使用glmulti拟合准家族? [英] fitting quasi family using glmulti?

问题描述

我使用了glmulti包中的glmulti函数来为泊松误差分布数据获得最佳的glm模型.那里没有问题.一旦获得最佳模型，就可以使用卡方检验来获得p值，并对输入模型的每个变量进行检验统计.我遇到的唯一问题是数据过于分散，Zuur书和Crawley都建议使用准族函数来校正过度分散.除了glmulti函数不允许拟合准函数外，这本身不是问题.

I used the glmulti function in the glmulti package to obtain the best glm model for poisson error distributed data. No problems there. Once I had obtained the best model, I used the Chi-square test to obtain p-values and test statistics for each of the variables entered into the model. The only problem I am encountering is that the data is overdispersed and the Zuur book and Crawley both suggest using the quasi family function to correct for overdispersion. This in itself is not a problem except that the glmulti function does not allow fits to quasi functions.

我的问题是，是否可以使用具有泊松误差分布的glmulti获取我的最佳模型，然后将最佳模型输出拟合为准函数，这是做事的不正确方法，并且如果还有其他建议，那么任何人都可以提供.

The question I have, is whether obtaining my best model using glmulti with a poisson error distribution and then fitting the best model output to a quasi function is the incorrect way to go about doing things and if there are any other suggestions anyone could offer.

我还运行了正态分布数据的glmulti(将家庭指定为高斯，将链接指定为身份)，这确实可行，但是如果我违反任何主要规则，请告诉我.

I also ran glmulti for normally distributed data (specifying family as gaussian and link as identity) and this did work, but if I am violating any major rules, please do let me know.

推荐答案

另一个建议的问题是"qaic"在glmulti中似乎不起作用. 对于我来说，这可行:

Problem with the other answer that was suggested is that "qaic" doesn't seem to work in glmulti. For me this worked though:

library(bbmle)
qaicmod = function (fit) qAIC(fit, dispersion=with(fit,sum((weights * residuals^2)[weights > 0])/df.residual) ) 

glmulti.out <- glmulti(XXX model formula XXX, 
  data = mydata,crit = "qaicmod", 
   confsetsize = 5, fitfunction = "glm", 
   family = poisson)

这使用常规的泊松GLM，但会基于估计的色散系数来计算QAIC. 在qaicmod函数的色散参数中，您还可以将包括所有变量在内的完整拟泊松GLM的估计色散系数(我见过一些统计数据建议这样做)，即代替

This uses a regular poisson GLM but calculates the QAIC based on the estimated dispersion coefficient. In the dispersion argument of the qaicmod function you could also put the estimated dispersion coefficient of a full quasipoisson GLM with all variables included (some statisticions I have seen recommend this), i.e. to use instead

disp <<- summary(fullmodel)$dispersion
qaicmod = function (fit) qAIC(fit, dispersion=disp) 

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