Code
print(out)
Output
Parameter | Log-Odds | SE | 95% CI | z | p
------------------------------------------------------------------------------
(Intercept) | -66.10 | 1.83e+05 | [-10644.72, 10512.52] | -3.60e-04 | > .999
x1 | 15.29 | 27362.84 | [ -3122.69, ] | 5.59e-04 | > .999
x2 | 6.24 | 81543.72 | [-12797.28, ] | 7.65e-05 | > .999
Message
Uncertainty intervals (profile-likelihood) and p-values (two-tailed)
computed using a Wald z-distribution approximation.
The model has a log- or logit-link. Consider using `exponentiate =
TRUE` to interpret coefficients as ratios.
Some coefficients are very large, which may indicate issues with
complete separation.
Code
print(out)
Output
Parameter | Log-Odds | SE | 95% CI | z | p
-------------------------------------------------------------------------
(Intercept) | -83.33 | 15505.03 | [ , 816.56] | -5.37e-03 | 0.996
gear | 21.01 | 3876.26 | [-248.93, ] | 5.42e-03 | 0.996
Message
Uncertainty intervals (profile-likelihood) and p-values (two-tailed)
computed using a Wald z-distribution approximation.
The model has a log- or logit-link. Consider using `exponentiate =
TRUE` to interpret coefficients as ratios.
Some coefficients are very large, which may indicate issues with
complete separation.
Code
print(out)
Output
Parameter | Log-Odds | SE | 95% CI | z | p
---------------------------------------------------------------
(Intercept) | -70.25 | 88.29 | [ , -16.06] | -0.80 | 0.426
qsec | 4.12 | 5.22 | [0.97, ] | 0.79 | 0.430
Message
Uncertainty intervals (profile-likelihood) and p-values (two-tailed)
computed using a Wald z-distribution approximation.
The model has a log- or logit-link. Consider using `exponentiate =
TRUE` to interpret coefficients as ratios.
Some coefficients seem to be rather large, which may indicate issues
with (quasi) complete separation. Consider using bias-corrected or
penalized regression models.
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