Tagged Questions

0
votes
0answers
18 views

Strategy to model, then predict / impute with very sparse variable?

Please excuse vague title. I am currently using an unsupervised SOM clustering approach to try to determine values for a variable that is mostly missing. I have ~8000 observations of 10 variables, the ...
3
votes
2answers
110 views

Imputing a missing variable based on common variables with another data set

I have 2 data sets: $A$ and $B$. The variables are common to both data sets with the exception of two, which are both missing in A. Let's call those two additional variables: $b_1$ and $b_2$. We ...
3
votes
1answer
217 views

Imputation with Random Forests

I have two questions on using random forest (specifically randomForest in R) for missing value imputation (in the predictor space). 1) How does the imputation algorithm work - specifically how and ...
1
vote
1answer
63 views

Multiple imputations via MICE package: is there an upper limit for number of imputations?

When performing mulitple imputations with the MICE package in R, I found some resources (http://www.statisticalhorizons.com/more-imputations) that recommend around 10 imputations. I was wondering if ...
2
votes
1answer
107 views

Hot deck imputation: validity of double imputation and selection of deck variables for a regression

Background: I had a data set containing 212 observations with a lots of missing values. Most of the IVs and DVs are categorical (DVs are ordinal) in nature. There are 3 DVs and about 30 IVs. My ...
1
vote
1answer
71 views

Confusion related to calculation of conditional distribution

I have this confusion related to the calculation of a conditional distribution suppose $y_n = N(0,w)$ $p(o_n|y_n) = N(D.y_n,\phi)$ How do I calculate $p(y_n|o_n)$ Actually I was reading this ...
3
votes
2answers
191 views

Should I use missing value using imputation or listwise or pairwise deletion methods?

I have 60,000 data and around 45% of them is missing and the missing values are random. Can I simply use listwise or pairwise deletion or do I have to use imputation? If imputation is recommended ...
2
votes
0answers
81 views

Strategies for Recovering Missing Data

I'm working on the following missing data problem to learn more about stats, probability, and machine learning, but I'm not really making progress solving it: I have a group of unordered, non-unique ...
1
vote
1answer
287 views

Adjustment for missing values of the categorical variables in a data set

I Have a data set containing about 40 categorical variables. I am trying to factor analyze them. But each categorical variable contains a good number of missing values. Some of them are simply because ...
2
votes
2answers
208 views

How to impute an ordinal variable with MICE but prevent it from taking one value?

I have an ordinal variable, overall_tumor_grade, that can take on values of 1, 2, 3, or X if the measurement is indeterminable. There are some NAs that I want to impute using the mice package in R, ...
1
vote
1answer
109 views

Why does a model perform worse after reintroducing observations with missing data imputed?

My dataset contains about 12% missing data, and much of the missing data is grouped along observations (not randomly scattered or along columns). I optimized a regression method after removing the ...
2
votes
0answers
237 views

How to impute data without missing at random?

Recently I got a global longitudinal data from several countries, and each county has one outcome variable and two predictors from 1995 to 2008. I found one of the predictors is always missing in each ...
4
votes
1answer
152 views

MCAR assumption is plausible should I do MI?

My data consists of measurements on patients with cancer and the variables are some indicators regarding the cancer as well as the stage and the grade of the cancer and some personal info about the ...
6
votes
2answers
332 views

Missing rates and multiple imputation

Is there a limit which is the least acceptable when using multiple imputation (MI)? For example can I use MI if the missing values in a variable are the 20% of the cases while and other variables ...
2
votes
0answers
163 views

Dealing with R type of variables when doing multiple imputation with the mi package

Volume 45 of the Journal of Statistical Software contains articles about packages that deal with imputation of missing data, one of which is the mi package. In the PDF article regarding that package ...
2
votes
1answer
147 views

Imputing/instrumenting for missing variables in a case-control study

I'm combining two surveys in a case-control design. Survey B is drawn from the "case" population, and includes all the variables I need for analysis, plus some extras. Survey A samples a general ...
2
votes
2answers
321 views

R function to use for multiple imputation and determining if data is MAR or MCAR

Can anyone tell me which R function to use for multiple imputation? Also, what should I do to determine if the missing data are MAR or MCAR or not?
4
votes
2answers
1k views

Multiple imputation on single subscale item or subscale scores?

Recently I am conducting a research on the relationship between motivation/attitude variables (Gardner's model) and English language proficiency in the Philippines. I encountered a problem: missing ...
1
vote
2answers
173 views

Best imputation method for stochastic noisy data?

What is the best imputation method for a dataset consisting of stochastic data? For example, let's say you have a table of security returns. In some cases the missings are random, in other cases are ...
6
votes
1answer
87 views

Getting an average measurement based on two raters for cases where data is missing for one rater

Context: I'm investigating behaviour in a clinical study involving children. I had both parents and teachers completing questionnaires to inform an understanding of the same underlying constructs, ...
7
votes
1answer
214 views

Dealing with missing data due to variable not being measured over initial period of a study

I was recently consulting a researcher in the following situation. Context: data were collected over four years at around 50 participants per year (participants had a specific diagnosed clinical ...