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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,20 +1,20 @@ | ||
| Package: hmm.discnp | ||
| Version: 0.1-9 | ||
| Date: 2013-04-17 | ||
| Version: 0.2-0 | ||
| Date: 2013-09-30 | ||
| Title: Hidden Markov models with discrete non-parametric observation | ||
| distributions. | ||
| Author: Rolf Turner <r.turner@auckland.ac.nz> and Limin Liu. | ||
| Maintainer: Rolf Turner <r.turner@auckland.ac.nz> | ||
| Depends: R (>= 0.99) | ||
| Description: Fits hidden Markov models with discrete non-parametric | ||
| observation distributions to data sets. Simulates data from | ||
| such models. Finds most probable underlying hidden states, the | ||
| most probable sequences of such states, and the log likelihood | ||
| of a collection of observations given the parameters of the | ||
| model. | ||
| Description: Fits hidden Markov models with discrete non-parametric | ||
| observation distributions to data sets. Simulates data | ||
| from such models. Finds most probable underlying hidden | ||
| states, the most probable sequences of such states, and the | ||
| log likelihood of a collection of observations given the | ||
| parameters of the model. | ||
| License: GPL (>= 2) | ||
| URL: http://www.math.unb.ca/~rolf/ | ||
| Packaged: 2013-04-18 02:02:12 UTC; rolf | ||
| Packaged: 2013-09-30 21:51:42 UTC; rolf | ||
| NeedsCompilation: yes | ||
| Repository: CRAN | ||
| Date/Publication: 2013-04-18 07:29:04 | ||
| Date/Publication: 2013-10-01 07:42:59 |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,31 +1,47 @@ | ||
| revise.ispd <- function(tpm=NULL,gamma=NULL,lns=NULL) { | ||
| revise.ispd <- function(tpm=NULL,gamma=NULL,lns=NULL,cis=TRUE) { | ||
| # Function revise.ispd. To revise the initial state probability | ||
| # distribution | ||
|
|
||
| if(is.null(tpm) + (is.null(gamma) & is.null(lns)) != 1) | ||
| stop(paste("Either \"tpm\" should be given OR\n", | ||
| "\"gamma\" and \"lns\" should be given.\n")) | ||
|
|
||
| if(cis) { | ||
| if(is.null(tpm) + (is.null(gamma) & is.null(lns)) != 1) | ||
| stop(paste("When \"cis\" is TRUE either \"tpm\" should be given\n", | ||
| "OR \"gamma\" and \"lns\" should be given.\n")) | ||
|
|
||
| # If ispd is taken to be the steady state distribution: | ||
| if(!is.null(tpm)) { | ||
| eee <- eigen(t(tpm)) | ||
| k <- match(1,round(eee$values,6)) | ||
| if(length(k) != 1) { | ||
| cat('Problems with eigenvalues:\n') | ||
| print(eee$values) | ||
| stop() | ||
| } | ||
| v <- Re(eee$vectors[,k]) | ||
| return(v/sum(v)) | ||
| } | ||
|
|
||
| if(!is.null(tpm)) { | ||
| eee <- eigen(t(tpm)) | ||
| k <- match(1,round(eee$values,6)) | ||
| if(length(k) != 1) { | ||
| cat('Problems with eigenvalues:\n') | ||
| print(eee$values) | ||
| stop() | ||
| } | ||
| v <- Re(eee$vectors[,k]) | ||
| ispd <- v/sum(v) | ||
| } else { | ||
| # Steady state not assumed: | ||
| v <- 0 | ||
| j <- 1 | ||
| nseq <- length(lns) | ||
| for(i in 1:nseq) { | ||
| v <- v + gamma[,j] | ||
| j <- j + lns[i] | ||
| v <- 0 | ||
| j <- 1 | ||
| nseq <- length(lns) | ||
| for(i in 1:nseq) { | ||
| v <- v + gamma[,j] | ||
| j <- j + lns[i] | ||
| } | ||
| ispd <- v/nseq | ||
| } | ||
| } else { | ||
| if(is.null(gamma) | is.null(lns)) | ||
| stop(paste("When \"cis\" is FALSE \"gamma\" and \"lns\"\n", | ||
| "must be given.\n")) | ||
| jg <- 1 | ||
| nseq <- length(lns) | ||
| K <- nrow(gamma) | ||
| ispd <- matrix(0,K,nseq) | ||
| for(j in 1:nseq) { | ||
| ispd[which.max(gamma[,jg]),j] <- 1 | ||
| jg <- jg + lns[j] | ||
| } | ||
| } | ||
| v/nseq | ||
| return(ispd) | ||
| } |
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