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daisy <- function(x, metric = c("euclidean", "manhattan", "gower"),
stand = FALSE, type = list(), weights = rep.int(1, p),
warnBin = warnType, warnAsym = warnType, warnConst = warnType,
warnType = TRUE)
{
## check type of input matrix
if(length(dx <- dim(x)) != 2 || !(is.data.frame(x) || is.numeric(x)))
stop("x is not a dataframe or a numeric matrix.")
n <- dx[1]# nrow
p <- dx[2]# ncol
varnms <- dimnames(x)[[2]]
pColl <- function(n) paste(n, collapse = ", ")
if(length(type)) {
if(!is.list(type) || is.null(ntyp <- names(type)) || any(ntyp == ""))
stop(gettextf("invalid %s; must be named list", sQuote("type")))
## check each component to be valid column names or numbers:
for(nt in ntyp) {
cvec <- type[[nt]]
ct <- paste0("type$", nt)
if(is.character(cvec)) {
if(!is.null(varnms) && !all(cvec %in% varnms))
stop(gettextf("%s has invalid column names", ct))
}
else if(is.numeric(cvec)) {
if(!all(1 <= cvec & cvec <= p))
stop(gettextf("%s must be in 1:ncol(x)", ct))
}
else stop(gettextf("%s must contain column names or numbers", ct))
}
tA <- type$asymm
tS <- type$symm
if(!is.null(tA) || !is.null(tS)) {
## tA and tS might be character and integer!
d.bin <- cbind(as.data.frame(x[, tA, drop= FALSE]),
x[, tS, drop= FALSE])
lenB <- sapply(lapply(d.bin, function(y)
levels(as.factor(y))), length)
if(any(lenB > 2))
stop("at least one binary variable has more than 2 levels.")
if(any(lenB < 2))
warning("at least one binary variable has not 2 different levels.")
## Convert factors to integer, such that ("0","1") --> (0,1):
if(any(is.f <- sapply(d.bin, is.factor)))
d.bin[is.f] <- lapply(d.bin[is.f],
function(f) as.integer(as.character(f)))
if(!all(sapply(d.bin, function(y)
is.logical(y) ||
all(sort(unique(as.numeric(y[!is.na(y)])))%in% 0:1))))
stop("at least one binary variable has values not in {0,1,NA}")
}
}
## transform variables and construct 'type' vector
if(is.data.frame(x)) {
type2 <- sapply(x, data.class)
x <- data.matrix(x)
} else { ## matrix
type2 <- rep("numeric", p)
names(type2) <- colnames(x)
}
if(length(type)) {
tT <- type$ ordratio
tL <- type$ logratio
x[, names(type2[tT])] <- unclass(as.ordered(x[, names(type2[tT])]))
x[, names(type2[tL])] <- log10( x[, names(type2[tL])])
type2[tA] <- "A"
type2[tS] <- "S"
type2[tT] <- "T" # was "O" (till 2000-12-14) accidentally !
}
type2[tI <- type2 %in% c("numeric", "integer") ] <- "I"
if(warnBin && n > 9 && any(tI) &&
any(iBin <- apply(x[, tI, drop = FALSE], 2,
function(v) length(table(v)) == 2)))
warning(gettextf("binary variable(s) %s treated as interval scaled",
pColl(which(tI)[iBin])))
type2[type2 == "ordered"] <- "O"
type2[type2 == "factor"] <- "N"
if(any(ilog <- type2 == "logical")) {
if(warnAsym) warning(sprintf(ngettext(sum(ilog),
"setting 'logical' variable %s to type 'asymm'",
"setting 'logical' variables %s to type 'asymm'"),
pColl(which(ilog))), domain = NA)
type2[ilog] <- "A"
}
## Note: We have 2 status codings: ndyst = (0,1,2) and jdat = (1,2);
## the latter is superfluous in principle
## standardize, if necessary
all.I <- all(type2 == "I")
if(all.I && { metric <- match.arg(metric); metric != "gower" }) {
if(stand) {
x <- scale(x, center = TRUE, scale = FALSE) #-> 0-means
sx <- colMeans(abs(x), na.rm = TRUE)# can still have NA's
if(0 %in% sx) {
if(warnConst) warning(gettextf(
"%s has constant columns %s; these are standardized to 0",
sQuote("x"), pColl(which(sx == 0))))
sx[sx == 0] <- 1
}
x <- scale(x, center = FALSE, scale = sx)
}
jdat <- 2L
ndyst <- if(metric == "manhattan") 2L else 1L # == diss_kind
}
else { ## mixed case or explicit "gower"
if(!missing(metric) && metric != "gower" && !all.I)
warning("with mixed variables, metric \"gower\" is used automatically")
## FIXME: think of a robust alternative scaling to
## Gower's (x - min(x)) / (max(x) - min(x))
colR <- apply(x, 2, range, na.rm = TRUE)
colmin <- colR[1,]
sx <- colR[2,] - colmin
if(any(sx == 0))
sx[sx == 0] <- 1
x <- scale(x, center = colmin, scale = sx)
jdat <- 1L
ndyst <- 0L ## diss_kind = "mixed | gower"
## weights only used in this "gower" case
if(length(weights) == 1)
weights <- rep.int(weights, p)
else if(length(weights) != p)
stop("'weights' must be of length p (or 1)")
}
## type2 <- paste(type2, collapse = "")
typeCodes <- c('A','S','N','O','I','T')
## 1 2 3 4 5 6 --> passed to Fortran below
type3 <- match(type2, typeCodes)# integer
if(any(ina <- is.na(type3)))
stop(gettextf("invalid type %s for column numbers %s",
type2[ina], pColl(which(ina))))
if((mdata <- any(inax <- is.na(x)))) { # TRUE if x[] has any NAs
jtmd <- integer(p)
jtmd[apply(inax, 2L, any)] <- -1L
## VALue for MISsing DATa
valmisdat <- 1.1* max(abs(range(x, na.rm=TRUE)))
x[inax] <- valmisdat
}
## call Fortran routine
storage.mode(x) <- "double"
disv <- .Fortran(cl_daisy, ## -> ../src/daisy.f
n,
p,
x,
if(mdata) rep(valmisdat, p) else double(1),
as.double(weights),
if(mdata) jtmd else integer(1),
jdat,
type3, # vtype
ndyst,
as.integer(mdata),
dis = double((n * (n - 1))/2),
NAOK = TRUE# only to allow "+- Inf"
)$dis
## adapt Fortran output to S:
## convert lower matrix, read by rows, to upper matrix, read by rows.
disv[disv == -1] <- NA
full <- matrix(0, n, n)
full[!lower.tri(full, diag = TRUE)] <- disv
disv <- t(full)[lower.tri(full)]
## give warning if some dissimilarities are missimg
if(anyNA(disv)) attr(disv, "NA.message") <-
"NA-values in the dissimilarity matrix !"
## construct S object -- "dist" methods are *there* !
class(disv) <- dissiCl # see ./0aaa.R
attr(disv, "Labels") <- dimnames(x)[[1]]
attr(disv, "Size") <- n
attr(disv, "Metric") <- if(!ndyst) "mixed" else metric
if(!ndyst) attr(disv, "Types") <- typeCodes[type3]
disv
}
print.dissimilarity <-
function(x, diag = NULL, upper = NULL,
digits = getOption("digits"), justify = "none", right = TRUE, ...)
{
cat("Dissimilarities :\n")
NextMethod("print")##-> stats:::print.dist(..)
cat("\n")
if(!is.null(attr(x, "na.message")))
cat("Warning : ", attr(x, "NA.message"), "\n")
cat("Metric : ", attr(x, "Metric"),
if(!is.null(aT <- attr(x,"Types")))
paste("; Types =", paste(aT, collapse=", ")), "\n")
cat("Number of objects : ", attr(x, "Size"), "\n", sep="")
invisible(x)
}
summary.dissimilarity <-
function(object, digits = max(3, getOption("digits") - 2), ...)
## 'digits': want a bit higher precision
{
sx <- summary(as.vector(object), digits = digits, ...)
at <- attributes(object)
r <- c(list(summ = sx, n = length(object)), at[names(at) != "class"])
class(r) <- "summary.dissimilarity"
r
}
print.summary.dissimilarity <- function(x, ...)
{
cat(x$n, "dissimilarities, summarized :\n")
print(x$summ, ...)
cat("Metric : ", x $ Metric,
if(!is.null(aT <- x $ Types))
paste("; Types =", paste(aT, collapse=", ")), "\n")
cat("Number of objects : ", x $ Size, "\n", sep="")
if(!is.null(x $ na.message))
cat("Warning : ", x $ NA.message, "\n")
invisible(x)
}
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