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Further to my previous post , it seems that one can/should use a logistic regression to model a proportion.

How do I interpret the coefficients of a logistic regression when the outcome variable is a proportion and not binary? Is it the same interpretation as if the outcome variable was binary?

Using the example below (in R), if the interpretation is the same as if the outcome (prop_survived) was binary, I would say "Controlling for all other variables, being a child nearly triples the odds of survival". If that's not the appropriate interpretation what is? What would be the interpretation for the inverse logit of the coefficients?

Is there a way to have an interpretation on the "original" variable i.e. the proportion itself? Say for example the outcome variable was a proportion/scale of pain (ranging continuously from 0 (no pain) to 1 (extreme pain)), it wouldn't make much sense to say that a one unit increase in X increases the odds of pain by Z... Instead I am interested in knowing by how much does pain increase (on my 0 to 1 scale) given a one unit increase in X.

Hope this makes sense, Thanks.

library(tidyverse)

data <- as_tibble(Titanic) %>% 
  group_by(Class, Sex, Age) %>%
  mutate(cohort_size = sum(n)) %>% 
  ungroup() %>%
  filter(cohort_size > 0 & Survived == "Yes") %>% 
  mutate(prop_survived = n / cohort_size)
data
#> # A tibble: 14 x 7
#>    Class Sex    Age   Survived     n cohort_size prop_survived
#>    <chr> <chr>  <chr> <chr>    <dbl>       <dbl>         <dbl>
#>  1 1st   Male   Child Yes          5           5        1     
#>  2 2nd   Male   Child Yes         11          11        1     
#>  3 3rd   Male   Child Yes         13          48        0.271 
#>  4 1st   Female Child Yes          1           1        1     
#>  5 2nd   Female Child Yes         13          13        1     
#>  6 3rd   Female Child Yes         14          31        0.452 
#>  7 1st   Male   Adult Yes         57         175        0.326 
#>  8 2nd   Male   Adult Yes         14         168        0.0833
#>  9 3rd   Male   Adult Yes         75         462        0.162 
#> 10 Crew  Male   Adult Yes        192         862        0.223 
#> 11 1st   Female Adult Yes        140         144        0.972 
#> 12 2nd   Female Adult Yes         80          93        0.860 
#> 13 3rd   Female Adult Yes         76         165        0.461 
#> 14 Crew  Female Adult Yes         20          23        0.870

model <- glm(
  prop_survived ~ Class + Sex + Age,
  weights = cohort_size,
  family = binomial(link = "logit"),
  data = data
)
summary(model)
#> 
#> Call:
#> glm(formula = prop_survived ~ Class + Sex + Age, family = binomial(link = "logit"), 
#>     data = data, weights = cohort_size)
#> 
#> Deviance Residuals: 
#>     Min       1Q   Median       3Q      Max  
#> -4.1356  -1.7126   0.7812   2.6800   4.3833  
#> 
#> Coefficients:
#>             Estimate Std. Error z value Pr(>|z|)    
#> (Intercept)   2.0438     0.1679  12.171  < 2e-16 ***
#> Class2nd     -1.0181     0.1960  -5.194 2.05e-07 ***
#> Class3rd     -1.7778     0.1716 -10.362  < 2e-16 ***
#> ClassCrew    -0.8577     0.1573  -5.451 5.00e-08 ***
#> SexMale      -2.4201     0.1404 -17.236  < 2e-16 ***
#> AgeChild      1.0615     0.2440   4.350 1.36e-05 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> (Dispersion parameter for binomial family taken to be 1)
#> 
#>     Null deviance: 671.96  on 13  degrees of freedom
#> Residual deviance: 112.57  on  8  degrees of freedom
#> AIC: 171.19
#> 
#> Number of Fisher Scoring iterations: 5
exp(coef(model))
#> (Intercept)    Class2nd    Class3rd   ClassCrew     SexMale    AgeChild 
#>  7.72017801  0.36128255  0.16901595  0.42414659  0.08891625  2.89082630
boot::inv.logit(coef(model))
#> (Intercept)    Class2nd    Class3rd   ClassCrew     SexMale    AgeChild 
#>  0.88532344  0.26539865  0.14457967  0.29782509  0.08165573  0.74298519

Created on 2024-02-01 with reprex v2.0.2

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