mice's pool.compare gives "Error: No glance method for objects of class call" for lmerTest models
mice's pool.compare gives "Error: No glance method for objects of class call" for lmerTest models
我正在尝试比较使用多重插补构建的两个模型。当我尝试比较模型时，鼠标的 pool.compare() 给出错误：class 调用的对象没有一目了然的方法或错误：'fit1' 和 [= 的插补数量不等14=]，即使我使用的是相同的估算数据集。这是一个可重现的例子：
library(mice)
library(miceadds)
library(lmerTest)
imp <- mice(nhanes, maxit = 2, m = 4)
summary(m0 <- pool(with(imp, lmerTest::lmer(bmi ~ 1 + (1 | chl)))))
summary(m1 <- pool(with(imp, lmerTest::lmer(bmi ~ 1 + hyp + (1 | chl)))))
pool.compare(m0, m1)
Error: No glance method for objects of class call
您需要在 pooling 之前比较对象。而且顺序很重要，m1 > m0。 （注意：我这里用的是lme4）
library(mice)
library(miceadds)
set.seed(42)
imp <- mice(nhanes, maxit = 2, m = 4)
summary(pool(m0 <- with(imp, lme4::lmer(bmi ~ 1 + (1 | chl)))))
# boundary (singular) fit: see ?isSingular
# estimate std.error statistic df p.value
# (Intercept) 26.60791 0.9722573 27.36715 18.24326 4.440892e-16
summary(pool(m1 <- with(imp, lme4::lmer(bmi ~ 1 + hyp + (1 | chl)))))
# boundary (singular) fit: see ?isSingular
# estimate std.error statistic df p.value
# (Intercept) 27.2308286 3.759095 7.2439857 5.181367 0.0006723643
# hyp -0.5310514 2.746281 -0.1933711 4.928222 0.8543848658
pool.compare(m1, m0)
# $call
# pool.compare(fit1 = m1, fit0 = m0)
#
# $call11
# with.mids(data = imp, expr = lme4::lmer(bmi ~ 1 + hyp + (1 |
# chl)))
#
# $call12
# mice(data = nhanes, m = 4, maxit = 2)
#
# $call01
# with.mids(data = imp, expr = lme4::lmer(bmi ~ 1 + (1 | chl)))
#
# $call02
# mice(data = nhanes, m = 4, maxit = 2)
#
# $method
# [1] "wald"
#
# $nmis
# age bmi hyp chl
# 0 9 8 10
#
# $m
# [1] 4
#
# $qbar1
# (Intercept) hyp
# 27.2308286 -0.5310514
#
# $qbar0
# (Intercept)
# 26.60791
#
# $ubar1
# [1] 6.916910 3.560812
#
# $ubar0
# [1] 0.8786098
#
# $deviances
# NULL
#
# $Dm
# [,1]
# [1,] 0.03739239
#
# $rm
# [1] 1.118073
#
# $df1
# [1] 1
#
# $df2
# [1] 10.76621
#
# $pvalue
# [,1]
# [1,] 0.850268
我正在尝试比较使用多重插补构建的两个模型。当我尝试比较模型时，鼠标的 pool.compare() 给出错误：class 调用的对象没有一目了然的方法或错误：'fit1' 和 [= 的插补数量不等14=]，即使我使用的是相同的估算数据集。这是一个可重现的例子：
library(mice)
library(miceadds)
library(lmerTest)
imp <- mice(nhanes, maxit = 2, m = 4)
summary(m0 <- pool(with(imp, lmerTest::lmer(bmi ~ 1 + (1 | chl)))))
summary(m1 <- pool(with(imp, lmerTest::lmer(bmi ~ 1 + hyp + (1 | chl)))))
pool.compare(m0, m1)
Error: No glance method for objects of class call
您需要在 pooling 之前比较对象。而且顺序很重要，m1 > m0。 （注意：我这里用的是lme4）
library(mice)
library(miceadds)
set.seed(42)
imp <- mice(nhanes, maxit = 2, m = 4)
summary(pool(m0 <- with(imp, lme4::lmer(bmi ~ 1 + (1 | chl)))))
# boundary (singular) fit: see ?isSingular
# estimate std.error statistic df p.value
# (Intercept) 26.60791 0.9722573 27.36715 18.24326 4.440892e-16
summary(pool(m1 <- with(imp, lme4::lmer(bmi ~ 1 + hyp + (1 | chl)))))
# boundary (singular) fit: see ?isSingular
# estimate std.error statistic df p.value
# (Intercept) 27.2308286 3.759095 7.2439857 5.181367 0.0006723643
# hyp -0.5310514 2.746281 -0.1933711 4.928222 0.8543848658
pool.compare(m1, m0)
# $call
# pool.compare(fit1 = m1, fit0 = m0)
#
# $call11
# with.mids(data = imp, expr = lme4::lmer(bmi ~ 1 + hyp + (1 |
# chl)))
#
# $call12
# mice(data = nhanes, m = 4, maxit = 2)
#
# $call01
# with.mids(data = imp, expr = lme4::lmer(bmi ~ 1 + (1 | chl)))
#
# $call02
# mice(data = nhanes, m = 4, maxit = 2)
#
# $method
# [1] "wald"
#
# $nmis
# age bmi hyp chl
# 0 9 8 10
#
# $m
# [1] 4
#
# $qbar1
# (Intercept) hyp
# 27.2308286 -0.5310514
#
# $qbar0
# (Intercept)
# 26.60791
#
# $ubar1
# [1] 6.916910 3.560812
#
# $ubar0
# [1] 0.8786098
#
# $deviances
# NULL
#
# $Dm
# [,1]
# [1,] 0.03739239
#
# $rm
# [1] 1.118073
#
# $df1
# [1] 1
#
# $df2
# [1] 10.76621
#
# $pvalue
# [,1]
# [1,] 0.850268