Error: provide initial values
I am doing binomial regression in R. I want to control the covariates in the model (age and BMI are continuous variables), whereas the dependent variable is the result (yes or no) and the independent variable is the group (1 or 2).
fit<-glm(Outcome~Group, data=data.1, family=binomial(link="log"))
and it works great.
When I try to put the age in the model, it still works fine. However, when I put BMI in the model, it gives me this:
Error: no valid set of coefficients has been found: please supply starting values
I was offered another combination of initial values such as:
fit<-glm(Outcome~Group+Age+BMI, data=data.1, family=binomial(link="log"), start=c(0,0,0,0)
or even start = (1,4) or start = 4, but it still gives me an error.
It also says:
Error in glm.fit(x = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, :
length of 'start' should equal 4 and correspond to initial coefs for c("(Intercept)", "group1", "age", "bmi")
...
Any help on this would be much appreciated!
Edited: Add a reproducible example.
N=50
data.1=data.frame(Outcome=sample(c(0,0,1),N, rep=T),Age=runif(N,8,58),BMI=rnorm(N,25,6),
Group=rep(c(0,1),length.out=N))
data.1$Group<-as.factor(data.1$Group)
fit<-glm(Outcome~Group, data=data.1, family=binomial(link="log"))
coefini=coef(glm(Outcome~Group+Age+BMI, data=data.1,family =binomial(link = "logit") ))
fit<-glm(Outcome~Group+Age+BMI, data=data.1, family=binomial(link="log"),start=coefini)
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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
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