Binary Logistic Regression with BFGS using package maxLik - r

Question

I tried binary logistic regression with BFGS using maxlik, but i have included the feature as per the syntax i attached below, but the result is, but i get output like this
Maximum Likelihood estimation
BFGS maximization, 0 iterations
*Return code 100: Initial value out of range.
https://docs.google.com/spreadsheets/d/1fVLeJznB9k29FQ_BdvdCF8ztkOwbdFpx/edit?usp=sharing&ouid=109040212946671424093&rtpof=true&sd=true (this is my data)*
library(maxLik)
library(optimx)
data=read_excel("Book2.xlsx")
data$JKLaki = ifelse(data$JK==1,1,0)
data$Daerah_Samarinda<- ifelse(data$Daerah==1,1,0)
data$Prodi2 = ifelse(data$Prodi==2,1,0)
data$Prodi3 = ifelse(data$Prodi==3,1,0)
data$Prodi4 = ifelse(data$Prodi==4,1,0)
str(data)
attach(data)
ll<- function(param){
mu <- param[1]
beta <- param[-1]
y<- as.vector(data$Y)
x<- cbind(1, data$JKLaki, data$IPK, data$Daerah_Samarinda, data$Prodi2, data$Prodi3, data$Prodi4)
xb<- x%*%beta
pi<- exp(xb)
val <- -sum(y * log(pi) + (1 - y) * log(1 - pi),log=TRUE)
return(val)
}
gl<- funtion(param){
mu <- param[1]
beta <- param[-1]
y <- as.vector(data$Y)
x <- cbind(0, data$JKLaki,data$IPK,data$Daerah_Samarinda,data$Prodi2,data$Prodi3,data$Prodi4)
sigma <- x*beta
pi<- exp(sigma)/(1+exp(sigma))
v= y-pi
vx=as.matrix(x)%*%as.vector(v)
gg= colSums(vx)
return(-gg)}
mle<-maxLik(logLik=ll, grad=gl,hess=NULL,
start=c(mu=1, beta1=0, beta2=0, beta3=0, beta4=0, beta5=0, beta6=0,beta7=0), method="BFGS")
summary(mle)
can i get some help, i tired get this solution, please.

Answer 1

I have been able to optimize the log-likelihood with the following code :
library(DEoptim)
library(readxl)
data <- read_excel("Book2.xlsx")
data$JKLaki <- ifelse(data$JK == 1, 1, 0)
data$Daerah_Samarinda <- ifelse(data$Daerah == 1, 1, 0)
data$Prodi2 <- ifelse(data$Prodi == 2, 1, 0)
data$Prodi3 <- ifelse(data$Prodi == 3, 1, 0)
data$Prodi4 <- ifelse(data$Prodi == 4, 1, 0)
ll <- function(param, data)
{
mu <- param[1]
beta <- param[-1]
y <- as.vector(data$Y)
x <- cbind(1, data$JKLaki, data$IPK, data$Daerah_Samarinda, data$Prodi2, data$Prodi3, data$Prodi4)
xb <- x %*% beta
pi <- exp(mu + xb)
val <- -sum(y * log(pi) + (1 - y) * log(1 - pi))
if(is.nan(val) == TRUE)
{
return(10 ^ 30)
}else
{
return(val)
}
}
lower <- rep(-500, 8)
upper <- rep(500, 8)
obj_DEoptim_Iter1 <- DEoptim(fn = ll, lower = lower, upper = upper,
control = list(itermax = 5000), data = data)
lower <- obj_DEoptim_Iter1$optim$bestmem - 0.25 * abs(obj_DEoptim_Iter1$optim$bestmem)
upper <- obj_DEoptim_Iter1$optim$bestmem + 0.25 * abs(obj_DEoptim_Iter1$optim$bestmem)
obj_DEoptim_Iter2 <- DEoptim(fn = ll, lower = lower, upper = upper,
control = list(itermax = 5000), data = data)
obj_Optim <- optim(par = obj_DEoptim_Iter2$optim$bestmem, fn = ll, data = data)
$par
par1 par2 par3 par4 par5 par6 par7
-350.91045436 347.79576145 0.05337466 0.69032735 -0.01089112 0.47465162 0.38284804
par8
0.42125664
$value
[1] 95.08457
$counts
function gradient
501 NA
$convergence
[1] 1
$message
NULL