cplot3d | R Documentation |
cplot3d
produces three dimensional scatter plot with classification labels of binary classes.
cplot3d(x1, x2, x3, ypred, yobs, colors = c("red", "blue"), symbols = c("circle", "o"), size = 10, xlab = NULL, ylab = NULL, zlab = NULL, title = NULL)
x1 |
the x coordinate of points in the plot. |
x2 |
the y coordinate of points in the plot. |
x3 |
the z coordinate of points in the plot. |
ypred |
a factor of the predicted binary response variable. |
yobs |
a factor of the observed binary response variable. |
colors |
a vector of two colors specifying the levels of the observed binary response variable. |
symbols |
a vector of two symbols specifying the levels of the predicted binary response variable. |
size |
the size of symbols. |
xlab |
a label for the x axis, defaults to a description of x1. |
ylab |
a label for the y axis, defaults to a description of x2. |
zlab |
a label for the z axis, defaults to a description of x3. |
title |
a main title of the plot. |
Symbols indicate the observed classes of binary response. Colors show TRUE or FALSE classification of the observations.
An object with class "plotly" and "htmlwidget".
Osman Dag
plot_ly
library(GMDH2) library(mlbench) data(BreastCancer) data <- BreastCancer # to obtain complete observations completeObs <- complete.cases(data) data <- data[completeObs,] x <- data.matrix(data[,2:10]) y <- data[,11] seed <- 12345 set.seed(seed) nobs <- length(y) # to split train, validation and test sets indices <- sample(1:nobs) ntrain <- round(nobs*0.6,0) nvalid <- round(nobs*0.2,0) ntest <- nobs-(ntrain+nvalid) train.indices <- sort(indices[1:ntrain]) valid.indices <- sort(indices[(ntrain+1):(ntrain+nvalid)]) test.indices <- sort(indices[(ntrain+nvalid+1):nobs]) x.train <- x[train.indices,] y.train <- y[train.indices] x.valid <- x[valid.indices,] y.valid <- y[valid.indices] x.test <- x[test.indices,] y.test <- y[test.indices] set.seed(seed) # to construct model via dce-GMDH algorithm model <- dceGMDH(x.train, y.train, x.valid, y.valid) # to obtain predicted classes for test set y.test_pred <- predict(model, x.test, type = "class") # to obtain confusion matrix and some statistics for test set confMat(y.test_pred, y.test, positive = "malignant") # to produce 3D scatter plot with classification labels for test set cplot3d(x.test[,1], x.test[,6], x.test[,3], y.test_pred, y.test, colors = c("red", "black"))
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