On Aug 12, 2010, at 10:35 AM, asdir wrote:
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
You have probably created litrate as a factor without realizing it.
That can easily happen if you just use read.table and one of the
values cannot be gracefully interpreted as a numeric. Either read in
with stringsAsFactors=FALSE or asIs=TRUE and then coerce it to
numeric. or if you want to fix an existing factor f%^&-up, then the
FAQ tells you to use something like:
cdmdata2$f_ed_variable <-
as.numeric(as.character(cdmdata2$f_ed_variable)
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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