ForLion (version 0.3.0)

GLM_Exact_Design: rounding algorithm for generalized linear models

Description

rounding algorithm for generalized linear models

Usage

GLM_Exact_Design(
  k.continuous,
  design_x,
  design_p,
  var_names = NULL,
  det.design,
  p,
  ForLion,
  bvec,
  Integral_based,
  b_matrix,
  joint_Func_b,
  Lowerbounds,
  Upperbounds,
  delta2,
  L,
  N,
  hfunc,
  link
)

Value

x.design matrix with rows indicating design point

ni.design 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

the corresponding approximate allocation

var_names

Names for the design factors. Must have the same length asfactor.level. Defaults to "X1", "X2", ...

det.design

the determinant of the approximate design

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

If ForLion==TRUE assumed parameter values of model parameters beta, same length of h(y)

Integral_based

TRUE or FALSE, whether or not integral-based EW D-optimality is used, FALSE indicates sample-based EW D-optimality is used.

b_matrix

matrix of bootstrapped or simulated parameter values.

joint_Func_b

prior distribution function of model parameters

Lowerbounds

vector of lower ends of ranges of prior distribution for model parameters.

Upperbounds

vector of upper ends of ranges of prior distribution for model parameters.

delta2

points with distance less than that will be merged

L

vector: rounding factors

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,Integral_based=TRUE,
 joint_Func_b=gjoint_b,Lowerbounds=paras_lowerbound, Upperbounds=paras_upperbound,
 delta2=0,L=1,N=100,hfunc=hfunc.temp,link=link.temp)