Questions tagged [correspondence-analysis]
Correspondence analysis is a dimensionality-reduction and mapping technique for nominal variables. It is often applied to a contingency table to explore visually affinities among row and column categories. If a table is 3+ dimensional the analysis is called Multiple Correspondence analysis.
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Preference Mapping using missing consumer data
I'm doing a preference mapping analysis using sensory and consumer liking data based on carto() function in R from SensoMineR ...
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Correspondence analysis
I have a question. I have the following categorical data from an open text diary study:
Individuals reported emotions in different situations.
Each individual reported their emotions in 3 to 5 ...
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MCA weights to construct a score
I have a set of variables all measured on a nominal scale. I have applied the MCA function within the FactoMineR package to reduce the dimension of my data set. Next I would like to calculate a score ...
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What is the difference between Multiple Correspondence Analysis and Principal Component Analysis result?
Several articles I've read stated that MCA and PCA both work as "reducing dimensionality" tools but MCA is used for categorical variables and PCA is used for numerical variables. But is ...
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Should low frequency categories be eliminated or amalgamated in multiple correspondence analysis?
This is just a theory question so no code or example is required I think. I want to know if there is a rule of thumb for what constitutes low frequency in MCA? Should the variable be eliminated? Could ...
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How to compare the socioeconomic status between two populations after applying multiple correspondence analysis?
I have two datasets of two populations (before and after)
with the task to create socioeconomic quintiles. Through many variables such as household assets and household quality, I used MCA (R language)...
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Identify groups/associations among longitudinal binary variables
This question is an extension of a prior question. I have a longitudinal dataset with several binary variables, along with an id variable and a ...
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MCA : interpretation for contribution and correlation
According to the definitions:
The correlation matrix reports the correlation of each variable with each dimension.
Contribution refers to the contribution of each category of each variable to the ...
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MCA : what is the difference between indicator method (default) and Burt method ? and which one to use?
I have a dataset with 28 ordinal variables and 1402 individuals and I am tasked to apply MCA method to create a socio-economic score SES in order to assign individuals into socio-economic groups based ...
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I need help interpreting MCA plots and data for a large language research project
I posted on the site a few days ago about my research and got some good advice from several users who suggested that an MCA was the most appropriate way to analyze my data (thank you btw!). I have ...
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Large Pearson residuals
My aim is to see if there exists a relation between the variables "Category" and "Types", spec. if there is a tendency for a particular category to use a particular type. These are ...
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MCA vs PCA - results for one factor are the same, but negative! [duplicate]
quick question regarding principal component analysis (PCA) and multiple correspondence analysis (MCA).
I am trying to extract scores to create index variables from ordinal data. I first used PCA, but ...
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What could cause having more dimensions than variables after MCA and dimensions explaining very little about data?
I have a dataset with 19 variables and 100k observations. All of my variables are categorical, some of them ordinal but I have not taken that into account here. To reduce its dimension, I performed a (...
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Correspondence Analysis R
I have a dataframe with 100k rows and 20 binary variables, one of which is my target.
I would like to apply a Correspondence Analysis (CA) on it, but I have a few doubts:
should the target column be ...
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GLM with scores/principal dimensions from MCA
I hope someone can help me understand how to run this analysis!
I have a dataset with many categorical variables (i.e. color, pattern, texture) associated to each animal in each interaction between ...
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How can I use projections (components) from multiple correspondence analysis in subsequent regression analysis, similar to PCA
I am trying to reduce the dimension of a matrix of several hundred binary/indicator/boolean variables, and then use the reduced components in subsequent regression modeling.
For continuous variables, ...
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What is the appropriate analysis method to test for differences between respondents' answer-profiles?
I am going to collect data from 8 people (T1, T2,..., T8) who each give reasons (a,b,c,d,...) for about 2000 decisions (O1,O2,O3,...,O2000). The decisions are binary (1 or 0). The reasons are from a ...
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How to find correlation between a dummy variable and a categorical variable?
I have a dataset with samples 0 and 1 data. Here each Id represents a sample no and 0 or 1 represents if the keyword(on the left: Water, Soil, etc) exists in the publication. The regional columns on ...
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What are the metrics to assess the quality of a multiple correspondence analysis (MCA) model?
We are trying to implement a multiple correspondence analysis (MCA) model. I was looking for metrics to assess the quality of an MCA to evaluate our model. Sadly, I didn’t find much literature about ...
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Should I consider the contribution of a variable in correspondance analysis if the $cos^2$ is weak?
So I have this table that represents contribution and cosinus of variable in CA
I noticed that the most popular choice ( 1-5 hours ) has the weakest contributions and the weakest cosinus.
It made ...
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What are the assumptions of Multiple Correspondence Analysis?
Is it possible to make Multiple Correspondence Analysis (MCA) with nominal data (such as country or gender) ? And more broadly, what are the assumptions of MCA?
For me, MCA is a type of factor ...
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Canonical Correspondence Analysis on non-ecological data
I have two datasets: one with samples (rows) taken at different months of the year and abundances or counts of different types of particles I found (columns), and the other with samples (rows) and the ...
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Feature selection with PCA and CA?
I am studying some factorial methods, namely, PCA and Correspondence Analysis and I have a few questions for you.
It is clear that the principal axes in PCA are linear combinations of the original ...
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Visualizing shared instances of p-values<alpha across large numbers of treatments
Assume a data table that presents the p-values of a large number of independent runs of a statistical hypothesis test. Each run represents a single test with two possible hypotheses (i.e., null and ...
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Multiple Correspondence Analysis probabilities class membership
Is it possible to get probabilities of class membership based on Multiple Correspondence Analysis which uses hierarchical clustering?
I am thinking along the lines of Latent Class Analysis that ...
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Detrended canonical correspondence analysis in R
Is it possible to perform a Detrended Canonical Correspondence Analysis in R? Im looking to analyse ecological (pollen) data to investigate beta diversity through time. As far as I can tell I have to ...
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Multiple Correspondence Analysis to inform a composite variable
I have 28 categorical variables, some binary, some with many levels (n=161).
I want to use some of these variables to make a composite variable to investigate a latent characteristic, and then test ...
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Multiple Correspondence Analysis as PCA
The book "Multiple Factor Analysis by Example Using R" states that MCA can be thought as an unstandardized PCA of the transformed Complete Disjunctive Table (basically an indicator matrix).
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Accounting for spatial structure in constrained ordination analysis (vegan)
I am looking to use ordination to test whether certain environmental measures influence the microbial community in soil samples. I am a bit confused about the correct way to define the model and ...
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How can I solve "horseshoe effect" on ordination analysis (Canonical Correspondence Analysis)? [duplicate]
I would like to ask for help in the analysis of Canonical Correspondence Analysis (CCA) that I did in the Vegan package in R, with my dataset of species and environmental parameters. The result ...
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Is it possible to code in a CA the additional variables in dummies variables
I'm doing a correspondence analysis as part of a study.
Unfortunately, one of the commands I use with R doesn't work because I have supplementary categorical variables that the package doesn't ...
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Interpreting the loads in a biplot
I came across a doubt about the interpretation of the biplot. I know that there are lot of questions related to this topic but I have a very concrete question, but I don't find any response.
Suppose ...
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Principal Component Analysis on Numerical Predictors alone for Dimension Reduction
I'm trying to reduce the number of dimensions for this 'Network Anamoly Detection' dataset: https://www.kaggle.com/anushonkar/network-anamoly-detection
The dataset has a total of 40 features out of ...
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How do I interpret the angles of two concentration ellipses?
Consider a map with two concentration ellipses like this below. The Vomit_y group is (almost?) perfectly vertical, while the Vomit_n group seems to be oriented at about 45 degrees. I understand that ...
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Using MCA/PCA together?
If I have a large dataset with continuous, discrete, and categorical data, is it appropriate to use MCA on the categorical features and PCA on the continuous, separately?
I'm preprocessing my data ...
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How to pool variables that rarely occur, particularly with respect to survey data
From the text : Multiple Correspondents Analysis by Brigette LeRoux
very infrequent categories of active variables need to be pooled with
others when feasible
The text doesn't explain how this ...
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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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Difference between two groups of people, each person "is" several characteristics
I have two groups of people, A and B (let's say 15 and 25 people).
Each person in each group is characterized by a bucket of features (bucket = 6-18 features). Each feature, during qualitative phase ...
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identifying what has changed in the data year on year
I have a dataset from one year and the 'same' dataset for the next year. I would like to identify what has changed in the datasets between the years. Both datasets have the same columns.
I currently ...
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How to conduct a principal component analysis on data set with large number of zeros
I have data for percentage cover of plant species in 500 sites. There are columns for 30 different species in the data set and I would like to drastically reduce this down to a manageable number of ...
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Display of dimensions in (multiple) correspondence analysis
I am getting into correspondence analysis and was wondering what the correct way is to display more than two dimensions in a biplot. I found examples with biplots showing dimensions plotted in order (...
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What assumptions about the data are require for Multiple Correspondence Analysis? [duplicate]
In multiple correspondence analysis, what assumptions about the data are necessary in order to find the principal coordinates for the rows and columns?
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Coding a categorical variable: 0-1 or multiple subcategories?
I'm working with R and want to run a correspondance analysis on a dataset containing, among the others, the following factors:
city district: 27 levels, each corresponding to a given district
...
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Having only binary variables, why is the use of PCA still appropriate?
I often see the use of PCA on large datasets with a lot of binary variables. As i recall, the computation of the principal components is done via eigenvalue decomposition (or SVD) of the correlation ...
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One-hot encoding for SOM
I have a question regarding how I should convert categorical data to numerical data. I'm using this kdd99cup intrusion detection dataset, which has a 41 attributes and class label is the type of ...
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Is MCA equivalent to PCA when all variables are binary?
I am looking to apply principal component analysis on binary (true/false) data, and I have come across the "equivalence between PCA and MCA" (Multiple Correspondense Analysis) for binary data, but ...
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Canonical Correspondence Analysis: how to interpret results
I am using Canonical Correspondence Analysis (CCA) to analyze phytolith abundances (similar to pollen) over environmental gradients. As I am new to CCA, I read some background info. The following ...
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R vegan: RDA vs CCA, which test to answer my research question and which results to report?
(if my question should be cut up into sub-questions please let me know, since all those questions are related I decided to ask them here together as one long question)
Main question
As part of my ...
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I have done crosstabs and a Correspondence Analysis, having trouble reporting and interpreting Chi-Squared and Inertia?
I have data on 1500 cases with two variables (color, genus) with 5 colors and 6 genera. I almost had generally equal spread across genus, but one is disproportionately represented and has about twice ...
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What do to after hierarchical clustering and finding number of clusters
I have a dataset with 10 categorical variables with over 5000 observations, I have clustered and then found the optimal number of clusters using elbow method.
Now I'm not sure what to do because I'm ...
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