notInExactSCI | R Documentation |
The function checks whether information about the precision of an approximation for the informative lower SCI-bounds can be collected.
notInExactSCI(
L,
randomShifts = 0,
shifts = NULL,
tolTrueSCI,
gMCP = NULL,
g = NULL,
weights = NULL,
q,
estimates = NULL,
Z = NULL,
pValues = NULL,
SE = NULL,
I = NULL,
mu_0,
alpha,
checkInput = TRUE
)
L |
An m-dimensional non-negative vector whose entries are the lower bounds of an approximation of the informative SCI. |
randomShifts |
A positive integer indicating how many random directions
of length |
shifts |
A matrix with m columns and any number of rows. Each entry must
be non-negative. Each row is a direction in the m-dimensional real space.
Each row must have at least one positive entry. |
tolTrueSCI |
The randomly generated shift-vectors and the row vectors
in |
gMCP |
An object of class |
g |
Numeric square matrix of transition weights for the graphical test
with m rows and m columns. The i-th row of the entered matrix defines the
arrows starting from the i-th hypothesis. Each entry has to be between
0 and 1 and each row must sum to a number less than or equal to 1. The
diagonal elements must be zero. Entering |
weights |
Numeric vector of weights of dimension m. It defines the
initial proportion of significance level which is assigned to each null
hypothesis. Entering |
q |
A numeric vector of dimension 1 or m defining the information
weights for each hypothesis. The entries have to be between 0 and 1
(inclusive). If |
estimates |
An m-dimensional numeric vector of unbiased point estimates for the parameters of interest. Each estimator is assumed to be (asymptotically) normal. |
Z |
An m-dimensional numeric vector of z-scores for testing the null
hypotheses. |
pValues |
An m-dimensional numeric vector of p-values from (asymptotic)
z-tests for testing the null hypotheses. |
SE |
A positive numeric vector of dimension 1 or m indicating the
standard errors of the point estimators. If |
I |
A positive numeric vector indicating the information of the
estimators. It can be entered as an alternative to the vector |
mu_0 |
A numeric vector of dimension 1 or m defining the bounds of the
null hypotheses of the underlying graphical test. If |
alpha |
A numeric defining the overall significance level for the
graphical test (i.e. SCIs will have coverage probability of at least
|
checkInput |
A boolean specifying whether the entered values should be checked. |
The function checks if it can be determined whether L
can be shifted
by a rescaled randomly generated direction or by a rescaled direction in the
shift matrix such that it describes valid lower informative SCI bounds.
If this is possible, the approximation L
is imprecise.
(The random directions are generated in such a way that all entries are
positive.)
Returns TRUE
if we can determine that the approximation is
imprecise. Returns FALSE
if we cannot determine that the
approximation is imprecise. (The approximation may still be imprecise.) Note
that inExactSCI
and notInExactSCI
could both return
FALSE
.
informSCI
explore_q
g <- matrix(c(0,0,1,0),2,2)
weights <- c(1,0)
q <- c(0.0068,1)
mu_0 <- c(-1,0)
pValues <- c(0.0002,0.01)
SE <- c(0.31,1.11)
alpha <- 0.025
L <- informSCI(g=g, weights=weights, q=q, mu_0=mu_0, pValues=pValues, SE=SE,
alpha=alpha, eps=1/10, tolBisec=1/10)$L
# When the randomShifts- or shift-parameter in the informSCI-function is
# specified, the notInExactSCI-function is called by the informSCI-function.
# It is also possible to analyse the accuracy of a calculated L (or an
# approximation of the lower informative SCI-bounds) by directly using
# the notInExactSCI-function:
notInExactSCI(L=L, randomShifts=100, tolTrueSCI=1/10^5, g=g, weights=weights,
q=q, pValues=pValues, SE=SE, mu_0=mu_0, alpha=alpha)
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