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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High Performance Classification or Similarity Algorithim for Mixed Data Types?

I have a database holding 10-ish features that describe different breeds of dogs. They are mostly categorical features, but some provide ranges for values. Here's a demo representation of the database,...
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Method to model the differences between two variables of different type using other independent variables

respondents were shown a video (chosen randomly for each respondent) of a speeding car and then asked to assess the car speed. For each respondent a set of background variables are also known. So the ...
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How to choose a fair gamma value when performing k-prototypes clustering?

In the k-prototypes clustering algorithm, the distance function consists of two dissimilarity components - one for the numerical elements of the observations, and one for their categorical elements. ...
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Statistical Analysis for mix of Categorical and Quantitative data

I have Air Freight data records for a period of 3 months that involves daily packages receiving for many companies scattered over 3 provinces, categories of materials (catalogs, docs, letters, ...
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131 views

PCA or Drop high correlated variables for clustering

I am performing clustering on mixed data type. I have few features which are high correlated. We generally use PCA before clustering and reduce the feature space, as its a mixed data I have used FAMD ...
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What is the data type of speed limit of a road network segment?

As you know the data type is one of the most important factors in selecting the Machine Learning algorithm. For example, K-means should not be employed for categorical data. I have a csv file ...
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Clustering a dataset with mixed, skewed, semi-correlated and unscaled values

I have a dataset containing six features, around 13000 records, and representing an urban road network. I imported data as a dataframe into Jupyter and table below demonstrates the sample of this data....
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Do we check for association between categorical variables before clustering mixed-type data?

I am clustering mixed-type data using hierarchical clustering and the Gower measure. My question is: Do we have to check for association (dependence) between categorical variables (e.g. using chi-...
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Unsupervised feature selection on mixed data

Is there any unsupervised feature selection possibility (for mixed data) ideally in Python? I have all kinds of data types in my dataset (scale, ordinal, binary, cathegorical). Is there any option how ...
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Use of Factor Analysis of Mixed Data for Regression Models

I was wondering if there was an implementation of FAMD for regression. I am trying to boost the performance of my candidate regression models. And, the only thing left in my To-Dos is performing ...
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Weighted metric for mixed binary (decomposed) data?

I have a large dataset with mixed type of data (example): Age Price Town Size Interests Small Middle Big Traveling Cooking TV 21 0 1 0 0 1 1 1 34 100 0 1 0 0 1 0 81 200 0 0 1 1 1 0 54 0 0 0 1 1 ...
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Best option for calculating distance/similarity for structured data with both categorical and numerical dimensions?

Say you have individual one with the following attributes: {age: 25, gender: Male, education: Bachelor's, residence: California, salary: 65,000}. Individual two has the following attributes: {age: 27, ...
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Should I aggregate explanatory variable?

The dataset I have is an aggregated outcome, e.g., the quarterly revenue of each firm (that manages a number of plants), revenue is measured by the end of each quarter a focal explanatory variable, e....
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Co-relation between non numeric and numeric variable

I am not good at stats. So accept my apology in advance if my question sounds silly or trivial. I have two data sets. One contains the positive reviews of the app which includes rating given by the ...
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Calculation of Correlation Matrix

If we have continuoues variables $X_{1},X_{2}$ and categorical variables $C_{1},C_{2}$ (with $L+1$ levels each $C_{i}$) variables, how do we define a Correlation Matrix where we have independence ...
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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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2 votes
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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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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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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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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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2 answers
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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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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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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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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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3 votes
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Bayesian logistic regression: mixed categorical and continuous predictors

I'm trying to model the probability of an event Y based on three independant variables, one (X) is continuous (a log count) and the others (A and B) are categorical (nominal). B is a subcategory of A. ...
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12 votes
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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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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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2 votes
1 answer
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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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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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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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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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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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Duplicated Rows in Mixed Data Type Clustering

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