Nothing
my_geoDA <-
function(X, y, learn, test)
{
# Perform a geometric predictive discriminant analysis
# X: matrix or data.frame with explanatory variables
# y: vector or factor with group membership
# learn: vector of learning observations
# test: vector of testing observations
# how many observations
n = nrow(X[learn,])
ntest = length(test)
# how many groups
ng = nlevels(y[learn])
glevs = levels(y[learn])
# group means
GM = my_groupMeans(X[learn,], y[learn])
# within-class covariance matrix
W = my_withinCov(X[learn,], y[learn])
# inverse of Within cov matrix
W_inv = solve(W)
# constant terms of classification functions
alphas = rep(0, ng)
# coefficients of classification functions
Betas = matrix(0, nrow(W_inv), ng)
for (k in 1:ng)
{
alphas[k] = -(1/2) * GM[k,] %*% W_inv %*% GM[k,]
Betas[,k] = t(GM[k,]) %*% W_inv
}
# Mahalanobis-Fisher Classification Rule
FDF = rbind(alphas, Betas)
rownames(FDF) = c("constant", colnames(X))
colnames(FDF) = glevs
# matrix of constant terms
A = matrix(rep(alphas,ntest), ntest, ng, byrow=TRUE)
# apply discrim functions
Disc = X[test,] %*% Betas + A
dimnames(Disc) = list(rownames(X[test,]), glevs)
# predicted class
pred = apply(Disc, 1, function(u) which(u == max(u)))
names(pred) = NULL
# assign class values
pred_class = factor(pred, levels=seq_along(glevs), labels=glevs)
# confusion matrix
conf = table(original=y[test], predicted=pred_class)
# results
res = list(FDF=FDF, conf=conf, Disc=Disc, pred_class=pred_class)
}
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.