Fixed unhelpful error message when optim.method is wrong

R/km.R

@@ -112,8 +112,14 @@
         }
 
         [email protected] <- TRUE
+        ##
+        optim.method <- tolower(optim.method)
+        if(!(optim.method %in% c("bfgs", "gen"))) {
+           stop("'optim.method' must be either 'BFGS' or 'gen'.  Currently it is '",
+                optim.method, "'")
+        }
         [email protected] <- optim.method
-
+        ##
         if ((length(lower) == 0) || (length(upper) == 0)) {
             bounds <- covParametersBounds(model@covariance, design)
             if (length(lower) == 0) lower <- bounds$lower
@@ -129,7 +135,7 @@
         model@upper <- as.numeric(upper)
         model@parinit <- as.numeric(parinit)
 
-        if (optim.method == "BFGS") {
+        if (optim.method == "bfgs") {
             if (length(control$pop.size) == 0) control$pop.size <- 20
             control$pop.size <- max(control$pop.size, multistart)
             if (identical(control$trace, FALSE)) control$trace <- 0
@@ -220,7 +226,7 @@
             model@penalty$value <- lambda
             model <- f(model, envir = envir.logLik)
         } else {
-            model <- f(model, envir = envir.logLik)
+           model <- f(model, envir = envir.logLik)
         }
 
         return(model)

R/kmEstimate.R

@@ -92,13 +92,13 @@
                 cat("  - variance bounds : ", c(lower[lp], upper[lp]), "\n")
             }
             cat("  - best initial criterion value(s) : ", initList$value, "\n")
-            if ([email protected]=="BFGS") cat("\n")
+            if ([email protected]=="bfgs") cat("\n")
         } # end printing
 
         # optimization
 
-        if ([email protected]=="BFGS") {
-            BFGSargs <- c("trace", "parscale", "ndeps", "maxit", "abstol", "reltol", "REPORT", "lnm", "factr", "pgtol")
+        if ([email protected]=="bfgs") {
+           BFGSargs <- c("trace", "parscale", "ndeps", "maxit", "abstol", "reltol", "REPORT", "lnm", "factr", "pgtol")
             commonNames <- intersect(BFGSargs, names(control))
             controlChecked <- control[commonNames]
             if (length(control$REPORT)==0) {
@@ -110,8 +110,7 @@
 
             # multistart in parallel with foreach
             multistart <- control$multistart
-
-            if (multistart==1){
+            if (multistart==1) {
                 model@parinit <- parinit <- as.numeric(parinit[, 1])
                 model@covariance <- initList$cov[[1]]
                 o <- optim(par = parinit, fn = fn, gr = gr,
@@ -120,16 +119,17 @@
                 model@control$convergence <- o$convergence
             } else {
                 # multistart with foreach
-                if (requireNamespace("foreach", quietly = TRUE)){
-                    olist <- foreach::"%dopar%"(foreach::foreach(i=1:multistart,
-                                                                 .errorhandling='remove'), {
-                                                                     model@covariance <- initList$cov[[i]]
-                                                                     optim(par = parinit[, i], fn = fn, gr = gr,
-                                                                           method = "L-BFGS-B", lower = lower, upper = upper,
-                                                                           control = controlChecked, hessian = FALSE, model, envir=envir)
+                if (requireNamespace("foreach", quietly = TRUE)) {
+                    olist <- foreach::"%dopar%"(foreach::foreach(i=seq(length=multistart),
+                                                                 .errorhandling='stop'), {
+                                                                    model@covariance <- initList$cov[[i]]
+                                                                    optim(par = parinit[, i], fn = fn, gr = gr,
+                                                                          method = "L-BFGS-B", lower = lower, upper = upper,
+                                                                          control = controlChecked, hessian = FALSE, model, envir=envir)
                                                                  })
+                    ## Note: if errorhandling drops the errors, the olist will be
+                    ## shorter.  I find it useful to have errors displayed.
                 }
-
                 # get the best result
                 bestValue <- Inf
                 bestIndex <- NA
@@ -142,8 +142,7 @@
                         bestValue <- currentValue
                         bestIndex <- i
                     }
-                } # end multistart
-
+                 } # end multistart
                 model@covariance <- initList$cov[[bestIndex]]
                 parinit <- parinit[, bestIndex]
                 model@parinit <- as.numeric(parinit)
@@ -195,11 +194,10 @@
                               lower = lower, upper = upper,
                               control = control, model=model, envir=envir)
         }
-
         model@logLik <- as.numeric(o$value)
 
 
-        if (model@method=="LOO"){
+        if (model@method=="LOO") {
 
             model@covariance <- vect2covparam(model@covariance, o$par)
 

R/kmStruct.R

@@ -429,7 +429,7 @@ update.km <- function(object,
         if (is.null(kmcontrol$penalty)) kmcontrol$penalty <- object@penalty
         if (length(object@penalty == 0)) kmcontrol$penalty <- NULL
         if (is.null(kmcontrol$optim.method)) kmcontrol$optim.method <- [email protected] 
-        if (length(kmcontrol$optim.method) == 0) kmcontrol$optim.method <- "BFGS"
+        if (length(kmcontrol$optim.method) == 0) kmcontrol$optim.method <- "bfgs"
         if (is.null(kmcontrol$control)) kmcontrol$control <- object@control
         if(length(object@gr) == 0) object@gr <- TRUE
         knots <- NULL; if(TheClass == "covScaling") knots <- object@covariance@knots

R/logLikFun.R

@@ -1,12 +1,10 @@
 `logLikFun` <-
 function(param, model, envir=NULL) {
-
   if ([email protected]=="Trend") {
     beta <- [email protected]
   } else {
     beta <- NULL
   }
-
 	if (identical(model@case, "LLconcentration_beta_sigma2")) {
 
 		model@covariance <- vect2covparam(model@covariance, param)
@@ -31,13 +29,12 @@ function(param, model, envir=NULL) {
 		}
 
 	} else if (identical(model@case, "LLconcentration_beta")) {
-
 		nparam <- length(param)
-    if (class(model@covariance) != "covAdditive0") {
+                if (class(model@covariance) != "covAdditive0") {
 		  model@covariance <- vect2covparam(model@covariance, param[1:(nparam-1)])
 		  model@covariance@sd2 <- param[nparam]
     } else {
-      model@covariance <- vect2covparam(model@covariance, param)
+       model@covariance <- vect2covparam(model@covariance, param)
     }
 
 # 		if (model@[email protected]) {
@@ -50,7 +47,6 @@ function(param, model, envir=NULL) {
 
 		C <- aux[[1]]
 		vn <- aux[[2]]
-
     T <- chol(C)
     x <- backsolve(t(T), model@y, upper.tri = FALSE)
     M <- backsolve(t(T), model@F, upper.tri = FALSE)

R/scalingFun.R

@@ -111,9 +111,9 @@ scalingFun <- function(X, knots, eta, plot = FALSE) {
     transX <- matrix(NA, nrow = NROW(X), ncol = NCOL(X))
     dimnames(transX) <- dimnames(X)
     X <- as.matrix(X)
-
     for (i in 1:d){
-        transX[ , i] <- scalingFun1d(x = X[ , i], knots = knots[[colnames(X)[i]]], eta = eta[[colnames(X)[i]]])
+       transX[ , i] <- scalingFun1d(x = X[ , i], knots = knots[[i]],
+                                    eta = eta[[i]])
     }
 
     if (plot) {

man/km.Rd

@@ -23,15 +23,21 @@ km(formula=~1, design, response, covtype="matern5_2",
   \item{response}{ a vector (or 1-column matrix or data frame) containing the values of the 1-dimensional output given by the objective function at the \code{design} points. }
   \item{covtype}{ an optional character string specifying the covariance structure to be used, to be chosen between \code{"gauss"}, \code{"matern5_2"}, \code{"matern3_2"}, \code{"exp"} or \code{"powexp"}. See a full description of available covariance kernels in \code{\link{covTensorProduct-class}}. Default is \code{"matern5_2"}. See also the argument \code{kernel} that allows the user to build its own covariance structure.}
   \item{coef.trend,}{ (see below)}
-  \item{coef.cov,}{ (see below)}
+  \item{coef.cov}{ (see below)}
   \item{coef.var}{ optional vectors containing the values for the trend, covariance and variance parameters. For estimation, 4 cases are implemented: 1. (All unknown) If all are missing, all are estimated. 2. (All known) If all are provided, no estimation is performed; 3. (Known trend) If \code{coef.trend} is provided but at least one of \code{coef.cov} or \code{coef.var} is missing, then BOTH \code{coef.cov} and \code{coef.var} are estimated; 4. (Unknown trend) If \code{coef.cov} and \code{coef.var} are provided but \code{coef.trend} is missing, then \code{coef.trend} is estimated (GLS formula).}
    \item{nugget}{ an optional variance value standing for the homogeneous nugget effect.}
   \item{nugget.estim}{ an optional boolean indicating whether the nugget effect should be estimated. Note that this option does not concern the case of heterogeneous noisy observations (see \code{noise.var} below). If \code{nugget} is given, it is used as an initial value. Default is \code{FALSE}.}
   \item{noise.var}{ for noisy observations : an optional vector containing the noise variance at each observation. This is useful for stochastic simulators. Default is \code{NULL}.}
   \item{estim.method}{  a character string specifying the method by which unknown parameters are estimated. Default is \code{"MLE"} (Maximum Likelihood). At this stage, a beta version of leave-One-Out estimation (\code{estim.method="LOO"}) is also implemented for noise-free observations.}
   \item{penalty}{ (beta version) an optional list suitable for Penalized Maximum Likelihood Estimation. The list must contain the item \code{fun} indicating the penalty function, and the item \code{value} equal to the value of the penalty parameter. At this stage the only available \code{fun} is \code{"SCAD"}, and \code{covtype} must be \code{"gauss"}. Default is \code{NULL}, corresponding to (un-penalized) Maximum Likelihood Estimation.}
-  \item{optim.method}{ an optional character string indicating which optimization method is chosen for the likelihood maximization. \code{"BFGS"} is the \code{optim} quasi-Newton procedure of package \code{stats}, with the method "L-BFGS-B". \code{"gen"} is the \code{genoud} genetic algorithm (using derivatives) from package \code{rgenoud} (>= 5.3.3). }
-  \item{lower, }{ (see below) }
+  \item{optim.method}{ an optional character string indicating which
+    optimization method to choose for the likelihood
+    maximization. \code{"BFGS"} is the \code{optim} quasi-Newton
+    procedure of package \code{stats}, with the method
+    "L-BFGS-B". \code{"gen"} is the \code{genoud} genetic algorithm
+    (using derivatives) from package \code{rgenoud} (>= 5.3.3).  Lower
+    case forms also supported.}
+  \item{lower}{ (see below) }
   \item{upper}{ optional vectors containing the bounds of the correlation parameters for optimization. The default values are given by \code{\link{covParametersBounds}}. }
   \item{parinit}{ an optional vector containing the initial values for the variables to be optimized over. If no vector is given, an initial point is generated as follows. For method \code{"gen"}, the initial point is generated uniformly inside the hyper-rectangle domain defined by \code{lower} and \code{upper}. For method \code{"BFGS"}, some points (see \code{control} below) are generated uniformly in the domain. Then the best point with respect to the likelihood (or penalized likelihood, see \code{penalty}) criterion is chosen. }
   \item{multistart}{ an optional integer indicating the number of initial points from which running the BFGS optimizer. These points will be selected as the best \code{multistart} one(s) among those evaluated (see above \code{parinit}). The multiple optimizations will be performed in parallel provided that a parallel backend is registered (see package \code{foreach}).} 

man/scalingFun.Rd

@@ -14,8 +14,10 @@ scalingFun(X, knots, eta, plot=FALSE)
 \arguments{
   \item{X}{ an n*d matrix standing for a design of n experiments in d-dimensional space }
   \item{knots}{ a list of knots parametrizing the transformation. }
-  \item{eta}{ a list of coefficients parametrizing the d marginal transformations. Each element stands for a set of marginal density values at the knots defined above.}
-  \item{plot}{ if TRUE plots the image of the columns of X according to the corresponding marginal transformations.}
+  \item{eta}{a list of coefficients parametrizing the d marginal
+  transformations. Each element stands for a set of marginal density
+  values at the knots defined above.}
+\item{plot}{ if TRUE plots the image of the columns of X according to the corresponding marginal transformations.}
 }
 
 \value{