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rdataframeregressiondata-analysis

Logistic regression on single subject data?

I have a data frame with n=18 participants. There are 90 observations across 3 IVs and 1 binary DV (see below for shortened example).

data <- data.frame(age = c(21, 30, 25, 41, 29, 33),IQ=c(60,70,80,90,100,200),SAT=(2400,2200,1400,1550,1470,1300), sex = factor(c(1, 2, 1, 2, 1, 2), labels = c("Female", "Male")))

I've already done a logistic glm regression using the entire sample. This was the code used:

model2<-glm(Sex~Age+IQ+SAT,family=binomial(link="logit"))
summary(model2)

I want to compute the beta coefficients for each participant-- Age, IQ, and SAT scores to predict Sex. I should have 18 coefficients to plot. Thanks in advance!!

Solution

  • You can try this approach using broom and tidyverse:

    library(tidyverse)
library(broom)
#Code
data %>% mutate(id=1:n()) %>% group_by(id) %>%
  do(fitmod = tidy(glm(sex~age+IQ+SAT,family=binomial(link="logit"), data = .))) %>% 
  unnest(fitmod)