Tagged Questions

When the data present lack of information (gaps), i.e., are not complete. Hence, it is important to consider this feature when performing a analysis or test.

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0answers
17 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 ...
1
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1answer
22 views

Imputation of a binary variable by Bayesian logistic regression

In the book "Flexible Imputation of Missing Data" by Van Buuren, the following algorithm is presented I think I understand the algorithm as given, but I would like to know what is the "more ...
0
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0answers
7 views

How to check file integrity of download if checksum is not posted [on hold]

I plan to download a large amount of files from a certain website, using iMacros to automate the process for me. However, this website is notorious for corrupted downloads. There are no checksums ...
0
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1answer
18 views

Missing factor levels after logistic regression glm()

I am quite new in the R universe, so please excuse me if the question is too simple.. I would like to perform a logistic regression on a marketing data set (only categorical variables), of the form ...
0
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0answers
18 views

MAR Vs. MNAR: attribute's missingness determined by the value of another attribute

Suppose I have a training data set that predicts the whether a person is unemployed or not using a decision tree. I have a certain set of attributes that I use to predict this. But for all samples ...
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2answers
36 views

Managing 'prefer not to says' in sensitive questionnaires

Consider a questionnaire where we ask someone about their sexuality. The five options, for simplicity, are: Heterosexual Homosexual Bisexual Other 'Prefer not to say' Assume we ask the population. ...
2
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2answers
109 views

How do I handle nonexistent or missing data?

enter link description hereI tried a forecasting method and want to check if my method is correct or not. My study is comparing different kinds of mutual funds. I want to use the GCC index as a ...
0
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0answers
21 views

(binary) Matrix completion with less known data

Recently, I meet such problems, I call it matrix completion problem. For example, the row denotes the users and the column denotes items. And If one user like the ...
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0answers
17 views

imputing missing values [closed]

My work involves mining multivariate time series data and i am dealing with clinical dataset with unequal intervals. I am confused with what type of method to employ for handling missing values.
2
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0answers
38 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 ...
0
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0answers
37 views

Identifying multivariate outliers in a large sample with missing data, using SPSS

I'm a psychology PhD student doing analysis on a relatively large set of data, obtained via online surveys. The purpose of the study is largely to determine normative data for a population of adults, ...
0
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0answers
9 views

How to aggregate daily data, with a large amount of missing data?

I have a conceptual problem in a rather large study of the following design: technical details of a telephone connection are monitored daily, and at the end of the month a questionnaire is filled out ...
1
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2answers
39 views

Handle missing values in factor variable

I have a huge dataset for a binary classification problem (about 1.5 million rows), and the feature space is quite large (145 dimension). Some of these features are factors (YES, NO), but there is ...
0
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1answer
81 views

Prediction when survey subsets create dramatically smaller Ns

Suppose you want to predict an outcome using a sample whose N is... 10,000 based on most demographic variables 9,000 based on Survey Question 1 3,000 who answered "Yes" to Question 1 and thus were ...
1
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0answers
14 views

Determine if missing values are informative(due to treatment) or just noise.

I'm not sure if this is those appropriate way to phrase this question. I have two populations of measurements. In my control population I have a quantitative measure of a proteins abundance. I expect ...
0
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0answers
14 views

Basic Help Needed in Coding Data Using Stata [migrated]

id Year Lat Long 1 1990 10 11 3 1994 2 8 . 1998 2 8 1 1993 . . 1 1991 10 . 3 1996 . 8 I recently started ...
1
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0answers
12 views

In R: Replace values of a row if missing with values of another row [migrated]

I am relatively new to R and probably the solution to this problem is rather simple. I have a dataframe that looks like this: ...
2
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1answer
79 views

Impute missing values using aregImpute

I have a data frame with 61 columns. Some data is missing. I read in Steyerberg's book about aregImpute in Hmisc. I used it with ...
0
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0answers
13 views

Joint model for ordinal repeated measures data and MNAR dropout using R

I have a dataset consisting of repeated measures data of graded toxicity scores (0-4) in a large number of patients being treated with a anti-cancer drug. We would like to identify predictors for ...
2
votes
1answer
149 views

Multiple imputation introduces negative values; dataset still valid?

After some detective work in my data sources, I realized the reasons for my previously reported 98% of missing data ratio. After implementing some data collection code fixes, the current missing data ...
0
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2answers
61 views

Error using mice() package in R for handling missing data [closed]

I am doing regression with a data with Y as target variable and 16 feature variables. I had two date feature variables which where as factor. I converted them to date format as shown below: ...
0
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1answer
47 views

NA produced in linear regression model

I have read similar posts to this but my problem is not resolved by the answers given. I want to do a v simple linear regression to see if bite incidence is related to district, zone (vacc or control) ...
0
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1answer
25 views

Missing data on Extreme Value Analysis

I am analyzing (extreme value analysis) the dataset which contain daily rainfall over 100 years of a single location. However there are around 500 missing values on the whole dataset. In this case the ...
1
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0answers
105 views

Ensuring exploratory study's validity with pseudo-simple random sampling

The context of my questions is as follows. I'm performing a cross-sectional secondary research study, involving open source software (OSS) projects. I collect data (information about the projects) ...
1
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1answer
34 views

Missing data analysis software

Does any standard statistical software like R, SAS or SPSS have procedures or codes to analyze log-linear models for missing data in contingency tables using maximum likelihood estimation (or EM ...
0
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0answers
15 views

Imputing values that depend on each other, such as percentiles or proportions

I have a data set with school level measures including test score percentiles. These percentiles are central to my analysis. For example, one measure is the test score of the 25th and 75th percentile ...
0
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0answers
21 views

GBM package: Why there is a missing node?

Why there is a missingNode as 3 as there are no missing values? I have the data in the following form: ...
0
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0answers
30 views

How to handle systematically missing values?

In my situation, one of two sources is not invoked if the confidence reported by the first source is higher than a threshold and hence it is missing in some examples. How can account for such missing ...
1
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0answers
15 views

Proper way to stack models when some models aren't always applicable?

Suppose you have two (or more) models that you want to ensemble together. However, some of the models are trained specifically on very specialized subsets of your data. If you do the stacking with a ...
3
votes
1answer
71 views

Missing data which simply cannot exist

I have read 80% of missing data in a single variable and understand the approach for dealing with missing data which simply cannot exist for 1 variable. I am trying to generalise this up to 2 or ...
0
votes
1answer
66 views

lasso with missing values and categorical variables

I have a dataset with a lot of missing values and mix of continues and categorical variables. I want to use something like group lasso to do features selection. Probably the output is binary 0,1 and ...
1
vote
1answer
51 views

How to determine whether a set of data are qualitative or quantitative?

Assume a data set with multiple columns, where the categorical data are coded. What is the best rule(s) or rule of thumb to determine whether each column contains qualitative data or quantitative ...
0
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0answers
25 views

how to handle the imbalanced data in regression analysis

The problem here is very similar to the problem asked by someben in 2012 (link:Sampling for Imbalanced Data in Regression). It involves the linear regression analysis using an unbalanced dataset. Say, ...
1
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0answers
43 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 ...
1
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0answers
28 views

How to handle uninterested missing (unobserved) data?

Suppose I have $N$ items $\{x_n\}_{n=1}^N$ (also denote the feature vector as $x_n$) and I have observed $M$ pairwise preferences $\{x_{i_m} \succ x_{j_m}\}_{m=1}^M$ from a user. Now I want to predict ...
0
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0answers
11 views

rebuild model based on what columns a record has

I've built a model in R using glm, and in the new dataset that I need to predict, there exist some new levels for the columns that are non-numeric. I know there are so many approaches to deal with ...
3
votes
1answer
200 views

Determining probability distribution for datasets with missing values

As a part of my exploratory data analysis (EDA) prior to further analysis, I'm trying to determine a probability distribution of my pilot dataset's variables. A particular feature of this dataset is a ...
1
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2answers
52 views

Missing values in SPSS for Chi-Square Tests

I'm a postgraduate student, doing a accent perception study, this is my first time using SPSS! I'm conducting the above tests on my data, where my participants used Likert scales to analyse the ...
0
votes
0answers
10 views

Missing values on clinical scoring system in SPSS

I have a SPSS database with 747 patients with sickness symptoms and lab results as variables. With those symptoms and results I have generate two scoring systems. I sum the scores of all the variables ...
0
votes
1answer
47 views

Measures of goodness-of-fit using multiply imputed data in Zelig

I am running a logistic regression model in R using multiply imputed data created using Amelia II, which I am then analyzing using Zelig. I would like to be able to report some measures of ...
0
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0answers
15 views

Categorical Missing Value Replacement

I want to ask how I could replace categorical missing values with reference please?
2
votes
0answers
15 views

Multiple Imputation Variance

I have been reading up on multiple imputation, and I am interested in the between-imputation variance. However, not in the estimation of the parameters, but in the imputed values themselves. (1) ...
1
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2answers
38 views

How to replace only SOME missing data

I have two types of missing data (non-responses and "Don't know or does not apply") and I want to replace only the non-responses (with mean or by using EM or by using Multiple Imputation) and to see ...
1
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0answers
47 views

Multiple imputation constraining for not applicable values

I want to do a multiple imputation of missing data using SPSS. I have a nominal (categorical) variable X with missing values codified as '9' and not applicable values codified as '8'. How can I ...
1
vote
1answer
45 views

Covariance matrix estimation in presence of missing data

I want to estimate a covariance matrix from data with some missing values. Ideally I'd like an R package but python could be ok. R has some built in ways of doing this. You can use ...
0
votes
1answer
12 views

Data Analysis of No-Opinion Options

In my questionnaire, I had a "no-opinion" option for several items on a 5-point-likert scale. I know, there is a lot of discussion about sense and non sense of those, but the fact is, i've got a data ...
0
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0answers
40 views

Estimate linear regression using items randomly selected from an item pool

I am asking this question against the background of a linear regression with single predicted variable $Y$ and multiple predictors $X$. $X$ comes from a survey using an "item pool" which suggests that ...
-1
votes
1answer
34 views

Marginal probability density of x (obs) obtained by integrating x(missing)

I am reading a text book on missing data, and a sentence below is slightly challenging for me to understand. The marginal probability density of $ \left ( x_{obs}\ \right)$ is obtained by ...
1
vote
1answer
74 views

LASSO or other regularized regression with censored (missing) data

Here is my problem. I am looking at various time series curves. Let's call them total spend aggregated over all customers on various products versus time. At any given time, I want to predict the ...
1
vote
2answers
71 views

How can I partition a distribution into two sub-populations with fixed bias? (simulation)

I am trying to simulate a selection model for a variable $Y$ dependent on covariate vector $X$, so that two groups/sub-sets $S=(0,1)$ of observations on $Y$ are created, which have a fixed difference ...