[EMAIL PROTECTED] writes: > I'm using R on Windows v2.0.1 with the nlme package (v3.1-53) and am finding > some unexpected discrepancies in the output of intervals.lme and coef.lme. > I've included a toy dataset at the end, but briefly, the data are > longitudinal data from couples in marital therapy. Each spouse's > relationship satisfaction is measured 4 times; I've fit both linear and > quadratic models to the change over time. The quadratic fits show the > discrpancies. > > Here's the call to lmList, coef, and intervals: > > tmp.lis1 <- lmList(dv ~ time + I(time^2)| id/sex, data = tmp.df, > na.action = na.omit) > coef(tmp.lis1) > intervals(tmp.lis1) > > Here is the coef() output: > (Intercept) time I(time^2) > 1/Husband 89.60 11.100000 -2.500000 > 1/Wife 69.80 5.300000 -0.500000 > 2/Husband 49.00 8.833333 -2.833333 > 2/Wife 45.00 28.666667 -9.666667 > 3/Husband 96.00 -6.000000 NA > 3/Wife 60.00 19.000000 NA > 4/Husband 70.00 48.500000 -16.500000 > 4/Wife 92.00 14.500000 -4.500000 > 5/Husband 75.00 43.500000 -14.500000 > 5/Wife 87.00 37.000000 -14.000000 > 6/Husband 66.75 1.250000 -1.250000 > 6/Wife 66.15 12.150000 -2.750000 > 7/Husband 92.75 6.750000 -0.750000 > 7/Wife 82.35 17.850000 -3.250000 > 8/Husband 76.15 -25.350000 11.750000 > 8/Wife 100.50 -12.000000 6.000000 > > And just the (Intercept) portion of the intervals() output: > , , (Intercept) > > lower est. upper > 1/Husband 72.4335719 89.6000000 106.7664281 > 1/Wife 52.6335719 69.8000000 86.9664281 > 2/Husband NaN 49.0000000 NaN > 2/Wife NaN 45.0000000 NaN > 3/Husband NaN 96.0000000 NaN > 3/Wife NaN NaN NaN > 4/Husband NaN NaN NaN > 4/Wife NaN NaN NaN > 5/Husband NaN NaN NaN > 5/Wife NaN NaN NaN > 6/Husband -3.8551591 -0.7453560 2.3644471 > 6/Wife -3.2306740 -1.4468675 0.3369390 > 7/Husband -2.1707917 -0.1916630 1.7874658 > 7/Wife -2.4667397 -1.0766253 0.3134891 > 8/Husband 4.0996388 4.5693563 5.0390738 > 8/Wife 0.9368527 1.7888544 2.6408560 > > Notice that the intercept estimates for couples 6-8 are wildly different > between the coef() and intervals() output. Granted, fitting an intercept, > slope, and quadratic to 4 data points doesn't leave much for an error term, > but it seems like the intercept coefficients ought to be the same. If the > quadratic is dropped, there is no discrepancy between coef() and intevals(), > so perhaps this is related to the complexity of the model vs. sparseness of > data? > > Any insights appreciated (data below).
Looks like the sort of things that happens if parts of code don't take notice of pivoting caused by singularities in the model. BTW: This wouldn't look like an issue with the lme methods, but with lmList counterparts. -- O__ ---- Peter Dalgaard Blegdamsvej 3 c/ /'_ --- Dept. of Biostatistics 2200 Cph. N (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918 ~~~~~~~~~~ - ([EMAIL PROTECTED]) FAX: (+45) 35327907 ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
