# Rectangular Confidence Regions 1.0

OS : Windows / Linux / Mac OS / BSD / Solaris

Script Licensing : Freeware

Created : Sep 14, 2007

Downloads : 4

Thank you for voting...

## R = RCR(S) computes the semi-edge-length of the ...

R = RCR(S) computes the semi-edge-length of the mean-centered hypercube with 95% probability given S, which is either a covariance matrix or a vector of standard deviations from a multivariate normal distribution. If S is a real, nonnegative vector, RCR(S) is equivalent to RCR(DIAG(S. ^2)). Scalar S is treated as a standard deviation.

R = RCR(S, P) computes the semi-edge-length of the hypercube with probability P instead of the default, which is 0. 95. R is the two-tailed, equicoordinate quantile corresponding to P. The hypercube edge-length is 2*R.

R = RCR(S, P, NP) uses NP quadrature points instead of the default, which is 2^11. Smaller values of NP result in faster computation, but may yield less accurate results. Use [] as a placeholder to obtain the default value of P.

R = RCR(S, P, NP, M) performs a bootstrap validation with M normally distributed random samples of size 1e6. Use [] as a placeholder to obtain the default value of NP.

R = RCR(S, P, NP, [M N]) performs a bootstrap validation with M normally distributed random samples of size N.

[R, E] = RCR(S, . . . ) returns an error estimate E.

R = RCR(S, P) computes the semi-edge-length of the hypercube with probability P instead of the default, which is 0. 95. R is the two-tailed, equicoordinate quantile corresponding to P. The hypercube edge-length is 2*R.

R = RCR(S, P, NP) uses NP quadrature points instead of the default, which is 2^11. Smaller values of NP result in faster computation, but may yield less accurate results. Use [] as a placeholder to obtain the default value of P.

R = RCR(S, P, NP, M) performs a bootstrap validation with M normally distributed random samples of size 1e6. Use [] as a placeholder to obtain the default value of NP.

R = RCR(S, P, NP, [M N]) performs a bootstrap validation with M normally distributed random samples of size N.

[R, E] = RCR(S, . . . ) returns an error estimate E.

**• MATLAB Release: R13**

**Demands:****Rectangular Confidence Regions 1.0 scripting tags:**distributed, rectangular, rcrs, rectangular confidence, confidence regions, performs, hypercube, bootstrap, statistics probability, default.

**What is new in Rectangular Confidence Regions 1.0 software script?**- Unable to find Rectangular Confidence Regions 1.0 news.

**What is improvements are expecting?**Newly-made Rectangular Confidence Regions 1.1 will be downloaded from here. You may download directly. Please write the reviews of the Rectangular Confidence Regions. License limitations are unspecified.