GAGAs (version 0.6.2)

cal.w.acc: Calculate the weighted ACC of the classification, the inputs must be characters

Description

Calculate the weighted ACC of the classification, the inputs must be characters

Usage

cal.w.acc(predictions, truelabels)

Value

weighted ACC

Arguments

predictions

predictions

truelabels

true labels

Examples

set.seed(2022)
p_size = 30
sample_size=300
R1 = 3
R2 = 2
ratio = 0.5 #The ratio of zeroes in coefficients
# Set the true coefficients
zeroNum = round(ratio*p_size)
ind = sample(1:p_size,zeroNum)
beta_true = runif(p_size,0,R2)
beta_true[ind] = 0
X = R1*matrix(rnorm(sample_size * p_size), ncol = p_size)
y=X%*%beta_true + rnorm(sample_size,mean=0,sd=2)
# Estimation
fit = GAGAs(X,y,alpha = 3,family="gaussian")
Eb = fit$beta
#Create testing data
X_t = R1*matrix(rnorm(sample_size * p_size), ncol = p_size)
y_t=X_t%*%beta_true + rnorm(sample_size,mean=0,sd=2)
#Prediction
Ey = predict.GAGA(fit,newx=X_t)

cat("\n err:", norm(Eb-beta_true,type="2")/norm(beta_true,type="2"))
cat("\n acc:", cal.w.acc(as.character(Eb!=0),as.character(beta_true!=0)))
cat("\n perr:", norm(Ey-y_t,type="2")/sqrt(sample_size))