计算ICC置信区间逻辑回归

时间:2017-09-20 06:37:43

标签: r logistic-regression mixed-models random-effects

我运行了逻辑回归模型,我试图确定随机效应在模型中的重要性。我正在为null和full模型执行此操作,但我将在此处显示null模型。

这是我到目前为止所做的:

> null_model <- glmer(disease ~ (1|origin), family = binomial(link='logit'), data = mydata)
> summary(null_model) 
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
 Family: binomial  ( logit )
Formula: disease ~ (1 | origin)
   Data: mydata

     AIC      BIC   logLik deviance df.resid 
   336.1    343.5   -166.0    332.1      294 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-1.8177 -0.5405 -0.5405  0.9260  2.3248 

Random effects:
 Groups Name        Variance Std.Dev.
 origin (Intercept) 1.47     1.212   
Number of obs: 296, groups:  origin, 22

Fixed effects:
            Estimate Std. Error z value Pr(>|z|)   
(Intercept)  -1.0916     0.3694  -2.955  0.00312 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1


> icc(null_model)
Generalized linear mixed model
 Family: binomial (logit)
Formula: disease ~ (1 | origin)

  ICC (origin): 0.308802

我现在想要确定的是 LRT (以确定ICC与零显着不同)和ICC的CI

我尝试使用bootMer创建一个自举分发来计算CI,但我不知道我是否已正确完成它以及结果是否正确。

> calc.icc <- function(y) {
    sumy <- summary(y)
    (sumy$varcor$origin[1]) / (sumy$varcor$origin[1] + sumy$sigma^2)
}

> calc.icc(null_model)

> boot.icc <- bootMer(null_model, calc.icc, nsim=1000)

> quantile(boot.icc$t, c(0.025, 0.975))
        2.5%        97.5% 
1.394684e-12 5.745278e-01 

如果有人可以请求协助,我们将不胜感激!

0 个答案:

没有答案