Hotelling (version 1.0-5)

hotelling.test: Two-sample Hotelling's T-squared test

Description

Performs a two-sample Hotelling's T-squared test for the difference in two multivariate means

Usage

hotelling.test(x, ...)
# S3 method for default
hotelling.test(x, y, shrinkage = FALSE, perm = FALSE,
  B = 10000, progBar = (perm && TRUE), ...)
# S3 method for formula
hotelling.test(x, data = NULL, pair = c(1, 2), ...)

Arguments

x

a matrix containing the data points from sample 1 or a formula specifying the elements to be used as a response and the grouping variable as a predictor

…

any additional arguments. This is useful to pass the optional arguments for the default call from the formula version

y

a matrix containing the data points from sample 2

shrinkage

if TRUE then Shaefer and Strimmer's James-Stein shrinkage estimator is used to calculate the sample covariance matrices

perm

if TRUE then permutation testing is used to estimate the non-parametric P-value for the hypothesis test

B

if perm is TRUE, then B is the number of permutations to perform

progBar

if TRUE and perm is TRUE then a progress bar will be displayed whilst the permutation procedure is carried out

data

a data frame needs to be specified if a formula is to be used to perform the test

pair

a vector of length two which can be used when the grouping factor has more than two levels to select different pairs of groups. For example for a 3-level factor, pairs could be set to c(1,3) to perform Hotelling's test between groups 1 an 3

Value

A list (which is also of class 'hotelling.test') with the following elements:

stats

a list containing all of the output from hotelling.stat

pval

the P-value from the test

results

if perm == TRUE, then all of the permuation test statisics are stored in results

Methods (by class)

• default: Two-sample Hotelling's T-squared test

• formula: Two-sample Hotelling's T-squared test

References

Hotelling, H. (1931). ``The generalization of Student's ratio.'' Annals of Mathematical Statistics 2 (3): 360--378.

Schaefer, J., and K. Strimmer (2005). ``A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics.'' Statist. Appl. Genet. Mol. Biol. 4: 32.

Opgen-Rhein, R., and K. Strimmer (2007). ``Accurate ranking of differentially expressed genes by a distribution-free shrinkage approach.'' Statist. Appl. Genet. Mol. Biol. 6: 9.

Campbell, G.P. and J. M. Curran (2009). ``The interpretation of elemental composition measurements from forensic glass evidence III.'' Science and Justice, 49(1),2-7.

See Also

hotelling.stat

Examples

# NOT RUN {
data(container.df)
fit = hotelling.test(.~gp, data = container.df)
fit

subs.df = container.df[1:10,]
subs.df$gp = rep(1:2, c(5,5))
fitPerm = hotelling.test(Al+Fe~gp, data  = subs.df, perm =  TRUE)
fitPerm
plot(fitPerm)

data(bottle.df)
fit12 = hotelling.test(.~Number, data = bottle.df)
fit12

fit23 = hotelling.test(.~Number, data = bottle.df, pair = c(2,3))
fit23

# }