Questions tagged [data-imputation]

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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70 views

Multiple Imputation in SPSS for RCT

I have conducted a randomised controlled trial design (2 groups - experimental and control) with data collection at two time points (T1 and T2). I want to use the Multiple Imputation Method in SPSS to ...
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536 views

missForest Data imputation vs. MICE using RF as imputation method?

Is the missForest package a special case of MICE using Random Forest as imputation (for just a single imputation)? The missForest algorithm is described here: https://academic.oup.com/bioinformatics/...
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Problems estimating a “Bayesian version of FIML”

I am anticipating that my question exposes some basic ignorance about how mcmc works, but here we go: In an attempt to deal with missing data I am trying to simultaneously estimate a regression model ...
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Best resources on imputation in R [duplicate]

This is my first question at stats. I need to impute some factors and numbers in my data set in R. What are my best options regarding packages and also a source to read more about the theory.
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What value to impute for informative NA values in R without misleading model

I'm building a model (random forest) in R to predict a rare event (scoring a goal in soccer). I have event-level data, which provides a log of all the actions (pass, tackle, foul, save, shot, goal) ...
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46 views

What type of imputation should I use?

I am learning how to handle missing values in a dataset. I have a table with ~1million entries. At the moment I am trying to deal with a small number of missing values. My data concerns a bicycle-...
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475 views

How to handle or impute large number of missing values?

I am trying to use this dataset to build a predictive model. The hubway.db file contains 3 tables. One of which is is bike_trips...
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48 views

Missing values in a variable depending on the values of another variable

I'm working on a public procurement dataset where I have information on all the participants that presented offers in 358 tenders. I'm analysing relationships between all the companies of the dataset (...
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Missing data imputation

I'm working on a public procurement dataset where I have information on all the participants that presented offers in 358 tenders. I'm analysing relationships between all the companies of the dataset (...
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3answers
525 views

How can missing values in the target variable be substituted using Python?

I have a dataset with some missing values in the target variable (label). Can I use clustering to find those missing label values? What other methods can be applied to resolve such an issue in Python? ...
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133 views

Machine Learning, Imputing values that should be blank

Sometimes data sets contain variables that indicate the presence of an event and the value that represented the event. As an example say a teacher wants to predict the grades of his students. Some of ...
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Is it ever recommended to use mean/multiple imputation when using tree-based predictive models?

Everytime that I am making some predictive model and I have missing data I impute categorical variables with something like "UNKNOWN" and numerical variables with some absurd number that will never be ...
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305 views

Target Encoding: missing value imputation before or after encoding

I want to perform a target encoding for my categorical features although I am not sure when to perform the data imputation if any of them has missing values. Let's say I have a few continuous features,...
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How do I create scale scores from item-level multiply imputed data in R?

I have been able to create 5 multiply imputed datasets using the mice package on R. I computed at the item level so now I have to calculate scale scores for each of the 5 imputed datasets before ...
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1answer
175 views

SPSS regression imputation

I have a dataset of 45 observations (participants), with variables on demographic data and standardized tests. Two standardized test variables are such that they have missing values on only one ...
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Can anyone help me with step by step procedure in MATLAB for missing data imputation using Singular Value Decomposition (SVD [duplicate]

I need step by step procedure for imputing data using SVD in Matlab. Please provide resources if any !
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406 views

Are VAE used for missing data imputation in multivariate time series? If not, what is used?

Multivariate time series are, to the best of my understanding, one of the few cases where Deep Learning still hasn't had its AlexNet moment. I'm especially interested to the case where most of the ...
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How do I impute data that is only partially missing?

I want to impute some missing data. I am interested in the number of months someone was unemployed between ages 18-21. This variable is bounded at 0-48. However, for some individuals, I have partial ...
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360 views

Is Structurally Missing Data a subset of Missing at Random Data?

I'm quite familiar with MCAR, MAR and MNAR (NMAR) data but I have just come across a new (for me) term: Structurally Missing Data (SMD). According to this page, Structurally missing data is data ...
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1answer
347 views

Polynomial regression for missing value imputation

I am trying to impute missing values by fitting higher degree polynomial. I have highly autocorralated time series meaning each value at t must be close to t-1. There are some noise and missing ...
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1answer
33 views

Recommended methods for replacing missing data?

We conducted a pre-post attitudinal survey measuring “Attitudes toward STEM” (28-items; α > .90) and “Multi-Ethnic Identity” (12-items; α > .90) among 50 middle schoolers. Students skipped items ...
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How to localize points from an incomplete distance matrix in R?

Suppose you have 3 shops and 2 supply units, and you only know the 6 pairwise (Euclidean, assuming 2D) distances between each shop and each supply unit, but not the pairwise distances between the ...
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527 views

Imputing and handling class imbalance

I have data with missing values. My $y$ is imbalanced (20% to 80%). a) is it at all possible to balance (e. via Smote) and Impute (e. via Mice) or will the results become too unreliable? b) if a) ...
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Partial imputation of missing dates

I'm working with dataframes (one for each of 185 locations) that shows sums of occurrences for each calendar date. There are no 0 values for occurrences in the entire dataset. There are several ...
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49 views

Missing data in regression

I am researching the effect of different marketing mix variables (e.g., price promotion, innovation) on the market share. More specifically, I want to analyze the effect of different marketing mix ...
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1answer
259 views

Predicting spendings overall and spendings for subcategories

I have a Dataset containing information about spendings of customers in various shops. There are 10 spending variables related to some categories (like spendings on clothing, spendings on hardware, ...
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127 views

Regression with missing Y’s

I use publicly available EU-Silc data to estimate the market price of social dwellings (subsidized dwellings). However my X variables are almost perfectly available,...
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292 views

Imputation and nested cross-validation

I am planning to do a nested cross-validation analysis using regularized regression. The inner loop will be used for model tuning and the outer loop for model assessment (test set). Because some data ...
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34 views

Can I correct for randomly missing data where missingness is has a known relationship to the error term?

Suppose I have a population of observations I want to model as being drawn from some distributional family, which I believe adequately represents the true distribution. My goal is to estimate the ...
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Imputed values into Shapefile / fortified shapefile

I currently have a shape file with approximately 20 numeric variables. Several of these variables have missing values. As this is a shapefile I do not think using the median or mean as a form of ...
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249 views

How to use MICE in R to fill missing values in test set?

It seems that MICE does not have a "predict" function which allows to use a fitted mids object to predict the missing values in test data set. I can certainly ...
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1answer
118 views

Imputing nested time series data with R

Does anyone know what is the superior algorithm to impute data in time series? I had strong dropouts over time because it was free to participants how many times to participate in my study (otherwise ...
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1answer
102 views

Proper Imputation and bias-correction on degrading signal with Kalman Filtering?

A signal degrades in its quality. Some signals are far more robust to degradation while others are not. We will simulate degradation by randomly removing values from a function and then applying ...
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407 views

Missing data imputation that can handle large data

I am looking for a reasonably scaling missing data imputation approach for big data (e.g. a well-scaling version of kNN - the standard versions we tried so far just ran out of memory) that fulfills ...
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619 views

How does Excel interpolate / imputate missing values in time-series when fitting a line to a plot?

I have a scatter plot in Excel (upper part of the screenshot) of time-series data. In-between the values that I plot (to the left), are some missings. I fit a (linear) line to those values and display ...
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Shouldn't we consider larger standard errors for effect measures or outcomes that are converted in meta-analysis?

There are methods to convert effect measures in meta-analysis (pdf). There are also methods to convert outcomes; at least, I am aware of the conversion described in Furukawa et al. (2005) from ...
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How do I impute clustered data that is not time-series data?

The goal of my research is to understand whether MRI imaging characteristics can predict tumor pathology. The data consists of resected tumor samples, with multiple samples per patient. On the MRI, we ...
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Why are missing values MNAR harder to impute than MCAR or MAR?

Reading papers related to the imputation of missing values related to the -omics field, systematically imputation algorithms were less accurate when imputing MNAR compared to imputing MCAR. My ...
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365 views

Imputing values with linear regression, valid strategy or creating biases?

I am practicing on the titanic competition from kaggle. In the dataset the Age variable has a number of missing values and I am now left with the choice of what to ...
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1answer
83 views

Imputing binary variable when no 0s, only 1s are available

I'm trying to impute missing values for a binary variable (values 0 and 1) with some challenging data (of about 1 million observations). The data can be divided into two groups: in group 1, we know ...
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1answer
1k views

Predictive Mean Matching as Single Imputation?

Multiple imputation is known to be advantageous compared to single imputation. However, in practice there are often non-statistical reasons why multiple imputation can not be used (e.g. the data ...
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data imputation of missing values in non-normally distributed explanatory variables

I have been told that mean imputation of missing values is inappropriate when the variables underlying distribution is non-normal. my variable is contiunous (but bound at 100) and most observations ...
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87 views

While performing label encoding or imputation, what should i do to the column with mostly 0's as values which is irrelevant to what column is about?

My DataFrame consists of 2919 rows. Now, for example I have this column "2ndFlrSF" 2ndFlrSF: Second floor's Area in square feet and these are the values in it after I run my Pandas command ...
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141 views

Handling missing data for participants who have not completed any standardised measures and have only provided demographic answers

When managing missing data, how many questions should participant have completed, at a minimum, before imputing the remainder of their missing data? For example, a number of my participants only ...
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24 views

Imputation to Result in Known Total

I am using R and Amelia to impute missing data for the number of homeless children in several locations. There is information ...
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472 views

Extracting Factor Scores of Latent Variables after CFA in AMOS

I plan to extract the latent variables' factor scores after conducting Confirmatory Factor Analysis (CFA). I will use these factor scores as explanatory variables for my next statistical procedure - ...
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1answer
74 views

Predicting the probabilities of sales opportunities

I want to predict the probabilities of sales opportunities using a binary classification algorithmn. However after using logistic regression my results do not seem realistic. This could be due to ...
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1answer
409 views

Impute binary outcome variable for GLM using Stan in R

My outcome variable is a series of Bernoulli trials where some values are missing y $\in$ {0, 1, NA} How do you impute NA values for an outcome variable in rstan in the context of a GLM, assuming ...
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1answer
626 views

Imputing missing outcome data

I saw the other link (Multiple imputation for outcome variables) discussing missing outcome data imputation for complete case analysis. However, I have missing outcome data as well as missing ...
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Is there a way to estimate regression coefficients?

I'm currently working on a simulation study (based on empirical data) and for this simulation I created a model with multiple interaction terms. The interaction terms are between categorical variables,...

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