Hello,

When I run the following glm model:

modelresult=glm(CID~WS+SS+DV+DS, data=kimu, family=binomial)

I get the following warning messages:

1: glm.fit: algorithm did not converge
2: glm.fit: fitted probabilities numerically 0 or 1 occurred

What I am trying to do is model my response variable (CID: correct bird
identification) as a function of the predictor variables weather state
(WS), sea state (SS), distance from the vessel (DV) and duration of the
sighting (DS). I defined both sea state and weather state as factors with
three levels (0, 1, or 2). Distance of the vessel values are 100, 80, 60,
40, and 20. Duration of the sighting ranges from 0 to 58 seconds.

The output R is giving me is:

Deviance Residuals:
       Min          1Q      Median          3Q         Max
-3.562e-05  -2.100e-08   2.100e-08   2.100e-08   3.632e-05

Coefficients:
              Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.000e+02  1.067e+06   0.000    1.000
WSf1         7.744e+01  9.086e+04   0.001    0.999
WSf2         1.285e+01  6.199e+04   0.000    1.000
SSf1        -1.042e+02  1.683e+05  -0.001    1.000
SSf2        -1.859e+02  1.432e+05  -0.001    0.999
DV           6.770e-01  9.394e+03   0.000    1.000
DS           9.822e+00  1.884e+04   0.001    1.000


What do the warning messages mean? Can I still use coefficient estimates
and standard error values?

Thank you!

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