SCSnp {BSagri} | R Documentation |
Calcualte simultaneous confidence sets according to Besag et al. (1995) from a empirical joint distribution of a parameter vector. Joint empirical distributions might be obtained from WinBUGS or OpenBUGS calls.
SCSnp(x,...) ## Default S3 method: SCSnp(x, conf.level = 0.95, alternative = "two.sided", ...) ## S3 method for class 'bugs': SCSnp(x, conf.level = 0.95, alternative = "two.sided", whichp = NULL, ...) ## S3 method for class 'CCRatio': SCSnp(x, ...) ## S3 method for class 'CCDiff': SCSnp(x, ...)
x |
a matrix N-times-P matrix or an object of class CCRatio or CCDiff |
conf.level |
a single numeric value between 0.5 and 1, the simultaneous confidence level |
alternative |
a single character string, one of "two.sided" , "less" , "greater" , for two-sided, upper and lower limits |
whichp |
a single character string, naming an element of the sims.list if x is a bugs object, ignored otherwise |
... |
further arguments, currently not used |
Let P be the number of parameters in the parameter vector and N be the total number of values obtained for the empirical joint distribution of the parameter vector, e.g. as can be obtaine e.g., from Gibbs sampling.
An object of class "SCSnp", a list with elements
conf.int |
a P-times-2 matrix containing the lower and upper confidence limits |
estimate |
a numeric vector of length P, containing the medians of the P marginal empirical distributions |
x |
the input object |
k |
the number of values outside the SCS, i.e. conf.level*N |
N |
the number of values used to construct the confidence set |
conf.level |
a single numeric value, the nominal confidence level, as input |
alternative |
a single character string, as input |
Frank Schaarschmidt
Besag J, Green P, Higdon D, Mengersen K (1995): Bayesian Computation and Stochastic Systems. Statistical Science 10 (1), 3-66.
CInp
for a wrapper to quantile
to compute elementwise intervals
# Assume a 1000 times 4 matrix of 4 mutually independent # normal variables: X<-cbind(rnorm(1000), rnorm(1000), rnorm(1000), rnorm(1000)) SCSts<-SCSnp(x=X, conf.level=0.9, alternative="two.sided") SCSts SCS<-SCSts$conf.int in1<-X[,1]>=SCS[1,1] & X[,1]<=SCS[1,2] in2<-X[,2]>=SCS[2,1] & X[,2]<=SCS[2,2] in3<-X[,3]>=SCS[3,1] & X[,3]<=SCS[3,2] in4<-X[,4]>=SCS[4,1] & X[,4]<=SCS[4,2] sum(in1*in2*in3*in4)