R: The estimate parameter is different between GLM model and optim() package

I want to find estimate parameter with optim() package in R. And I compare my result with GLM model in R. The code is

d <- read.delim("http://dnett.github.io/S510/Disease.txt")  d$disease=factor(d$disease)  d$ses=factor(d$ses)  d$sector=factor(d$sector)  str(d)    oreduced <- glm(disease~age+sector, family=binomial(link=logit), data=d)  summary(oreduced)    y<-as.numeric(as.character(d$disease))  x1<-as.numeric(as.character(d$age))  x2<-as.numeric(as.character(d$sector))    nlldbin=function(param){    eta<-param[1]+param[2]*x1+param[3]*x2    p<-1/(1+exp(-eta))    -sum(y*log(p)+(1-y)*log(1-p),na.rm=TRUE)  }  MLE_estimates<-optim(c(Intercept=0.1,age=0.1,sector2=0.1),nlldbin,hessian=TRUE)    MLE_estimatesenter  

The result with GLM model

Coefficients:              Estimate Std. Error z value Pr(>|z|)      (Intercept) -2.15966    0.34388  -6.280 3.38e-10 ***  age          0.02681    0.00865   3.100 0.001936 **   sector2      1.18169    0.33696   3.507 0.000453 ***  ---  Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1  

And with optim()

$par    Intercept         age     sector2   -3.34005918  0.02680405  1.18101449   

Can someone please tell me why its different and how to fix this? Thank you

https://stackoverflow.com/questions/66937680/r-the-estimate-parameter-is-different-between-glm-model-and-optim-package April 04, 2021 at 11:05AM