ForLion (version 0.1.0)

design_initial_self: function to generate random initial design with design points and the approximate allocation

Description

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

Usage

design_initial_self(
  k.continuous,
  factor.level,
  lvec,
  uvec,
  bvec,
  h.func,
  link = "continuation",
  Fi.func = Fi_MLM_func,
  delta = 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 random initial design

Arguments

k.continuous

number of continuous variables

factor.level

lower, upper limit of continuous variables, and discrete levels of categorical variables, continuous factors come first

lvec

lower limit of continuous variables

uvec

upper limit of continuous variables

bvec

assumed parameter values of beta

h.func

function, is used to transfer the design point to model matrix (e.g. add interaction term, add intercept)

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

delta

tuning parameter, the distance threshold, || x_i(0) - x_j(0) || >= delta

epsilon

or determining f.det > 0 numerically, f.det <= epsilon will be considered as f.det <= 0

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, lvec=lvec.temp,
uvec=uvec.temp, bvec=bvec.temp, h.func=hfunc.temp, link=link.temp)