infmat: Information matrix of a design
In OptimalDesign: A Toolbox for Computing Efficient Designs of Experiments

Description Usage Arguments Value Note Author(s) See Also Examples

Computes the information matrix of a design w in the model determined by the matrix Fx of candidate regressors.

1	infmat(Fx, w, echo=TRUE)

`Fx`	the `n` times `m` matrix of candidate regressors (as rows), where `n` is the number of candidate design points and `m` (where `m>=2`, `m<=n`) is the number of parameters.
`w`	a non-negative vector of length `n` representing the design.
`echo`	Print the call of the function?

The information matrix of the design w in the model with all candidate regresors given by the rows of Fx.

The information matrix is standardized, i.e., it assumes that the variance of the errors is 1.

Radoslav Harman, Lenka Filova

optcrit

# Compute its information matrix for the design that is
# uniform on all the points with at most two levels equal to 1
# in the main effects model with 2 factors.

Fx <- Fx_cube(~x1 + x2 + x3 + x4 + x5, lower = rep(0, 5))
w <- rep(0, 2^5)
for (i in 1:(2^5)) if (sum(Fx[i, 2:6]) <= 2) w[i] <- 1
print(M <- infmat(Fx, w))

## Not run: 
# Visualize the correlation matrix of the parameter estimators

V <- solve(M); Y <- diag(1/sqrt(diag(V)))
library(corrplot); corrplot(Y %*% V %*% Y)

## End(Not run)