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"discrimin" <- function (dudi, fac, scannf = TRUE, nf = 2) {
if (!inherits(dudi, "dudi"))
stop("Object of class dudi expected")
if (!is.factor(fac))
stop("factor expected")
lig <- nrow(dudi$tab)
if (length(fac) != lig)
stop("Non convenient dimension")
rank <- dudi$rank
dudi <- redo.dudi(dudi, rank)
deminorm <- as.matrix(dudi$c1) * dudi$cw
deminorm <- t(t(deminorm)/sqrt(dudi$eig))
cla.w <- tapply(dudi$lw, fac, sum)
mean.w <- function(x) {
z <- x * dudi$lw
z <- tapply(z, fac, sum)/cla.w
return(z)
}
tabmoy <- apply(dudi$l1, 2, mean.w)
tabmoy <- data.frame(tabmoy)
row.names(tabmoy) <- levels(fac)
cla.w <- cla.w/sum(cla.w)
X <- as.dudi(tabmoy, rep(1, rank), as.vector(cla.w), scannf = scannf,
nf = nf, call = match.call(), type = "dis")
res <- list()
res$eig <- X$eig
res$nf <- X$nf
res$fa <- deminorm %*% as.matrix(X$c1)
res$li <- as.matrix(dudi$tab) %*% res$fa
w <- scalewt(dudi$tab, dudi$lw)
res$va <- t(as.matrix(w)) %*% (res$li * dudi$lw)
res$cp <- t(as.matrix(dudi$l1)) %*% (dudi$lw * res$li)
res$fa <- data.frame(res$fa)
row.names(res$fa) <- names(dudi$tab)
names(res$fa) <- paste("DS", 1:X$nf, sep = "")
res$li <- data.frame(res$li)
row.names(res$li) <- row.names(dudi$tab)
names(res$li) <- names(res$fa)
w <- apply(res$li, 2, mean.w)
res$gc <- data.frame(w)
row.names(res$gc) <- as.character(levels(fac))
names(res$gc) <- names(res$fa)
res$cp <- data.frame(res$cp)
row.names(res$cp) <- names(dudi$l1)
names(res$cp) <- names(res$fa)
res$call <- match.call()
class(res) <- "discrimin"
return(res)
}
"plot.discrimin" <- function (x, xax = 1, yax = 2, ...) {
if (!inherits(x, "discrimin"))
stop("Use only with 'discrimin' objects")
if ((x$nf == 1) || (xax == yax)) {
if (inherits(x, "coadisc")) {
appel <- as.list(x$call)
df <- eval.parent(appel$df)
fac <- eval.parent(appel$fac)
lig <- nrow(df)
if (length(fac) != lig)
stop("Non convenient dimension")
lig.w <- apply(df, 1, sum)
lig.w <- lig.w/sum(lig.w)
cla.w <- as.vector(tapply(lig.w, fac, sum))
mean.w <- function(x) {
z <- x * lig.w
z <- tapply(z, fac, sum)/cla.w
return(z)
}
w <- apply(df, 2, mean.w)
w <- data.frame(t(w))
sco.distri(x$fa[, xax], w, clabel = 1, xlim = NULL,
grid = TRUE, cgrid = 1, include.origin = TRUE, origin = 0,
sub = NULL, csub = 1)
return(invisible())
}
appel <- as.list(x$call)
dudi <- eval.parent(appel$dudi)
fac <- eval.parent(appel$fac)
lig <- nrow(dudi$tab)
if (length(fac) != lig)
stop("Non convenient dimension")
sco.quant(x$li[, 1], dudi$tab, fac = fac)
return(invisible())
}
if (xax > x$nf)
stop("Non convenient xax")
if (yax > x$nf)
stop("Non convenient yax")
fac <- eval.parent(as.list(x$call)$fac)
def.par <- par(no.readonly = TRUE)
on.exit(par(def.par))
layout(matrix(c(1, 2, 3, 4, 4, 5, 4, 4, 6), 3, 3),
respect = TRUE)
par(mar = c(0.2, 0.2, 0.2, 0.2))
s.arrow(x$fa, xax = xax, yax = yax, sub = "Canonical weights",
csub = 2, clabel = 1.25)
s.corcircle(x$va, xax = xax, yax = yax, sub = "Cos(variates,canonical variates)",
csub = 2, cgrid = 0, clabel = 1.25)
scatterutil.eigen(x$eig, wsel = c(xax, yax))
s.class(x$li, fac, xax = xax, yax = yax, sub = "Scores and classes",
csub = 2, clabel = 1.5)
s.corcircle(x$cp, xax = xax, yax = yax, sub = "Cos(components,canonical variates)",
csub = 2, cgrid = 0, clabel = 1.25)
s.label(x$gc, xax = xax, yax = yax, sub = "Class scores",
csub = 2, clabel = 1.25)
}
"print.discrimin" <- function (x, ...) {
if (!inherits(x, "discrimin"))
stop("to be used with 'discrimin' object")
cat("Discriminant analysis\n")
cat("call: ")
print(x$call)
cat("class: ")
cat(class(x), "\n")
cat("\n$nf (axis saved) :", x$nf)
cat("\n\neigen values: ")
l0 <- length(x$eig)
cat(signif(x$eig, 4)[1:(min(5, l0))])
if (l0 > 5)
cat(" ...\n\n")
else cat("\n\n")
sumry <- array("", c(5, 4), list(1:5, c("data.frame", "nrow",
"ncol", "content")))
sumry[1, ] <- c("$fa", nrow(x$fa), ncol(x$fa), "loadings / canonical weights")
sumry[2, ] <- c("$li", nrow(x$li), ncol(x$li), "canonical scores")
sumry[3, ] <- c("$va", nrow(x$va), ncol(x$va), "cos(variables, canonical scores)")
sumry[4, ] <- c("$cp", nrow(x$cp), ncol(x$cp), "cos(components, canonical scores)")
sumry[5, ] <- c("$gc", nrow(x$gc), ncol(x$gc), "class scores")
print(sumry, quote = FALSE)
cat("\n")
}
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