Joachim Schork
  • Member for 6 years, 10 months
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6 votes
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Imputation by regression in R
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11 votes

Even though this thread is a bit old, I am sure some people are still trying to find a solution in this thread. Therefore I want to add an example how you could use the mice package for regression ...

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2 answers
4 votes
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Simulate MAR (Missing at Random) data
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3 votes

With your data, MAR could be generated as follows: # Create a normal data frame (not necessary, but makes the following easier) data <- data.frame(y) colnames(data) <- c("y", "x") # Generate ...

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1 answers
6 votes
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Can you perform a multiple imputation on data that is missing not at random (MNAR)?
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Is there a way to identify if your data is MNAR, MAR, or MCAR? There is Little's MCAR test, which can evaluate if your missings are MCAR. More informations can be found here on page 12. As far as I ...

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1 answers
4 votes
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Should I run variable selection within MICE for Multiple Imputation?
2 votes

In addition to your source you could have a look at van Buuren (2012, p. 128). Here, the author of mice explains some reasons to select a subset of variables in more detail. I don't know a built in ...

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2 answers
4 votes
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How to combine multiply imputed datasets created with MICE from different cohorts?
2 votes

I suggest to combine the data of the 2 cohorts in the forefront and impute afterwards. To include the information about the 2 different cohorts into your imputation, you could add a cohort variable. ...

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2 votes
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Missing values in a large data set
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One common way to deal with missing values is (multiple) imputation. A popular R package is mice, which by default uses a multinomial logistic regression for the imputation of categorical variables ...

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1 answers
0 votes
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What is a convincing way to replace missing values in income data in R
2 votes

Dropping your observations with NA's and negative values would not be a good idea, since there is probably a systematic related to the missingness. If you don't take care about that your estimates ...

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1 answers
5 votes
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Multiple Imputation and "Conditional Missing" values
2 votes

I suggest to impute in 2 steps. Impute Condition first and then impute Dependent1-3 for the subset of Condition = Yes. # I extended your data a bit, to get more cases. TestData <- data.frame(...

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1 answers
2 votes
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Difference in success of mice imputation across variables
1 votes

Based on your plots, you can not say which of the distributions are correct or wrong. These kind of plots are usually used to see whether the multiple imputations lead to similar imputed values (i.e. ...

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1 answers
2 votes
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How to handle missing data for observations occurring before data collection of certain features started?
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Imputation is one of the standard approaches to deal with missing values. In the following, you can find an example how to perform imputation in R. # Example data RecID <- 1:6 Speed <- c(25, 30,...

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1 votes
1k views
Missing values for multiple columns
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A common approach is Multivariate Imputation by Chained Equations (MICE). A paper about the topic can be found here. There are several statistical softwares, which are able to perform MICE. Below you ...

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