[R] Regression Error: Otherwise good variable causes singularity. Why?

[asdir]

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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