Type: Package
Title: Global Adaptive Generative Adjustment Algorithm for Generalized Linear Models
Version: 0.6.2
Language: en-US
Description: Fits linear regression, logistic and multinomial regression models, Poisson regression, Cox model via Global Adaptive Generative Adjustment Algorithm. For more detailed information, see Bin Wang, Xiaofei Wang and Jianhua Guo (2022) <doi:10.48550/arXiv.1911.00658>. This paper provides the theoretical properties of Gaga linear model when the load matrix is orthogonal. Further study is going on for the nonorthogonal cases and generalized linear models. These works are in part supported by the National Natural Foundation of China (No.12171076).
License: GPL-2
URL: https://arxiv.org/abs/1911.00658
Encoding: UTF-8
Depends: R (≥ 3.6.0)
Imports: Rcpp (≥ 1.0.9), survival, utils
Suggests: mvtnorm
SystemRequirements: C++17
LinkingTo: Rcpp, RcppEigen
RoxygenNote: 7.2.3
Maintainer: Bin Wang <eatingbeen@hotmail.com>
NeedsCompilation: yes
Packaged: 2024-01-23 06:45:10 UTC; Administrator
Author: Bin Wang [aut, cre], Xiaofei Wang [ctb], Jianhua Guo [ths]
Repository: CRAN
Date/Publication: 2024-01-23 07:02:53 UTC

GAGAs: A package for fiting a GLM with GAGA algorithm

Description

Fits linear, logistic and multinomial, poisson, and Cox regression models via Global Adaptive Generative Adjustment algorithm.

Fits linear, logistic and multinomial, poisson, and Cox regression models via the Global Adaptive Generative Adjustment algorithm.

Usage

GAGAs(
  X,
  y,
  family = c("gaussian", "binomial", "poisson", "multinomial", "cox"),
  alpha = 2,
  itrNum = 100,
  thresh = 0.001,
  QR_flag = FALSE,
  flag = TRUE,
  lamda_0 = 0.001,
  fdiag = TRUE,
  frp = TRUE,
  subItrNum = 20
)

Arguments

X

Input matrix, of dimension nobs*nvars; each row is an observation. If the intercept term needs to be considered in the estimation process, then the first column of X must be all 1s.

y

Response variable. Quantitative for family="gaussian", or family="poisson" (non-negative counts). For family="binomial" should be either a factor with two levels. For family="multinomial" should be a one-hot matrix or a nc>=2 level factor. For family="cox" should be an n*2 matrix, one column should be named "time", indicating the survival time; the other column must be named "status", and consists of 0 and 1, 0 indicates that the row of data is censored, 1 is opposite.

family

Either a character string representing one of the built-in families, "gaussian", "binomial", "poisson", "multinomial" or "cox".

alpha

Hyperparameter. In general, alpha can be set to 1, 2 or 3. for family="gaussian" and family="cox", the suggested value for alpha is 2 or 3. for family="binomial", family="poisson" and family="multinomial", the suggested value for alpha is 1 or 2. but when the collinearity of the load matrix is serious, the hyperparameters can be selected larger, such as 5.

itrNum

The number of iteration steps. In general, 20 steps are enough. If the condition number of X is large, it is recommended to greatly increase the number of iteration steps.

thresh

Convergence threshold for beta Change, if max(abs(beta-beta_old))<threshold, return.

QR_flag

It identifies whether to use QR decomposition to speed up the algorithm. Currently only valid for linear models.

flag

It identifies whether to make model selection. The default is TRUE.

lamda_0

The initial value of the regularization parameter for ridge regression. The running result of the algorithm is not sensitive to this value.

fdiag

It identifies whether to use diag Approximation to speed up the algorithm.

frp

Identifies if a method is preprocessed to reduce the number of parameters

subItrNum

Maximum number of steps for subprocess iterations.

Value

Regression coefficients

Mypackage functions

GAGAs

Examples

# Gaussian
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))

# binomial
set.seed(2022)
cat("\n")
cat("Test binomial GAGA\n")
p_size = 30
sample_size=600
test_size=1000
R1 = 1
R2 = 3
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,R2*0.2,R2)
beta_true[ind] = 0
X = R1*matrix(rnorm(sample_size * p_size), ncol = p_size)
X[1:sample_size,1]=1
t = 1/(1+exp(-X%*%beta_true))
tmp = runif(sample_size,0,1)
y = rep(0,sample_size)
y[t>tmp] = 1
fit = GAGAs(X,y,family = "binomial", alpha = 1)
Eb = fit$beta
#Generate test samples
X_t = R1*matrix(rnorm(test_size * p_size), ncol = p_size)
X_t[1:test_size,1]=1
t = 1/(1+exp(-X_t%*%beta_true))
tmp = runif(test_size,0,1)
y_t = rep(0,test_size)
y_t[t>tmp] = 1
#Prediction
Ey = predict(fit,newx = X_t)
cat("\n--------------------")
cat("\n err:", norm(Eb-beta_true,type="2")/norm(beta_true,type="2"))
cat("\n acc:", cal.w.acc(as.character(Eb!=0),as.character(beta_true!=0)))
cat("\n pacc:", cal.w.acc(as.character(Ey),as.character(y_t)))
cat("\n")
# multinomial
set.seed(2022)
cat("\n")
cat("Test multinomial GAGA\n")
p_size = 20
C = 3
classnames = c("C1","C2","C3","C4")
sample_size = 500
test_size = 1000
ratio = 0.5 #The ratio of zeroes in coefficients
Num = 10 # Total number of experiments
R1 = 1
R2 = 5
#Set the true coefficients
beta_true = matrix(rep(0,p_size*C),c(p_size,C))
zeroNum = round(ratio*p_size)
for(jj in 1:C){
  ind = sample(1:p_size,zeroNum)
  tmp = runif(p_size,0,R2)
  tmp[ind] = 0
  beta_true[,jj] = tmp
}
#Generate training samples
X = R1*matrix(rnorm(sample_size * p_size), ncol = p_size)
X[1:sample_size,1]=1
z = X%*%beta_true
t = exp(z)/(1+rowSums(exp(z)))
t = cbind(t,1-rowSums(t))
tt = t(apply(t,1,cumsum))
tt = cbind(rep(0,sample_size),tt)
# y = matrix(rep(0,sample_size*(C+1)),c(sample_size,C+1))
y = rep(0,sample_size)
for(jj in 1:sample_size){
  tmp = runif(1,0,1)
  for(kk in 1:(C+1)){
    if((tmp>tt[jj,kk])&&(tmp<=tt[jj,kk+1])){
      # y[jj,kk] = 1
      y[jj] = kk
      break
    }
  }
}
y = classnames[y]
fit = GAGAs(X, y,alpha=1,family = "multinomial")
Eb = fit$beta
#Prediction
#Generate test samples
X_t = R1*matrix(rnorm(test_size * p_size), ncol = p_size)
X_t[1:test_size,1]=1
z = X_t%*%beta_true
t = exp(z)/(1+rowSums(exp(z)))
t = cbind(t,1-rowSums(t))
tt = t(apply(t,1,cumsum))
tt = cbind(rep(0,test_size),tt)
y_t = rep(0,test_size)
for(jj in 1:test_size){
  tmp = runif(1,0,1)
  for(kk in 1:(C+1)){
    if((tmp>tt[jj,kk])&&(tmp<=tt[jj,kk+1])){
      y_t[jj] = kk
      break
    }
  }
}
y_t = classnames[y_t]
Ey = predict(fit,newx = X_t)
cat("\n--------------------")
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 pacc:", cal.w.acc(as.character(Ey),as.character(y_t)))
cat("\n")

# Poisson
set.seed(2022)
p_size = 30
sample_size=300
R1 = 1/sqrt(p_size)
R2 = 5
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)
X[1:sample_size,1]=1
y = rpois(sample_size,lambda = as.vector(exp(X%*%beta_true)))
y = as.vector(y)
# Estimate
fit = GAGAs(X,y,alpha = 2,family="poisson")
Eb = fit$beta
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)))

# cox
p_size = 50
sample_size = 500
test_size = 1000
R1 = 3
R2 = 1
ratio = 0.5 #The ratio of zeroes in coefficients
censoringRate = 0.25 #Proportion of censoring data in observation data
# Set the true coefficients
zeroNum = round(ratio*p_size)
ind = sample(1:p_size,zeroNum)
beta_true = runif(p_size,-R2,R2)
beta_true[ind] = 0
# Generate training samples
X = R1*matrix(rnorm(sample_size * p_size), ncol = p_size)
z = X%*%beta_true
u = runif(sample_size,0,1)
t = ((-log(1-u)/(3*exp(z)))*100)^(0.1)
cs = rep(0,sample_size)
csNum = round(censoringRate*sample_size)
ind = sample(1:sample_size,csNum)
cs[ind] = 1
t[ind] = runif(csNum,0,0.8)*t[ind]
y = cbind(t,1 - cs)
colnames(y) = c("time", "status")
#Estimation
fit = GAGAs(X,y,alpha=2,family="cox")
Eb = fit$beta

#Generate testing samples
X_t = R1*matrix(rnorm(test_size * p_size), ncol = p_size)
z = X_t%*%beta_true
u = runif(test_size,0,1)
t = ((-log(1-u)/(3*exp(z)))*100)^(0.1)
cs = rep(0,test_size)
csNum = round(censoringRate*test_size)
ind = sample(1:test_size,csNum)
cs[ind] = 1
t[ind] = runif(csNum,0,0.8)*t[ind]
y_t = cbind(t,1 - cs)
colnames(y_t) = c("time", "status")
#Prediction
pred = predict(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 Cindex:", cal.cindex(pred,y_t))

Fit a linear model via the GAGA algorithm

Description

Fit a linear model with a Gaussian noise via the Global Adaptive Generative Adjustment algorithm

Usage

LM_GAGA(
  X,
  y,
  alpha = 3,
  itrNum = 50,
  thresh = 0.001,
  QR_flag = FALSE,
  flag = TRUE,
  lamda_0 = 0.001,
  fix_sigma = FALSE,
  sigm2_0 = 1,
  fdiag = TRUE,
  frp = TRUE
)

Arguments

X

Input matrix, of dimension nobs*nvars; each row is an observation. If the intercept term needs to be considered in the estimation process, then the first column of X must be all 1s.

y

Quantitative response vector.

alpha

Hyperparameter. The suggested value for alpha is 2 or 3. When the collinearity of the load matrix is serious, the hyperparameters can be selected larger, such as 5.

itrNum

The number of iteration steps. In general, 20 steps are enough. If the condition number of X is large, it is recommended to greatly increase the number of iteration steps.

thresh

Convergence threshold for beta Change, if max(abs(beta-beta_old))<threshold, return.

QR_flag

It identifies whether to use QR decomposition to speed up the algorithm. Currently only valid for linear models.

flag

It identifies whether to make model selection. The default is TRUE.

lamda_0

The initial value of the regularization parameter for ridge regression. The running result of the algorithm is not sensitive to this value.

fix_sigma

It identifies whether to update the variance estimate of the Gaussian noise or not. fix_sigma=TRUE uses the initial variance as the variance estimate in each loop. fix_sigma=FALSE updates the variance estimate in each loop.

sigm2_0

The initial variance of the Gaussian noise.

fdiag

It identifies whether to use diag Approximation to speed up the algorithm.

frp

Identifies whether pre-processing is performed by the OMP method to reduce the number of parameters

Value

Coefficient vector.

Examples

# Gaussian
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))

Calculate F1 score for classification, the inputs must be characters, and each of these elements must be either 'FALSE' or 'TRUE'.

Description

Calculate F1 score for classification, the inputs must be characters, and each of these elements must be either 'FALSE' or 'TRUE'.

Usage

cal.F1Score(predictions, truelabels)

Arguments

predictions

predictions

truelabels

true labels

Value

F1 score

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
cat("\n F1 score:", cal.F1Score(as.character(Eb!=0),as.character(beta_true!=0)))

Calculate ACC for classification, the inputs must be characters

Description

Calculate ACC for classification, the inputs must be characters

Usage

cal.acc(predictions, truelabels)

Arguments

predictions

predictions

truelabels

true labels

Value

ACC

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

compute C index for a Cox model

Description

Computes Harrel's C index for predictions from a "cox" object.

Usage

cal.cindex(pred, y, weights = rep(1, nrow(y)))

Arguments

pred

Predictions from a "cox" object

y

a survival response object - a matrix with two columns "time" and "status"; see documentation for "glmnet" or see documentation for "GAGA"

weights

optional observation weights

Details

Computes the concordance index, taking into account censoring. This file fully references the Cindex.R file in glmnet package.

Value

Harrel's C index

Author(s)

Trevor Hastie <hastie@stanford.edu>

References

Harrel Jr, F. E. and Lee, K. L. and Mark, D. B. (1996) Tutorial in biostatistics: multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing error, Statistics in Medicine, 15, pages 361–387.

See Also

cv.glmnet

Examples


set.seed(10101)
N = 1000
p = 30
nzc = p/3
x = matrix(rnorm(N * p), N, p)
beta = rnorm(nzc)
fx = x[, seq(nzc)] %*% beta/3
hx = exp(fx)
ty = rexp(N, hx)
tcens = rbinom(n = N, prob = 0.3, size = 1)  # censoring indicator
y = cbind(time = ty, status = 1 - tcens)  # y=Surv(ty,1-tcens) with library(survival)
fit = GAGAs(x, y, family = "cox")
pred = predict(fit, newx = x)
cat("\n Cindex:", cal.cindex(pred, y))

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)

Arguments

predictions

predictions

truelabels

true labels

Value

weighted ACC

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))

Fit a Cox model via the GAGA algorithm.

Description

Fit a Cox model via the Global Adaptive Generative Adjustment algorithm. Part of this function refers to the coxphfit function in MATLAB 2016b.

Usage

cox_GAGA(
  X,
  t,
  alpha = 2,
  itrNum = 20,
  thresh = 0.001,
  flag = TRUE,
  lamda_0 = 0.5,
  fdiag = TRUE,
  subItrNum = 20
)

Arguments

X

Input matrix, of dimension nobs*nvars; each row is an observation. If the intercept term needs to be considered in the estimation process, then the first column of X must be all 1s.

t

A n*2 matrix, one column should be named "time", indicating the survival time; the other column must be named "status", and consists of 0 and 1, 0 indicates that the row of data is censored, 1 is opposite.

alpha

Hyperparameter. The suggested value for alpha is 2 or 3.

itrNum

Maximum number of iteration steps. In general, 20 steps are enough. If the condition number of X is large, it is recommended to greatly increase the number of iteration steps.

thresh

Convergence threshold for beta Change, if max(abs(beta-beta_old))<threshold, return.

flag

It identifies whether to make model selection. The default is TRUE.

lamda_0

The initial value of the regularization parameter for ridge regression. The running result of the algorithm is not sensitive to this value.

fdiag

It identifies whether to use diag Approximation to speed up the algorithm.

subItrNum

Maximum number of steps for subprocess iterations.

Value

Coefficient vector.

Examples

set.seed(2022)
p_size = 50
sample_size = 500
test_size = 1000
R1 = 3
R2 = 1
ratio = 0.5 #The ratio of zeroes in coefficients
censoringRate = 0.25 #Proportion of censoring data in observation data
# Set the true coefficients
zeroNum = round(ratio*p_size)
ind = sample(1:p_size,zeroNum)
beta_true = runif(p_size,-R2,R2)
beta_true[ind] = 0
# Generate training samples
X = R1*matrix(rnorm(sample_size * p_size), ncol = p_size)
z = X%*%beta_true
u = runif(sample_size,0,1)
t = ((-log(1-u)/(3*exp(z)))*100)^(0.1)
cs = rep(0,sample_size)
csNum = round(censoringRate*sample_size)
ind = sample(1:sample_size,csNum)
cs[ind] = 1
t[ind] = runif(csNum,0,0.8)*t[ind]
y = cbind(t,1 - cs)
colnames(y) = c("time", "status")
#Estimation
fit = GAGAs(X,y,alpha=2,family="cox")
Eb = fit$beta

#Generate testing samples
X_t = R1*matrix(rnorm(test_size * p_size), ncol = p_size)
z = X_t%*%beta_true
u = runif(test_size,0,1)
t = ((-log(1-u)/(3*exp(z)))*100)^(0.1)
cs = rep(0,test_size)
csNum = round(censoringRate*test_size)
ind = sample(1:test_size,csNum)
cs[ind] = 1
t[ind] = runif(csNum,0,0.8)*t[ind]
y_t = cbind(t,1 - cs)
colnames(y_t) = c("time", "status")
#Prediction
pred = predict(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 Cindex:", cal.cindex(pred,y_t))

Fit a Cox model via the GAGA algorithm using cpp.

Description

Fit a Cox model via the Global Adaptive Generative Adjustment algorithm. Part of this function refers to the coxphfit function in MATLAB 2016b.

Usage

cpp_COX_gaga(
  X,
  y,
  cens,
  alpha = 2,
  itrNum = 50L,
  thresh = 0.001,
  flag = TRUE,
  lamda_0 = 0.5,
  fdiag = TRUE,
  subItrNum = 20L
)

Arguments

X

Input matrix, of dimension nobs*nvars; each row is an observation. If the intercept term needs to be considered in the estimation process, then the first column of X must be all 1s.

y

A n*1 matrix, indicating the survival time;

cens

A n*1 matrix, consists of 0 and 1, 1 indicates that the row of data is censored, 0 is opposite.

alpha

Hyperparameter. The suggested value for alpha is 2 or 3.

itrNum

Maximum number of iteration steps. In general, 20 steps are enough.

thresh

Convergence threshold for beta Change, if max(abs(beta-beta_old))<threshold, return.

flag

It identifies whether to make model selection. The default is TRUE.

lamda_0

The initial value of the regularization parameter for ridge regression.

fdiag

It identifies whether to use diag Approximation to speed up the algorithm.

subItrNum

Maximum number of steps for subprocess iterations.

Value

Coefficient vector

Fit a logistic model via the Global Adaptive Generative Adjustment algorithm using cpp

Description

Fit a logistic model via the Global Adaptive Generative Adjustment algorithm using cpp

Usage

cpp_logistic_gaga(
  X,
  y,
  s_alpha,
  s_itrNum,
  s_thresh,
  s_flag,
  s_lamda_0,
  s_fdiag,
  s_subItrNum
)

Arguments

X

Input matrix, of dimension nobs*nvars; each row is an observation. If the intercept term needs to be considered in the estimation process, then the first column of X must be all 1s.

y

should be either a factor with two levels.

s_alpha

Hyperparameter. The suggested value for alpha is 1 or 2. When the collinearity of the load matrix is serious, the hyperparameters can be selected larger, such as 5.

s_itrNum

The number of iteration steps. In general, 20 steps are enough. If the condition number of X is large, it is recommended to greatly increase the number of iteration steps.

s_thresh

Convergence threshold for beta Change, if max(abs(beta-beta_old))<threshold, return.

s_flag

It identifies whether to make model selection. The default is TRUE.

s_lamda_0

The initial value of the regularization parameter for ridge regression. The running result of the algorithm is not sensitive to this value.

s_fdiag

It identifies whether to use diag Approximation to speed up the algorithm.

s_subItrNum

Maximum number of steps for subprocess iterations.

Value

Coefficient vector.

Fit a multinomial model via the GAGA algorithm using cpp

Description

Fit a multinomial model the Global Adaptive Generative Adjustment algorithm

Usage

cpp_multinomial_gaga(
  X,
  y,
  s_alpha,
  s_itrNum,
  s_thresh,
  s_flag,
  s_lamda_0,
  s_fdiag,
  s_subItrNum
)

Arguments

X

Input matrix, of dimension nobs*nvars; each row is an observation. If the intercept term needs to be considered in the estimation process, then the first column of X must be all 1s.

y

a One-hot response matrix or a nc>=2 level factor

s_alpha

Hyperparameter. The suggested value for alpha is 1 or 2. When the collinearity of the load matrix is serious, the hyperparameters can be selected larger, such as 5.

s_itrNum

The number of iteration steps. In general, 20 steps are enough. If the condition number of X is large, it is recommended to greatly increase the number of iteration steps.

s_thresh

Convergence threshold for beta Change, if max(abs(beta-beta_old))<threshold, return.

s_flag

It identifies whether to make model selection. The default is TRUE.

s_lamda_0

The initial value of the regularization parameter for ridge regression. The running result of the algorithm is not sensitive to this value.

s_fdiag

It identifies whether to use diag Approximation to speed up the algorithm.

s_subItrNum

Maximum number of steps for subprocess iterations.

Value

Coefficient matrix with K-1 columns, where K is the class number. For k=1,..,K-1, the probability

Pr(G=k|x)=exp(x^T beta_k) /(1+sum_{k=1}^{K-1}exp(x^T beta_k))

. For k=K, the probability

Pr(G=K|x)=1/(1+sum_{k=1}^{K-1}exp(x^T beta_k))

.

Fit a poisson model via the GAGA algorithm using cpp

Description

Fit a poisson model the Global Adaptive Generative Adjustment algorithm

Usage

cpp_poisson_gaga(
  X,
  y,
  s_alpha,
  s_itrNum,
  s_thresh,
  s_flag,
  s_lamda_0,
  s_fdiag,
  s_subItrNum
)

Arguments

X

Input matrix, of dimension nobs*nvars; each row is an observation. If the intercept term needs to be considered in the estimation process, then the first column of X must be all 1s. In order to run the program stably, it is recommended that the value of X should not be too large. It is recommended to preprocess all the items in X except the intercept item by means of preprocessing, so that the mean value of each column is 0 and the standard deviation is 1/ colnum(X).

y

Non-negative count response vector.

s_alpha

Hyperparameter. The suggested value for alpha is 1 or 2. When the collinearity of the load matrix is serious, the hyperparameters can be selected larger, such as 5.

s_itrNum

The number of iteration steps. In general, 20 steps are enough. If the condition number of X is large, it is recommended to greatly increase the number of iteration steps.

s_thresh

Convergence threshold for beta Change, if max(abs(beta-beta_old))<threshold, return.

s_flag

It identifies whether to make model selection. The default is TRUE.

s_lamda_0

The initial value of the regularization parameter for ridge regression. The running result of the algorithm is not sensitive to this value.

s_fdiag

It identifies whether to use diag Approximation to speed up the algorithm.

s_subItrNum

Maximum number of steps for subprocess iterations.

Value

Coefficient vector.

Fit a logistic model via the Global Adaptive Generative Adjustment algorithm

Description

Fit a logistic model via the Global Adaptive Generative Adjustment algorithm

Usage

logistic_GAGA(
  X,
  y,
  alpha = 1,
  itrNum = 30,
  thresh = 0.001,
  flag = TRUE,
  lamda_0 = 0.001,
  fdiag = TRUE,
  subItrNum = 20
)

Arguments

X

Input matrix, of dimension nobs*nvars; each row is an observation. If the intercept term needs to be considered in the estimation process, then the first column of X must be all 1s.

y

should be either a factor with two levels.

alpha

Hyperparameter. The suggested value for alpha is 1 or 2. When the collinearity of the load matrix is serious, the hyperparameters can be selected larger, such as 5.

itrNum

The number of iteration steps. In general, 20 steps are enough. If the condition number of X is large, it is recommended to greatly increase the number of iteration steps.

thresh

Convergence threshold for beta Change, if max(abs(beta-beta_old))<threshold, return.

flag

It identifies whether to make model selection. The default is TRUE.

lamda_0

The initial value of the regularization parameter for ridge regression. The running result of the algorithm is not sensitive to this value.

fdiag

It identifies whether to use diag Approximation to speed up the algorithm.

subItrNum

Maximum number of steps for subprocess iterations.

Value

Coefficient vector.

Examples

# binomial
set.seed(2022)
cat("\n")
cat("Test binomial GAGA\n")
p_size = 30
sample_size=600
test_size=1000
R1 = 1
R2 = 3
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,R2*0.2,R2)
beta_true[ind] = 0
X = R1*matrix(rnorm(sample_size * p_size), ncol = p_size)
X[1:sample_size,1]=1
t = 1/(1+exp(-X%*%beta_true))
tmp = runif(sample_size,0,1)
y = rep(0,sample_size)
y[t>tmp] = 1
fit = GAGAs(X,y,family = "binomial", alpha = 1)
Eb = fit$beta
#Generate test samples
X_t = R1*matrix(rnorm(test_size * p_size), ncol = p_size)
X_t[1:test_size,1]=1
t = 1/(1+exp(-X_t%*%beta_true))
tmp = runif(test_size,0,1)
y_t = rep(0,test_size)
y_t[t>tmp] = 1
#Prediction
Ey = predict(fit,newx = X_t)
cat("\n--------------------")
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 pacc:", cal.w.acc(as.character(Ey),as.character(y_t)))
cat("\n")

Fit a multinomial model via the GAGA algorithm

Description

Fit a multinomial model the Global Adaptive Generative Adjustment algorithm

Usage

multinomial_GAGA(
  X,
  y,
  alpha = 1,
  itrNum = 50,
  thresh = 0.001,
  flag = TRUE,
  lamda_0 = 0.001,
  fdiag = TRUE,
  subItrNum = 20
)

Arguments

X

Input matrix, of dimension nobs*nvars; each row is an observation. If the intercept term needs to be considered in the estimation process, then the first column of X must be all 1s.

y

a One-hot response matrix or a nc>=2 level factor

alpha

Hyperparameter. The suggested value for alpha is 1 or 2. When the collinearity of the load matrix is serious, the hyperparameters can be selected larger, such as 5.

itrNum

The number of iteration steps. In general, 20 steps are enough. If the condition number of X is large, it is recommended to greatly increase the number of iteration steps.

thresh

Convergence threshold for beta Change, if max(abs(beta-beta_old))<threshold, return.

flag

It identifies whether to make model selection. The default is TRUE.

lamda_0

The initial value of the regularization parameter for ridge regression. The running result of the algorithm is not sensitive to this value.

fdiag

It identifies whether to use diag Approximation to speed up the algorithm.

subItrNum

Maximum number of steps for subprocess iterations.

Value

Coefficient matrix with K-1 columns, where K is the class number. For k=1,..,K-1, the probability

Pr(G=k|x)=exp(x^T beta_k) /(1+sum_{k=1}^{K-1}exp(x^T beta_k))

. For k=K, the probability

Pr(G=K|x)=1/(1+sum_{k=1}^{K-1}exp(x^T beta_k))

.

Examples

# multinomial
set.seed(2022)
cat("\n")
cat("Test multinomial GAGA\n")
p_size = 20
C = 3
classnames = c("C1","C2","C3","C4")
sample_size = 500
test_size = 1000
ratio = 0.5 #The ratio of zeroes in coefficients
Num = 10 # Total number of experiments
R1 = 1
R2 = 5
#Set the true coefficients
beta_true = matrix(rep(0,p_size*C),c(p_size,C))
zeroNum = round(ratio*p_size)
for(jj in 1:C){
  ind = sample(1:p_size,zeroNum)
  tmp = runif(p_size,0,R2)
  tmp[ind] = 0
  beta_true[,jj] = tmp
}
#Generate training samples
X = R1*matrix(rnorm(sample_size * p_size), ncol = p_size)
X[1:sample_size,1]=1
z = X%*%beta_true
t = exp(z)/(1+rowSums(exp(z)))
t = cbind(t,1-rowSums(t))
tt = t(apply(t,1,cumsum))
tt = cbind(rep(0,sample_size),tt)
# y = matrix(rep(0,sample_size*(C+1)),c(sample_size,C+1))
y = rep(0,sample_size)
for(jj in 1:sample_size){
  tmp = runif(1,0,1)
  for(kk in 1:(C+1)){
    if((tmp>tt[jj,kk])&&(tmp<=tt[jj,kk+1])){
      # y[jj,kk] = 1
      y[jj] = kk
      break
    }
  }
}
y = classnames[y]
fit = GAGAs(X, y,alpha=1,family = "multinomial")
Eb = fit$beta
#Prediction
#Generate test samples
X_t = R1*matrix(rnorm(test_size * p_size), ncol = p_size)
X_t[1:test_size,1]=1
z = X_t%*%beta_true
t = exp(z)/(1+rowSums(exp(z)))
t = cbind(t,1-rowSums(t))
tt = t(apply(t,1,cumsum))
tt = cbind(rep(0,test_size),tt)
y_t = rep(0,test_size)
for(jj in 1:test_size){
  tmp = runif(1,0,1)
  for(kk in 1:(C+1)){
    if((tmp>tt[jj,kk])&&(tmp<=tt[jj,kk+1])){
      y_t[jj] = kk
      break
    }
  }
}
y_t = classnames[y_t]
Ey = predict(fit,newx = X_t)
cat("\n--------------------")
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 pacc:", cal.w.acc(as.character(Ey),as.character(y_t)))
cat("\n")

Fit a Poisson model via the GAGA algorithm

Description

Fit a Poisson model the Global Adaptive Generative Adjustment algorithm

Usage

poisson_GAGA(
  X,
  y,
  alpha = 1,
  itrNum = 30,
  thresh = 0.001,
  flag = TRUE,
  lamda_0 = 0.5,
  fdiag = TRUE,
  subItrNum = 20
)

Arguments

X

Input matrix, of dimension nobs*nvars; each row is an observation. If the intercept term needs to be considered in the estimation process, then the first column of X must be all 1s. In order to run the program stably, it is recommended that the value of X should not be too large. It is recommended to preprocess all the items in X except the intercept item by means of preprocessing, so that the mean value of each column is 0 and the standard deviation is 1/ colnum(X).

y

Non-negative count response vector.

alpha

Hyperparameter. The suggested value for alpha is 1 or 2. When the collinearity of the load matrix is serious, the hyperparameters can be selected larger, such as 5.

itrNum

The number of iteration steps. In general, 20 steps are enough. If the condition number of X is large, it is recommended to greatly increase the number of iteration steps.

thresh

Convergence threshold for beta Change, if max(abs(beta-beta_old))<threshold, return.

flag

It identifies whether to make model selection. The default is TRUE.

lamda_0

The initial value of the regularization parameter for ridge regression. The running result of the algorithm is not sensitive to this value.

fdiag

It identifies whether to use diag Approximation to speed up the algorithm.

subItrNum

Maximum number of steps for subprocess iterations.

Value

Coefficient vector.

Examples

# Poisson
set.seed(2022)
p_size = 30
sample_size=300
R1 = 1/sqrt(p_size)
R2 = 5
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)
X[1:sample_size,1]=1
y = rpois(sample_size,lambda = as.vector(exp(X%*%beta_true)))
y = as.vector(y)
# Estimate
fit = GAGAs(X,y,alpha = 2,family="poisson")
Eb = fit$beta
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)))

Get predictions from a GAGA fit object

Description

Gives fitted values from a fitted GAGA object.

Usage

## S3 method for class 'GAGA'
predict(object, newx, ...)

Arguments

object

Fitted "GAGA" object.

newx

Matrix of new values for x at which predictions are to be made. Must be a matrix. If the intercept term needs to be considered in the estimation process, then the first column of X must be all 1s.

...

some other params

Value

Predictions

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))

Get predictions from a GAGA linear model fit object

Description

Get predictions from a GAGA linear model fit object

Usage

predict_LM_GAGA(fit, newx)

Arguments

fit

Fitted "GAGA" object.

newx

Matrix of new values for x at which predictions are to be made. Must be a matrix. If the intercept term needs to be considered in the estimation process, then the first column of X must be all 1s.

Value

Predictions

Get predictions from a GAGA cox model fit object

Description

Get predictions from a GAGA cox model fit object

Usage

predict_cox_GAGA(fit, newx)

Arguments

fit

Fitted "GAGA" object.

newx

Matrix of new values for x at which predictions are to be made. Must be a matrix. If the intercept term needs to be considered in the estimation process, then the first column of X must be all 1s.

Value

Predictions

Get predictions from a GAGA logistic model fit object

Description

Get predictions from a GAGA logistic model fit object

Usage

predict_logistic_GAGA(fit, newx)

Arguments

fit

Fitted "GAGA" object.

newx

Matrix of new values for x at which predictions are to be made. Must be a matrix. If the intercept term needs to be considered in the estimation process, then the first column of X must be all 1s.

Value

Predictions

Get predictions from a GAGA multinomial model fit object

Description

Get predictions from a GAGA multinomial model fit object

Usage

predict_multinomial_GAGA(fit, newx)

Arguments

fit

Fitted "GAGA" object.

newx

Matrix of new values for x at which predictions are to be made. Must be a matrix. If the intercept term needs to be considered in the estimation process, then the first column of X must be all 1s.

Value

Predictions

Get predictions from a GAGA poisson model fit object

Description

Get predictions from a GAGA poisson model fit object

Usage

predict_poisson_GAGA(fit, newx)

Arguments

fit

Fitted "GAGA" object.

newx

Matrix of new values for x at which predictions are to be made. Must be a matrix. If the intercept term needs to be considered in the estimation process, then the first column of X must be all 1s.

Value

Predictions

Fit a linear model via the GAGA algorithm using cpp.

Description

Fit a linear model via the GAGA algorithm using cpp.

Usage

rcpp_lm_gaga(
  X,
  y,
  s_alpha,
  s_itrNum,
  s_thresh,
  s_QR_flag,
  s_flag,
  s_lamda_0,
  s_fix_sigma,
  s_sigm2_0,
  s_fdiag,
  s_frp
)

Arguments

X

Input matrix, of dimension nobs*nvars; each row is an observation. If the intercept term needs to be considered in the estimation process, then the first column of X must be all 1s.

y

Quantitative response N*1 matrix.

s_alpha

Hyperparameter. The suggested value for alpha is 2 or 3.

s_itrNum

The number of iteration steps. In general, 20 steps are enough.

s_thresh

Convergence threshold for beta Change, if max(abs(beta-beta_old))<threshold, return.

s_QR_flag

It identifies whether to use QR decomposition to speed up the algorithm.

s_flag

It identifies whether to make model selection. The default is TRUE.

s_lamda_0

The initial value of the regularization parameter for ridge regression.

s_fix_sigma

It identifies whether to update the variance estimate of the Gaussian noise or not.

s_sigm2_0

The initial variance of the Gaussian noise.

s_fdiag

It identifies whether to use diag Approximation to speed up the algorithm.

s_frp

Pre-processing by OMP method to reduce the number of parameters

Value

Coefficient vector

Print a summary of GAGA object

Description

Print a summary of GAGA object

Usage

## S3 method for class 'GAGA'
summary(object, ...)

Arguments

object

Fitted "GAGA" object.

...

some other params

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")
summary(fit)

mirror server hosted at Truenetwork, Russian Federation.