compute_map_rejection_probs | R Documentation |
\delta_x \times \delta_y=0
" against its
alternative "\delta_x \times \delta_y\neq 0
" based on the test
statistic in the real plane. The Bayes risk is induced by either the 0-1
or the bounded quadratic loss function.Computes the "map" of rejection probabilities for the Bayes risk optimal
test of the composite null "\delta_x \times \delta_y=0
" against its
alternative "\delta_x \times \delta_y\neq 0
" based on the test
statistic in the real plane. The Bayes risk is induced by either the 0-1
or the bounded quadratic loss function.
compute_map_rejection_probs(
alpha = 0.05,
K = 16,
loss = c("0-1", "quadratic"),
return_solver = FALSE
)
alpha |
A positive |
K |
An |
loss |
A |
return_solver |
A |
A '2K x 2K' matrix
whose '(i,j)' coefficient is the
probability to reject the null when the test statistic falls in the
square [b(i-1-K)/K, b(i-K)/K] \times [b(j-1-K)/K, b(j-K)/K]
. If
'return_solver' is 'TRUE', then the matrix has an attribute called
'solver' which is the complete output of the optimization function
'lpSolve::lp' used to determine the probabilities.
## one of the four outputs of 'compute_map_rejection_probs' stored in the package
head(map_01_0.05, c(5, 5))
map <- compute_map_rejection_probs(alpha = 0.05, K = 16, loss = "0-1")
plot(map)
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