svm_mdsplot | R Documentation |
Plots 2D MDS configuration including facets as determined by an SVM.
svm_mdsplot(mds_object, svm_object, class, legend1 = TRUE, legend2 = TRUE, inset = c(-0.2, 0.5), plot.dim = c(1,2), by = 0.01, main, xlab, ylab, xlim, ylim, ...)
mds_object |
Object of class |
svm_object |
Object of class |
class |
Vector of class assignments (facets) for each object. |
legend1 |
If |
legend2 |
If |
inset |
Inset distance from the margins for both legends as a fraction of the plot region when legend is placed by keyword. |
plot.dim |
Vector with dimensions to be plotted. |
by |
Scaling factor for resolution (the smaller, the higher the resolution). |
main |
Plot title. |
xlab |
Label of x-axis. |
ylab |
Label of y-axis. |
xlim |
Scale x-axis. |
ylim |
Scale y-axis. |
... |
Further plot arguments passed: see |
Using the SVM implementation of e1071
one can determine facets in an MDS configuration based on an SVM fit. This function plots the resulting facets on top of the 2D MDS configuration. Note that this function is work in progress.
svm
, tune.svm
## Guttman intelligence data Delta <- sim2diss(Guttman1965[[1]]) class <- Guttman1965[[2]] ## ordinal MDS fit mds_gut <- mds(Delta, ndim = 2, type = "ordinal") mds_gut cols <- rainbow_hcl(4)[as.numeric(class)] plot(mds_gut, col = cols, label.conf = list(col = cols)) legend("bottomright", legend = levels(class), cex = 0.7, col = rainbow_hcl(4), pch = 19) ## radial SVM fit X <- mds_gut$conf ## extract configuration dat <- data.frame(class = class, X) ## merge with class vector costvec <- 2^seq(-4, 4) ## tuning parameter grid gamma <- seq(0.01, 0.5, 10) set.seed(111) svm_gut <- tune.svm(class ~ D1 + D2, data = dat, kernel = "radial", cross = 10, cost = costvec)$best.model svm_gut preds <- predict(svm_gut, data = dat) ## predicted classes table(obs = class, pred = preds) ## confusion matrix svm_mdsplot(mds_gut, svm_gut, dat$class, inset = c(-0.3, 0.5))
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