augment_design | R Documentation |
Augments a design. The user gives an initial design for which he would like to add points and specifies the weight of the new points. Then he is prompted to choose a minimum efficiency. After that, the candidate points region is calculated and the user can choose the points and weights to add.
augment_design(
criterion,
init_design,
alpha,
model,
parameters,
par_values,
design_space,
calc_optimal_design,
par_int = NA,
matB = NULL,
distribution = NA,
weight_fun = function(x) 1
)
criterion |
character variable with the chosen optimality criterion. Can be one of the following:
|
init_design |
dataframe with "Point" and "Weight" columns that represents the initial design to augment |
alpha |
combined weight of the new points |
model |
formula that represents the model with x as the independent variable |
parameters |
character vector with the unknown parameters of the model to estimate |
par_values |
numeric vector with the initial values of the unknown parameters |
design_space |
numeric vector with the limits of the space of the design |
calc_optimal_design |
boolean parameter, if TRUE, the optimal design is calculated and efficiencies of the initial and augmented design are given |
par_int |
optional numeric vector with the index of the |
matB |
optional matrix of dimensions k x k, for L-optimality. |
distribution |
character specifying the probability distribution of the response. Can be one of the following:
|
weight_fun |
optional one variable function that represents the square of the structure of variance, in case of heteroscedastic variance of the response |
A dataframe that represents the D-augmented design
init_des <- data.frame("Point" = c(30, 60, 90), "Weight" = c(1/3, 1/3, 1/3))
augment_design("D-Optimality", init_des, 0.25, y ~ 10^(a-b/(c+x)), c("a","b","c"),
c(8.07131, 1730.63, 233.426), c(1, 100), TRUE)
augment_design("D-Optimality", init_des, 0.25, y ~ 10^(a-b/(c+x)), c("a","b","c"),
c(8.07131, 1730.63, 233.426), c(1, 100), FALSE)
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