Description Usage Arguments Value Author(s) Examples
Calculates the parameter estimates associated with quadratic discriminant analysis
1 |
X |
A list where each element contains the data of a different class |
Lambda1 |
Ridge tuning parameter, must be greater than or equal to 0 |
Lambda2 |
Ridge Fusion tuning parameter, must be greater than or equal to 0 |
scaleC |
If |
An object of class RidgeFusedQDA
, basically a list including elements
Omega |
a list where each element is the inverse covariance matrix estimate for the corresponding element of X |
Means |
A list of class means |
Pi |
Class Proportions |
Lambda1 |
|
Lambda2 |
|
iter |
Number of iterations until convergence |
Brad Price
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ## Creating a toy example with 5 variables
library(mvtnorm)
set.seed(526)
p=5
Sig1=matrix(0,p,p)
for(j in 1:p){
for(i in j:p){
Sig1[j,i]=.7^abs(i-j)
Sig1[i,j]=Sig1[j,i]
}
}
Sig2=diag(c(rep(2,p-5),rep(1,5)),p,p)
X1=rmvnorm(100,rep(2*log(p)/p,p),Sig1)
Y=rmvnorm(100,,Sig2)
Z=list(X1,Y)
A2=FusedQDA(Z,10,10,scale=TRUE)
names(A2)
|
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