 0.5375 > litrate0.847 -2.620e+01 3.948e+01 -0.664 0.5075 > litrate0.86 -3.576e+01 4.950e+01 -0.722 0.4707 > litrate0.864 -4.482e+01 6.274e+01 -0.714 0.4755 > litrate0.872 -1.946e+01 2.715e+01 -0.717 0.4739 > litrate0.877 -2.710e+01 3.702e+01 -0.732 0.4646 > litrate0.879 -3.460e+01 5.147e+01 -0.672 0.5020 > litrate0.886 -3.276e+01 4.860e+01 -0.674 0.5008 > litrate0.889 -4.120e+01 5.755e+01 -0.716 0.4746 > litrate0.904 -2.282e+01 2.985e+01 -0.764 0.4453 > litrate0.91 -3.478e+01 5.037e+01 -0.691 0.4904 > litrate0.923 -1.762e+01 2.551e+01 -0.691 0.4902 > litrate0.925 -2.445e+01 3.611e+01 -0.677 0.4990 > litrate0.926 -2.995e+01 4.565e+01 -0.656 0.5123 > litrate0.928 -2.839e+01 3.933e+01 -0.722 0.4710 > litrate0.937 -2.571e+01 3.795e+01 -0.677 0.4986 > litrate0.94 -2.109e+01 3.051e+01 -0.691 0.4900 > litrate0.959 -2.078e+01 2.895e+01 -0.718 0.4735 > litrate0.96 -3.403e+01 4.798e+01 -0.709 0.4787 > litrate0.962 -4.084e+01 5.755e+01 -0.710 0.4785 > litrate0.971 -3.743e+01 5.247e+01 -0.713 0.4761 > litrate0.98 -3.709e+01 5.170e+01 -0.717 0.4737 > litrate0.986 -2.663e+01 4.437e+01 -0.600 0.5488 > litrate0.991 -3.045e+01 4.166e+01 -0.731 0.4654 > litrate1 -2.732e+01 4.459e+01 -0.613 0.5405 > africa NA NA NA NA > imr 2.160e+00 9.357e-01 2.309 0.0216 * although it should result in something similar to this: Coefficients: (1 not defined because of singularities) > Estimate Std. Error t value Pr(>|t|) > (Intercept) 1.216e+01 5.771e+01 0.211 0.8332 > factor(year)2006 -1.403e+00 5.777e-01 -2.429 0.0157 * > factor(year)2007 -2.799e-01 7.901e-01 -0.354 0.7234 > log(gdppcpppconst) 2.762e-01 5.517e+00 0.050 0.9601 > log(gdppcpppconstAII) -1.344e-01 9.025e-01 -0.149 0.8817 > log(co2eemisspc) 5.655e+00 2.903e+00 1.948 0.0523 . > log(co2eemisspcAII) -1.411e-01 4.245e-01 -0.332 0.7399 > log(dist) -2.938e-01 4.023e-01 -0.730 0.4658 > fdiboth 1.326e-04 1.133e-04 1.171 0.2425 > odapartnertohost 2.319e-03 1.437e-03 1.613 0.1078 > corrupt 1.875e+00 3.313e+00 0.566 0.5718 > log(infraindex) 4.783e+00 1.091e+01 0.438 0.6615 > litrate -2.485e+01 3.190e+01 -0.779 0.4365 > africa -2.732e+01 4.459e+01 -0.613 0.5405 > imr 2.160e+00 9.357e-01 2.309 0.0216 * In fact, if I don't use the litrate variable, the regression runs just fine. If I use the variable in a different regression, it also works fine. I just can't find the point where it turns ugly. I tested the litrate-variable for everything I know to test for: The structure is numerical and it does not contain any missings. It has the same length as every other variable in the set and is a continuous variable with values between 0 and 1. Does anyone have an idea? -- View this message in context: http://r.789695.n4.nabble.com/Regression-Error-Otherwise-good-variable-causes-singularity-Why-tp2322780p2322780.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ 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. 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