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 <dirkroettgers@gmail.com> 
Sent by: r-help-bounces@r-project.org
08/12/2010 10:35 AM

To
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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?
-- 
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