View source: R/mean2_1931Hotelling.R
mean2.1931Hotelling | R Documentation |
Given two multivariate data X and Y of same dimension, it tests
H_0 : μ_x = μ_y\quad vs\quad H_1 : μ_x \neq μ_y
using the procedure by Hotelling (1931).
mean2.1931Hotelling(X, Y, paired = FALSE, var.equal = TRUE)
X |
an (n_x \times p) data matrix of 1st sample. |
Y |
an (n_y \times p) data matrix of 2nd sample. |
paired |
a logical; whether you want a paired Hotelling's test. |
var.equal |
a logical; whether to treat the two covariances as being equal. |
a (list) object of S3
class htest
containing:
a test statistic.
p-value under H_0.
alternative hypothesis.
name of the test.
name(s) of provided sample data.
hotelling_generalization_1931SHT
## CRAN-purpose small example smallX = matrix(rnorm(10*3),ncol=3) smallY = matrix(rnorm(10*3),ncol=3) mean2.1931Hotelling(smallX, smallY) # run the test ## generate two samples from standard normal distributions. X = matrix(rnorm(50*5), ncol=5) Y = matrix(rnorm(77*5), ncol=5) ## run single test print(mean2.1931Hotelling(X,Y)) ## empirical Type 1 error niter = 1000 counter = rep(0,niter) # record p-values for (i in 1:niter){ X = matrix(rnorm(50*5), ncol=5) Y = matrix(rnorm(77*5), ncol=5) counter[i] = ifelse(mean2.1931Hotelling(X,Y)$p.value < 0.05, 1, 0) } ## print the result cat(paste("\n* Example for 'mean2.1931Hotelling'\n","*\n", "* number of rejections : ", sum(counter),"\n", "* total number of trials : ", niter,"\n", "* empirical Type 1 error : ",round(sum(counter/niter),5),"\n",sep=""))
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