Thank you so much for your help.
The data I am using is the last file called l_yx.RData at this link (the
second file contains the plots from earlier):
http://scientia.crescat.net/static/ben/
Seems like the warning went away with pmin(x,1) but now the OR is over
15k. If I multiple my x's by 1000 I get a much more realistic OR. So I
guess this brings me to a much different question: aren't OR's comparable
between factors/data? In this case they don't seem to be. However, with
different data the OR's only change a very small amount (+8.0e-4) when I
multiply the x's by 1000. I don't understand.
Anyways, here is a run with the raw data and a run with your suggestion
(pmin(x,1)) that removed the error:
> l_logit = glm(y~x, data=as.data.frame(l_yx),
family=binomial(link="logit"))
> l_logit
Call: glm(formula = y ~ x, family = binomial(link = "logit"), data =
as.data.frame(l_yx))
Coefficients:
(Intercept) x
-2.293 8.059
Degrees of Freedom: 690302 Total (i.e. Null); 690301 Residual
Null Deviance: 448800
Residual Deviance: 447100 AIC: 447100
> l_exp_coef = exp(l_logit$coefficients)[2]
> l_exp_coef
x
3161.781
> dim(l_yx)
[1] 690303 2
> l_yx = cbind(l_yx[,1],pmin(l_yx[,2],1))
> dim(l_yx)
[1] 690303 2
> colnames(l_yx) = c('y','x')
> mean(l_yx[,2])
[1] 0.01117248
> range(l_yx[,2])
[1] 0 1
> head(l_yx[,2])
[1] 0.00302316 0.07932130 0.00000000 0.01779657 0.16083735 0.00000000
> unique(l_yx[,1])
[1] 0 1
> l_logit = glm(y~x, data=as.data.frame(l_yx),
family=binomial(link="logit"))
> l_logit
Call: glm(formula = y ~ x, family = binomial(link = "logit"), data =
as.data.frame(l_yx))
Coefficients:
(Intercept) x
-2.312 9.662
Degrees of Freedom: 690302 Total (i.e. Null); 690301 Residual
Null Deviance: 448800
Residual Deviance: 446800 AIC: 446800
> l_exp_coef = exp(l_logit$coefficients)[2]
> l_exp_coef
x
15709.52
Thanks,
Ben
On Thu, Dec 1, 2011 at 4:32 PM, peter dalgaard <pda...@gmail.com> wrote:
>
> On Dec 1, 2011, at 23:43 , Ben quant wrote:
>
> > I'm not proposing this as a permanent solution, just investigating the
> warning. I zeroed out the three outliers and received no warning. Can
> someone tell me why I am getting no warning now?
>
> It's easier to explain why you got the warning before. If the OR for a one
> unit change is 3000, the OR for a 14 unit change is on the order of 10^48
> and that causes over/underflow in the conversion to probabilities.
>
> I'm still baffled at how you can get that model fitted to your data,
> though. One thing is that you can have situations where there are fitted
> probabilities of one corresponding to data that are all one and/or fitted
> zeros where data are zero, but you seem to have cases where you have both
> zeros and ones at both ends of the range of x. Fitting a zero to a one or
> vice versa would make the likelihood zero, so you'd expect that the
> algorithm would find a better set of parameters rather quickly. Perhaps the
> extremely large number of observations that you have has something to do
> with it?
>
> You'll get the warning if the fitted zeros or ones occur at any point of
> the iterative procedure. Maybe it isn't actually true for the final model,
> but that wouldn't seem consistent with the OR that you cited.
>
> Anyways, your real problem lies with the distribution of the x values. I'd
> want to try transforming it to something more sane. Taking logarithms is
> the obvious idea, but you'd need to find out what to do about the zeros --
> perhaps log(x + 1e-4) ? Or maybe just cut the outliers down to size with
> pmin(x,1).
>
> >
> > I did this 3 times to get rid of the 3 outliers:
> > mx_dims = arrayInd(which.max(l_yx), dim(l_yx))
> > l_yx[mx_dims] = 0
> >
> > Now this does not produce an warning:
> > l_logit = glm(y~x, data=as.data.frame(l_yx),
> family=binomial(link="logit"))
> >
> > Can someone tell me why occurred?
> >
> > Also, again, here are the screen shots of my data that I tried to send
> earlier (two screen shots, two pages):
> > http://scientia.crescat.net/static/ben/warn%20num%200%20or%201.pdf
> >
> > Thank you for your help,
> >
> > Ben
> >
> > On Thu, Dec 1, 2011 at 3:25 PM, Ben quant <ccqu...@gmail.com> wrote:
> > Oops! Please ignore my last post. I mistakenly gave you different data I
> was testing with. This is the correct data:
> >
> > Here you go:
> >
> > > attach(as.data.frame(l_yx))
> > > range(x[y==0])
> > [1] 0.00000 14.66518
> > > range(x[y==1])
> > [1] 0.00000 13.49791
> >
> >
> > How do I know what is acceptable?
> >
> > Also, here are the screen shots of my data that I tried to send earlier
> (two screen shots, two pages):
> > http://scientia.crescat.net/static/ben/warn%20num%200%20or%201.pdf
> >
> > Thank you,
> >
> > Ben
> >
> > On Thu, Dec 1, 2011 at 3:07 PM, Ben quant <ccqu...@gmail.com> wrote:
> > Here you go:
> >
> > > attach(as.data.frame(l_yx))
> > > range(x[y==1])
> > [1] -22500.0000 746.6666
> > > range(x[y==0])
> > [1] -10076.5303 653.0228
> >
> > How do I know what is acceptable?
> >
> > Also, here are the screen shots of my data that I tried to send earlier
> (two screen shots, two pages):
> > http://scientia.crescat.net/static/ben/warn%20num%200%20or%201.pdf
> >
> > Thank you,
> >
> > Ben
> >
> >
> > On Thu, Dec 1, 2011 at 2:24 PM, peter dalgaard <pda...@gmail.com> wrote:
> >
> > On Dec 1, 2011, at 21:32 , Ben quant wrote:
> >
> > > Thank you for the feedback, but my data looks fine to me. Please tell
> me if I'm not understanding.
> >
> > Hum, then maybe it really is a case of a transition region being short
> relative to the range of your data. Notice that the warning is just that: a
> warning. I do notice that the distribution of your x values is rather
> extreme -- you stated a range of 0--14 and a mean of 0.01. And after all,
> an odds ratio of 3000 per unit is only a tad over a doubling per 0.1 units.
> >
> > Have a look at range(x[y==0]) and range(x[y==1]).
> >
> >
> > >
> > > I followed your instructions and here is a sample of the first 500
> values : (info on 'd' is below that)
> > >
> > > > d <- as.data.frame(l_yx)
> > > > x = with(d, y[order(x)])
> > > > x[1:500] # I have 1's and 0's dispersed throughout
> > > [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
> 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
> 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0
> > > [101] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
> 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
> 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
> > > [201] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
> 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
> 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 0 0 1 0 0 0 1 0 0
> > > [301] 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
> 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 1 0
> 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
> > > [401] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0
> 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
> 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
> > >
> > > # I get the warning still
> > > > l_df = as.data.frame(l_yx)
> > > > l_logit = glm(y~x, data=l_df, family=binomial(link="logit"))
> > >
> > > Warning message:
> > > glm.fit: fitted probabilities numerically 0 or 1 occurred
> > >
> > > # some info on 'd' above:
> > >
> > > > d[1:500,1]
> > > [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
> 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
> 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
> > > [101] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
> 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
> 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
> > > [201] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
> 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
> 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
> > > [301] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
> 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
> 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
> > > [401] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
> 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
> 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
> > > > d[1:500,2]
> > > [1] 3.023160e-03 7.932130e-02 0.000000e+00 1.779657e-02 1.608374e-01
> 0.000000e+00 5.577064e-02 7.753926e-03 4.018553e-03 4.760918e-02
> 2.080511e-02 1.642404e-01 3.703720e-03 8.901981e-02 1.260415e-03
> > > [16] 2.202523e-02 3.750940e-02 4.441975e-04 9.351171e-03 8.374567e-03
> 0.000000e+00 8.440448e-02 5.081017e-01 2.538640e-05 1.806017e-02
> 2.954641e-04 1.434859e-03 6.964976e-04 0.000000e+00 1.202162e-02
> > > [31] 3.420300e-03 4.276100e-02 1.457324e-02 4.140121e-03 1.349180e-04
> 1.525292e-03 4.817502e-02 9.515717e-03 2.232953e-02 1.227908e-01
> 3.293581e-02 1.454352e-02 1.176011e-03 6.274138e-02 2.879205e-02
> > > [46] 6.900926e-03 1.414648e-04 3.446349e-02 8.807174e-03 3.549332e-02
> 2.828509e-03 2.935003e-02 7.162872e-03 5.650050e-03 1.221191e-02
> 0.000000e+00 2.126334e-02 2.052020e-02 7.542409e-02 2.586269e-04
> > > [61] 5.258664e-02 1.213126e-02 1.493275e-02 8.152657e-03 1.774966e-02
> 2.433198e-02 1.371257e-02 6.732339e-02 9.747677e-03 8.180594e-03
> 1.882473e-01 1.682596e-03 1.410170e-02 8.429676e-03 3.520589e-01
> > > [76] 1.865160e-02 2.389941e-02 3.301558e-02 4.896605e-03 6.826244e-03
> 9.151337e-02 1.722270e-02 1.764039e-01 3.554063e-02 7.338532e-03
> 9.577793e-03 3.632366e-03 1.422400e-02 3.020497e-02 1.797096e-02
> > > [91] 2.577899e-03 1.338365e-01 8.202223e-03 1.088335e-02 2.137259e-02
> 6.829797e-03 3.104860e-04 8.927160e-03 5.626881e-02 1.197682e-02
> 6.140553e-02 2.729364e-02 1.377913e-02 3.018277e-03 1.188304e-02
> > > [106] 2.029268e-03 2.108815e-02 1.765503e-03 2.449253e-02 3.677046e-03
> 1.013463e-02 4.286642e-02 1.238931e-02 3.072090e-03 1.509613e-02
> 3.609885e-02 5.537268e-03 3.151614e-02 3.703148e-03 1.409401e-01
> > > [121] 1.473365e-02 9.160461e-03 1.035099e-01 3.005865e-02 1.332199e-02
> 6.936535e-03 2.433539e-02 4.935373e-03 4.822740e-03 1.597643e-03
> 3.805619e-03 1.092683e-02 1.760635e-01 5.565614e-03 7.739213e-04
> > > [136] 4.529198e-03 5.110359e-03 7.391258e-02 5.018207e-03 2.071417e-02
> 1.825787e-02 9.141405e-03 1.078386e-01 2.171470e-04 7.164583e-03
> 2.522077e-02 1.994428e-03 6.044653e-03 1.778078e-04 5.797706e-03
> > > [151] 1.526778e-02 1.595897e-02 1.995627e-01 1.946735e-03 6.711582e-02
> 2.722350e-02 3.127499e-02 1.074904e-01 2.477414e-03 5.015375e-02
> 3.417810e-02 2.046643e-02 1.644832e-02 5.220166e-03 1.217752e-02
> > > [166] 1.187352e-02 1.634016e-02 2.349568e-03 3.451811e-02 2.593540e-03
> 6.868595e-03 1.311236e-02 6.457757e-03 2.942391e-04 1.628352e-02
> 8.288831e-03 3.170856e-04 1.251331e+00 1.706954e-02 1.063723e-03
> > > [181] 1.374416e-02 2.140507e-02 2.817009e-02 2.272793e-02 4.365562e-02
> 6.089414e-03 2.498083e-02 1.360471e-02 1.884079e-02 1.448660e-02
> 2.341314e-02 8.167064e-03 4.109117e-02 2.660633e-02 7.711723e-03
> > > [196] 9.590278e-03 2.515490e-03 1.978033e-02 3.454990e-02 8.072748e-03
> 4.718885e-03 1.621131e-01 4.547743e-03 1.081195e-02 9.572051e-04
> 1.790391e-02 1.618026e-02 1.910230e-02 1.861914e-02 3.485475e-02
> > > [211] 2.844890e-03 1.866889e-02 1.378208e-02 2.451514e-02 2.535044e-03
> 3.921364e-04 1.557266e-03 3.315892e-03 1.752821e-03 6.786187e-03
> 1.360921e-02 9.550702e-03 8.114506e-03 5.068741e-03 1.729822e-02
> > > [226] 1.902033e-02 8.196564e-03 2.632880e-03 1.587969e-02 8.354079e-04
> 1.050023e-03 4.236195e-04 9.181120e-03 4.995919e-04 1.092234e-02
> 1.207544e-02 2.187243e-01 3.251349e-02 1.269134e-03 1.557751e-04
> > > [241] 1.232498e-02 2.654449e-02 1.049324e-03 8.442729e-03 6.331691e-03
> 1.715609e-02 1.017800e-03 9.230006e-03 1.331373e-02 5.596195e-02
> 1.296551e-03 5.272687e-03 2.805640e-02 4.790665e-02 2.043011e-02
> > > [256] 1.047226e-02 1.866499e-02 9.323001e-03 8.920536e-03 1.582911e-03
> 2.776238e-03 2.914762e-02 4.402356e-03 9.555274e-04 1.681966e-03
> 7.584319e-04 6.758914e-02 1.505431e-02 2.213308e-02 1.329330e-02
> > > [271] 7.284363e-03 2.687818e-02 2.997535e-03 7.470007e-03 2.070569e-03
> 3.441944e-02 1.717768e-02 4.523364e-02 1.003558e-02 1.365111e-02
> 1.906845e-02 1.676223e-02 3.506809e-04 9.164257e-02 9.008416e-03
> > > [286] 1.073903e-02 4.855937e-03 8.618043e-03 2.529247e-02 1.059375e-02
> 5.834253e-03 2.004309e-02 1.460387e-02 2.899190e-02 5.867984e-03
> 1.983956e-02 6.834339e-03 1.925821e-03 9.231870e-03 6.839616e-03
> > > [301] 1.029972e-02 2.009769e-02 9.458785e-03 1.183901e-02 8.911549e-03
> 1.264745e-02 2.995451e-03 7.657983e-04 5.315853e-03 1.325039e-02
> 1.044103e-02 2.307236e-02 2.780789e-02 1.735145e-02 9.053126e-03
> > > [316] 5.847638e-02 3.815715e-03 5.087690e-03 1.040513e-02 4.475672e-02
> 6.564791e-02 3.233571e-03 1.076193e-02 8.283819e-02 5.370256e-03
> 3.533256e-02 1.302812e-02 1.896783e-02 2.055282e-02 3.572239e-03
> > > [331] 5.867681e-03 5.864974e-04 9.715807e-03 1.665469e-02 5.082044e-02
> 3.547168e-03 3.069631e-03 1.274717e-02 1.858226e-03 3.104809e-04
> 1.247831e-02 2.073575e-03 3.544110e-04 7.240736e-03 8.452117e-05
> > > [346] 8.149151e-04 4.942461e-05 1.142303e-03 6.265512e-04 3.666717e-04
> 3.244669e-02 7.242018e-03 6.335951e-04 2.329072e-02 3.719716e-03
> 2.803425e-02 1.623981e-02 6.387102e-03 8.807679e-03 1.214914e-02
> > > [361] 6.699341e-03 1.148082e-02 1.329736e-02 1.537364e-03 2.004390e-02
> 1.562065e-02 1.655465e-02 9.960172e-02 2.174588e-02 1.209472e-02
> 2.328413e-02 2.012760e-04 1.422327e-02 2.194455e-03 2.307362e-02
> > > [376] 4.315764e-03 3.208576e-02 3.826598e-02 1.828001e-02 3.935978e-03
> 5.294211e-04 1.392423e-02 6.588394e-03 1.040147e-03 1.260787e-02
> 9.051757e-04 5.353215e-02 6.049058e-02 1.382630e-01 1.064124e-01
> > > [391] 3.380742e-03 1.798038e-02 1.557048e-01 1.217146e-02 4.140520e-02
> 4.707564e-02 2.786042e-02 8.836988e-03 5.542879e-03 1.862664e-02
> 8.858770e-03 1.026681e-03 1.692105e-02 8.849238e-03 7.143816e-03
> > > [406] 1.630118e-02 1.165920e-01 9.471496e-03 4.879998e-02 1.388216e-02
> 1.453267e-02 4.845224e-04 1.415190e-03 1.208627e-02 1.372348e-02
> 2.573131e-02 1.169595e-02 1.825447e-02 2.574299e-02 5.301360e-02
> > > [421] 6.961110e-03 7.781891e-03 1.013308e-03 3.160916e-03 1.090344e-02
> 1.530841e-02 9.398088e-04 9.143726e-04 1.286683e-02 2.006193e-02
> 1.774378e-02 5.681591e-02 9.584676e-03 7.957152e-02 4.485609e-03
> > > [436] 1.086684e-02 2.930273e-03 6.085481e-03 4.342320e-03 1.319999e-02
> 2.120402e-02 4.477545e-02 1.991814e-02 8.893947e-03 7.790133e-03
> 1.610199e-02 2.441280e-02 2.781231e-03 1.410080e-02 1.639912e-02
> > > [451] 1.797498e-02 1.185382e-02 2.775063e-02 3.797315e-02 1.428883e-02
> 1.272659e-02 2.390500e-03 7.503478e-03 8.965356e-03 2.139452e-02
> 2.028536e-02 6.916416e-02 1.615986e-02 4.837412e-02 1.561731e-02
> > > [466] 7.130332e-03 9.208406e-05 1.099934e-02 2.003469e-02 1.395857e-02
> 9.883482e-03 4.110852e-02 1.202052e-02 2.833039e-02 1.233236e-02
> 2.145801e-02 7.900161e-03 4.663819e-02 4.410819e-03 5.115056e-04
> > > [481] 9.100270e-04 4.013683e-03 1.227139e-02 3.304697e-03 2.919099e-03
> 6.112390e-03 1.922229e-02 1.208282e-03 1.164037e-02 2.166888e-02
> 4.381615e-02 5.318929e-03 7.226343e-03 2.732819e-02 2.385092e-04
> > > [496] 4.905250e-02 1.159876e-02 4.068228e-03 3.349013e-02 1.273468e-03
> > >
> > >
> > > Thanks for your help,
> > >
> > > Ben
> > >
> > > On Thu, Dec 1, 2011 at 11:55 AM, peter dalgaard <pda...@gmail.com>
> wrote:
> > >
> > > On Dec 1, 2011, at 18:54 , Ben quant wrote:
> > >
> > > > Sorry if this is a duplicate: This is a re-post because the pdf's
> mentioned
> > > > below did not go through.
> > >
> > > Still not there. Sometimes it's because your mailer doesn't label them
> with the appropriate mime-type (e.g. as application/octet-stream, which is
> "arbitrary binary"). Anyways, see below
> > >
> > > [snip]
> > > >
> > > > With the above data I do:
> > > >> l_logit = glm(y~x, data=as.data.frame(l_yx),
> > > > family=binomial(link="logit"))
> > > > Warning message:
> > > > glm.fit: fitted probabilities numerically 0 or 1 occurred
> > > >
> > > > Why am I getting this warning when I have data points of varying
> values for
> > > > y=1 and y=0? In other words, I don't think I have the linear
> separation
> > > > issue discussed in one of the links I provided.
> > >
> > > I bet that you do... You can get the warning without that effect (one
> of my own examples is the probability of menarche in a data set that
> includes infants and old age pensioners), but not with a huge odds ratio as
> well. Take a look at
> > >
> > > d <- as.data.frame(l_yx)
> > > with(d, y[order(x)])
> > >
> > > if it comes out as all zeros followed by all ones or vice versa, then
> you have the problem.
> > >
> > >
> > > >
> > > > PS - Then I do this and I get a odds ratio a crazy size:
> > > >> l_sm = summary(l_logit) # coef pval is $coefficients[8], log odds
> > > > $coefficients[2]
> > > >> l_exp_coef = exp(l_logit$coefficients)[2] # exponentiate the
> > > > coeffcients
> > > >> l_exp_coef
> > > > x
> > > > 3161.781
> > > >
> > > > So for one unit increase in the predictor variable I get 3160.781%
> > > > (3161.781 - 1 = 3160.781) increase in odds? That can't be correct
> either.
> > > > How do I correct for this issue? (I tried multiplying the predictor
> > > > variables by a constant and the odds ratio goes down, but the
> warning above
> > > > still persists and shouldn't the odds ratio be predictor variable
> size
> > > > independent?)
> > >
> > >
> > > --
> > > Peter Dalgaard, Professor,
> > > Center for Statistics, Copenhagen Business School
> > > Solbjerg Plads 3, 2000 Frederiksberg, Denmark
> > > Phone: (+45)38153501
> > > Email: pd....@cbs.dk Priv: pda...@gmail.com
> > >
> > >
> > >
> > >
> > >
> > >
> > >
> > >
> > >
> >
> > --
> > Peter Dalgaard, Professor,
> > Center for Statistics, Copenhagen Business School
> > Solbjerg Plads 3, 2000 Frederiksberg, Denmark
> > Phone: (+45)38153501
> > Email: pd....@cbs.dk Priv: pda...@gmail.com
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
>
> --
> Peter Dalgaard, Professor,
> Center for Statistics, Copenhagen Business School
> Solbjerg Plads 3, 2000 Frederiksberg, Denmark
> Phone: (+45)38153501
> Email: pd....@cbs.dk Priv: pda...@gmail.com
>
>
>
>
>
>
>
>
>
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