Nothing
`msoplot` <-
function (x, alpha = 0.05, explained = FALSE, ylim = NULL,
legend = "topleft", ...)
{
if (is.data.frame(x$vario)) {
vario <- x$vario
hasSig <- is.numeric(x$vario$CA.signif)
z <- qnorm(alpha/2)
if (is.numeric(vario$CA.signif)) {
vario <- vario[, -ncol(vario)]
}
ymax <- max(vario[, -1:-3], na.rm = TRUE)
b <- ncol(vario) - 3
label <- c("", "", "", "Total variance", "Explained plus residual",
"Residual variance", "Explained variance", "Conditioned variance")
ci.lab <- "C.I. for total variance"
sign.lab <- if(hasSig) "Sign. autocorrelation" else NULL
if (is.numeric(x$CCA$rank)) {
if (!explained)
b <- b - 1
if (is.numeric(x$vario$se))
b <- b - 1
figmat <- cbind(vario$All + z * vario$se,
vario$All - z * vario$se,
vario$Sum,
vario[, 6:(b + 3)])
matplot(vario$Dist, cbind(0,figmat), type = "n",
xlab = "Distance", ylab = "Variance",
ylim = ylim, ...)
lines(vario$Dist, vario$All + z * vario$se, lty = 1, ...)
lines(vario$Dist, vario$All - z * vario$se, lty = 1, ...)
lines(vario$Dist, vario$Sum, type = "b", lty = 2,
pch = 3, ...)
## Legend
legend(legend,
legend=c(label[c(2,3:b)+3], ci.lab, sign.lab),
lty=c(c(1,2,1,1,1)[2:b], 1, if(hasSig) NA),
pch=c(3, (6:(b+3))-6, NA, if(hasSig) 15)
)
matlines(vario$Dist, figmat[,-c(1:3)], type = "b", lty = 1,
pch = 6:(b+3)-6, ...)
text(x = c(vario$Dist), y = par("usr")[3], pos = 3,
label = c(vario$n), cex = 0.8, ...)
abline(v = max(x$H/2), lty = 3, ...)
}
else {
if (is.null(ylim))
ylim <- c(0, ymax)
plot(vario$Dist, vario$All, type = "b", lty = 1,
pch = 0, xlab = "Distance", ylab = "Variance",
ylim = ylim, ...)
abline(h = x$tot.chi, lty = 5, ...)
text(x = c(vario$Dist), y = par("usr")[3], pos = 3,
label = c(vario$n), cex = 0.8)
abline(v = max(x$H)/2, lty = 3, ...)
legend(legend,
legend=c("Total variance","Global variance estimate",
if(hasSig) "Sign. autocorrelation"),
lty=c(1,5, if(hasSig) NA),
pch = if(hasSig) c(NA,NA,15) else NULL)
}
}
if (hasSig) {
a <- c(1:nrow(x$vario))[x$vario$CA.signif <
alpha]
points(vario$Dist[a], x$vario$CA[a], pch = 15, ...)
if (is.numeric(x$CCA$rank)) {
inflation <- 1 - weighted.mean(x$vario$CA, x$vario$n)/
weighted.mean(x$vario$CA[-a],
x$vario$n[-a])
cat("Error variance of regression model underestimated by",
round(inflation * 100, 1), "percent", "\n")
}
}
invisible()
}
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