[英]How to bootstrap R-squared of a mixed model?
I'm trying to obtain bootstrapped R^2 for a mixed effect model.我正在尝试为混合效果 model 获得自举 R^2。 As there is already only a workaround to obtain the conditional and marginal R^2, I tried to bootstrap these statistic based on an example given by statmethods for bootstrapping a single statistic.
由于已经只有一种解决方法来获得条件和边际 R^2,因此我尝试根据statmethods给出的用于引导单个统计信息的示例来引导这些统计信息。 The code works but the bias and standard error are always zero.
该代码有效,但偏差和标准误差始终为零。
library(lme4)
library(boot)
data(Dyestuff, package = "lme4")
model <- lmer(Yield ~ 1|Batch, data=Dyestuff)
summary(model)
r.squaredGLMM(model)
rsq <- function(formula, data, indices) {
d <- data[indices,]
model.fit <- lmer(Yield ~ 1|Batch, data=Dyestuff)
fit.r.squared <- r.squaredGLMM(model.fit)
return(summary(fit.r.squared[,2]))
}
set.seed(101)
results <- boot(data=Dyestuff, statistic=rsq,
R=1000, formula=Yield ~ 1|Batch)
results
Bootstrap Statistics :
original bias std. error
t1* 0.4184874 0 0
t2* 0.4184874 0 0
t3* 0.4184874 0 0
t4* 0.4184874 0 0
t5* 0.4184874 0 0
t6* 0.4184874 0 0
Shouldn't the conditional and marginal R^2 also change when I bootstrap a model?当我引导 model 时,条件和边际 R^2 不应该也改变吗? And is there any other way to obtain bootstrapped conditional and marginal R^2?
还有其他方法可以获得自举条件和边际 R^2 吗?
rsq <- function(formula, data, indices) {
d <- data[indices,]
model.fit <- lmer(Yield ~ 1|Batch, data = d)
fit.r.squared <- r.squaredGLMM(model.fit)
return(fit.r.squared[,2])
}
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