Cross Validated Asked on November 26, 2021
I have conducted a logistic regression in R
> Model <- glm(A ~ B + C, family = "binomial", data = Data)
> summary(Model)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.6138 678.6939 -0.002 0.9981
BPu 1.0003 0.5539 1.806 0.0709 .
C.L 21.2450 2146.2181 0.010 0.9921
C.Q 1.2210 1813.8853 0.001 0.9995
C.C 9.8965 1073.1091 0.009 0.9926
C^4 -0.3275 405.5973 -0.001 0.9994
exp(coef(Model))
(Intercept) BPu C.L C.Q
1.991295e-01 2.719031e+00 1.684921e+09 3.390646e+00
C.C C^4
1.986151e+04 7.207529e-01
As I understand it when the independent variable, B, (a binary variable) changes to Pu this is associated with an increase in the log odds of a "success" in the dependent by 1.0003, or that odds of increasing the dependent variable are multiplied by 3.22 relative to the intercept, and this change is near significant.
Can I say a similar statement for the variable CR, a 5 level ordinal variable? I’ve found online that L, Q, C, ^4 represent linear quadratic, cubic … but I haven’t found an answer outlining what I can practically say about these coefficients or how to interpret them.
I understand that the influence is likely to be insignificant, but which P value do I use? What can I say about the other coefficients?
I think a lot of your answers can be found in the answer to a similar question. The one caveat is that the linear, quadratic, cubic, and quartic terms do not directly correspond to numeric effects as though you used the factor levels as a raw number. You can see this by typing contr.poly(5)
in $R$ to see how the various levels are coded (beyond the intercept = constant = all 1's).
Answered by kurtosis on November 26, 2021
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