MICE中的地带图 [英] Stripplot in MICE

问题描述

我在R中使用软件包MICE进行多次插补.我只用数值变量完成了几种插补，插补方法是预测均值匹配，当我使用命令stripplot(插补数据集的名称)时，我可以看到所有变量的观测值和插补值.

I´m using the package MICE in R to do multiple imputations. I´ve done several imputations with only numerical variables, the imputation method is predictive mean matching, and when I use the command stripplot(name of imputed dataset) I get to see the observed and imputed values of all the variables.

当我尝试对分类变量和数值变量进行归因时，就会出现问题.然后，插补方法是将PMM用于数字变量，将逻辑回归用于分类变量. stripplot-command仅显示数字变量.我尝试使用以下命令指定edu是具有2个值的类别变量:

The problem occurs when I try to do imputation on a combination of categorical and numerical variables. The imputation method then is PMM for the numerical variables, and logistical regression for the categorical ones. The stripplot-command only shows me the numerical variables. I tried to specify with these commands were edu is a categorical variable with 2 values:

stripplot(imp, imp$edu)
stripplot(imp, names(imp$edu))

我得到了这个错误:

stripplot.mids(imp，imp $ edu)错误:无法填充扩展公式.

Error in stripplot.mids(imp, imp$edu) : Cannot pad extended formula.

有人知道我该如何为数字变量和分类变量绘制观测值和推算值?

Does anyone know how I can plot the values of the observed and the imputed values for both the numerical and the categorical variables?

推荐答案

您可以尝试做的一件事是将估算出的dataset作为data.frame进行检索，并仅使用常规的绘图功能即可.首先检索包含缺少值的原始数据集的数据集(imp是mouses.mids对象，即正在运行的鼠标的结果)

One thing you can try is to retrieve the imputed dataset as a data.frame and just use normal plotting functions. First retrieve the datasets including the original dataset with missing values (imp is the mice.mids object i.e. result of running mice)

impL <- complete(imp,"long",include = T)

接下来添加一个虚拟值，指示要估算哪些数据集

Next add a dummy indicating which datasets are imputed

impL$Imputed <- factor(impL$.imp >0,labels = c("Observed","Imputed"))

然后，您可以仅对每个变量使用绘图功能.这样的好处是您可以创建更好的图.例如，使用ggplot(软件包ggplot2)在分类变量上创建条形图:

Then you can just use plotting functions for each variable. This has the benefit that you can create nicer plots. For example using ggplot (package ggplot2) to create a barplot on a categorical variable:

ggplot(impL[which(!is.na(impL$var1)),],aes(x = var1)) + 
geom_bar(aes(y = ..prop.., group = Imputed)) + facet_wrap(Imputed ~ .,ncol=1,nrow=2)

包含!is.na是为了避免绘制NA条. var1是要绘制的变量.对于连续变量，您可以创建一个密度图.

The !is.na is included to avoid the plotting of an NA bar. var1 is the variable you want to plot. For a continuous variable you might create a density plot.

ggplot(impL, aes(x = var2, colour = Imputed)) + geom_density()

要查看所有唯一的插补，可以在aes括号内添加group = .imp.希望这会有所帮助

To look at all the unique imputations you can add group = .imp within the aes brackets. Hope this helps

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