泊松回归AIC表

时间:2015-03-05 20:21:48

标签: r regression glm model-comparison

我在R中进行了一系列泊松回归,然后根据AIC对我的模型进行排名。但是我得到了这个结果:

 > aictab(cand.set = Cand.models, sort = TRUE)

  Model selection based on AICc :

     K AICc Delta_AICc AICcWt Cum.Wt   LL
Mod7 4  Inf        NaN    NaN     NA -Inf
Mod6 3  Inf        NaN    NaN     NA -Inf
Mod5 3  Inf        NaN    NaN     NA -Inf
Mod4 3  Inf        NaN    NaN     NA -Inf
Mod3 2  Inf        NaN    NaN     NA -Inf
Mod2 2  Inf        NaN    NaN     NA -Inf
Mod1 2  Inf        NaN    NaN     NA -Inf

每个模型分别给出拦截结果,但没有给出AIC ...

> Cand.models[[1]]

Call:  glm(formula = D ~ A, family = poisson(), data = d)

Coefficients:
(Intercept)        Slope  
   -0.17356      0.07058  

Degrees of Freedom: 251 Total (i.e. Null);  250 Residual
Null Deviance:      55.35 
Residual Deviance: 54.99    AIC: Inf

当我用family = gaussian(identity)做同样的事情时,我得到了结果。当我进行泊松回归时,AIC怎么会不工作?

任何帮助将不胜感激。

2 个答案:

答案 0 :(得分:1)

很难理解为什么在没有看到您的数据或代码的情况下获得结果(下次提示)。但AIC(c)模型选择肯定可以与泊松回归一起使用 - 下面是一个例子:

library(AICcmodavg)

# make some dummy data (taken from: http://stats.stackexchange.com/questions/11096/how-to-interpret-coefficients-in-a-poisson-regression)
treatment     <- factor(rep(c(1, 2), c(43, 41)), 
                    levels = c(1, 2),
                    labels = c("placebo", "treated"))
improved      <- factor(rep(c(1, 2, 3, 1, 2, 3), c(29, 7, 7, 13, 7, 21)),
                    levels = c(1, 2, 3),
                    labels = c("none", "some", "marked"))    
numberofdrugs <- rpois(84, 10) + 1    
healthvalue   <- rpois(84, 5)   
y             <- data.frame(healthvalue, numberofdrugs, treatment, improved)


# Model selection using AICc
# setup a list of candidate models
Cand.models <- list( )

Cand.models[[1]] <- glm(healthvalue~numberofdrugs+treatment+improved, data=y, family=poisson)
Cand.models[[2]] <- glm(healthvalue~treatment, data=y, family=poisson)

# create a vector of names to trace back models in set
Modnames <- paste("mod", 1:length(Cand.models), sep = " ")

# generate AICc table
aictab(cand.set = Cand.models, modnames = Modnames, sort = TRUE)

答案 1 :(得分:0)

确保公式中的D由整数非0值组成,否则Poisson glm LL倾向于爆炸。