ForLion (version 0.1.0)

GLM_Exact_Design: Approximation to exact design algorithm for generalized linear model

Description

Approximation to exact design algorithm for generalized linear model

Usage

GLM_Exact_Design(
  k.continuous,
  design_x,
  design_p,
  det.design,
  p,
  ForLion,
  bvec,
  joint_Func_b,
  Lowerbounds,
  Upperbounds,
  rel.diff,
  L,
  N,
  hfunc,
  link
)

Value

x.design matrix with rows indicating design point

ni.design EW D-optimal or D-optimal exact allocation

rel.efficiency relative efficiency of the Exact and Approximate Designs

Arguments

k.continuous

number of continuous factors

design_x

the matrix with rows indicating design point which we got from the approximate design

design_p

D-optimal approximate allocation

det.design

the determinant of D-optimal approximate allocation

p

number of parameters

ForLion

TRUE or FALSE, TRUE: this approximate design was generated by ForLion algorithm, FALSE: this approximate was generated by EW ForLion algorithm

bvec

assumed parameter values of model parameters beta, same length of h(y)

joint_Func_b

The prior joint probability distribution of the parameters

Lowerbounds

The lower limit of the prior distribution for each parameter

Upperbounds

The upper limit of the prior distribution for each parameter

rel.diff

points with distance less than that will be merged

L

rounding factor

N

total number of observations

hfunc

function for obtaining model matrix h(y) for given design point y, y has to follow the same order as n.factor

link

link function, default "logit", other links: "probit", "cloglog", "loglog", "cauchit", "log", "identity"

Examples

k.continuous=1
design_x=matrix(c(25, -1, -1,-1, -1 ,
                 25, -1, -1, -1, 1,
                 25, -1, -1, 1, -1,
                 25, -1, -1, 1, 1,
                 25, -1, 1, -1, -1,
                 25, -1, 1, -1, 1,
                 25, -1, 1, 1, -1,
                 25, -1, 1, 1, 1,
                 25, 1, -1, 1, -1,
                 25, 1, 1, -1, -1,
                 25, 1, 1, -1, 1,
                 25, 1, 1, 1, -1,
                 25, 1, 1, 1, 1,
                 38.9479, -1, 1, 1, -1,
                 34.0229, -1, 1, -1, -1,
                 35.4049, -1, 1, -1, 1,
                 37.1960, -1, -1, 1, -1,
                 33.0884, -1, 1, 1, 1),nrow=18,ncol=5,byrow = TRUE)
hfunc.temp = function(y) {c(y,y[4]*y[5],1);};   # y -> h(y)=(y1,y2,y3,y4,y5,y4*y5,1)
link.temp="logit"
design_p=c(0.0848, 0.0875, 0.0410, 0.0856, 0.0690, 0.0515,
          0.0901, 0.0845, 0.0743, 0.0356, 0.0621, 0.0443,
          0.0090, 0.0794, 0.0157, 0.0380, 0.0455, 0.0022)
det.design=4.552715e-06
paras_lowerbound<-c(0.25,1,-0.3,-0.3,0.1,0.35,-8.0)
paras_upperbound<-c(0.45,2,-0.1,0.0,0.4,0.45,-7.0)
 gjoint_b<- function(x) {
 Func_b<-1/(prod(paras_upperbound-paras_lowerbound))
 ##the prior distributions are follow uniform distribution
return(Func_b)
}
 GLM_Exact_Design(k.continuous=k.continuous,design_x=design_x,
 design_p=design_p,det.design=det.design,p=7,ForLion=FALSE,joint_Func_b=gjoint_b,
 Lowerbounds=paras_lowerbound, Upperbounds=paras_upperbound,rel.diff=0,L=1,
 N=100,hfunc=hfunc.temp,link=link.temp)