ForLion (version 0.3.0)

design_initial_self: 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
)

Value

X matrix of initial design point

p0 initial random approximate allocation

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

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

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)