Refers to a general class of methods used to "fill in" missing data. Methods used for doing this typically are related to interpolation (http://en.wikipedia.org/wiki/Interpolation) and require assumptions about why the data is missing (e.g. "missing at random")

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24 views

How to perform SVD to impute missing values, a concrete example

This might be a very stupid question, but I have read the great comments regarding how to deal with missing values before applying svd, but I would like to know how it is going to work, if I apply it ...
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8 views

Imputation for Time Series of Accumulated Value

I have a regular time series of accumulated values of a variable (usage) with some missing (sometimes consecutive) intervals. Is there an imputation method that methodologically considers this ...
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0answers
16 views

Should data be normalized before or after imputation of missing data?

I am working on a metabolomics data set of 81 samples x 407 variables with ~17% missing data. I would like to compare a number of imputation methods to see which is best for my data. Is there a ...
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16 views

Why do I get an error when trying to impute missing data using PMM in MICE package in R?

I am trying to compare imputation methods for an 81 samples x 407 variables data set with ~17% missing values. Some of the variables will be correlated, some highly, that is the nature of the data. ...
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1answer
15 views

Which Imputation method to use in MI

I'm making a predictive model. I'm thinking of using MI but not sure which imputation method to use. Is there some metrics or graphs one can compute on the data to see which method is best for which ...
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7 views

RBF SVM image classification with missing features

I have been working on a image classification problem (face recognition especifically) and my test set has some missing values: for some face images only the upper half is avaliable and for others the ...
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1answer
17 views

When is it ok to MI Data with MNAR predictor without further instructions

I have a data set with predictors that are mostly MAR(supposedly), however I do also have one that is likely to be MNAR in the sense that the missing of that predictor depends on an unobserved ...
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1answer
27 views

Opinion on when to impute data

I work with longitudinal data that tends to be “messy.” For example, we collect eye-tracking and physiological measures at multiple time points in young children. This causes data to be missing not ...
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0answers
4 views

efficiently locf by groups in a single R data.table [migrated]

I have a large, wide data.table (20m rows) keyed by a person ID but with lots of columns (~150) that have lots of null values. Each column is a recorded state / ...
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0answers
24 views

Unequal timepoints longitudinal data with missing values

I have a longitudinal data with unequal time points with missing values. I am looking for methods to impute the missing data. I looked at R packages NORM and AMELIA II and SAS procedures PROC MI. All ...
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0answers
22 views

Flexible prediction with neural network or other method

I want to use neural network for my first time, but I need to check if it fits for my case. So, my idea is to teach a model on data like Y = f(X1, X2...XN) and ...
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1answer
47 views

How to deal with dropouts from a waiting list control group?

Many treatment studies compare a treatment group with a waiting list control group, for example to adjust for spontaneous remissions. Unfortunately, many more participants drop out from the waiting ...
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1answer
33 views

Imputation using regression

I have a table of 4 variables where some of the data is missing and I need to impute the missing values, my table is as follows: ...
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18 views

Distinguishing between zeros and missing data

I have a panel data set with 12,000 observations of daily counts of visitors to a number of recreational sites. The data has been given to me with missing values recorded as zeroes. There are also ...
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1answer
50 views

Problems with Missing values

I have a data set for a predictive model(predicting survival rate with certain acute medical condition on some animals) with 25 predictors where around 30% of the predictors are complete, 3 predictors ...
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2answers
27 views

Holt-Winters for Imputation

I have found Holt-Winters seasonal method a very decent method for forecast, specifically for cases where more recent observations are more representative of the near future. The method equally sounds ...
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5answers
3k views

A 6th response option (“I don't know”) was added to a 5-point Likert scale. Is the data lost?

I need a little bit of help salvaging the data from a questionnaire. One of my colleagues applied a questionnaire, but inadvertently, instead of using the original 5-point Likert scale (strongly ...
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0answers
8 views

Can I use estimated relationships from one model to impute predicted response in another model?

I simplified the problem below to the core issue: I need to estimate the future expected incremental response (Sales/Web visits) for a company (Call it Company B) due to introducing a new medium, ...
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1answer
34 views

Popular (single) imputation methods for ordinal variable

I am setting up a monte carlo simulation study in R for a comparison between several imputation methods for ordinal variables. So far, I am planning to use the following imputation methods: Multiple ...
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0answers
10 views

What are some graphical devices for observing effects of imputing data?

I am interested in various imputation techniques and how certain imputation techniques work better than others. Say I had some data set with missing values. I usually try to determine the reason why ...
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0answers
12 views

Randomly erase data given sparseness

I would like to sparsify a data frame given the sparseness value. That is, I would like to randomly delete some data from the dataframe to later use an imputation algorithm to impute the missing ...
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0answers
36 views

Missing value imputation with nearest neighbour

I'm using k-nearest neighbour imputation method for missing values. This method has two tuning parameters: k and the distance metric. I see two options for applying this imputation method: Inside ...
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0answers
24 views

How to test whether social network properties predict a binary outcome?

I'm looking to see if whether social network properties (such as different measures of centrality) predict a binary choice. The first part of the question is, what is the best method to do this? I ...
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47 views

Multiple imputation for predictive analysis using mice package in R

I am using the mice package to impute some missing values, and it works nicely. I am facing a tricky strategic question though. Basically, I am working on predictors of myocardial infarction (time ...
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1answer
61 views

K-nearest neighbour imputation of missing values

I have a dataset where the columns correspond to features and the rows correspond to data points. I have around 5'000 data points and 8 features. Now, I would like to impute the missing values with ...
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14 views

Imputation and sampling weights

a student of mine has around 1650 cases with missing data (under 5% per variable). She wanted to do a single regression imputation in SPSS (I know that this is not the best method but that does not ...
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105 views

Difference between imputation and interpolation?

When dealing with data sets that have missing values, imputation replaces missing values with substituted values while interpolation replaces missing values with calculated values within some range. ...
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12 views

SPSS Mixed model with imputed data and model LLC scores

I am supposed to build several models (it is multilevel analysis of students from different schools). I used multiple imputation for missing data after running MVA and pattern analysis. Now I have 5 ...
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59 views

Data Imputation with Amelia on large dataset: Taking very long time [closed]

I have 761,592 obs for 31 variables on users behaviours towards online ads. Out of 31 variables, 28 are categorical. Many cat. variables have more than 10 categories. I am using Amelia for missing ...
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2answers
76 views

Framework of multiple imputation

I read this paper about ("Multiple Imputation For Missing Data: What Is It And How Can I Use It?")(http://www.csos.jhu.edu/contact/staff/jwayman_pub/wayman_multimp_aera2003.pdf) Does any one have ...
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15 views

Imputation of predictors missing data for logistic modelling

I never used imputation of missing data and I would like to understand the effect of imputation in a specific scenario. Lets suppose that I have a dataset whit some predictors variable and one binary ...
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17 views

Lower Denomination Imputation

Before I start, I will point out that I am very new to imputing data and so any advice would be greatly appreciated. Apologies if there is an obvious answer that I am overlooking. I have a data set ...
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2answers
73 views

Change mean imputation in MICE package

MICE Steps The chained equation process can be broken down into six general steps: Step 1: A simple imputation, such as imputing the mean, is performed for every missing value in the dataset. These ...
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1answer
54 views

Financial time series data: Imputing before or after calculating returns?

I've got several time series of daily prices ($(p_t^j)_{t=1,\dots,n}$ ) of different tradable cards $j=1,\dots,k$. I'd like to calculate the time series of the (log)returns $r_t= ...
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46 views

compare different Imputation method by RMSE

My original dataset : ...
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2answers
87 views

How to create a variable that is present in test data set but not in train?

Im try to do a classification but i have a variable production budget which is present in test dataset and not in train. so how do i proceed. could i impute that variable somehow. i dont want to drop ...
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0answers
20 views

Imputation of a (weird) multivariate outcome

I am working with a dataset in which the outcome of interest is a vector of dates of particular events: (date_1,date_2,date_3,...,date_n). Some of these outcome vectors are completely missing, but I ...
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1answer
182 views

How to know which imputation is best for impute my dataset from Multiple imputation by using mice

I used mice package to impute the missing value as follows: ...
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0answers
17 views

Multiple imputation: manual calculation of new values

I need to impute some data but I find myself in the impossibility of using the model I will eventually use for my analysis as a model of imputation. I have reasons to believe that the var to be ...
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58 views

How to use cross validation when you have missing data & rare events?

I am trying to use repeated cross validation to test my classifier. Moreover, I want to use imputation due to missing values and downsampling due to unbalanced data (I have 88% of my data in the ...
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0answers
31 views

SAS HELP: Imputing both continuous and categorical variables in a single dataset

I am trying to impute data for missing observations within a longitudinal dataset following an arbitrary missing data pattern. Both continuous and categorical variables have missing data. I am ...
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22 views

Estimating unexplained variance from multivariate probit output (for imputation protocol)

I have a use case in which survey data underreports program participation, and I need to impute new recipients from within the survey data. There are two (exogenously provided) sub-objectives: ...
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81 views

python classification without having to impute missing values

I have a dataset that is working nicely in weka. It has a lot of missing values represented by '?'. Using a decision tree, I am able to deal with the missing values. However, on sci-kit learn, I see ...
3
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1answer
199 views

ML covariance estimation from Expectation-Maximization with missing data

Assuming a multivariate normal distribution with missing data, is there a straightforward way to find the maximum likelihood estimate for covariance using an Expectation-Maximization algorithm? NOTE: ...
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0answers
45 views

Missing data in time series

What is the minimum percentage of missing values that is permitted in time series models like ARIMA or Exponential smoothing? What is the best method to impute missing time series data?
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32 views

How to combine multiply imputed datasets created with MICE from different cohorts?

I have data from two separate cohorts. If not imputed I would just rbind() the two datasets and analyse. But due to non-random missing imputation was needed for ...
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1answer
87 views

Analysis of imputed datasets in Stata 14

I am relatively new to multiple imputation (and statistical analysis in general), so I apologize if my question seems naïve to more experienced users. I am dealing with a somewhat large dataset ...
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1answer
77 views

Imputation during pre-processing in data mining

I'm trying to comprehend the most appropriate way to utilize machine learning imputation (e.g., KNN) as a pre-processing step in data mining, and I've run into a few questions that don't have clear ...
3
votes
1answer
383 views

Uncertainty in random forest imputations from R missForest package

I am in the process of imputing missing values for my data set that contains approximately 20 variables and 3,000 observations. Most of the missing data values are contained in 2 of the variables (one ...
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1answer
812 views

Data imputation with preProcess in caret returns less observations than expected

I wonder why preProcess function from R's caret package used for imputation of dataset's missing values returns less observations than in original dataset? For example: ...