design | R Documentation |
Construct optimal approximate designs as well as efficient exact designs for crossover model with subject dropout, crossover model with proportional residual effect, and interference model.
design( model = c("dropout", "proportional", "interference"), n, opt, t, p, ..., max_iter = 40 )
model |
an model indicator, must be one of 'dropout', 'proportional', or 'interference'. |
n |
Positive Integer, total number of observations needed. |
opt |
Integer. optimal criterion indicator, opt = 0 means D-opt, opt = 1 means A-opt |
t |
Positive interger,number or levels of treatment, the default coding is integer from 1 to t |
p |
Numeric, number of periods for crossover model or number of blocks for intereference model |
... |
other necessary control parameters required by specific model
For crossover with dropout, |
max_iter |
a positive integer. Controls maximum iteration time of exchange. Default is 40. |
A S3 object of one of classes 'dropout', 'proportional' or 'interference'.
model |
the model name |
n |
total number of observations of exact design |
opt |
optimal criterion |
t |
number of levels of treaments |
p |
number of periods or plots in a block |
... |
other inputs |
initial_design |
a randomly chosen design as a starting point for newton's method |
exact_design |
an exact design rounded from approximate design |
approx_design |
optimal approximate design |
verify_equivalence |
result of general equivalence theorem, the last entry is the value of directional derivative |
time |
computing time for approximate design |
eff
, effLB
, summary
# NOTE: max_iter is usually set to 40. # Here max_iter = 5 is for demenstration only. # crossover dropout model ## D-optimal example1 <- design('dropout',10,0,3,3,drop=c(0,0,0.5), max_iter = 5) summary(example1) eff(example1) # efficiency from rounding effLB(example1) # obtain lower bound of efficiency ## A-optimal design('dropout',10,1,3,3,drop=c(0,0,0.5), max_iter = 5) # proportional model ## D-optimal design('proportional',10,0,3,3, sigma = diag(1,3),tau = matrix(sqrt(1+3), nrow=3, ncol=1),lambda = 0.2, max_iter = 5) ## A-optimal design('proportional',10,1,3,3, sigma = diag(1,3), tau = matrix(sqrt(1+3), nrow=3, ncol=1),lambda = 0.2, max_iter = 5) # interference model ## D-optimal design('interference',10,0,3,3, sigma = diag(1,3), max_iter = 5) ## A-optimal design('interference',10,1,3,3, sigma = diag(1,3), max_iter = 5)
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