# [R] question regarding logit regression using glm

From: Haibo Huang <edhuang00_at_yahoo.com>
Date: Sat 06 Aug 2005 - 06:17:21 EST

I got the following warning messages when I did a binomial logit regression using glm():

Warning messages:
1: Algorithm did not converge in: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart,
2: fitted probabilities numerically 0 or 1 occurred in: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart,

Can some one share your thoughts on how to solve this problem? Please read the following for details. Thank you very much!

Best,
Ed

> Lease\$ET = factor(Lease\$EarlyTermination)
> SICCode=factor(Lease\$SIC.Code)
> TO=factor(Lease\$TenantHasOption)
> LO=factor(Lease\$LandlordHasOption)
> TEO=factor(Lease\$TenantExercisedOption)
>
> RegA=glm(ET~1+MSA,

Warning messages:
1: Algorithm did not converge in: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart,
2: fitted probabilities numerically 0 or 1 occurred in: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart,
> summary(RegA)

Call:
glm(formula = ET ~ 1 + MSA, family = binomial(link = logit),

data = Lease, weights = Origil.SQFT)

Deviance Residuals:

Min 1Q Median 3Q Max
-6.038e+03 -2.066e-06 0.000e+00 0.000e+00 6.720e+03

Coefficients:

Estimate Std. Error    z value
Pr(>|z|)
(Intercept)          5.711e+00  8.466e-02  6.745e+01

<2e-16 ***
MSAAnchorage -6.493e+00 8.541e-02 -7.602e+01
<2e-16 ***
MSAAtlanta 6.894e+14 2.310e+04 2.985e+10
<2e-16 ***
MSAAustin -9.362e+14 4.954e+04 -1.890e+10
<2e-16 ***
MSABoston -2.474e+15 2.151e+04 -1.150e+11
<2e-16 ***
MSACharlotte -2.150e+15 7.265e+04 -2.960e+10
<2e-16 ***
MSAChicago -1.174e+15 2.057e+04 -5.707e+10
<2e-16 ***
MSACleveland -7.607e+14 7.046e+04 -1.080e+10
<2e-16 ***
MSAColumbus -2.768e+15 1.685e+05 -1.642e+10
<2e-16 ***
<2e-16 ***
<2e-16 ***
MSAEast Bay -6.191e+01 1.344e+05 -4.61e-04 1 MSAFt. Worth -6.565e+00 8.483e-02 -7.739e+01
<2e-16 ***
MSAHouston -2.735e+15 3.576e+04 -7.648e+10
<2e-16 ***
MSAIndianapolis -7.483e+14 6.588e+04 -1.136e+10
<2e-16 ***
MSALos Angeles -1.388e+15 2.887e+04 -4.809e+10
<2e-16 ***
MSAMinneapolis -1.011e+15 2.731e+04 -3.702e+10
<2e-16 ***
MSANashville 2.143e+01 9.395e+04 2.28e-04 1 MSANew Orleans -3.370e+15 5.038e+04 -6.689e+10
<2e-16 ***
MSANew York -2.526e+15 2.969e+04 -8.507e+10
<2e-16 ***
MSANorfolk -5.614e+01 2.020e+06 -2.78e-05 1

MSAOakland-East Bay -2.272e+15 3.642e+04 -6.239e+10
<2e-16 ***

MSAOrange County -5.165e+14 2.428e+04 -2.128e+10
<2e-16 ***

MSAOrlando -3.215e+15 1.096e+05 -2.933e+10
<2e-16 ***

<2e-16 ***

MSAPhoenix -1.156e+01 8.807e-02 -1.313e+02
<2e-16 ***

MSAPortland 7.604e+14 3.841e+04 1.980e+10
<2e-16 ***

MSARaleigh-Durham -4.312e+01 1.294e+05 -3.33e-04
1
MSARiverside         1.626e+15  4.645e+05  3.500e+09

<2e-16 ***
MSASacramento -9.873e+14 5.345e+04 -1.847e+10

<2e-16 ***

MSASalt Lake City 1.793e+15 2.029e+05 8.839e+09
<2e-16 ***

MSASan Antonio 9.451e+14 9.473e+04 9.977e+09
<2e-16 ***

MSASan Diego -3.740e+15 6.651e+04 -5.623e+10
<2e-16 ***

MSASan Francisco 3.109e+14 2.394e+04 1.299e+10
<2e-16 ***

MSASan Jose 7.392e+14 2.961e+04 2.497e+10
<2e-16 ***

MSASeattle -2.250e+15 1.581e+04 -1.423e+11
<2e-16 ***

MSASt. Louis -2.606e+15 1.801e+05 -1.447e+10
<2e-16 ***

MSAStamford -6.592e+00 8.469e-02 -7.784e+01
<2e-16 ***

MSAWashington DC 8.460e+13 3.319e+04 2.549e+09
<2e-16 ***

MSAWest Palm Beach -3.924e+01 2.308e+05 -1.70e-04

1

---
Signif. codes:  0 `***' 0.001 `**' 0.01 `*' 0.05 `.'
0.1 ` ' 1

(Dispersion parameter for binomial family taken to be
1)

Null deviance:  123111026  on 9302  degrees of
freedom
Residual deviance: 3028559052  on 9263  degrees of
freedom
AIC: 3028559132

Number of Fisher Scoring iterations: 25

> anova(RegA)
Analysis of Deviance Table

Response: ET

Terms added sequentially (first to last)

Df   Deviance Resid. Df Resid. Dev
NULL                      9302  123111026
MSA    39          0      9263 3028559052
>

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