glmDataGen: Data generator under the framework of generalized linear...

In PingYangChen/elnglm: Generalized Linear Model with Elastic Net Penalty

Description

Data generator under the framework of generalized linear model

Usage

glmDataGen(
  n,
  d,
  family = c("gaussian", "binomial", "multinomial"),
  trueb0,
  trueb,
  s = 0.5,
  seed = NULL
)

Arguments

n	integer. The number of observations.
d	integer. The number of predictors.
family	string. The value of starting inertia weight in PSO updating procedure. The default is 1.2.
trueb0	double.
trueb	vector.
s	double.
seed	integer. The random seed.

Value

An List.

• x the matrix of the predictors.

• y the vector of the response variable. For family = 'multinomial', the output is a matrix of size $n\times$#(categories).

Examples

# Intercept
trueb0 <- 1
# Regression Coefficients (the first 3 are active)
trueact <- c(1, 1, 1, 0, 0, 0, 0, 0, 0, 0)
trueb <- runif(10, -1, 1)*10
trueb[which(trueact == 0)] <- 0 

# Generate data of continuous response
df <- glmDataGen(n = 500, d = 10, family = "gaussian", trueb0, trueb, s = 0.5, seed = 1)

# Generate data of binary response
dfb <- glmDataGen(n = 500, d = 10, family = "binomial", trueb0, trueb, seed = 1)

# Generate data of multi-categorical response (not run)
#
# Intercepts of the three log-linear models 
# trueb0 <- c(1, 1, 1)
# Regression Coefficients of the three log-linear models 
# trueact <- cbind(
#   c(1, 1, 1, 0, 0, 0, 0, 0, 0, 0),
#   c(0, 0, 0, 1, 1, 1, 1, 0, 0, 0),
#   c(0, 0, 0, 0, 0, 1, 1, 1, 1, 0)
# )
# trueb <- matrix(runif(10*3, -1, 1)*10, 10, 3)
# for (m in 1:3) { trueb[which(trueact[,m] == 0),m] <- 0 }
# 
# Generate data. The response y is a matrix of 3 columns.
# dfm <- glmDataGen(n = 500, d = 10, family = "multinomial", trueb0, trueb, seed = 1)

PingYangChen/elnglm documentation built on Jan. 6, 2022, 5:50 a.m.