I am conducting a log binomial regression in R. I want to control for covariates in the model (age and BMI- both continuous variables) whereas the dependent variable is Outc
After some trial and error, using set.seed(123)
:
coefini=coef(glm(Outcome~Group+Age, data=data.1,family =binomial(link = "log") ))
fit2<-glm(Outcome~Group+Age+BMI, data=data.1, family=binomial(link="log"),start=c(coefini,0))
summary(fit2)
Call:
glm(formula = Outcome ~ Group + Age + BMI, family = binomial(link = "log"),
data = data.1, start = c(coefini, 0))
Deviance Residuals:
Min 1Q Median 3Q Max
-1.2457 -0.9699 -0.7725 1.2737 1.6799
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.5816964 1.0616813 -1.490 0.136
Group1 0.4987848 0.3958399 1.260 0.208
Age 0.0091428 0.0138985 0.658 0.511
BMI -0.0005498 0.0331120 -0.017 0.987
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 65.342 on 49 degrees of freedom
Residual deviance: 63.456 on 46 degrees of freedom
AIC: 71.456
Number of Fisher Scoring iterations: 3