# R/hypo_matrix_MANOVA.R In MANOVA.RM: Resampling-Based Analysis of Multivariate Data and Repeated Measures Designs

#### Defines functions HN_MANOVAHC_MANOVA

```#### function for generating hypotheses matrices in the MANOVA setting
# nf: number of factors involved
# fl: vector of factorlevels, i.e. (a, b, c, ....) of size nf
# p: Number of dimensions

#--------------------- crossed designs ------------------------------
HC_MANOVA <- function(fl, perm_names, names, p, nh){
nf <- length(fl)
# centering matrix
P <- function(x){
P <- diag(x) - matrix(1 / x, ncol = x, nrow = x)
return(P)
}
# scaled one-matrices
One <- function(x){
I <- matrix(1 / x, ncol = x, nrow = x)
return(I)
}

# calculate the permutation of the names
Z <- 0:(nf - 1)
position <- rep(0, nh)
for (i in 1:nh) {
position[i] <- position[i] + sum(2 ^ Z[which(perm_names[i, ] == 1)])
}
Vektor <- rep(NA, nh)
for (j in 1:nh) {
Vektor[position[j]] <- j
}
fac_names <- names[Vektor]
# function for calculating the kronecker product of several matrices
kp <- function(A) {
kp <- A[[1]]
for (i in 2: length(A)) {
kp <- kp %x% A[[i]]
}
return(kp)
}
# scaled One-matrices for all factors
A <- list()
for (i in 1:nf) {
A[[i]] <- One(fl[i])
}
A_alt <- A
# P-matrices for all factors
B <- list()
for (i in 1:nf) {
B[[i]] <- P(fl[i])
}
# calculate all combinations of elements of A and B
# (except all from A and all from B)
n1 <- c(0, 1)
liste <- vector("list", nf)
for (i in 1:nf) {
liste[[i]] <- n1
}
G <- expand.grid(liste)
G <- G[2:(dim(G)[[1]] - 1), ]        # 1 means A
# list of all combinations of A and B
C <- list()
for (i in 1:dim(G)[1]) {
index <- which(G[i, ] == 1)
for (j in 1:length(index)) {
A[[index[j]]] <- B[[index[j]]]
}
C[[i]] <- A
A <- A_alt
}
# calculation of the hypotheses matrices (except for the nf-fold interaction)
hypo <- vector("list", nh)
for (i in 1:(nh - 1)) {
C_tmp <- C[[i]]
hypo[[i]] <- kp(C_tmp)
}
# nf-fold interaction
hypo[[nh]] <- kp(B)

# Kronecker product with I_p
for (i in 1:nh){
hypo[[i]] <- hypo[[i]] %x% diag(p)
}

return(list(hypo, fac_names))
}

# ----------------------- nested designs -------------------------------------
HN_MANOVA <- function(fl, p){
nf <- length(fl)
# centering matrix
P <- function(x){
P <- diag(x) - matrix(1 / x, ncol = x, nrow = x)
return(P)
}
# scaled one-matricess
One <- function(x){
I <- matrix(1/x, ncol = x, nrow = x)
return(I)
}
# function for calculating the kronecker product of several matrices
kp <- function(A) {
kp <- A[[1]]
for (i in 2: length(A)) {
kp <- kp %x% A[[i]]
}
return(kp)
}
hypo <- vector("list", nf)
if (nf == 2) {
hypo[[1]] <- P(fl[1]) %x% One(fl[2]) %x% diag(p)
hypo[[2]] <- diag(fl[1]) %x% P(fl[2]) %x% diag(p)
} else if (nf == 3) {
hypo[[1]] <- P(fl[1]) %x% One(fl[2]) %x% One(fl[3]) %x% diag(p)
hypo[[2]] <- diag(fl[1]) %x% P(fl[2]) %x% diag(fl[3]) %x% diag(p)
hypo[[3]] <- diag(fl[1]) %x% diag(fl[2]) %x% P(fl[3]) %x% diag(p)
} else {
print("Error")
}
return(hypo)
}
```

## Try the MANOVA.RM package in your browser

Any scripts or data that you put into this service are public.

MANOVA.RM documentation built on Aug. 28, 2019, 9:03 a.m.