design_initial_self: function to generate initial design with design points and...

In ForLion: 'ForLion' Algorithm to Find D-Optimal Designs for Experiments

function to generate initial design with design points and the approximate allocation

Description

function to generate initial design with design points and the approximate allocation

Usage

design_initial_self(
  k.continuous,
  factor.level,
  MLM,
  xlist_fix = NULL,
  lvec,
  uvec,
  bvec,
  h.func,
  link = "continuation",
  Fi.func = Fi_MLM_func,
  delta0 = 1e-06,
  epsilon = 1e-12,
  maxit = 1000
)

Arguments

k.continuous	number of continuous variables
factor.level	list of distinct factor levels, “(min, max)” for continuous factors that always come first, finite sets for discrete factors.
MLM	TRUE or FALSE, TRUE: generate initial design for multinomial logistic model, FALSE: generate initial design for generalized linear model
xlist_fix	list of discrete factor experimental settings under consideration, default NULL indicating a list of all possible discrete factor experimental settings will be used.
lvec	lower limit of continuous variables
uvec	upper limit of continuous variables
bvec	assumed parameter values of beta
h.func	function for generating the corresponding model matrix or predictor vector, given an experimental setting or design point.
link	link function, default "continuation", other options "baseline", "adjacent" and "cumulative"
Fi.func	function, is used to calculate Fisher inforamtion for a design point, default to be Fi_MLM_func() in the package
delta0	tuning parameter, the distance threshold, || x_i(0) - x_j(0) || >= delta0
epsilon	tuning parameter as converging threshold, such that, a nonnegative number is regarded as numerical zero if less than epsilon, default 1e-12.
maxit	maximum number of iterations

Value

X matrix of initial design point

p0 initial random approximate allocation

f.det the determinant of Fisher information matrix for the initial design

Examples

k.continuous.temp=5
link.temp = "cumulative"
n.factor.temp = c(0,0,0,0,0,2)  # 1 discrete factor w/ 2 levels + 5 continuous
## Note: Always put continuous factors ahead of discrete factors,
## pay attention to the order of coefficients paring with predictors
lvec.temp = c(-25,-200,-150,-100,0,-1)
uvec.temp = c(25,200,0,0,16,1)
hfunc.temp = function(y){
if(length(y) != 6){stop("Input should have length 6");}
 model.mat = matrix(NA, nrow=5, ncol=10, byrow=TRUE)
 model.mat[5,]=0
 model.mat[1:4,1:4] = diag(4)
 model.mat[1:4, 5] =((-1)*y[6])
 model.mat[1:4, 6:10] = matrix(((-1)*y[1:5]), nrow=4, ncol=5, byrow=TRUE)
 return(model.mat)
 }
bvec.temp=c(-1.77994301, -0.05287782,  1.86852211, 2.76330779, -0.94437464, 0.18504420,
-0.01638597, -0.03543202, -0.07060306, 0.10347917)

design_initial_self(k.continuous=k.continuous.temp, factor.level=n.factor.temp,
MLM=TRUE,xlist_fix=NULL, lvec=lvec.temp,uvec=uvec.temp, bvec=bvec.temp,
h.func=hfunc.temp,link=link.temp)

ForLion documentation built on June 28, 2025, 1:09 a.m.