Multivariate Quadratic

Generates a sample of observations having user defined correlation matrix R. Optionally, the user may also define the means and variances. If unspecified, these two arguments will default to a mean vector of zeros and variance vector of ones.

The stats toolbox is required for the 'linear' and 'quadratic' options only.function out=addFitLine(poolFits,modelType,lineprops,ax) Purpose Add one or more fit lines to some or all data in axes "ax" or, without ax specified, to the current axes. Inputs *poolFits [optional 0 by default] - if 1 we...

Henze and Zirkler (1990) introduce a multivariate version of the univariate There are many tests for assessing the multivariate normality in the statistical literature (Mecklin and Mundfrom, 2003). Unfortunately, there is no known uniformly most powerful test and it is recommended to perform...


Platforms: Matlab

The response variable is discrete ( it could be binary or multinomial), it could have "k" groups (k=2 binary, k>2 multinomial). the categorical can have "c" categories. The routine finds the weights for each category using the Fisher's method described inHubert J. Carl, Wisenbaker M. Joseph,...


Platforms: Matlab

Other hypothesis used with the Mauchly's sphericity test is on the orthonormalized contrasts,H0: Cd-deOaoSC = s^2I , where C is any full-rank (p-1) x p matrix of orthonormal contrasts.A is the nonorthonormalized contrasts matrix.C = GMO(Ad-deOao) is a (p-1) doOCo p column-orthonormal matrix (by...


Platforms: Matlab

It is well known that many multivariate statistical procedures call upon the assumption of multivariate normality (MVN). At least 50 different procedures have been proposed for this problem. Unfortunately, different conclusions about the MVN of a data set can be reached by different procedures...


Platforms: Matlab

Doornik-Hansen (1994) introduce a multivariate version of the univariate omnibus test for normality of Shenton and Bowman (1977), based on the transformed skewness and kurtosis. Due that the skewness and kurtosis are not independently distributed and the kurtosis approaches very slowly to...


Platforms: Matlab

Calculates the Mardia's multivariate skewness and kurtosis coefficients as well as their corresponding statistical tests. For large sample size the multivariate skewness is asymptotically distributed as a Chi-square random variable; here it is corrected for small sample size. Likewise, the...


Platforms: Matlab

Creates a number of samples from a specified number of dimensions and centers them around a given mean, and within a given covariance range. You might not find it very useful, but hey, I need something to do this so why not.To use:You need to generate 1000 samples from a 3 dimensional Gaussian...


Platforms: Matlab

This m-file is used in experiments where there is uncertain about the assumption of linearity over the region of exploration, and when the experimenter decides to conduct a 2^2 factorial design with a single or several replicates of each factorial run, augmented with some center points. A formal...


Platforms: Matlab

MEDOUTLIERFILT - remove outliers from a multivariate data set using themedian of each column[stats_data, filtered_data] = medoutlierfilt(x,outlier_cut,plot_state) removes possible outliers froma data set, X, by specifiying a cut off. OUTLIER_CUT is a cut off is a multiple of the inter quartile...


Platforms: Matlab

This test is based on the Wilks'method (1963) designed for detection of a single outlier from a normal multivariate sample and approaching the maximun squared Mahalanobis distance to a F distribution function by the Yang and Lee (1987) formulation. A significative squared Mahalanobis distance...


Platforms: Matlab

Helps choose a Box-Cox power transformation for a multivariate linear regression.Assume you are looking at the residuals of [b,bint,r] = regress(y,X) and it seems a transformation is in place. Use:boxcoxlm(y,X) to find the best lambda for a Box-Cox power transformation (y^lambda, or log(y) for...


Platforms: Matlab

Discriminant Analysis is a multivariate technique concerned with separating distinct sets of observations to previously defined groups; rather exploratory in nature, it is a separatory procedure. Here, we develop this technique considering normal populations with equal covariance matrices. So,...


Platforms: Matlab

On the literature there are available several tests of the multivariate normality (ca. 50). Among them is the graphic approach based on a chi-square quantile-quantile plot of the observations' squared Mahalanobis distances. Besides the graphic q-q approaching, in this file we proposes an...


Platforms: Matlab

Estimating of the statistical power of a performed (a posteriori) single factor Multivariate Analysis of Variance (MANOVA).It needs to input the observed parameters among two possible options. Option 1: multivariate observed F-statistic, numerator degrees of freedom, denominator degrees of...


Platforms: Matlab

From a total multivariate data matrix and a significance level, the homogeneity covariance matrices are testing by the Box's M, given a Chi-square or F approximation in function of the group sample-size.


Platforms: Matlab

Using the Cholesky decomposition, it generates n iterations of multivariate Gaussian random variables for a given mean vector (mu) and variance-covariance matrix (sigma).mvgrnd(mu,sigma,n)


Platforms: Matlab

Statistical power of a performed (a posteriori) multivariate Hotelling's T-Squared test. File needs to input the observed multivariate F-statistic, number of data of interested sample (group) 1, number of data of complement sample (group) 2, number of variables, and significance (default =...


Platforms: Matlab

This file estimates the a priori statistical power of a single-factor Multivariate Analysis of Variance (MANOVA).The MATLAB macro needs to Input the R2-alternate-hypothetical population proportion of variance of the source-proportion of residual variance (error) ratio [PVs/PVe], p-number of...


Platforms: Matlab