tda {transDA} | R Documentation |
Transformation Discriminant Analysis
Description
Implements discriminant analysis methods including traditional linear (LDA), quadratic (QDA), transformation (TDA), mixture (MDA) discriminant analysis, and their combinations such as TQDA or TLMDA. The user chooses a specific method by specifying options for common or varying transformation parameters as well as covariance matrices.
Usage
tda(x, max_k, ID, trans = TRUE, common_lambda = FALSE,
common_sigma = FALSE, iter = 50, subgroup = NULL,
tol= 0.001, lambda0 = 0.015)
Arguments
x |
A frame or matrix containing a training data set |
max_k |
The maximum number of mixture components within each class to be fitted |
ID |
A variable containing class memberships for all observations |
trans |
A transformation indicator: |
common_lambda |
A parameter that regulates transformations. If |
common_sigma |
A homoscedasticity parameter: if |
iter |
A maximum number of iterations of the EM algorithm; the default value is 50 |
subgroup |
A vector containing the number of mixture components per each class to be fitted |
tol |
Tolerance level for a stopping critetion based on the relative difference in two consecutive log-likelihood values |
lambda0 |
Starting value for transformation parameters |
Value
BIC |
Values of the Bayesian Information Criterion calculated for each evaluated model |
subprior |
Estimated component priors for each class |
mu |
Estimated component means for each class |
sigma |
Estimated component covariance matrices for each group |
lambda |
Estimated transformation parameters |
loglik |
The log-likelihood value for the model with the lowest BIC |
pred_ID |
Estimated classification of observations in the training data set |
prior |
Estimated class priors |
misclassification_rate |
Misclassification rate for the training data set |
ARI |
Adjusted Rand index value |
Z |
Matrix of posterior probabilities for the training data set |
See Also
Examples
set.seed(123)
# Example 1:
MDA <- tda(x = iris[,1:4], max_k = 2,ID = iris$Species, trans = FALSE)
print(MDA)
summary(MDA)
# Example 2:
LDA <- tda(x = iris[,1:4], max_k = 1, ID = iris$Species, trans = FALSE,
common_sigma = TRUE)
print(LDA)
summary(LDA)
# Example 3:
QDA <- tda(x = iris[,1:4], subgroup = c(1, 1, 1), ID = iris$Species,
trans = FALSE, common_sigma = FALSE)
print(QDA)
summary(QDA)
# Example 4:
TQDA <- tda(x = iris[,1:4], subgroup = c(1, 1, 1), ID = iris$Species,
trans = TRUE, common_sigma = FALSE, common_lambda = TRUE)
print(TQDA)
summary(TQDA)