最初の答えについては、関数を書き直すことができますplot.spei
plot.spei <-
function (x, ...)
{
## label <- ifelse(as.character(x$call)[1] == "spei", "SPEI",
## "SPI")
ser <- ts(as.matrix(x$fitted[-c(1:x$scale), ]), end = end(x$fitted),
frequency = frequency(x$fitted))
ser[is.nan(ser - ser)] <- 0
se <- ifelse(ser == 0, ser, NA)
tit <- dimnames(x$coefficients)[2][[1]]
if (start(ser)[2] == 1) {
ns <- c(start(ser)[1] - 1, 12)
}
else {
ns <- c(start(ser)[1], start(ser)[2] - 1)
}
if (end(ser)[2] == 12) {
ne <- c(end(ser)[1] + 1, 1)
}
else {
ne <- c(end(ser)[1], end(ser)[2] + 1)
}
n <- ncol(ser)
if (is.null(n))
n <- 1
par(mar = c(4, 4, 2, 1) + 0.1)
if (n > 1 & n < 5)
par(mfrow = c(n, 1))
if (n > 1 & n >= 5)
par(mfrow = c({
n + 1
}%/%2, 2))
for (i in 1:n) {
datt <- ts(c(0, ser[, i], 0), frequency = frequency(ser),
start = ns, end = ne)
datt.pos <- ifelse(datt > 0, datt, 0)
datt.neg <- ifelse(datt <= 0, datt, 0)
plot(datt, type = "n", xlab = "", main = tit[i], ...)
if (!is.null(x$ref.period)) {
k <- ts(5, start = x$ref.period[1, ], end = x$ref.period[2,
], frequency = 12)
k[1] <- k[length(k)] <- -5
polygon(k, col = "light grey", border = NA, density = 20)
abline(v = x$ref.period[1, 1] + (x$ref.period[1,
2] - 1)/12, col = "grey")
abline(v = x$ref.period[2, 1] + (x$ref.period[2,
2] - 1)/12, col = "grey")
}
grid(col = "black")
polygon(datt.pos, col = "blue", border = NA)
polygon(datt.neg, col = "red", border = NA)
lines(datt, col = "dark grey")
abline(h = 0)
points(se, pch = 21, col = "white", bg = "black")
}
}
そして、ylab
パラメータを使用します
plot(spi1, ylab = "SPI")
個別にプロットする場合は、クラスの適合値を抽出しts
、R の時系列オブジェクトに基本的なプロットを適用できます。
par(mfrow = c(3, 4))
listofmonths <- split(fitted(spi1), cycle(fitted(spi1)))
names(listofmonths) <- month.abb
require(plyr)
l_ply(seq_along(listofmonths), function(x) {
plot(x = seq_along(listofmonths[[x]]), y = listofmonths[[x]],
type = "l", xlab = "", ylab = "SPI")
title(names(listofmonths)[x])
})
これらのタイプのプロットを試すこともできます
monthplot(fitted(spi1), labels = month.abb, cex.axis = 0.8)
boxplot(fitted(spi1) ~ cycle(fitted(spi1)), names = month.abb, cex.axis = 0.8)