SPCCtest {MVET}

Srivastava Plot Correlation Coefficient(SPCC) Test for Multivariate Normality Test

Description

Using principal component analysis, the number of eigenvalues is selected such that the ratio of eigenvalues exceeds 70%. The principal component score vectors corresponding to these selected eigenvalues are used, and testing is conducted using the threshold defined by Filliben (1975). Users have the option to select the number of eigenvalues for the analysis based on their requirements.

Usage

SPCCtest(data,
         k = 0,
         level = 0.05)

Arguments

data

A numeric matrix or data frame.

k

The number of principal components can be manually selected. If 0 is entered, it automatically finds k components such that the explained variance ratio is at least 70%. (default = 0)

level

At the 0.01 or 0.05 significance level, the critical value. (default = 0.05)

Value

Srivastava.QQplot

Shows a chi-Square Q-Q plot for each PCs using ggplot2.

data.cnt

Observation n data count.

explain.ratio

Displays all explained variance ratios.

critical.value

Critical value proposed by Filliben (1975), corresponding to data.cnt and PPCC.value.

result

Final result of multivariate normality.

References

Srivastava, M. S. (1984), A measure of skewness and kurtosis and a graphical method for assessing multivariate normality. Statistics & Probability Letters, 2(5), 263-267.

Filliben, J. J. (1975), The Probability Plot Correlation Coefficient Test for Normality, Technometrics 17, 111-117.

Examples

data(wine)
class1.wine <- subset(wine, class == 1)[, -1]
SPCCtest(class1.wine, k = 5, level = 0.05)