chorussell.lp | R Documentation |
chorussell
procedureThis function computes the (1-α)-confidence
interval in the chorussell
procedure by solving the
optimization problem.
chorussell.lp( lb.can1, lb.can2, ub.can1, ub.can2, n, R, alpha, ub, lb, logical.ub, logical.lb, remove.const, ci, kappa, k = 0, progress, df.lb, df.ub )
lb.can1 |
The vector of values that corresponds to √{n}≤ft(\hat{θ}^b_{\rm lb} - \hat{θ}_{\rm lb}\right). |
lb.can2 |
The vector of values that corresponds to √{n}≤ft(\hat{θ}^b_{\rm lb} - \hat{θ}_{\rm lb} - Δ \right). |
ub.can1 |
The vector of values that corresponds to √{n}≤ft(\hat{θ}^b_{\rm ub} - \hat{θ}_{\rm ub}\right). |
ub.can2 |
The vector of values that corresponds to √{n}≤ft(\hat{θ}^b_{\rm ub} - \hat{θ}_{\rm ub} + Δ \right). |
n |
The sample size. This is only required if |
R |
The number of bootstrap replications. |
alpha |
The significance level. This can be a vector. |
ub |
The sample upper bound. |
lb |
The sample lower bound. |
logical.ub |
The logical upper bound. |
logical.lb |
The logical lower bound. |
remove.const |
A boolean variable. This determine whether the constraints are to be removed. |
ci |
A boolean variable that indicates whether a p-value or a
(1-α)-confidence interval is returned. If |
kappa |
The tuning parameter used in the second step of the two-step procedure for obtaining the bounds subject to the shape constraints. It can be any nonnegative number or a vector of nonnegative numbers. |
k |
Iteration number. |
progress |
The boolean variable for whether the progress bars should
be displayed. If it is set as |
df.lb |
The list of lower bounds that are obtained from the simplified program. |
df.ub |
The list of upper bounds that are obtained from the simplified program. |
Returns the following list of objects:
bd |
A vector that represents the (1-α)-confidence interval. |
unique |
An indicator variable of whether the solution is unique. |
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