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edited May 22, 2020 at 16:42

EDIT:

On advice from a friend I used the effects package and the plot(allEffects(model)) function in order to evaluate the effects in a model plot. This results in:

Moreover, based on output from the allEffects function:

 model: GotoPB ~ ZF_Pb + ZF_NotPb

 ZF_Pb effect
ZF_Pb
        0       7.5        15        22        30 
0.4460287 0.8372560 0.9704774 0.9946413 0.9992555 

 ZF_NotPb effect
ZF_NotPb
        0        10        20        30        40 
0.6283205 0.5424455 0.4539719 0.3683128 0.2902256

Leads me to believe that my initial assumption was correct, and that indeed an increase of ZF_Pb coincides with an increased probability of GotoPB=Y.

EDIT:

On advice from a friend I used the effects package and the plot(allEffects(model)) function in order to evaluate the effects in a model plot. This results in:

Moreover, based on output from the allEffects function:

 model: GotoPB ~ ZF_Pb + ZF_NotPb

 ZF_Pb effect
ZF_Pb
        0       7.5        15        22        30 
0.4460287 0.8372560 0.9704774 0.9946413 0.9992555 

 ZF_NotPb effect
ZF_NotPb
        0        10        20        30        40 
0.6283205 0.5424455 0.4539719 0.3683128 0.2902256

Leads me to believe that my initial assumption was correct, and that indeed an increase of ZF_Pb coincides with an increased probability of GotoPB=Y.

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asked May 22, 2020 at 10:56

R. Iersel

Interpreting glm intercept and estimates

First off I will state that I have looked at similar threads (such as this (referenced later) and this) yet I still don't fully understand. My situation is as follows, I am trying to report the results of the following glm:

glm(GotoPB ~ ZF_Pb + ZF_NotPb, data = DF_DL_5, family = binomial(link = "logit"))

In which the variables are:

GotoPB = A factor with two levels: "N" or "Y"

ZF_Pb = A numeric variable

ZF_NotPb = A numeric variable

My summary is as follows:

Call:
glm(formula = GotoPB ~ ZF_Pb + ZF_NotPb, family = binomial(link = "logit"), 
    data = DF_DL_5)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.4285  -1.1333   0.3953   1.1467   1.7764  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)    
(Intercept)  0.07267    0.12283   0.592 0.554124    
ZF_Pb        0.24729    0.04173   5.926  3.1e-09 ***
ZF_NotPb    -0.03548    0.01057  -3.357 0.000788 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 840.93  on 608  degrees of freedom
Residual deviance: 778.57  on 606  degrees of freedom
AIC: 784.57

Number of Fisher Scoring iterations: 5

Initially I understood this as: An increase in ZF_Pb increases the chance that GotoPB = Y. But I am a bit confused on the interpretation of the intercept. Referring back to the thread referenced earlier, which states that the intercept is determined alphabetically, does that mean that my intercept is in fact GoToPB-N.

And following on that, would that mean that an increase in ZF_Pb instead equates the opposite; The higher ZF_Pb the larger the chance that GotoPB = N?

I should state that this last conclusion would be in stark contrast to what we would expect, which is why this has left me rather confused. I am worried that I do not understand these results properly. Could someone help me clarify this?