candisc: Visualizing Generalized Canonical Discriminant and Canonical Correlation Analysis. Functions for computing and visualizing generalized canonical discriminant analyses and canonical correlation analysis for a multivariate linear model.

Table of Contents

## What is Candisc?

candisc: Visualizing Generalized Canonical Discriminant and Canonical Correlation Analysis. Functions for computing and visualizing generalized canonical discriminant analyses and canonical correlation analysis for a multivariate linear model.

**What is canonical discriminant analysis?**

Canonical discriminant analysis (CLIA) is a multi- variate technique which can be used to determine the relation- ships among a categorical variable and a group of independent variables. One primary purpose of CDA is to separate classes (pop- ulations) in a lower dimensional discriminant space.

### What are the differences between Manova and discriminant analysis?

MANOVA can say how groups are significantly different i.e. how valid are the groups but Discriminant analysis can let us know how do groups differ i.e. which variables best distinguish among the groups. Discriminant Analysis operates on data sets for which pre-specified, well defined groups already exist.

**What is the difference between MANOVA and regression?**

We do regression when we are interested in prediction And there is multivariate linear regression technique However we use Manova when we are interested to study the effect of independent variables on the dependent variables I.e. whether there is an effect or no and what is the cause of the effect.

#### What is the relationship between discriminant and MANOVA?

**Is LDA and logistic regression the same?**

LDA works when all the independent/predictor variables are continuous (not categorical) and follow a Normal distribution. Whereas in Logistic Regression this is not the case and categorical variables can be used as independent variables while making predictions.

## Is LDA supervised or unsupervised?

Linear discriminant analysis (LDA) is one of commonly used supervised subspace learning methods.

**What is the difference between discriminant analysis and logistic regression?**

Thus, linear discriminant analysis and logistic regression can be used to assess the same research problems. Their functional form is the same but they differ in the method of the estimation of their coefficient. Discriminant analysis produces a score, similar to the production of logit of the logistic regression.

### Is MANOVA multivariate regression?

You have three outcomes and one input variable, you can’t use multiple regression. Peter has clearly explained, you need to choose between three simple regression (taking one output at a time) or MANOVA (Multivariate regression).

**What is one difference between MANOVA and DFA?**

In considering DFA, it does exactly the same thing as described above for the MANOVA, but provides all of the remaining output. This allows you to determine not only which variables are best at discriminating groups, but also which groups are different and which are not.

#### Which is better PCA or LDA?

PCA performs better in case where number of samples per class is less. Whereas LDA works better with large dataset having multiple classes; class separability is an important factor while reducing dimensionality.

**What is difference between PCA and LDA?**

LDA focuses on finding a feature subspace that maximizes the separability between the groups. While Principal component analysis is an unsupervised Dimensionality reduction technique, it ignores the class label. PCA focuses on capturing the direction of maximum variation in the data set.

## Why is logistic regression better than discriminant analysis?

So, when the assumptions of the discriminant analysis are violated, we should always avoid the discriminant analysis and analyze our data with logistic regression, which gives robust results since it can handle both continuous and categorical variables [8].

**When should a MANOVA be used?**

MANOVA can be used when we are interested in more than one dependent variable. MANOVA is designed to look at several dependent variables (outcomes) simultaneously and so is a multivariate test, it has the power to detect whether groups differ along a combination of dimensions.

### Why would I use a MANOVA?

Summary MANOVA is used when there are multiple dependent variables as well as independent variables in the study. MANOVA combines the multiple dependent variables in a linear manner to produce a combination which best separates the independent variable groups.