Ihavenothing
我使用flexmix估计的结果和这个程序的结果完全不一样，将代码粘贴如下，希望您能够给予指导，非常感谢：
#prepare data
slope1 <- -.3;slope2 <- .3;slope3 <- 1.8; slope4 <- 0.5;intercept1 <- 1.5
age <- sample(seq(18,60,len=401), 200)
grade <- sample(seq(0,100,len=401), 200)
not_smsa <- sample(seq(-2,2,len=401), 200)
unemployment <- rnorm(200,mean=0,sd=1)
wage <- intercept1 + slope1*age +slope2*grade + slope3*not_smsa + rnorm(length(age),0,.15)
y <- wage
X <- cbind(1, age , grade , not_smsa)
mydata <- cbind.data.frame(X,y)
anso <- lm(wage ~ age + grade + not_smsa,
data = mydata)
vi <- c(coef(anso),0.01,0.02,0.03,0.04,0.1)
#function
fmm <- function(beta) {
mu1 <- c(X %*% beta[1:4])
mu2 <- c(X %*% beta[5:8])
p1 <- 1 / (1 + exp(-beta[9]))
p2 <- 1-p1
llk <- p1*dnorm(y,mu1)+p2*dnorm(y,mu2)
-sum(log(llk),na.rm=T)
}
fit <- optim(vi,fmm , method = "BFGS", control = list(maxit=50000), hessian = TRUE)
fit$par
library(flexmix)
flexfit <- flexmix(wage ~ age + grade + not_smsa, data = mydata, k = 2)
flexfit$par
c1 <- parameters(flexfit,component=1)
c2 <- parameters(flexfit, component=2)
