有限混合tweedie

Question

我试图估计一个有限混合的tweedie (或复合Poisson-γ)分布。我已经搜索了我能想到的任何资源，却没有找到任何关于如何做到这一点的资源。

我目前正在尝试使用flexmix软件包在R编写一个不同的M步驱动程序，如第12-14页的flexmix所概述的那样。下面是我的代码，它依赖于cplm包：

tweedieClust <- function(formula = .~.,offset = NULL){
require(tweedie)
require(cplm)
require(plyr)
require(dplyr)
retval <- new("FLXMC", weighted = TRUE, formula = formula, dist = "tweedie",
              name = "Compound Poisson Clustering")

retval@defineComponent <- expression ({
    predict <- function(x, ...) {
        pr <- mu
    }
    logLik <- function(x, y, ...){
        dtweedie(y, xi = p, mu = mu, phi = phi) %>%
             log
    }
    new("FLXcomponent",
        parameters=list(coef=coef),
        logLik=logLik, predict=predict,
        df=df)
})
retval@fit <- function (x, y, w, component) {
    fit <- cpglm(formula = y ~ x, link = "log", weights=w, offset=offset)
    with(list(coef = coef(fit), df = ncol(x),mu = fit$fitted.values,
              p = fit$p, phi = fit$phi),
         eval(retval@defineComponent))
}
retval
}

但是，这会导致以下错误：

dtweedie芯片中的错误(y，xi = p，mu = mu，phi = phi)：对不整合数组的二进制操作

有没有人做过或看到了有限混合的tweedie分布？你能指出正确的方向来完成这一点，使用柔性混合或其他吗？

Answer 1

问题就在重量部分的某个地方，如果你去掉它，它就会起作用：

tweedieClust <- function(formula = .~.,offset = NULL){
  require(tweedie)
  require(statmod)
  require(cplm)
  require(plyr)
  require(dplyr)
  retval <- new("FLXMC", weighted = F, formula = formula, dist = "tweedie",
            name = "Compound Poisson Clustering")

  retval@defineComponent <- expression ({
    predict <- function(x, ...) {
      pr <- mu
    }
    logLik <- function(x, y, ...){
      dtweedie(y, xi = p, mu = mu, phi = phi) %>%
        log
    }
    new("FLXcomponent",
        parameters=list(mu=mu,xi=p,phi=phi),
        logLik=logLik, predict=predict,
        df=df)
  })
  retval@fit <- function (x, y, w, component) {
    fit <- cpglm(formula = End~.,data=dmft, link = "log")
    with(list(df = ncol(x), mu = fit$fitted.values,
              p = fit$p, phi = fit$phi),
         eval(retval@defineComponent))
  }
  retval
}

示例：

library(flexmix)
data("dmft", package = "flexmix")
m1 <- flexmix(End ~ .,data=dmft, k = 4, model = tweedieClust())