Questions tagged [mixed-type-data]

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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9 views

How to analyze repeated measures data with categorical and continuous IVs

I'm trying to determine the best way to analyze my data. Participants completed a working memory task under two conditions-the order of which was counterbalanced. All participants completed both ...
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1answer
20 views

Treat year as a fixed effect, random effect, or covariate?

I have a dataset of fish species from different sites within a harbour collected over 17 years. The dataset consists of 1,042 sampling transects collected at 6 specific locations where fish were ...
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1answer
21 views

Can machine learning be used with data where each dimension is different?

Let's suppose that I have some data, and I have a vector representation of each data point. For example, one data point might look like this: [0, 1, 0, 3, -2, 2.3]. Now suppose that for each vector, ...
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27 views

Testing for Collinearity in a Dataset with Categorical and Continuous Variables

I have a dataset that has 17 variables. 9 categorical and 8 continuous. Some have more than 2 levels. I've reduced the dimensionality significantly. I am looking for strategies to test for colinearity ...
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2answers
138 views

Why is betareg() giving “invalid dependent variable” error?

I am trying to run a beta regression using the betareg package and I am using the following script: ...
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14 views

How to combine continuous and discrete variables for bayesian network? [duplicate]

Basically the title says the question. I have a data set with both types of variables and AFAIK bayesian networks are constructed for discrete variables. Is it possible to somehow use them together?
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35 views

This mixed-type family of random variables has no dominating measure, so a likelihood function can't be defined?

Let $$ X = \begin{cases}\theta & \text{with probability 1/2}\\ Z\sim N(0,1) & \text{with probability 1/2.} \end{cases}$$ Here, $\theta\in\mathbb{R}$ is the parameter to be estimated. It ...
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17 views

gap statistic for mixed datasets - how to generate reference samples?

In the Tibshirani's paper on Gap Statistic for choosing optimal number of clusters, authors mention that one can generate reference samples by sampling uniformly from the range of each feature. How ...
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3 views

Suggestions for Data Wrangling with Large Dataset of String Types

I have a fairly large dataset (1934X150000) which I got from this Kaggle challenge. https://www.kaggle.com/c/springleaf-marketing-response/data I have no problem reading the file into my SparkSession, ...
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60 views

How does one choose “good” gamma in K-prototypes algorithm?

The k-prototypes algorithm uses a cost function which is a weighted sum of costs on numerical and categorical attributes. I wonder how can one choose an appropriate weight? The paper introducing the ...
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8 views

Mixed design with continuous between participants measure

Context: I commonly used mixed factorial designs that consist of two of more repeated-measures factors, that participants experience within the same experimental session, and a between-participants ...
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1answer
29 views

What is a natural way to define RKHS over mixed spaces (discrete and continuous)?

It is well known that given a kernel $k$ over any space $\mathcal{X}$, there is a corresponding RKHS (Reproducing Kernel Hilbert Space) associated with the kernel $k$. For example, Radial basis ...
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16 views

Categorical x continuous variable interaction in regression?

I have a sample containing two subgroups and I would like to measure the relationship between two continuous variables across the entire group and check if there's an interaction with subgroup. First, ...
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52 views

What is the difference between a factorial analysis for mixed data (FAMD) and a PCA on a dataset where qualitative variable are dummy-encoded?

There are many variants of the principal component analysis (PCA) framework for discrete variables or a mixture of quantitative and discrete variables. Image from this book. However, I am not ...
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48 views

Robust RM ANOVA [closed]

I am an R beginner and I needed to run robust two way mixed ANOVAs with R using the WRS2 package and the function btwin because my data has nonnormal residuals (I looked at Q-Q plots) and there is a ...
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17 views

Factor analysis when I have mixture of continuous and dichotomous variables

I am trying to determine if there is a significant difference between two groups for a single continuous variable while accounting for baseline differences in dichotomous variables in each group. To ...
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1answer
200 views

What post-hoc test should be used for a glmer model with a binary response, and a continuous and categorical predictor?

I'm a bit of a newbie with stats and R, so need a bit of direction to find a suitable post-hoc test for my glmer model. I'm trying to find if presence is affected by environmental factors for each ...
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1answer
172 views

PCR after PCA with mixed data - how to extract/export the PCs as new variables in R?

As the title/question implies I ran a PCA with mixed data, i.e. categorical and numeric, in R, once with the "FactoMineR" and "factoextra" packages (analysis version 1) and once with the "PCAmixdata" ...
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14 views

Scaling for mixed data and reverse transform

I have mixed type data (numerical, one hot encoded, ordinal). My data also has outliers. I am trying to scale them using a StandardScaler from sklearn, but I also need to revese transform the test ...
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48 views

Self organized maps on mixed dataset (categoricals / numerics features )

i have a dataset of mixed variables and i want to apply self organized maps on it how can i extend som to mixed dataset? can i use the gower distance instead of euclidienne distance in order to ...
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44 views

How to prove properties about mixed (discrete and continuous inputs) function spaces?

I am using Gaussian Processes for data with mixed inputs (discrete and continuous input variables) i.e. $𝑥=[𝑥_𝑐,𝑥_𝑑]$ where $𝑥_𝑐 \in \Re$ and $x_d \in \mathbb{Z}$. There is a lot of work on ...
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13 views

AUROC and Equality in Scores

The task at hand is to calculate the empirical AUROC for scores assigned to $N + M$ samples from two classes A and B. Most scores lie within the interval (0,1) and are pairwise different, but some ...
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379 views

How to use filter-based feature selection for mixed data types?

The idea behind filter-based methods of feature selection is that we assign a value to each feature, where the value indicates how important the feature is for predicting the outcome variable. We can ...
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1answer
33 views

Two way ANOVA when one independant variable is not Categorical

I am doing a test on doctors' training level and the number on duty. One IV is training level, the other IV is the number of doctors on duty. The DV is the number of patients seen. There are two ...
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1answer
230 views

Mixture model on binary + continuous data

If I have a dataset of continuous variables (that I can assume are normally distributed), I can identify subgroups using a Gaussian mixture model and implement. Likewise if I have binary data I can ...
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29 views

Clustering algorithm for mixed data with non constant categorical variables

I have the following scenario, imagine that I have a dataset as follows: ...
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2answers
94 views

Statistical test to find association between two variables

I'm dealing with ecological data. Broadly speaking, i've counted the plant abundance (discrete variable) in a number of points (small blocks,one number for each ...
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271 views

Input data for Canonical Correspondence Analysis (CCA)

I'm going to conduct Canonical Correspondence Analysis (CCA). In the tutorial I've found at: CCA environmental data are discrete ...
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2answers
81 views

Determination of statistically relevant quantitative/qualitative variables

I have a database containing records of several parameters: one is a quantitative parameter $D$ (e.g. average fuel consumption), others are qualtitative parameters $X_1, ..., X_n$ (e.g. make, color, ...
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71 views

Assessing a binary decission based on continuos and multi-level categorical variables

I have been asked to generate a tool to assess if a particular new set of measurements fit within a list of already accepted ones. The problem is that there are different categorical variables with ...
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202 views

Need PCA or other dimensionality reduction with mixed type variables, before doing clustering

I have a fairly large dataset of 171 mixed variables (104 dichotomous/qualitative, and 67 continuous). My goal is to build a typology of agricultural systems. I originally used a PCA to reduce the ...
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151 views

How to adjust continuous and categorical variables for categorical variable?

I am performing a metaanalysis where I am trying to find predictors for an ordinal response variable. Additionally, I want to perform pair-wise correlations on some of the variables. I have a ...
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1answer
2k views

t-SNE with mixed continuous and binary variables

I am currently investigating the visualisation of high-dimensional data using t-SNE. I have some data with mixed binary and continuous variables and the data appears to cluster the binary data much ...
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1answer
604 views

How to validate clusters after calculating Gower distances and Ward's clustering in R

I am trying to apply Ward's clustering on a mixed types dataset, and wanna explain what I did (maybe helpful to others), and I have some questions regarding this analysis, mainly how to validate my ...
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1answer
94 views

Why does Random Forest not find this simple categorical interaction?

The questions: Why is Random Forest not better at finding an interaction of a simple indicator $\times$ continuous variable? What kind of machine learning model would be better at finding this ...
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1answer
757 views

How to deal with mixed data type in deep neural network?

My dataset has 300 numeric features, each of them ranges from 1 to 500. In addition, I have 1000 categorical features (0 or 1), around 90% are 0's (kind of sparse). To run deep neural network, I ...
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75 views

Using non-parametric transformation (ranking) on variables for factor analysis in R

I have been wrestling with how to simplify a set of mixed variables (some orthogonal, some numeric) some (of both types) of which are significantly skewed. I have run a factor analysis anyway and ...
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17 views

Different conditions on data measurement for ml

Is it possible to train a prediction model (on my case classifier with four classes) between data taken on different conditions? To be more specific I have two data sets and for my task I am allowed ...
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1answer
19 views

How to perform prediction on a mixed type label (i.e. continuous but optional)

I have a set of simulations in which an event may or may not happen. I recorded the time the event occurs and whether it does. I would like to perform regression on the time variable, including ...
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1answer
162 views

Duplicated Rows in Mixed Data Type Clustering

I have a dataset which has ~200k rows and looks like the following - ...
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1answer
101 views

Analysis of impact between 2 observations

I have 2 columns of ~18000 observations. First column contains a value that shows the delay of a filed document (in days) of year 2017. For example: if the document had to be filed on May 5th 2017, ...
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1answer
54 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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2answers
679 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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1answer
5k views

Can a logistic regression have both continuous and discrete variables as covariates or as regressors?

My dependent variable should be binary, but can I include discrete and continuous variables simultaneously in my equation? For example let's say X1 is discrete and X2 is continuous?
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97 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
37 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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1answer
137 views
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1answer
56 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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2answers
333 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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200 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)...