看来mi
在过去几年的某个时候,软件包进行了相当大的重写。
以下教程详细概述了“旧”的处理方式:http://thomasleeper.com/Rcourse/Tutorials/mi.html http://thomasleeper.com/Rcourse/Tutorials/mi.html
“新”的做事方式(坚持 Leeper 的模拟演示)看起来像这样:
#load mi
library(mi)
#set seed
set.seed(10)
#simulate some data (with some observations missing)
x1 <- runif(100, 0, 5)
x2 <- rnorm(100)
y <- 2*x1 + 20*x2 + rnorm(100)
mydf <- cbind.data.frame(x1, x2, y)
mydf$x1[sample(1:nrow(mydf), 20, FALSE)] <- NA
mydf$x2[sample(1:nrow(mydf), 10, FALSE)] <- NA
# Convert to a missing_data.frame
mydf_mdf <- missing_data.frame(mydf)
# impute
mydf_imp <- mi(mydf_mdf)
尽管函数名称已更改,但这实际上与“旧”的处理方式非常相似。
最大的变化(从我的角度来看)是替换了以下“旧”功能
lm.mi(formula, mi.object, ...)
glm.mi(formula, mi.object, family = gaussian, ...)
bayesglm.mi(formula, mi.object, family = gaussian, ...)
polr.mi(formula, mi.object, ...)
bayespolr.mi(formula, mi.object, ...)
lmer.mi(formula, mi.object, rescale=FALSE, ...)
glmer.mi(formula, mi.object, family = gaussian, rescale=FALSE, ...)
.
以前,用户可以使用这些函数之一为每个估算数据集计算模型,然后使用mi.pooled()
(or coef.mi()
如果我们遵循 Leeper 的例子)。
在当前版本中mi
(我安装了 v1.0),最后这些步骤似乎已合并为一个函数,pool()
. The pool()
函数似乎读取在上述插补过程中分配给变量的族和链接函数,然后使用以下方法估计模型bayesglm
使用如下所示的指定公式。
# run models on imputed data and pool the results
summary(pool(y ~ x1 + x2, mydf_imp))
##
## Call:
## pool(formula = y ~ x1 + x2, data = mydf_imp)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.98754 -0.40923 0.03393 0.46734 2.13848
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.34711 0.25979 -1.336 0.215
## x1 2.07806 0.08738 23.783 1.46e-13 ***
## x2 19.90544 0.11068 179.844 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.7896688)
##
## Null deviance: 38594.916 on 99 degrees of freedom
## Residual deviance: 76.598 on 97 degrees of freedom
## AIC: 264.74
##
## Number of Fisher Scoring iterations: 7
看起来我们即将恢复模拟 Beta 值(2 和 20)。换句话说,它的行为符合预期。
让我们采用一组稍微大一些的数据,并简单地模拟随机效应,只是为了获得分组变量。
mydf2 <- data.frame(x1 = rep(runif(100, 0, 5), 20)
,x2 = rep(rnorm(100, 0, 2.5), 20)
,group_var = rep(1:20, each = 100)
,noise = rep(rnorm(100), 20))
mydf2$y <- 2*mydf2$x1 + 20*mydf2$x2 + mydf2$noise
mydf2$x1[sample(1:nrow(mydf2), 200, FALSE)] <- NA
mydf2$x2[sample(1:nrow(mydf2), 100, FALSE)] <- NA
# Convert to a missing_data.frame
mydf2_mdf <- missing_data.frame(mydf2)
show(mydf2_mdf)
## Object of class missing_data.frame with 2000 observations on 5 variables
##
## There are 4 missing data patterns
##
## Append '@patterns' to this missing_data.frame to access the corresponding pattern for every observation or perhaps use table()
##
## type missing method model
## x1 continuous 200 ppd linear
## x2 continuous 100 ppd linear
## group_var continuous 0 <NA> <NA>
## noise continuous 0 <NA> <NA>
## y continuous 0 <NA> <NA>
##
## family link transformation
## x1 gaussian identity standardize
## x2 gaussian identity standardize
## group_var <NA> <NA> standardize
## noise <NA> <NA> standardize
## y <NA> <NA> standardize
Since missing_data.frame()
似乎正在翻译group_var
作为连续的,我使用change()
函数来自mi
重新分配给"un"
对于“无序分类”,然后按上述方式进行。
mydf2_mdf <- change(mydf2_mdf, y = "group_var", what = "type", to = "un" )
# impute
mydf2_imp <- mi(mydf2_mdf)
现在,除非1.0版本mi
删除了以前版本的功能(即可用的功能lmer.mi
and glmer.mi
),我假设在公式中添加随机效应应该指出pool()
到适当的lme4
功能。然而,最初的错误消息表明情况并非如此。
# run models on imputed data and pool the results
summary(pool(y ~ x1 + x2 + (1|group_var), mydf2_imp))
## Warning in Ops.factor(1, group_var): '|' not meaningful for factors
## Warning in Ops.factor(1, group_var): '|' not meaningful for factors
## Error in if (prior.scale[j] < min.prior.scale) {: missing value where TRUE/FALSE needed
按照我的警告消息并从我的因子中提取整数确实可以让我得到一个估计,但结果表明pool()
仍在估计固定效应模型bayesglm
并保持我尝试的随机效应不变。
summary(pool(y ~ x1 + x2 + (1|as.numeric(as.character(group_var))), mydf2_imp))
##
## Call:
## pool(formula = y ~ x1 + x2 + (1 | as.numeric(as.character(group_var))),
## data = mydf2_imp)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.93633 -0.69923 0.01073 0.56752 2.12167
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 1.383e-01 2.596e+02 0.001
## x1 1.995e+00 1.463e-02 136.288
## x2 2.000e+01 8.004e-03 2499.077
## 1 | as.numeric(as.character(group_var))TRUE -3.105e-08 2.596e+02 0.000
## Pr(>|t|)
## (Intercept) 1
## x1 <2e-16 ***
## x2 <2e-16 ***
## 1 | as.numeric(as.character(group_var))TRUE 1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.8586836)
##
## Null deviance: 5384205.2 on 1999 degrees of freedom
## Residual deviance: 1713.9 on 1996 degrees of freedom
## AIC: 5377
##
## Number of Fisher Scoring iterations: 4
我的问题是:
- 是否可以使用以下方法轻松生成汇总随机效应估计
mi
?, and
- 如果是,怎么办?