View source: R/MLM_Exact_Design.R
MLM_Exact_Design | R Documentation |
Approximation to exact design algorithm for multinomial logit model
MLM_Exact_Design(
J,
k.continuous,
design_x,
design_p,
det.design,
p,
ForLion,
bvec,
bvec_matrix,
rel.diff,
L,
N,
hfunc,
link
)
J |
number of response levels in the multinomial logit model |
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 |
If ForLion==TRUE assumed parameter values of model parameters beta, same length of h(y) |
bvec_matrix |
If ForLion==FALSE the matrix of the bootstrap parameter values of beta |
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 "continuation", other choices "baseline", "cumulative", and "adjacent" |
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
J=3
k.continuous=1
design_x<-c(0.0000,103.5451,149.2355)
design_p<-c(0.2027, 0.3981, 0.3992)
det.design=54016609
p=5
theta = c(-1.935, -0.02642, 0.0003174, -9.159, 0.06386)
hfunc.temp = function(y){
matrix(data=c(1,y,y*y,0,0,0,0,0,1,y,0,0,0,0,0), nrow=3,
ncol=5, byrow=TRUE)
}
link.temp = "continuation"
MLM_Exact_Design(J=J, k.continuous=k.continuous,design_x=design_x,
design_p=design_p,det.design=det.design,p=p,ForLion=TRUE,bvec=theta,
rel.diff=1,L=0.5,N=1000,hfunc=hfunc.temp,link=link.temp)
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