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Dataset including variables of different measurement nature (e.g. continuous, categorical, binary, count etc.) analyzed together in one variable set. Use this tag when this presents a challenge for the analysis. Do NOT use to refer to [mixed-model].

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How can I print out the design matrix from lm in R?

Assume x2-x4 are continuous predictors occupying one column each in the design matrix created using lm() in R. I want to include x1 a categorical variable which has 3 levels. Regression R code whould ...
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1answer
15 views

Comparing the results of 2 different type but identical samples from 2 devices

I have 2 columns of of the concentration of fat in fish which is measured by 2 different labs. I need to know if the difference of the results from these 2 labs are statistically significant. The data ...
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1answer
5 views

Non-negative matrix factorization (NMF) on mixed data using 1-hot encoding

From a standpoint of interpretation, can I use NMF on one-hot encoded categorical data for dimension reduction? I have mixed data and was thinking about one-hot encoding the categorical features and ...
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0answers
25 views

Avoiding multicollinearity with dummy encoding of ordinal variable

I was having trouble finding this exact circumstance; hopefully I haven't missed an obvious previous answer. I have a target variable, Y (discrete counts), and two ...
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0answers
29 views

How to compute distances with both categorical and continuous attributes?

I have to handle with a datast containing both categorical attributes (around 25) and continuous attributes (around 25). I would like to do outliers detection. I think that it would be a good idea to ...
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0answers
11 views

Is it a problem if marginal estimates with instrumental variable approach are way higher than the marginal estimates without instrument?

I am estimating a model with instrumental variable / exclusion variable. I have tested the instrument and it satisfies all the assumptions. The marginal effects of the model without instrument: cmp (...
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1answer
15 views

Cluster Analysis for mixed data and using a large sample

I wanna perform Cluster Analysis and I have Mixed data (a couple of dummy variables). I've found some information about Gower's distance, but when I tried to use it, R broke down (i guess) because I ...
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0answers
125 views

factor-analysis on mixed-type data: Most factor extraction methods produce error

I am working on a project in which we try to develop a questionnaire. Our Item Pool consisted of Items that have a binary response format (yes/no) and items that were responded to on a 5-point-likert ...
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12 views

Customer data with mixed attributes

I know this is not proper question, but im kinda despair. I'm working on clustering for mixed data, and trying to make segmentation. But, it is difficult to get the data. Anyone has the data?
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2answers
69 views

binary distance k prototype [closed]

I am using k-prototype for my mixed data set. I've chosen Euclidean Distance for my numeric variables; which (dis)similiraty measure can I chose for my categorical variable? The variables ask e.g. for ...
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0answers
90 views

How to test for conditional independence with mix of categorical and continuous data

I wish to test for conditional independence between $X$ and $Y$ given $Z$. However $X$ is continuous, while $Y$ and $Z$ are categorical variables. My idea is to test if $P(X|Z = z) = P(X|Y = y, Z = z)...
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1answer
105 views

Is (a) multicollinearity and/or (b) binary variables an issue for DBSCAN? if so, how can one correct for these issues?

I have read some related questions, such as: Why are mixed data a problem for euclidean-based clustering algorithms?, What data structure to use for my cluster analysis or what cluster analysis to use ...
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2answers
319 views

Mixed data in Gaussian Mixture Models

Is it possible to use a dataset with mixed variables such as continuous, ordered, and categorical variables and cluster the data using the Gaussian Mixed Model with EM algorithm. I cannot find ...
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3answers
2k views

Interpreting Silhouette plot for Cluster Analysis

I am running a mixed type data cluster analysis in R and I am trying to interpret the Silhouette Plot. For whatever reason, it is telling me that more clusters is ideal for analysis. Why could this be?...
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0answers
71 views

how to aggregate composite indicators of mixed data using weight based on factor analysis of mixed data (famd)?

how to aggregate composite indicators of mixed data using weight based on factor analysis of mixed data (famd)? i just know for principal component analysis (pca) or numerical data can using weighted ...
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1answer
440 views

Difference Between Cubic Clustering Criterion, Silhouette Score, and Calinski Harabasz

I am clustering a mixed geological data set containing numeric (pump pressure, bit speed, mud temperature), nominal (presence or absence of a specific stones), and ordinal data (relative concentration ...
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1answer
213 views

PCA on part of dataset

I have read numerous questions/answers on this site and others debating the use of PCA and other dimensionality reduction techniques on mixed data (containing both continuous and categorical variables)...
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1answer
415 views

Latent class analysis (LCA) with mixed data

first of all I am sorry if a similar question has already been asked, but I have not found a thread dealing with this topic. I want to use latent class analysis on 4 variables as an approach to ...
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1answer
251 views

How to classify mixed data?

I am trying to do some classification tasks on mixed data set (Hepatitis data set)from UCI ,I will apply SVM and Naive Bayes in R & WEKA, both of them can not handle mixed data directly. Naive ...
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1answer
116 views

Number of components in Principal Components Analysis with mixed data

How can I decide about the optimal number of components in Principal component analysis when I have mixed data of of categorical and continuous? I'm using ...
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0answers
644 views

Autoencoder with Mixed Data

Is it reasonable? The categorical features can be binary ("true" or "false") or strings, which are one-hot encoded. Some continuous features may be integers, which are treated as real values. If an ...
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1answer
240 views

Multidimensional similarity space

Does anyone know about multidimensional similarity space? I am working on mixed data set (numerical and categorical), I want to create multidimensional similarity space, contains pairs of similarity ...
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0answers
54 views

Running chi-squared test on mixed data

I have a set of data with high dimension = 80. The set contains mixed features: numerical and categorical data. I'd like to extract all relevant features to an output $Y$ (which is categorical as ...
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1answer
869 views

Can I use Clustering with mixed data type in R? [duplicate]

I know there is same question in cross validated. But it is somewhat different. Clustering of mixed type data with R At there Q&A, as using daisy funtion(), we can use categorical data type in ...
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42 views

FA for mixed data types, including dichotomous

I wish to conduct factor analysis on data that I have. The data is collected from a questionnaire and contains different types of data, for example, there are answers to the following questions: <...
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0answers
299 views

Computing similarity matrix with mixed data

I would like to compute similarity matrix (which I will further use for clustering purposes) from my data (failure data from automotive company). The data consist of these variables: START DATE + ...
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0answers
337 views

Which dimensionality reduction technique for mixed data? [duplicate]

I have a binary dependent variable and 18 independent variables which I want to use as regressors in a logistic regression. Prior to that, I want to reduce the dimensionality of the data set to shield ...
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0answers
85 views

Mixture probabilistic model on mixed data

When dealing with homogeneous types of data, we can employ mixtures of gaussians (for continous) or some kind of k-proptotyping (ordinal-nominal). I am investigating around the statistical/machine ...
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0answers
72 views

Clustering for mixed data including string attributes [duplicate]

Suppose to have a dataset containing feature vectors representing some people. Each feature vector contains mixed type of attributes (e.g. sex, age, height, hair color, favourite film, ...). For ...
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1answer
124 views

Data Preparation

I faced a mixed data set which contained both continuous and categorical variables (totally more than 200 variables). Now I have chosen 60 variables out of them by the business specification. Then is ...
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1answer
519 views

Same kernel for mixed/categorical data?

I know it's common practice, but is it right to apply the common kernels to categorical/mixed data? If not, are there alternatives? I'm expecting answers from both theoretical and practical points of ...
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5answers
1k views

Clustering of variables: but they are mixed type, some are numeric, some are categorical

I have a dataset with 15 variables. Some variables are numeric, continuous. Other variables are boolean, dichotomous (true/false). There's also one variable categorical, nominal. ...
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2answers
2k views

How to calculate the distance in KNN for mixed data types?

when the data is from different types (numerical and categorical) of course euclidean distance alone or hamming distance alone can't help. so i have 2 approaches: standardize all the data with ...
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1answer
370 views

Cluster Analysis for Website Data [duplicate]

I want to perform cluster analysis on the data of a website. The data is mainly visitor history(97000 rows) and has following variables: a)User Device Category b) Traffic Marketing Channel c) Traffic ...
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0answers
49 views

What is a vector of effects?

The journal article in question is this article (General mixed-data model: extension of general location and grouped continuous models, de Leon and Carriere), on p. 535 (or, based on the PDF file, p. ...
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1answer
871 views

Detect unusual trends and anomalies using mixed data (categorical and numerical)

I've been asked to detect "unusual trends and anomalies" using data similar to ATM transaction data. Each entry has a mixture of numerical and categorical variables, things like transaction ID, ...
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1answer
1k views

Is continuous inputs an assumption of factor analysis?

Should we use only continuous inputs for factor analysis (FA)? My data is a mix of continuous and categorical inputs: one of the inputs has only 600, 700 and 1000 as values. I found that principal ...
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1answer
3k views

PCA/factor analysis of mixed (quantitative + qualitative) data: inconsistent results

I have a dataset composed of 4 variables, 2 being numerical and 2 categorical (ordinal in fact). They all represent 4 types of indicators/measures of the same phenomenon . I want to analyse them in a ...
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1answer
517 views

Kernels for Categorical or Mixed Data

It appears that when data sets have a combination of categorical and continuous attributes, the common way to apply kernel algorithms to such data sets is to use a one hot encoding scheme for each ...
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0answers
132 views

Using PCA to find most 'similar' points to a given observation (mixed data)

I am trying to find the most 'similar' points to each other in a dataset of mixed data. I understand that if these were all numeric variables on the same scale, one could simply use Euclidean Distance ...
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3answers
5k views

Ask for suggestions on clustering methods on a large dataset with mixed types of variables

I need to build segmentation on a large customer dataset with more than 300K records and many variables, including continuous like income and age, ordinal like education level and membership level, ...
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0answers
2k views

Gower's dissimilarity measure and Ward's clustering method

I have read some threads on this website saying that it is not OK to use Gower's dissimilarity matrix for Ward's clustering algorithm. I have mixed type variables, first I had a dissimilarity matrix ...
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2answers
18k views

How to use both binary and continuous variables together in clustering?

I need to use binary variables (values 0 & 1) in k-means. But k-means only works with continuous variables. I know some people still use these binary variables in k-means ignoring the fact that k-...
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0answers
1k views

Gower distance with R

I have 17 numeric and 5 binary (0-1) variables, with 73 samples in my dataset. I know that the Gower distance is a good metric for datasets with mixed variables. When I use daisy function in cluster ...
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4answers
4k views

Why are mixed data a problem for euclidean-based clustering algorithms?

Most classical clustering and dimensionality reduction algorithms (hierarchical clustering, principal component analysis, k-means, self-organizing maps...) are designed specifically for numeric data, ...
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2answers
10k views

How does the Gower distance calculate the difference between binary variables'?

I have 17 numeric and 5 binary (0-1) variables, with 73 samples in my dataset. I need to run a cluster analysis. I know that the Gower distance is a good metric for datasets with mixed variables. ...
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1answer
466 views

How to do multivariate outlier detection in mixed data with category?

I have a data table where the entries are in the following format. The first column is category, which represent the product category. I have 5 such categories. ...
115
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6answers
206k views

Correlations with unordered categorical variables

I have a dataframe with many observations and many variables. Some of them are categorical (unordered) and the others are numerical. I'm looking for associations between these variables. I've been ...
2
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1answer
457 views

Clustering variables of mixed types in R [duplicate]

I need to analyse questionnaire survey data with mixed data types (nominal, ordinal, continuous). I want to cluster the variables. So far I only have dead ends. I know I can use daisy in the cluster ...
2
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2answers
725 views

Unsupervised Dimensional reduction for mixed data types

I have a data set with about 50K rows and 100 columns. You can consider every row to be representing one restaurant. My goal is to calculate dissimilarities between all the restaurants - Gower's ...