Nothing
embed.boot <- function(x1, x2, B = 999) {
## x contains all the data
## ina is an indicator variable of each sample
n1 <- dim(x1)[1] ; n2 <- dim(x2)[1]
x <- rbind(x1, x2)
ina <- c( rep(1, n1), rep(2, n2) )
ni <- c(n1, n2)
p <- dim(x)[2] ## dimensionality of the data
n <- n1 + n2 ## sample size of the data
S <- rowsum(x, ina) / ni
Rbi <- sqrt( Rfast::rowsums(S^2) ) ## the mean resultant length of each group
S <- Rfast::colmeans(x)
Rbar <- sqrt( sum(S^2) ) ## the mean resultant length based on all the data
Ft <- (n - 2) * ( sum(ni * Rbi^2) - n * Rbar^2) / ( n - sum(ni * Rbi^2) )
m1 <- S[1, ] ; m2 <- S[2, ]
m1 <- m1 / sqrt( sum(m1^2) )
m2 <- m2 / sqrt( sum(m2^2) )
m <- S / Rbar
rot1 <- t( Directional::rotation(m1, m) )
rot2 <- t( Directional::rotation(m2, m) )
y1 <- x1 %*% rot1
y2 <- x2 %*% rot2
ftb <- numeric(B)
for (i in 1:B) {
b1 <- Rfast2::Sample.int(n1, n1, replace = TRUE)
b2 <- Rfast2::Sample.int(n2, n2, replace = TRUE)
yb <- rbind(y1[b1, ], y2[b2, ])
S <- rowsum(yb, ina) / ni
Rbi <- sqrt( Rfast::rowsums(S^2) ) ## the mean resultant length of each group
S <- Rfast::colmeans(yb)
Rbar <- sqrt( sum(S^2) ) ## the mean resultant length based on all the data
ftb[i] <- (n - 2) * ( sum(ni * Rbi^2) - n * Rbar^2) / ( n - sum(ni * Rbi^2) )
}
p.value <- ( sum(ftb > Ft) + 1 ) / (B + 1)
statistic <- Ft ; names(statistic) <- "Bootstrap embed test statistic"
parameter <- "NA" ; names(parameter) <- "df"
alternative <- "The 2 directional mean vectors differ"
method <- "Bootstrap ANOVA for 2 directional mean vectors using the embedding approach"
data.name <- c("data ", " groups")
result <- list( statistic = statistic, parameter = parameter, p.value = p.value,
alternative = alternative, method = method, data.name = data.name )
class(result) <- "htest"
return(result)
}
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