Given multivariate data split into several subsamples (classes) the analysis finds linear combinations of variables, called discriminant functions, which discriminate between classes and are uncorrelated. The functions are applied then to assign old or new observations to the classes. Discriminant ...

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Sample lower bound for binary classification in Linear Discriminant Analysis?

Below is a description of this problem: Suppose the label $Y\in\{1,0\}$ in binary classification satisfies $\Pr[Y=1]=\Pr[Y=0]=\frac{1}{2}$, and $p(X|Y=1)=\mathcal{N}(\mu_1,\Sigma)$, ...
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10 views

Plotting QDA projections in R

When doing discriminant analysis using LDA or PCA it is straightforward to plot the projections of the data points by using the two strongest factors. This can be done in R by using the x component ...
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16 views

What is the heuristic to decide number of components for LDA dimensionality reduction?

In the PCA case, I prefer to plot the variance and choose number of components regarding that plot's breaking point. In the LDA (linear disriminant analysis) case, what can be used for such an ...
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10 views

Different coefficients of linear discriminants with the same raw data

I have just tried two ways to perform a linear discriminant analysis. Mode 1 This is the data, divided in two tables (printed from screen using R commander): They belong respectively to the two ...
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32 views

Using results of Canonical Discriminant Analysis to get overall variable importance?

I have a dataset with thousands of observations pre-assigned to 18 groups and with measures for 8 different variables. I am using canonical discriminant analysis to see how separable my 18 groups are. ...
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38 views

Discrimination between measurements made at different points in time

I would like to ascertain what variables discriminate best between experimental conditions in a repeated-measures experimental design. I have performed Repeated Measures MANOVA to determine whether ...
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20 views

How to preprocessing category data for discriminant analysis

I want to do discriminant analysis for five set observation data (five category) using LDA. From references, most of them suggesting to apply some preprocessing first (i.e. transformation, center, ...
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40 views

Multivariate normality in Discriminant Analysis when using dummy variables

I've studied statistics now for almost two years and I'm starting to believe I have missed something very fundamental. I'm doing discriminant analysis where, as I understand it, I can use dummy ...
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1answer
73 views

LDA, PCA and k-means: how are they related?

I am trying to understand how linear discriminant analysis (LDA) is related to principal component analysis (PCA) and k-means clustering method. As an example, here is a comparison between PCA and ...
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26 views

LDA with three variabilities

In a classical linear discriminant analysis (LDA), we usually have only two variabilities to handle (the between classes and the between individual variability). Usually LDA is used to maxmize the ...
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15 views

Individual factor significance in multilevel sPLS-DA

I recently was asked by reviewers to "include p-values" with my multilevel sparse partial least squares analysis. In brief, I have a nested design with two factors, say treatment and sampling region. ...
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59 views

Explanatory variables with linear discriminant analysis

I have a set of data which contains a mix of continuous, categorical variables etc. I wanted to apply linear discriminant analysis however, from some research on the Internet I understand that ...
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2answers
108 views

Dimensionality reduction technique similar to LDA when class labels are probabilistic

Given discrete class labels, say True and False, LDA (linear discriminant analysis) can be used to perform discriminant dimensionality reduction and attempt to find a subspace that best separates the ...
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1answer
246 views

How to follow up a factorial MANOVA with discriminant analysis?

This is a follow-up to to my previous question: How can MANOVA report a significant difference when none of the univariate ANOVAs reaches significance? I have two IVs with each having three levels ...
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1answer
60 views

When to use linear discriminant function and when logistic regression? [duplicate]

I am trying to find out when for creating classification rule to use linear discriminant function and when to use logistic regression? I need to help to find information sources to this topic. Any ...
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20 views

Posterior probabilities as LDA output [closed]

My colleague found @MichaelChernick's post on collinear variables in multiclass lda training. It may be the answer to a very tough question I have had a hard time getting answered. Can someone confirm ...
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45 views

Best time to contact: logistic regression loop or discriminant analysis

I want to do a contactability model which gives for each phone the best combination of time-day to be called. My data is the register of each trial and the result (contacted or not) by day and time. ...
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1answer
90 views

What is the correct formula for covariance matrix in quadratic discriminant analysis (QDA)?

I know that in quadratic discriminant analysis (QDA) we use the variance of each class, so is the formula different than that in linear discriminant analysis (LDA)? Is it $$\frac{1}{N-K} \sum (x - ...
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1answer
72 views

Testing Logistic Regression Classifier in R

I am testing the logistic regression classifier in R. I created some test data like this: x=runif(10000) y=runif(10000) df=data.frame(x,y,as.factor(x-y>0)) ...
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145 views

How do we generate the ROC curve for Linear Discriminant Analysis method

I know the method to generate the ROC curve for other methods such as naive Bayes where the tuning parameter is the threshold like also in logistic regression. If we want to generate the ROC curve ...
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1answer
15 views

Extract features to explain different states of the world

I have a problem that can be seen as the inverse of a classification problem. I don't need to classify points, but to explain the differences (if any) between points in different, pre-specified ...
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1answer
313 views

What is the correct formula for between-class scatter matrix in LDA?

At one point in the process of applying linear discriminant analysis (LDA), one has to find the vector $v$ that maximizes the ratio $vBv'/vWv'$, where $B$ is the "between-class scatter" matrix, and ...
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2answers
62 views

Log transformation for data?

If the data is between (0,1) because of some kind of vector normalization to get rid of background noise, is it still OK to do log transformation to improve normality? Or we have to do logit ...
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1answer
46 views

Cannot perform tests for multivariate normality. Is my data set too large?

I'm examining the performance of quadratic and linear discriminant models at classification. My dataset has 250,000 observations, 2 groups and 30 explanatory variables. I thought it would be worth ...
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1answer
124 views

How can MANOVA report a non-significant p-value while LDA results in perfect separation of two groups?

I am new to statistics and currently got a dataset which contains $80$ dependent variables and $1$ independent variable with $2$ groups. MANOVA reports a $p$-value of $> 0.6$ on this dataset. But ...
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17 views

linear discriminant analysis, Bayes approach authors?

I know that in 1936 Fisher proposed the LDA that minimizes the variance within and maximizes between. My question is, the Bayes approach of LDA is attributed to a particular(s) author(s)? and what ...
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17 views

Canonical discriminant analysis - lack of equality of covariance matrices [duplicate]

I have a dataset with 92 observations and two groups that corresponde to two analytical fractions of soil samples (i.e., light fraction or LF, and mineral-associated fraction or MoM). Each group has ...
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45 views

What are F values of the canonical functions in discriminant analysis?

Recently, I read a paper (Pell et al. 2009) in which the authors use Discriminant Analysis, and I quote: The discriminant analysis produced three significant canonical functions (Function 1, ...
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320 views

More recognizable Python implementation of Linear Discriminant Analysis?

I have been using scikit-learn's LDA implementation to do some experiments, and recently wanted to test out some modifications to the LDA derivation. I was looking at the Python implementation that ...
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82 views

What is a “discriminant function” and how to interpret it?

I want to test a model using discriminant function analysis. My question, as the title states, is very basic: What is a discriminant function? That is, how can I interpret the different discriminant ...
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1answer
297 views

Does Fisher linear discriminant analysis (LDA) require normal distribution of the data in each class?

Does Fisher linear discriminant analysis really require the data distribution in each category to be normal? I see two versions. The first one states that it requires the normal distribution and ...
0
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1answer
43 views

Bayes' factor vs. Bayes' Discriminant Rule

When we are comparing two models against some data, will we obtain the same (set of) posterior odds for the models both when we use the Bayes' factor and when we use the discriminant rule? If not, ...
3
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2answers
557 views

Reproduce linear discriminant analysis projection plot

I'm struggling with projection points in linear discriminant analysis (LDA). Many books on multivariate statistical methods illustrate the idea of the LDA with the figure below. The problem ...
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2answers
929 views

Post-hoc tests for MANOVA: univariate ANOVAs or discriminant analysis?

I am using a MANOVA test to compare nine different dependent variables (from neuropsychological and neuropsychiatric assessment) between three groups. The output shows a significant influence from ...
2
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1answer
433 views

Does it make sense to calculate Q2 and R2 values on PLS-DA models?

Since PLS-DA is a computational technique which deals with outcomes expressed as a categorical variable (e.g. "Yellow","Brown","Black","Green") I cannot understand how it is possible to calculate Q2 ...
3
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1answer
277 views

Linear Discriminant Analysis and non-normal distributed data

If I understand correctly, a Linear Discriminant Analysis (LDA) assumes normal distributed data, independent features, and identical covariances for every class for the optimality criterion. Since ...
7
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3answers
1k views

Why Python's scikit-learn LDA results are different from LDA in R or a step-by-step approach

I was using the Linear Discriminant Analysis (LDA) from the scikit-learn machine learning library (Python) for dimensionality reduction and was a little bit curious about the results. I am wondering ...
2
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1answer
347 views

Standardizing before/after/at all when using multi-class LDA for pre-processing step

If a multi-class Linear Discriminant Analysis (or I also read Multiple Discriminant Analysis sometimes) is used for dimensionality reduction (or transformation after dimensionality reduction via PCA), ...
3
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3answers
279 views

Is linear discriminant analysis (LDA) more likely to overfit than support vector machine (SVM)?

I went to a short talk and the speaker quickly mentioned something like 'LDA (linear discriminant analysis) is more likely to be overfitted than SVM (support vector machine)'. Is this true? And why?
3
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1answer
173 views

Multi-class classification via all pairwise classifications with LDA

I have trained linear discriminant analysis (LDA) classifiers for three classes of the IRIS data and struggling with how to make the classification. Here is the procedure: For the Iris data, I have 3 ...
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63 views

How to check discriminant analysis assumption in R using lda?

I want to use lda in MASS package in R. According to the theory behind that, first need to validate the assumption. Actually I've found some example from the net but they did not bother to validate ...
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2answers
644 views

Does it make sense to combine PCA and LDA?

Assume I have a dataset for a supervised statistical classification task, e.g., via a Bayes' classifier. This dataset consists of 20 features and I want to boil it down to 2 features via ...
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0answers
87 views

Multiple Discriminant Analysis, Linear Discriminant Analysis, and Multidimensional scaling - how are they related?

Some time ago when I took a Pattern Classification class, the "concept" was introduced as Multiple Discriminant Analysis: You want to project your data onto a subspace (if you are interested in ...
2
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1answer
133 views

A question on discriminant analysis- Linear discriminant function

Above is part of an examination paper. I am not sure how to understand this SAS output. Especially what is there in the last table which looks to me like two discriminant functions. Can someone ...
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98 views

Discriminant analysis

I want to do a discriminant analysis for my study. This study consist of one dependent variable and 8 independent variables. The dependent variable is categorical and has 2 groups. The independent ...
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0answers
129 views

Why do we not look at the covariance matrix when choosing between LDA or QDA

I understand the difference between LDA and QDA (linear and quadratic discriminant analysis), being that with LDA assume that your features have the same covariance matrix in each class. I wonder why ...
0
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1answer
77 views

Modeling: Option to cross-validate and predict afterwards

This is beyond a R question, But why doesn't it make sense to fit a model, say, a linear discriminant (LDA) model, with leave-out-one cross validation, and afterwards to use this model to predict a ...
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1answer
175 views

How to correctly apply LDA following PCA?

I have a high dimensional dataset ($n \times p$: $30 \times 100$) which I want to use as an testing dataset to build a two group classifier (LDA or QDA). I've read that you can do PCA to do an ...
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16 views

Statistical technique to assess nutrition

My goal is to find risk factors for a disease. I think that a malnutrition is a risk factor for this disease. I have 5 variables that indicate the frequencies of ...
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2answers
1k views

Logistic regression vs. LDA as two-class classifiers

I am trying to wrap my head around the statistical difference between Linear discriminant analysis and Logistic regression. Is my understanding right that, for a two class classification problem, LDA ...