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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16 views

univariate imputation

I have a problem imputing a variable tobacco use for a survey dataset with replicate weights. Stata does not have a code for this due to statistical issues related to this matter: ...
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0answers
10 views

Multiple Imputation for Spatial Models

I'm trying to estimate various spatial models (SAR, SDM, SEM) but have missing data throughout my variables. The mice package in R gives a straightforward solution when none of the variables with a ...
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0answers
26 views

R MICE imputation failing

I am really baffled about why my imputation is failing in R's Mice 2.22 package. I am attempting a very simple operation with the following data frame: ...
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0answers
23 views

Taking only a single data set from a multiple imputation?

If I use a package like R's mice to do multiple imputation, then only select the first of the resulting imputations and use that as a single imputation -- ignoring ...
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14 views

EM algorithm - data imputation

Suppose we have iid sample of random vector $(X,Y,Z)$, where $x_i, y_i, z_i$ denotes corresponding realizations for the components. Assume also, that $(X,Y,Z)$ has a three-dimensional Gaussian ...
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0answers
42 views

Missing data not at random - Advice needed on method

I have been developing a logistic regression model based on retrospective data from a national trauma database of head injury in the UK. The key outcome is 30 day mortality (denoted as "Survive" ...
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1answer
57 views

How to handle missing data in a small $n$ large $k$ machine learning scenario?

I have a sample size $N=130$ and $1000$ variables. I am using machine learning techniques (SVM) for analysing the data. Some variables in the dataset have values that are so huge that they must be ...
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1answer
11 views

Group variables seeing the class

I need to perform a classification model (logistic regression, pnn or neural network). I'm doing the part of data preparation in R. I have a nominal variable, that has more than 800 levels. I need to ...
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0answers
60 views

What are the pros and cons of using median imputation to handle missing value?

I have to choose between median or mean imputation to handle missing values. I feel median imputation will work better because it is a number that is already present in the data set and is less ...
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23 views

Impute missing data of a variable

I'm currently working on spatial spillovers in agriculture at the municpal level with cross-section data. But, I do have missing values for the investment in capital at the municipalities though the ...
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0answers
45 views

Imputation of missing values for doing PCA in R [duplicate]

I have a dataset with approximately 4000 rows and 150 columns. I want to predict the values of a single column (= target). The data is on cities (demography, social, economic, ... indicators). A lot ...
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59 views

R - basic multiple imputation with the mi package

Consider the following R code: ...
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1answer
54 views

Measuring longitudinal data where individuals have missing observations

We have a longitudinal panel of X users with their online spending patterns and are trying to measure certain metrics within the panel. We have time series information about the users such as their ...
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24 views

R- yaimpute - standarize value before KNN for imputation

I need to impute data before running a logistic regression. I'm trying the package yaimpute but I realized that it doesn't standarize before imputing. I created a sample matrix to view so: ...
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2answers
82 views

Semantics rules? A classification challenge

Suppose we make interviews on a large number of households in which we ask, among other things, the sex and age of the individuals living in the household, and also who among these individuals is the ...
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0answers
18 views

Missing data and evaluating parametric assumptions when using MPlus

I am conducting in my study a paired samples t-test and a path analysis using MPlus. I have read that MPlus uses FIML, which is a strong method for dealing with missing data, and because of it I don't ...
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0answers
19 views

Are pooled results from multiple imputation equivalent to a posterior mean?

I am fairly new to multiple imputation and trying to be sure I understand the approach. Say I have a data set with missing values, so I create 5 imputed data sets using multiple imputation by ...
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1answer
69 views

Data imputation for meta analysis using mice package in R

I have a data-set with 32 effect size estimates- only 11 of which report a value for the continuous moderator of interest (the samples anxiety level). A complete case analysis (restricted to the 11 ...
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2answers
29 views

Single-Imputation on Age Needed?

I am hoping someone can help me with this answer. I did a Single Imputation on my data set for age (<5% missing). My adviser asked the following "It’s strange to me to impute a demographic ...
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1answer
30 views

Data imputation for statistical analysis

Here is the situation: I have an individual level data set $X$ where each row is a person $i$ and each column denote characteristics of $i$. The problem is that my data is missing an important ...
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68 views

Random Forest for data imputation

Currently I am using Random Forest approach for Missing Values Imputation from missForest package in R. I faced the following problem: the algorithm works much longer than any other imputation ...
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1answer
66 views

Imputation model: Pooled model is insignificant. How to interpret?

I have ordinal data on three IVs ranging from 1 to 5 as below: IV1: Not at all Important - Very Important IV2: Not at all Satisfied - Very Satisfied IV3: Performs much Worse - Performs much better ...
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0answers
45 views

Mean imputation/estimation of missing data

Could someone please refer me to papers that have imputed the mean to missing values of a continuous variable? (i.e. papers that have used mean imputation) I have imputed my missing IMD values using ...
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1answer
44 views

Difference between imputation and forecast

what is the difference between imputation and forecasting? All i know, forecasting is the term used in time series analysis, which means predicting the future value by considering the trend of the ...
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1answer
116 views

R regression with categorical response variable

I have four variables, two are categorical and two are numeric: ...
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1answer
40 views

how to impute missing values on numpy array created by train_test_split from pandas.DataFrame?

I'm working on the dataset with lots of NA values with sklearn and pandas.DataFrame. I implemented different imputation strategies for different columns of the dataFrame based column names. For ...
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1answer
72 views

Stepwise regression modeling using multiply imputed data sets

After multiply imputing data, it is natural to estimate regression models on the data. When multiple predictors are available, sometimes stepwise regression is used for model building (forward ...
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18 views

Missing Value in Commodities Prices

I am trying to model the prices of four energetic commodities with ARIMA models in R. Unfortunately the price series is not regular, as for some days, like Christmas, no price is given. My series is ...
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1answer
36 views

Overfitting on Missing Value Imputations

When performing Missing Value imputations, should we be concerned about overfitting the data? Why or why not? For example: If I impute a variables missing value using a CART regression tree, should ...
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114 views

J48 Handling Missing Value with Tree based Imputation

Aloha, currently i have some trouble and question zu implement some kind of special missing value handling in WEKA J48 algorithm using WEKA JAVA API. I want to test the performance of SHAPIRO ...
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24 views

Predict/impute one cell of matrix using all other cells

The question: I want to predict/impute one missing cell of a matrix using the contents of all other cells. Anyone have ideas on how to do this? The context: The matrix is n people's responses to m ...
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1answer
38 views

Imputation and Distributions

Suppose you impute a variable using a normal distribution with mean 10 and sd 5. Is it better to draw 1000 random samples from this normal distribution, take the average, and then use this to impute ...
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1answer
65 views

How to get only positive values when imputing data?

Suppose age is normally distributed with mean 20 and standard deviation 5. How do you ensure that you get only positive values when you sample age from this distribution? I am trying to impute ...
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1answer
90 views

using cluster information in multiple imputation

i need to impute a dataset all categorical variables before doing analysis. I can just simply impute with mode of all data or a variable. I belief that better option will be to classify the subjects ...
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3answers
470 views

using random forest for missing data imputation in categorical variables ( in R)

I have following type of associated data. The following example step to generate associated variable. p number of variables and n is number of observations. ...
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1answer
97 views

Individual and overall RMSE for multivariate data

I have a dataset which contains missing values, and I'm using imputation packages (Rs mi and ...
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3answers
519 views

How to handle with missing values in order to prepare data for feature selection with LASSO?

My situation: small sample size: 116 binary outcome variable long list of explanatory variables: 44 explanatory variables did not come from the top of my head; their choice was based on the ...
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1answer
46 views

How do you suppress imputed data and only display results for the pooled data?

I've done 15 imputations and am now running various tests on the new data, but all I'm really interested in is the pooled results at the end. The long list of imputations (especially in a test like an ...
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2answers
82 views

Imputing missing observation in multivariate time series

Suppose I have a dataframe consisting of six time series. In this dataframe, some observations are missing, meaning at some timepoints all time series contain a NA-value. In R, one possible imputation ...
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1answer
57 views

Why is collinearity a problem when imputing missing values?

I'm imputing missing values using R's mice package. My data has three numeric variables and a class variable so I am using a ...
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0answers
54 views

Validation - correctly compare and validated imputation models

I've seen a lot of interesting questions here about multiple imputation and also great answers that helped me a lot to impute my data. I've used Predictive Mean Matching, EMB and I would like to use ...
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183 views

Imputation by regression in R

Say I have below example data, where rows are observations and columns are variables, and NAs stand for missing values. ...
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0answers
55 views

Adding values for missing dates in time-series data using SAS

I am having trouble figuring out how to create entries for missing dates in my time-series data. I'm trying to use proc expand and I'm able to get entries for dates between the already existing dates ...
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1answer
24 views

svm manual implementation to find missing values

how svm algorithm used to impute the missing data in a dataset. I need a manual implementation of svm algorithm to find missing values with example
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2answers
117 views

using neighbor information in imputing data or find off-data (in R)

I have dataset with assumption that nearest neighbors are best predictors. Just a perfect example of two-way gradient visualized- Suppose we have case where few values are missing, we can easily ...
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5answers
322 views

How to perform imputation of values in very large number of data points?

I have a very large dataset and about 5% random values are missing. These variables are correlated with each other. The following example R dataset is just a toy example with dummy correlated data. ...
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1answer
133 views

optimal scaling / CATREG for imputed data

I have a data set with 5 different kinds of nutrient statuses and I want to see whether they are associated with categorical / ordinal grades at school. I have multiple covariates which I will ...
2
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2answers
188 views

Fast missing data imputation in R for big data that is more sophisticated than simply imputing the means?

I need a package for missing data imputation in R. But since I am dealing with big data, the number of missing data entries can also be high. The packages which impute using mean or median are of ...
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2answers
233 views

Simultaneous imputation of multiple binary variables in R

I have a dataset with multiple correlated binary variables (0/1). Can anyone point me toward a solution for imputing completely random missing values based on information in the other variables? ...
4
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0answers
103 views

Is the following procedure to measure the quality of an imputation ok?

I'd like to compare different kinds of imputation techniques, i.e. methods which allow to fill missing data fields in a data frame. For now, I'm only using the R package ...