Merge pull request #76 from rkillick/dev

Merging 2.2.5 from dev

DESCRIPTION

@@ -1,8 +1,8 @@
 Package: changepoint
 Type: Package
 Title: Methods for Changepoint Detection
-Version: 2.2.4
-Date: 2022-10-31
+Version: 2.2.5
+Date: 2022-11-08
 Authors@R: c(person("Rebecca", "Killick",
         role=c("aut","cre"),email="r.killick@lancs.ac.uk"),
 				person("Kaylea", "Haynes", role="aut"),        
@@ -12,17 +12,18 @@ Maintainer: Rebecca Killick <r.killick@lancs.ac.uk>
 BugReports: https://github.com/rkillick/changepoint/issues
 URL: https://github.com/rkillick/changepoint/
 Description: Implements various mainstream and specialised changepoint methods for finding single and multiple changepoints within data.  Many popular non-parametric and frequentist methods are included.  The cpt.mean(), cpt.var(), cpt.meanvar() functions should be your first point of call.
-Depends: R(>= 3.1), methods, stats, zoo(>= 0.9-1)
+Depends: R(>= 3.2), methods, stats, zoo(>= 0.9-1)
 Suggests: testthat, vdiffr
 License: GPL
 LazyData: true
-Packaged: 2022-10-31 14:14:13 UTC; killick
+Packaged: 2022-11-08 14:14:13 UTC; killick
 NeedsCompilation: yes
 Repository: CRAN
-Date/Publication: 2022-10-31 15:50:02 UTC
+Date/Publication: 2022-11-08 15:50:02 UTC
 Author: Rebecca Killick [aut, cre],
   Kaylea Haynes [aut],
   Idris Eckley [ths],
   Paul Fearnhead [ctb, ths],
   Robin Long [ctb],
   Jamie Lee [ctr]
+

NEWS

@@ -1,7 +1,12 @@
+Version 2.2.5
+=============
+* Thanks to Toby Hocking for highlighting that when pen=0, BinSeg doesn't need to warn that ncpts=Q as this is by design. Warning for pen=0 removed.
+* Thanks to Toby Hocking and his students for spotting that when minseglen=1 BinSeg would not allow the maximum Q.  This was due to a hardcoding of minseglen=2 in the check function.  This has been corrected.
+
 Version 2.2.4
 =============
-* Updated C code to be compliant with new CRAN flags
 * Updated check for 1D objects to also check for 1D arrays.  Thanks to Github user ChristopherEeles for highlighting the bug.
+* Updated C code to be compliant with new CRAN flags
 
 Version 2.2.3
 =============
@@ -17,7 +22,6 @@ Version 2.2.3
 * Removed a feature from the direct call to "segnigh.XX.YY" functions where a vector of penalties could be supplied.  This caused a warning to occur in a dependency.
 * Re-evaluated the maximum Q that could be given to all segneigh functions.  Thanks to Aaron Lowther for highlighting.
 
-
 Version 2.2.2
 =============
 * Updated R dependency, thanks to Mateusz Konieczny to identifying the problem (and solution).
@@ -222,3 +226,6 @@ Version 0.2
 Version 0.1
 ===========
 * Original
+
+
+

R/BinSeg_one_func_minseglen.R

@@ -1,8 +1,9 @@
 BINSEG = function(sumstat, pen = 0, cost_func = "norm.mean", shape = 1, minseglen = 2,  Q=5){
-
-  n=length(sumstat[,1])-1
-  if(n<2){stop('Data must have atleast 2 observations to fit a changepoint model.')}
-  if(Q>((n/2)+1)){stop(paste('Q is larger than the maximum number of segments',floor(n/2)+1))}
+
+  n = length(sumstat[,1]) - 1
+  if(n<2){stop('Data must have at least 2 observations to fit a changepoint model.')}
+  if(Q>((n/minseglen)+1)){stop(paste('Q is larger than the maximum number of segments',(n/minseglen)+1))}
+  if(Q>n){stop(paste("Q is larger than the length of the data length"))}
   if(Q<=0){stop(paste('Q is the maximum number of changepoints so should be greater than 0'))}
 
   storage.mode(sumstat) = 'double'
@@ -11,16 +12,13 @@ BINSEG = function(sumstat, pen = 0, cost_func = "norm.mean", shape = 1, minsegle
   likeout=rep(0,Q) # sets up null vector for likelihood of changepoints in cptsout
   storage.mode(cptsout)='integer'
   storage.mode(likeout)='double'
-  op_cps=0
-
-  #on.exit(.C("FreeBinSeg",answer[[6]],PACKAGE='changepoint'))
-  # answer=.C('PELT',cost_func, y3, y2,y,as.integer(n),as.double(pen),cptsout,as.integer(error),as.double(shape))
-  answer=.C('binseg',cost_func, sumstat,as.integer(n),as.double(pen),as.integer(Q),cptsout, as.integer(minseglen), likeout, as.integer(op_cps), as.double(shape))
-  if(answer[[9]]==Q){warning('The number of changepoints identified is Q, it is advised to increase Q to make sure changepoints have not been missed.')}
-  if(answer[[9]]==0){cpts=n}
-  else{cpts=c(sort(answer[[6]][1:answer[[9]]]),n)}
-  return(list(cps=rbind(answer[[6]],2*answer[[8]]),cpts=cpts,op.cpts=answer[[9]],pen=pen))
-  ##answer[6] is cptsout, answer[8] is likeout ("beta value")
+  op_cps = 0
+
+  answer=.C('binseg', cost_func = cost_func, sumstat = sumstat, n = as.integer(n), pen = as.double(pen), Q = as.integer(Q), cptsout = cptsout, minseglen = as.integer(minseglen), likeout = likeout, op_cps = as.integer(op_cps), shape = as.double(shape))
+  if((answer$op_cps == Q)&(pen!=0)){warning('The number of changepoints identified is Q, it is advised to increase Q to make sure changepoints have not been missed.')}
+  if(answer$op_cps == 0){cpts=n}
+  else{cpts=c(sort(answer$cptsout[1:answer$op_cps]),n)}
+  return(list(cps=rbind(answer$cptsout,2*answer$likeout),cpts=cpts,op.cpts=answer$op_cps,pen=pen))
   }
 
 

R/class_input.R

@@ -5,7 +5,7 @@ class_input <- function(data, cpttype, method, test.stat, penalty, pen.value, mi
     ans=new("cpt")
   }
 
-  data.set(ans)=data;cpttype(ans)=cpttype;method(ans)=method; test.stat(ans)=test.stat;pen.type(ans)=penalty;pen.value(ans)=pen.value;minseglen(ans)=minseglen;
+  data.set(ans)=data;cpttype(ans)=cpttype;method(ans)=method; test.stat(ans)=test.stat;pen.type(ans)=penalty;pen.value(ans)=pen.value;minseglen(ans)=minseglen;ans@date=date();
   if(penalty!="CROPS"){ # crops is only one that doesn't give a single set of cpts
     cpts(ans)=out[[2]]
 
@@ -49,4 +49,4 @@ class_input <- function(data, cpttype, method, test.stat, penalty, pen.value, mi
   }
 
   return(ans)
-}
+}

R/cpt.class.R

@@ -1,4 +1,4 @@
-	setClass("cpt",slots=list(data.set="ts", cpttype="character", method="character", 	test.stat="character",pen.type="character",pen.value="numeric",minseglen="numeric",cpts="numeric",ncpts.max="numeric",param.est="list",date="character",version="character"),prototype=prototype(cpttype="Not Set",date=date(),version=as(packageVersion("changepoint"),'character')))
+setClass("cpt",slots=list(data.set="ts", cpttype="character", method="character", 	test.stat="character",pen.type="character",pen.value="numeric",minseglen="numeric",cpts="numeric",ncpts.max="numeric",param.est="list",date="character",version="character"),prototype=prototype(cpttype="Not Set",date=date(),version=as(packageVersion("changepoint"),'character')))
 
 	setClass("cpt.reg",slots=list(data.set="matrix", cpttype="character", method="character", test.stat="character",pen.type="character",pen.value="numeric",minseglen="numeric",cpts="numeric",ncpts.max="numeric",param.est="list",date="character",version="character"),prototype=prototype(cpttype="regression",date=date(),version=as(packageVersion("changepoint"),"character")))
 
@@ -587,9 +587,8 @@
 	    }
 	    return(tmpscale)			
 	  }
-	  param.trend=function(object){
-	    cpts=c(0,object@cpts)
-	    seglen=seg.len(object)
+	  param.trend=function(object,cpts){
+	    seglen=cpts[-1]-cpts[-length(cpts)]
 	    data=data.set(object)
 	    n=length(data)
 	    sumstat=cbind(cumsum(c(0,data)),cumsum(c(0,data*c(1:n))))

man/changepoint-package.Rd

@@ -12,8 +12,8 @@ Implements various mainstream and specialised changepoint methods for finding si
 \tabular{ll}{
 Package: \tab changepoint\cr
 Type: \tab Package\cr
-Version: \tab 2.2.4 \cr
-Date: \tab 2022-10-31\cr
+Version: \tab 2.2.5 \cr
+Date: \tab 2022-11-08\cr
 License: \tab GPL\cr
 LazyLoad: \tab yes\cr
 }

man/cpt.var.Rd

@@ -15,7 +15,7 @@ test.stat="Normal",class=TRUE,param.estimates=TRUE,minseglen=2)
 	A vector, ts object or matrix containing the data within which you wish to find a changepoint.  If data is a matrix, each row is considered a separate dataset.
 }
   \item{penalty}{
-  Choice of "None", "SIC", "BIC", "MBIC", AIC", "Hannan-Quinn", "Asymptotic", "Manual" and "CROPS" penalties.  If Manual is specified, the manual penalty is contained in the pen.value parameter. If Asymptotic is specified, the theoretical type I error is contained in the pen.value parameter.  If CROPS is specified, the penalty range is contained in the pen.value parameter; note this is a vector of length 2 which contains the minimum and maximum penalty value.  Note CROPS can only be used if the method is "PELT". The predefined penalties listed DO count the changepoint as a parameter, postfix a 0 e.g."SIC0" to NOT count the changepoint as a parameter.
+  Choice of "None", "SIC", "BIC", "MBIC", "AIC", "Hannan-Quinn", "Asymptotic", "Manual" and "CROPS" penalties.  If Manual is specified, the manual penalty is contained in the pen.value parameter. If Asymptotic is specified, the theoretical type I error is contained in the pen.value parameter.  If CROPS is specified, the penalty range is contained in the pen.value parameter; note this is a vector of length 2 which contains the minimum and maximum penalty value.  Note CROPS can only be used if the method is "PELT". The predefined penalties listed DO count the changepoint as a parameter, postfix a 0 e.g."SIC0" to NOT count the changepoint as a parameter.
 }
   \item{pen.value}{
   The theoretical type I error e.g.0.05 when using the Asymptotic penalty.  A vector of length 2 (min,max) if using the CROPS penalty.  The value of the penalty when using the Manual penalty option - this can be a numeric value or text giving the formula to use.  Available variables are, n=length of original data, null=null likelihood, alt=alternative likelihood, tau=proposed changepoint, diffparam=difference in number of alternatve and null parameters.