= (Data, CurrGauss,
Means, Covariances, MainAxesAngle,
Colors, Cls,
Shapes, ShapeText, AxNames = ("X1", "X2"),
ShowAxis = , ShowEllipsoids = ,
ShowGaussNr = , Source = "D"){
# DESCRIPTION
#
#
# INPUT
# Data [1:n, 1:2] Numeric matrix with n observations and 2 features.
# Means List with l [1:2] numerical vector defining the means of
# the l GMM components.
# Covariances List with l [1:2, 1:2] numerical matrices defining the
# covariance matrices of the l GMM
# components.
# MainAxesAngle[1:4] Numeric vector with 1st and 2nd main axes of a 2D
# ellipsoid and the respective angles
# measured to the first unit vector c(0,1).
# Colors [1:n] Numerical vector of size n containing the colors of each observation.
# Cls [1:n] Numerical vector of size n containing the classes of each observation.
# Shapes List of List with 4 attributes (type, fillcolor, opacity, path)
# for a shape for plotting. Here it is used for plotting an ellipsoid.
# AxNames Character vector with names for each dimension ax of 2D
# plot.
# ShowAxis Boolean (Default=TRUE). T: Show main axes of the GMM
# ellipsoid/circle.
# Source Character indicating plot source. Important attribute for plotly in
# shiny in order to keep control of specific panels.
#
# OUTPUT
# plotOut Plotly object containing plot for direct visualization.
#
# Author: QMS 15.12.2021
((Data)){
("Parameter Data is missing. Returning.")
()
}{
(!(Data)){
("Parameter Data is not of type matrix. Returning.")
()
} ((Data)[2] != 2){
("Parameter Data does not have exactly two feature columns. Returning.")
()
}
}
(!(Means)){
(!(Means)){
("Parameter Means is not of type list. Returning.")
()
}{
(i 1:(Means)){
(!(Means[i]])){
("Parameter Means can only contain vectors. Returning.")
()
} ((Means[i]]) != 2){
("Parameter Means can only contain vectors of dimension 2. Returning.")
()
}
}
}
}
(!(Covariances)){
(!(Covariances)){
("Parameter Cov is not of type list. Returning.")
()
}{
(i 1:(Covariances)){
(!(Covariances[i]])){
("Parameter Cov can only contain matrices. Returning.")
()
} (((Covariances[i]])[1] != 2) | ((Covariances[i]])[2] != 2)){
("Parameter Cov can only contain matrices of dimension 2x2. Returning.")
()
}
}
}
}
((MainAxesAngle)){
("Parameter MainAxesAngle is missing. Returning.")
()
}{
(!(MainAxesAngle)){
("Parameter MainAxesAngle is not of type list. Returning.")
()
}
}
((Colors)){
("Parameter Colors is missing. Returning.")
()
}{
(!(Colors)){
("Parameter Colors is not of type vector. Returning.")
()
}
}
((Cls)){
("Parameter Cls is missing. Returning.")
()
}
((Data)[1] != (Cls)){
("Number of rows of parameter Data must match length of vector Cls. Returning.")
()
}
((Colors) < ((Cls))){
("Length of parameter Colors must be greater than or equal to the number of unique entries in Cls. Returning.")
()
}
(!(Shapes)){
("Parameter Shapes must be of type list. Returning.")
()
}
((ShowAxis)){
ShowAxis =
}
(!(Source)){
("Parameter Source is not a character type. Returning.")
()
}
plotOut = plotly::plot_ly( = Source)
plotOut = plotly::add_markers(p = plotOut,
x = Data,1],
y = Data,2],
marker = (color = Colors[Cls]), = "scatter")
(ShowGaussNr){
plotOut = plotly::add_text(p = plotOut,
x = ShapeText,1],
y = ShapeText,2],
= ShapeText,3],
textfont = (color = "white", size = 40))
}
(ShowAxis){
(i 1:(Means)){
plotOut = plotly::add_markers(p = plotOut,
x = Means[i]][1],
y = Means[i]][2],
marker = (color = "white", size = 7,
= (color = "black", = 2)),
= "scatter")
((Covariances[i]] != ((1,1)))){
MySVD = (Covariances[i]]) # Compute singular value decomposition for Princ. Component Axes
SD1 = MySVD$d[1]*MySVD$u,1] # Extract 1st PCA component vector
SD2 = MySVD$d[2]*MySVD$u,2] # Extract 2nd PCA component vector
NormSD1 = (SD1, = "2")
TopCircle1 = acos((SD1 * (0,1))/NormSD1)*(180/) # See if 1st PCA is on the upper part of the cartesian coord. sys.
BottomCircle1 = acos((SD1 * (0,-1))/NormSD1)*(180/) # See if 1st PCA is on the lower part of the cartesian coord. sys.
Angle1 = acos((SD1 * (1,0))/NormSD1)*(180/)
(BottomCircle1<TopCircle1){
Angle1 = 360 - Angle1 # This would be the angle for the lower part
}
(((Angle1-MainAxesAngle[i]][3])) > 5 & MainAxesAngle[i]][3] != 360){
SD1 = -SD1
}
NormSD2 = (SD2, = "2")
TopCircle2 = acos((SD2 * (0,1))/NormSD2)*(180/) # See if 1st PCA is on the upper part of the cartesian coord. sys.
BottomCircle2 = acos((SD2 * (0,-1))/NormSD2)*(180/)
Angle2 = acos((SD2 * (1,0))/NormSD2)*(180/)
(BottomCircle2<TopCircle2){
Angle2 = 360 - Angle2 # This would be the angle for the lower part
}
((Angle2-((MainAxesAngle[i]][3]+90)%%360)) > 5){
SD2 = -SD2
}
PC1A = SD1[1]; PC1B = SD1[2]; PC2A = SD2[1]; PC2B = SD2[2] # Eigenvector components
plotOut = plotly::add_annotations(p = plotOut,
standoff=0,
x = Means[i]][1] + PC1A, y = Means[i]][2] + PC1B,
ax = Means[i]][1], ay = Means[i]][2],
xref = "x", yref = "y",
axref = "x", ayref = "y",
= "", showarrow = ,
arrowcolor = "bisque", arrowhead = 0.7, arrowsize = 2)
#plotOut = plotly::add_annotations(p = plotOut,
# x = Means[[i]][1] + PC2A, y = Means[[i]][2] + PC2B,
# ax = Means[[i]][1], ay = Means[[i]][2],
# xref = "x", yref = "y",
# axref = "x", ayref = "y",
# text = "", showarrow = TRUE,
# arrowcolor="bisque", arrowhead = 0.7, arrowsize = 1)
}
}
}
((Shapes) == (Means)){
(i 1:(Means)){
(i != CurrGauss){
Shapes[i]]$fillcolor = "black"
}{
Shapes[i]]$opacity = 0.7
}
}
}
(ShowEllipsoids != ){
Shapes =
}
plotOut = plotly::(p = plotOut,
= "2D Scatter Plot of Dataset",
shapes = Shapes,
xaxis = ( = AxNames[1], fixedrange = T, scaleanchor="y", scaleratio=1),
yaxis = ( = AxNames[2], fixedrange = T),
plot_bgcolor = "rgb(254, 254, 254)", # plot_bgcolor = "rgb(254, 247, 234)",
paper_bgcolor = "rgb(254, 254, 254)") # paper_bgcolor = "rgb(254, 247, 234)"
plotOut = plotly::hide_colorbar(p = plotOut)
plotOut = plotly::hide_legend(p = plotOut)
plotOut = plotly::config(p = plotOut, displayModeBar=F, editable=T)
(plotOut)
}
