Q-Q plot to assess normality of data
qqmat plots Mahalanobisdistances of a given sample against those expected from a Gaussian distribution
qqmat(x, output = FALSE, square = FALSE)
x |
sample data: matrix or vector |
output |
logical: if TRUE results are returned |
square |
plot in a square window - outliers might be cut off. |
if output=TRUE, the following values are returned
x |
distances from an expected Gaussian distribution |
y |
observed distances - sorted |
d |
observed distances - unsorted |
Stefan Schlager
require(MASS) ### create normally distributed data data <- mvrnorm(100,mu=rep(0,5),Sigma = diag(5:1)) qqmat(data) ###create non normally distributed data data1 <- rchisq(100,df=3) qqmat(data1,square=FALSE)
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