r-mice | 易学教程

R - how to pass formula to a with(df, glm(y ~ x)) construction inside a function

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问题 I'm using the mice package in R to multiply-impute some missing data. I need to be able to specify a formula that is passed to a with(df, glm(y ~ x)) construction inside of a function. This with() construction is the format used by the mice package to fit the regression model separately within each of the imputed datasets. However, I cannot figure out the scoping problems preventing me from successfully passing the formula as an argument. Here is a reproducible example: library(mice) data

Multiple Imputed datasets - pooling results

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问题 I have a dataset containing missing values. I have imputed this dataset, as follows: library(mice) id <- c(1,2,3,4,5,6,7,8,9,10) group <- c(0,1,1,0,1,1,0,1,0,1) measure_1 <- c(60,80,90,54,60,61,77,67,88,90) measure_2 <- c(55,NA,88,55,70,62,78,66,65,92) measure_3 <- c(58,88,85,56,68,62,89,62,70,99) measure_4 <- c(64,80,78,92,NA,NA,87,65,67,96) measure_5 <- c(64,85,80,65,74,69,90,65,70,99) measure_6 <- c(70,NA,80,55,73,64,91,65,91,89) dat <- data.frame(id, group, measure_1, measure_2, measure_3

Run glm.mids on a subset of imputed data from mice (R)

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问题 I get an error when I try to run glm.mids on a subset of a mids imputation object: library(mice) imp2 = mice(nhanes) glm.mids( (hyp==2)~bmi+chl, data=imp2, subset=(age==1) ) gives the cryptic error message "Error in eval(expr, envir, enclos) : ..1 used in an incorrect context, no ... to look in" even though the syntax works with regular glm on the original dataset: glm( (hyp==2)~bmi+chl, data=nhanes, subset=(age==1) ) The documentation ?glm.mids doesn't specifically address subset but says

Leftover NAs after imputing using mice

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问题 What is going on in the following? #create some data library(data.table) library(mice) myData = data.table(invisible.covariate=rnorm(10), visible.covariate=rnorm(10), category=factor(sample(1:3,10, replace=TRUE)), treatment=sample(0:1,10, replace=TRUE)) myData[,outcome:=invisible.covariate+visible.covariate+treatment*as.integer(category)] myData[,invisible.covariate:=NULL] myData[treatment == 0,untreated.outcome:=outcome] myData[treatment == 1,treated.outcome:=outcome] #impute missing values

recode mice mids object

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问题 I have a mids object created from mice . I would like to recode some imputed variables and retain the mids object. I know that I could convert the mids object to "long" with complete() , but I want to keep the mids object since it has some additional uses. Here's an example using the nhanes dataset. Running mice() creates 5 imputed datasets for the variables in nhanes . I'm focusing on hyp . library(mice) names(nhanes) nhanes$hyp #[1] NA 1 1 NA 1 NA 1 1 1 NA NA NA 1 2 1 NA 2 2 1 2 NA 1 1 1

Stripplot in MICE

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问题 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. 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

Can MICE pool complete GLM output binary logistic regression?

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问题 I am running a logistic regression with a binary outcome variable on data that has been multiply imputed using MICE. It seems straightforward to pool the coefficients of the glm model: imp=mice(nhanes2, print=F) imp$meth fit0=with(data=imp, glm(hyp~age, family = binomial)) fit1=with(data=imp, glm(hyp~age+chl, family = binomial)) summary(pool(fit1)) However, I can't figure out a way to pool other output generated by the glm. For instance, the glm function produces AIC, Null deviance and

Error with svydesign using imputed data sets

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问题 I am analyzing an imputed dataset using svydesign but I am getting an error. Below is the code: library(mitools) library(survey) data(nhanes) nhanes$hyp <- as.factor(nhanes$hyp) imp <- mice(nhanes,method=c("polyreg","pmm","logreg","pmm"), seed = 23109) des<-svydesign(id=~1, strat=~age, data=imputationList(imp)) Error in as.data.frame.default(data, optional = TRUE) : cannot coerce class ""call"" to a data.frame I am following the tutorial from this page: http://r-survey.r-forge.r-project.org

Automatically compare nested models from mice's glm.mids

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问题 I have a multiply-imputed model from R's mice package in which there are lots of factor variables. For example: library(mice) library(Hmisc) # turn all the variables into factors fake = nhanes fake$age = as.factor(nhanes$age) fake$bmi = cut2(nhanes$bmi, g=3) fake$chl = cut2(nhanes$chl, g=3) head(fake) age bmi hyp chl 1 1 <NA> NA <NA> 2 2 [20.4,25.5) 1 [187,206) 3 1 <NA> 1 [187,206) 4 3 <NA> NA <NA> 5 1 [20.4,25.5) 1 [113,187) 6 3 <NA> NA [113,187) imput = mice(nhanes) # big model fit1 = glm

Automatically compare nested models from mice's glm.mids

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I have a multiply-imputed model from R's mice package in which there are lots of factor variables. For example: library(mice) library(Hmisc) # turn all the variables into factors fake = nhanes fake$age = as.factor(nhanes$age) fake$bmi = cut2(nhanes$bmi, g=3) fake$chl = cut2(nhanes$chl, g=3) head(fake) age bmi hyp chl 1 1 <NA> NA <NA> 2 2 [20.4,25.5) 1 [187,206) 3 1 <NA> 1 [187,206) 4 3 <NA> NA <NA> 5 1 [20.4,25.5) 1 [113,187) 6 3 <NA> NA [113,187) imput = mice(nhanes) # big model fit1 = glm.mids((hyp==2) ~ age + bmi + chl, data=imput, family = binomial) I want to test the significance of each