Rsolnp bug with multiple start points when unif+cos(1) models are

specified
DistanceDevelopment · Oct 3, 2014 · cb5f248 · cb5f248
1 parent 8c62020
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -22,8 +22,8 @@ Description: This package implements mark-recapture distance sampling
     J.L., Southwell, C. and Paxton, C.L.G. "Accommodating unmodelled
     heterogeneity in double-observer distance sampling surveys". 2006.
     Biometrics 62:372-378.)
-Version: 2.1.10
-Date: 2014-09-27
+Version: 2.1.11
+Date: 2014-10-3
 Depends:
     R (>= 3.0)
 Suggests:

diff --git a/NEWS b/NEWS
@@ -1,3 +1,9 @@
+mrds 2.1.11
+-----------
+
+  * Bug in unif+cos(1) models when using monotonicity constraints and randomised starting points. Since the model only has 1 parameter, there is a bug in selecting columns in Rsolnp starting value code that makes the result be a vector, which then doesn't work with an apply later. Workaround of not using randomised starting values in mrds for that model. Thanks to Nathalie Cavada for finding this bug.
+
+
 mrds 2.1.10
 -----------
 

diff --git a/R/detfct.fit.opt.R b/R/detfct.fit.opt.R
@@ -135,27 +135,34 @@ detfct.fit.opt <- function(ddfobj, optim.options, bounds, misc.options,
         lowerbounds.ic <- rep(0,2*misc.options$mono.points)
         upperbounds.ic <- rep(10^10,2*misc.options$mono.points)
 
-        #lt<-try(solnp(pars=initialvalues, fun=flnl, eqfun=NULL, eqB=NULL,
-        #              ineqfun=flnl.constr,
-        #              ineqLB=lowerbounds.ic, ineqUB=upperbounds.ic,
-        #              LB=lowerbounds, UB=upperbounds,
-        #              ddfobj=ddfobj, misc.options=misc.options,
-        #              control=list(trace=as.integer(showit),
-        #                           tol=misc.options$mono.tol,
-        #                           delta=misc.options$mono.delta)))
-
-        # this code randomly generates starting values see ?gosolnp
-        lt<-try(gosolnp(pars=initialvalues, fun=flnl, eqfun=NULL, eqB=NULL,
-                      ineqfun=flnl.constr,
-                      ineqLB=lowerbounds.ic, ineqUB=upperbounds.ic,
-                      LB=lowerbounds, UB=upperbounds,
-                      ddfobj=ddfobj, misc.options=misc.options,
-                      control=list(trace=as.integer(showit),
-                                   tol=misc.options$mono.tol,
-                                   delta=misc.options$mono.delta),
-                      distr = rep(1, length(lowerbounds)),
-                      n.restarts = 2, n.sim = 200,
-                      rseed=as.integer(runif(1)*1e9)))
+        if(length(initialvalues)==1){
+          ## gosolnp (below) doesn't work when there is only 1 parameter
+          ## since there is a bug that leaves the optimisation with a vector
+          ## when it is expecting a matrix. To avoid this bug, don't do the
+          ## multiple start points in that case (should only be unif+cos(1))
+          ## models for us.
+          lt<-try(solnp(pars=initialvalues, fun=flnl, eqfun=NULL, eqB=NULL,
+                        ineqfun=flnl.constr,
+                        ineqLB=lowerbounds.ic, ineqUB=upperbounds.ic,
+                        LB=lowerbounds, UB=upperbounds,
+                        ddfobj=ddfobj, misc.options=misc.options,
+                        control=list(trace=as.integer(showit),
+                                     tol=misc.options$mono.tol,
+                                     delta=misc.options$mono.delta)))
+        }else{
+          # this code randomly generates starting values see ?gosolnp
+          lt<-try(gosolnp(pars=initialvalues, fun=flnl, eqfun=NULL, eqB=NULL,
+                        ineqfun=flnl.constr,
+                        ineqLB=lowerbounds.ic, ineqUB=upperbounds.ic,
+                        LB=lowerbounds, UB=upperbounds,
+                        ddfobj=ddfobj, misc.options=misc.options,
+                        control=list(trace=as.integer(showit),
+                                     tol=misc.options$mono.tol,
+                                     delta=misc.options$mono.delta),
+                        distr = rep(1, length(lowerbounds)),
+                        n.restarts = 2, n.sim = 200,
+                        rseed=as.integer(runif(1)*1e9)))
+        }
 
 
         # if that failed then make a dummy object