print.bcp {bcp} | R Documentation |
print method for class ``bcp''.
print.bcp(x, digits = max(3, .Options$digits - 3), ...)
x |
the result of a call to `bcp'. |
digits |
an optional specification of the number of digits displayed in the summary statistics. |
... |
additional arguments. |
The function returns the posterior probability of a change point for each position, the posterior means and standard deviations. These results are modeled after the `summary.mcmc' output of the coda package (Plummer et al., 2006). If return.mcmc=TRUE (i.e., if full mcmc results are returned), `bcp' objects can be converted into `mcmc' objects to view `mcmc' summaries – see examples below.
Chandra Erdman
Bai J., Perron P. (2003), Computation and Analysis of Multiple Structural Change Models, Journal of Applied Econometrics, 18, 1-22. url: http: //qed.econ.queensu.ca/jae/2003-v18.1/bai-perron/.
Daniel Barry and J. A. Hartigan (1993), A Bayesian Analysis for Change Point Problems, Journal of The American Statistical Association, 88, 309-19.
Olshen, A. B., Venkatraman, E. S., Lucito, R., Wigler, M. (2004), Circular binary segmentation for the analysis of array-based DNA copy number data, Biostatistics, 5, 557-572. url: http://www.bioconductor.org/repository/release1.5/package/html/DNAcopy.html.
Martyn Plummer, Nicky Best, Kate Cowles, and Karen Vines (2006), The coda Package, version 0.10-7, CRAN: The Comprehensive R Network.
Snijders et al. (2001), Assembly of microarrays for genome-wide measurement of DNA copy number, Nature Genetics, 29, 263-264.
Achim Zeileis, Friedrich Leisch, Bruce Hansen, Kurt Hornik, Christian Kleiber (2006), The strucchange Package, version 1.3-1, CRAN: The Comprehensive R Network.
`bcp', `summary.bcp', and `plot.bcp'.
##### A random sample from a few normal distributions ##### testdata <- c(rnorm(20), rnorm(10, 5, 1), rnorm(20)) bcp.0 <- bcp(testdata) print.bcp(bcp.0) plot.bcp(bcp.0) ##### An MCMC summary from the ``coda'' package ##### if(require("coda")) { if(is.na(bcp.0$mcmc.means)==FALSE) { bcp.mcmc <- as.mcmc(bcp.0$mcmc.means) summary(bcp.mcmc) } } ##### Coriell chromosome 11 ##### data(coriell) chrom11 <- as.vector(na.omit(coriell$Coriell.05296[coriell$Chromosome==11])) bcp.11 <- bcp(chrom11) print.bcp(bcp.11) plot.bcp(bcp.11) # to see bcp and Circular Binary Segmentation results run: if(require("DNAcopy")) { bcp.11$posterior.prob[length(bcp.11$posterior.brob)] <- 0 n <- length(chrom11) cbs <- segment(CNA(chrom11, rep(1, n), 1:n), verbose = 0) cbs.ests <- rep(unlist(cbs$output[6]), unlist(cbs$output[5])) op <- par(mfrow=c(2,1),col.lab="black",col.main="black") plot(1:n, bcp.11$posterior.mean, type="l", xlab="Location", ylab="Posterior Mean", main="Posterior Means") lines(cbs.ests, col="red") points(chrom11) plot(1:n, bcp.11$posterior.prob, type="l", ylim=c(0,1), xlab="Location", ylab="Posterior Probability of a Change", main="Change Point Locations") for(i in 1:(dim(cbs$output)[1]-1)) abline(v=cbs$output$loc.end[i], col="red") par(op) } else { cat("DNAcopy is not loaded") } ##### RealInt ##### data("RealInt") bcp.ri <- bcp(as.vector(RealInt)) print.bcp(bcp.ri) plot.bcp(bcp.ri) # to see bcp and Bai and Perron results run: if(require("strucchange")) { bcp.ri$posterior.prob[length(bcp.ri$posterior.brob)] <- 0 bp <- breakpoints(RealInt ~ 1, h = 2)$breakpoints rho <- rep(0, length(RealInt)) rho[bp] <- 1 b.num<-1 + c(0,cumsum(rho[1:(length(rho)-1)])) bp.mean <- unlist(lapply(split(RealInt,b.num),mean)) bp.ri <- rep(0,length(RealInt)) for(i in 1:length(bp.ri)) bp.ri[i] <- bp.mean[b.num[i]] op <- par(mfrow=c(2,1),col.lab="black",col.main="black") xax <- seq(1961, 1987, length=103) plot(xax, bcp.ri$posterior.mean, type="l", xlab="Time", ylab="Mean", main="Posterior Means") lines(xax, bp.ri, col="blue") points(RealInt) plot(xax, bcp.ri$posterior.prob, type="l", ylim=c(0,1), xlab="Time", ylab="Posterior Probability", main="Posterior Probability of a Change") for(i in 1:length(bp.ri)) abline(v=xax[bp[i]], col="blue") par(op) } else { cat("strucchange is not loaded") }