DQDA
In [1]:
suppressMessages(library(sparsediscrim))
load("../transformed data/golub3571.rda")
load("../transformed data/paper9.rda")
# Settings as specified in the paper
p = 40 # number of genes for FLDA
B = 50 # Aggregation predictors
N = 200 # repeat classification N times
d = c(0.05, 0.1,0.25, 0.5, 0.75, 1) # CPD parameter
set.seed(2017)
In [2]:
cbine_data = data.frame(response = factor(total3571_response), scale_golub_merge)
dqda_error = numeric(N)
for(i in 1:200){
dqda_index = mysplit(nrow(cbine_data))
dqda_train = cbine_data[-dqda_index,]
dqda_test = cbine_data[dqda_index,]
# gene selection
temp_bw = order(BW(dqda_train[, -1], dqda_train$response), decreasing = T)[1:p]
dqda_train_t = data.frame(response = dqda_train$response, dqda_train[,temp_bw+1])
dqda_test_t= data.frame(response = dqda_test$response, dqda_test[,temp_bw+1])
dqda_md = dlda(response~., data = dqda_train_t)
dqda_pred = predict(dqda_md, dqda_test_t[,-1])$class
dqda_error[i] = sum(dqda_pred != dqda_test_t$response)
}
resultDQDA = c(Median = median(dqda_error), Upper_quartile = quantile(dqda_error, 0.75))
In [3]:
resultDQDA