R:使用新的lme4软件包的bootMer()进行自举二进制混合模型逻辑回归 [英] R: Bootstrapped binary mixed-model logistic regression using bootMer() of the new lme4 package
问题描述
我想使用新的lme4软件包(当前为开发人员版本)的新bootMer()功能.我是R的新手，不知道应该为它的FUN参数编写哪个函数.它说它需要一个数值向量，但我不知道该函数将执行什么操作.因此，我有一个混合模型公式，该公式强制转换为bootMer()，并具有许多重复项.因此，我不知道该外部函数有什么作用?它应该是引导方法的模板吗?引导程序中是否尚未实现引导方法?那么为什么他们需要一个外部的利益统计"呢?我应该使用哪个感兴趣的统计数据?
I want to use the new bootMer() feature of the new lme4 package (the developer version currently). I am new to R and don't know which function should I write for its FUN argument. It says it needs a numerical vector, but I have no idea what that function will perform. So I have a mixed-model formula which is cast to the bootMer(), and have a number of replicates. So I don't know what that external function does? Is it supposed to be a template for bootstrapping methods? Aren't bootstrapping methods already implemented in he bootMer? So why they need an external "statistic of interest"? And which statistic of interest should I use?
以下语法是否适合工作? R不断产生错误，指出FUN必须是数值向量.我不知道如何将估算值与拟合值"分开，甚至我应该首先这样做吗?我只能说我对有趣"的说法迷失了.我也不知道该使用变量"Mixed5"传递混合模型glmer()公式，还是应该传递一些指针和引用?我在示例中看到X(bootMer()的第一个参数是* lmer()对象.我想编写* Mixed5，但它导致了错误.
Is the following syntax proper to work on? R keeps on error generating that the FUN must be a numerical vector. I don't know how to separate the estimates from the "fit" and even should I do that in the first place? I can just say I am lost with that "FUN" argument. Also I don't know should I pass the mixed-model glmer() formula using the variable "Mixed5" or should I pass some pointers and references? I see in the examples that X (the first argument of bootMer() is a *lmer() object. I wanted to write *Mixed5 but it rendered an error.
非常感谢.
我的代码是:
library(lme4)
library(boot)
(mixed5 <- glmer(DV ~ (Demo1 +Demo2 +Demo3 +Demo4 +Trt)^2
+ (1 | PatientID) + (0 + Trt | PatientID)
, family=binomial(logit), MixedModelData4))
FUN <- function(formula) {
fit <- glmer(DV ~ (Demo1 +Demo2 +Demo3 +Demo4 +Trt)^2
+ (1 | PatientID) + (0 + Trt | PatientID)
, family=binomial(logit), MixedModelData4)
return(coef(fit))
}
result <- bootMer(mixed5, FUN, nsim = 3, seed = NULL, use.u = FALSE,
type = c("parametric"),
verbose = T, .progress = "none", PBargs = list())
result
FUN
fit
错误:
Error in bootMer(mixed5, FUN, nsim = 3, seed = NULL, use.u = FALSE, type = c("parametric"), :
bootMer currently only handles functions that return numeric vectors
--------------------------------------------------- - - - - - 更新 - - - - - - - - - - - - - - - - - - - - -------------
-------------------------------------------------------- Update -----------------------------------------------------
我按照Ben的指示编辑了代码.该代码运行得很好，但SE和Biase均为零.您还知道如何从此输出中提取P值(对我来说很奇怪)?我应该使用afex包的mixed()吗?
I edited the code like what Ben instructed. The code ran very good but the SEs and Biases were all zero. Also do you know how to extract P values from this output (strange to me)? Should I use mixed() of afex package?
我的修改代码:
library(lme4)
library(boot)
(mixed5 <- glmer(DV ~ (Demo1 +Demo2 +Demo3 +Demo4 +Trt)^2
+ (0 + Trt | PatientID)
, family=binomial(logit), MixedModelData4))
FUN <- function(fit) {
fit <- glmer(DV ~ (Demo1 +Demo2 +Demo3 +Demo4 +Trt)^2
+ (1 | PatientID) + (0 + Trt | PatientID)
, family=binomial(logit), MixedModelData4)
return(fixef(fit))
}
result <- bootMer(mixed5, FUN, nsim = 3)
result
--------------------------------------------------- ---------更新2 --------------------------------------- --------------
-------------------------------------------------------- Update 2 -----------------------------------------------------
我也尝试了以下操作，但是代码生成警告，但未给出任何结果.
I also tried the following but the code generated warnings and didn't give any result.
(mixed5 <- glmer(DV ~ Demo1 +Demo2 +Demo3 +Demo4 +Trt
+ (1 | PatientID) + (0 + Trt | PatientID)
, family=binomial(logit), MixedModelData4))
FUN <- function(mixed5) {
return(fixef(mixed5))}
result <- bootMer(mixed5, FUN, nsim = 2)
警告消息:
In bootMer(mixed5, FUN, nsim = 2) : some bootstrap runs failed (2/2)
> result
Call:
bootMer(x = mixed5, FUN = FUN, nsim = 2)
Bootstrap Statistics :
WARNING: All values of t1* are NA
WARNING: All values of t2* are NA
WARNING: All values of t3* are NA
WARNING: All values of t4* are NA
WARNING: All values of t5* are NA
WARNING: All values of t6* are NA
--------------------------------------------------- ---------更新3 --------------------------------------- --------------
-------------------------------------------------------- Update 3 -----------------------------------------------------
此代码还生成警告:
FUN <- function(fit) {
return(fixef(fit))}
result <- bootMer(mixed5, FUN, nsim = 2)
警告和结果:
Warning message:
In bootMer(mixed5, FUN, nsim = 2) : some bootstrap runs failed (2/2)
> result
Call:
bootMer(x = mixed5, FUN = FUN, nsim = 2)
Bootstrap Statistics :
WARNING: All values of t1* are NA
WARNING: All values of t2* are NA
WARNING: All values of t3* are NA
WARNING: All values of t4* are NA
WARNING: All values of t5* are NA
WARNING: All values of t6* are NA
推荐答案
这里基本上有两个(简单的)混淆.
There are basically two (simple) confusions here.
- 第一个在
coef()(返回矩阵列表)和fixef()(返回固定效果的向量)之间 系数):我假设您想要的是fixef()，尽管您可能想要类似c(fixef(mixed),unlist(VarCorr(mixed)))的东西. - 第二个是
FUN应该将拟合的模型对象作为输入...
- The first is between
coef()(which returns a list of matrices) andfixef()(which returns a vector of the fixed-effect coefficients): I assume thatfixef()is what you wanted, although you might want something likec(fixef(mixed),unlist(VarCorr(mixed))). - the second is that
FUNshould take a fitted model object as input ...
例如:
library(lme4)
library(boot)
mixed <- glmer(incidence/size ~ period + (1|herd),
weights=size, data=cbpp, family=binomial)
FUN <- function(fit) {
return(fixef(fit))
}
result <- bootMer(mixed, FUN, nsim = 3)
result
## Call:
## bootMer(x = mixed, FUN = FUN, nsim = 3)
## Bootstrap Statistics :
## original bias std. error
## t1* -1.398343 -0.20084060 0.09157886
## t2* -0.991925 0.02597136 0.18432336
## t3* -1.128216 -0.03456143 0.05967291
## t4* -1.579745 -0.08249495 0.38272580
##
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