Fixed spelling, other bugs found using R CMD check

DESCRIPTION

@@ -17,10 +17,12 @@ Depends:
     igraph,
     Rcpp,
     fastcluster,
-    pryr
+    pryr,
+    nnls
 Imports:
     ggplot2,
-    Matrix
+    Matrix,
+    methods
 LinkingTo: Rcpp
 Collate:
     'Rcpp_exceptions.R'
@@ -34,6 +36,7 @@ Collate:
     'mpcross-class.R'
     'biparentalDominant.R'
     'combineGenotypes.R'
+    'compressedProbabilities.R'
     'detailedPedigree-class.R'
     'eightWayPedigreeRandomFunnels.R'
     'eightWayPedigreeSingleFunnel.R'
@@ -42,6 +45,7 @@ Collate:
     'expand.R'
     'f2Pedigree.R'
     'finals.R'
+    'fixedNumberOfFounderAlleles.R'
     'formGroups.R'
     'founders.R'
     'fourParentPedigreeRandomFunnels.R'
@@ -50,6 +54,7 @@ Collate:
     'hetData.R'
     'impute.R'
     'imputeFounders.R'
+    'jitterMap.R'
     'mapFunctions.R'
     'markers.R'
     'mpcross.R'

DESCRIPTION.in

@@ -15,7 +15,9 @@ Depends:
     methods,
     igraph,
     fastcluster,
-    pryr
+    pryr,
+    nnls
 Imports: 
     ggplot2,
-    Matrix
+    Matrix,
+    methods

R/biparentalDominant.R

@@ -11,7 +11,7 @@ setClass("biparentalDominant", contains="NULL")
 #' cross <- simulateMPCross(map = map, pedigree = f2Pedigree, mapFunction = haldane, seed = 1)
 #' founders(cross)
 #' finals(cross)[1:10,]
-#' The heterozygotes are initially coded as 3
+#' #The heterozygotes are initially coded as 3
 #' hetData(cross)[[1]]
 #' #Make all markers dominant
 #' dominantCross <- cross + biparentalDominant()

R/detailedPedigree-class.R

@@ -74,6 +74,12 @@ NULL
 .detailedPedigree <- setClass("detailedPedigree", contains = "pedigree", slots = list(initial = "integer", observed = "logical"), validity = checkDetailedPedigree)
 #' @export
 #' @describeIn detailedPedigree-class Construct object of class detailedPedigree
+#' @param lineNames The names assigned to the lines
+#' @param mother The female parent of this line
+#' @param father The male parent of this line
+#' @param initial The indices of the founder lines in the pedigree
+#' @param observed A logical vector determining which lines are observed in the final population
+#' @param selfing Value determining whether or not subsequent analysis of populations generated from this pedigree should assume infinite generations of selfing. Possible values are \code{"finite"} and \code{"infinite"}.
 detailedPedigree <- function(lineNames, mother, father, initial, observed, selfing)
 {
 	mother <- as.integer(mother)

R/estimateMap.R

@@ -36,8 +36,6 @@ estimateMap <- function(mpcrossLG, mapFunction = rfToHaldane, maxOffset = 1)
 		b <- rfData[indices]
 		#Values of 0.5 result in infinite estimated distance, which doesn't really work. 
 		b[b == 0.5] <- 0.49
-		if (!requireNamespace("nnls", quietly=TRUE))
-			stop("nnls needed for computemap to work. Please install it.\n", call.=FALSE) 
 		result <- nnls::nnls(d, mapFunction(b)) 
 		map[[as.character(group)]] <- c(0, cumsum(result$x[which(indices[,1] == indices[,2]+1)]))
 		names(map[[as.character(group)]]) <- rfData@markers

R/mpcross.R

@@ -134,7 +134,7 @@ setMethod(f = "+", signature = c("mpcrossRF", "mpcross"), definition = function(
       stop("Internal error: Markers should have been combined as two blocks")
     }
     marker2Range <- range(match(markers(e2), markers(combined)))
-    extraRFData <- estimateRFInternal(object = combined, recombValues = e1@rf@r, lineWeights = rep(1, nLines(e2)), marker1Range = marker1Range, marker2Range = marker2Range, keepLod = keepLod, keepLkhd = keepLkhd, gbLimit = e1@rf@gbLimit, verbose = list(verbose = FALSE, progressStyle = 1L))
+    extraRFData <- estimateRFInternal(object = combined, recombValues = e1@rf@r, lineWeights = rep(1, nLines(e2)), markerRows = marker1Range, markerColumns = marker2Range, keepLod = keepLod, keepLkhd = keepLkhd, gbLimit = e1@rf@gbLimit, verbose = list(verbose = FALSE, progressStyle = 1L))
     stop("Need to check this section")
   }
   #If the markers are all the same, keep them in the same order

R/roxygen.R

@@ -1,9 +1,12 @@
 #' @import qtl
 #' @import igraph
+#' @import methods
 #' @importFrom pryr address
+#' @importFrom nnls nnls
 #' @exportClass pedigreeGraph
 #' @exportMethod subset 
 #' @exportMethod plot
 #' @importClassesFrom Matrix index dspMatrix dppMatrix
+#' @importFrom methods setClass
 #' @useDynLib mpMap2
 NULL

src/estimateRFSpecificDesign.cpp

@@ -733,7 +733,7 @@ bool toInternalArgs(estimateRFSpecificDesignArgs&& args, rfhaps_internal_args& i
 		{
 			try
 			{
-				Rcpp::Rcout << "Input data had hetrozygotes but was analysed assuming infinite selfing. " << std::endl << "    All hetrozygotes were ignored." << std::endl;
+				Rcpp::Rcout << "Input data had heterozygotes but was analysed assuming infinite selfing. " << std::endl << "    All hetrozygotes were ignored." << std::endl;
 			}
 			catch(...)
 			{}

src/estimateRFSpecificDesign.h

@@ -62,7 +62,7 @@ bool estimateRFSpecificDesign(rfhaps_internal_args& internal_args, unsigned long
  * 3. Check that the data is consistent with the input funnels.
  * 4. Recode the founders and finals data in a normalised form, also working out the maximum number of alleles per marker, across the dataset
  * 5. Assign a unique value to each marker pattern and funnel. 
- * 6. In the case of infinite generations of selfing, replace hetrozygote values with NA. 
+ * 6. In the case of infinite generations of selfing, replace heterozygote values with NA. 
  * @param args Input arguments
  * @param internalArgs preprocessed arguments
  * @return A boolean value, with true indicating success. False indicates an error.

src/imputeFounders.cpp

@@ -14,7 +14,7 @@
 #include "funnelHaplotypeToMarker.hpp"
 #include "viterbi.hpp"
 #include "recodeHetsAsNA.h"
-template<int nFounders, bool infiniteSelfing> void imputedFoundersInternal2(Rcpp::IntegerMatrix founders, Rcpp::IntegerMatrix finals, Rcpp::S4 pedigree, Rcpp::List hetData, Rcpp::List map, Rcpp::IntegerMatrix results, double homozygoteMissingProb, double hetrozygoteMissingProb, Rcpp::IntegerMatrix key)
+template<int nFounders, bool infiniteSelfing> void imputedFoundersInternal2(Rcpp::IntegerMatrix founders, Rcpp::IntegerMatrix finals, Rcpp::S4 pedigree, Rcpp::List hetData, Rcpp::List map, Rcpp::IntegerMatrix results, double homozygoteMissingProb, double heterozygoteMissingProb, Rcpp::IntegerMatrix key)
 {
 	//Work out maximum number of markers per chromosome
 	int maxChromosomeMarkers = 0;
@@ -66,7 +66,7 @@ template<int nFounders, bool infiniteSelfing> void imputedFoundersInternal2(Rcpp
 			//Technically a warning could lead to an error if options(warn=2). This would be bad because it would break out of our code. This solution generates a c++ exception in that case, which we can then ignore. 
 			try
 			{
-				warning("Input data had hetrozygotes but was analysed assuming infinite selfing. All hetrozygotes were ignored. \n");
+				warning("Input data had heterozygotes but was analysed assuming infinite selfing. All heterozygotes were ignored. \n");
 			}
 			catch(...)
 			{}
@@ -157,7 +157,7 @@ template<int nFounders, bool infiniteSelfing> void imputedFoundersInternal2(Rcpp
 	viterbi.results = results;
 	viterbi.key = key;
 	viterbi.homozygoteMissingProb = homozygoteMissingProb;
-	viterbi.hetrozygoteMissingProb = hetrozygoteMissingProb;
+	viterbi.heterozygoteMissingProb = heterozygoteMissingProb;
 	viterbi.intercrossingSingleLociHaplotypeProbabilities = &intercrossingSingleLociHaplotypeProbabilities;
 	viterbi.funnelSingleLociHaplotypeProbabilities = &funnelSingleLociHaplotypeProbabilities;
 
@@ -188,18 +188,18 @@ template<int nFounders, bool infiniteSelfing> void imputedFoundersInternal2(Rcpp
 		cumulativeMarkerCounter += (int)positions.size();
 	}
 }
-template<int nFounders> void imputedFoundersInternal1(Rcpp::IntegerMatrix founders, Rcpp::IntegerMatrix finals, Rcpp::S4 pedigree, Rcpp::List hetData, Rcpp::List map, Rcpp::IntegerMatrix results, bool infiniteSelfing, double homozygoteMissingProb, double hetrozygoteMissingProb, Rcpp::IntegerMatrix key)
+template<int nFounders> void imputedFoundersInternal1(Rcpp::IntegerMatrix founders, Rcpp::IntegerMatrix finals, Rcpp::S4 pedigree, Rcpp::List hetData, Rcpp::List map, Rcpp::IntegerMatrix results, bool infiniteSelfing, double homozygoteMissingProb, double heterozygoteMissingProb, Rcpp::IntegerMatrix key)
 {
 	if(infiniteSelfing)
 	{
-		imputedFoundersInternal2<nFounders, true>(founders, finals, pedigree, hetData, map, results, homozygoteMissingProb, hetrozygoteMissingProb, key);
+		imputedFoundersInternal2<nFounders, true>(founders, finals, pedigree, hetData, map, results, homozygoteMissingProb, heterozygoteMissingProb, key);
 	}
 	else
 	{
-		imputedFoundersInternal2<nFounders, false>(founders, finals, pedigree, hetData, map, results, homozygoteMissingProb, hetrozygoteMissingProb, key);
+		imputedFoundersInternal2<nFounders, false>(founders, finals, pedigree, hetData, map, results, homozygoteMissingProb, heterozygoteMissingProb, key);
 	}
 }
-SEXP imputeFounders(SEXP geneticData_sexp, SEXP map_sexp, SEXP homozygoteMissingProb_sexp, SEXP hetrozygoteMissingProb_sexp)
+SEXP imputeFounders(SEXP geneticData_sexp, SEXP map_sexp, SEXP homozygoteMissingProb_sexp, SEXP heterozygoteMissingProb_sexp)
 {
 BEGIN_RCPP
 	Rcpp::S4 geneticData;
@@ -296,16 +296,16 @@ BEGIN_RCPP
 	}
 	if(homozygoteMissingProb < 0 || homozygoteMissingProb > 1) throw std::runtime_error("Input homozygoteMissingProb must be a number between 0 and 1");
 
-	double hetrozygoteMissingProb;
+	double heterozygoteMissingProb;
 	try
 	{
-		hetrozygoteMissingProb = Rcpp::as<double>(hetrozygoteMissingProb_sexp);
+		heterozygoteMissingProb = Rcpp::as<double>(heterozygoteMissingProb_sexp);
 	}
 	catch(...)
 	{
-		throw std::runtime_error("Input hetrozygoteMissingProb must be a number between 0 and 1");
+		throw std::runtime_error("Input heterozygoteMissingProb must be a number between 0 and 1");
 	}
-	if(hetrozygoteMissingProb < 0 || hetrozygoteMissingProb > 1) throw std::runtime_error("Input hetrozygoteMissingProb must be a number between 0 and 1");
+	if(heterozygoteMissingProb < 0 || heterozygoteMissingProb > 1) throw std::runtime_error("Input heterozygoteMissingProb must be a number between 0 and 1");
 
 	std::vector<std::string> foundersMarkers = Rcpp::as<std::vector<std::string> >(Rcpp::colnames(founders));
 	std::vector<std::string> finalsMarkers = Rcpp::as<std::vector<std::string> >(Rcpp::colnames(finals));
@@ -378,19 +378,19 @@ BEGIN_RCPP
 	{
 		if(nFounders == 2)
 		{
-			imputedFoundersInternal1<2>(founders, finals, pedigree, hetData, map, results, infiniteSelfing, homozygoteMissingProb, hetrozygoteMissingProb, key);
+			imputedFoundersInternal1<2>(founders, finals, pedigree, hetData, map, results, infiniteSelfing, homozygoteMissingProb, heterozygoteMissingProb, key);
 		}
 		else if(nFounders == 4)
 		{
-			imputedFoundersInternal1<4>(founders, finals, pedigree, hetData, map, results, infiniteSelfing, homozygoteMissingProb, hetrozygoteMissingProb, key);
+			imputedFoundersInternal1<4>(founders, finals, pedigree, hetData, map, results, infiniteSelfing, homozygoteMissingProb, heterozygoteMissingProb, key);
 		}
 		else if(nFounders == 8)
 		{
-			imputedFoundersInternal1<8>(founders, finals, pedigree, hetData, map, results, infiniteSelfing, homozygoteMissingProb, hetrozygoteMissingProb, key);
+			imputedFoundersInternal1<8>(founders, finals, pedigree, hetData, map, results, infiniteSelfing, homozygoteMissingProb, heterozygoteMissingProb, key);
 		}
 		else if(nFounders == 16)
 		{
-			imputedFoundersInternal1<16>(founders, finals, pedigree, hetData, map, results, infiniteSelfing, homozygoteMissingProb, hetrozygoteMissingProb, key);
+			imputedFoundersInternal1<16>(founders, finals, pedigree, hetData, map, results, infiniteSelfing, homozygoteMissingProb, heterozygoteMissingProb, key);
 		}
 		else
 		{

src/probabilities16.h

@@ -6,7 +6,7 @@ template<> struct probabilityData<16>
 public:
 	/*See Karl Bromans paper on intermediate generations. This mask converts allele encodings (0 - 2) into indices into 
 	the array of 4 different probabilities. In terms of indices, 
-	Zero = homozygote, One = other homozygote, Two = hetrozygote
+	Zero = homozygote, One = other homozygote, Two = heterozygote
 	In terms of values, see Table one of the paper. Zero = first equation of table, one = second equation, etc. Note that
 	we combine equations 4 and 5 into a single state. 
 	*/

src/probabilities2.cpp

@@ -30,14 +30,14 @@ template<> void genotypeProbabilitiesNoIntercross<2, false>(std::array<double, 5
 	prob[0] = (1/(2*(1 + 2*r))) -std::pow(0.5, selfingGenerations+2)*(2 - quadraticPower + oneMinusTwoRPower*(1 - 2 *r)/ ( 1 + 2*r));
 	//Different homozygotes at both
 	prob[1] = (r/(1 + 2*r)) -std::pow(0.5, selfingGenerations+2)*(2 - quadraticPower - oneMinusTwoRPower*(1 - 2 *r)/ ( 1 + 2*r));
-	//One homozygote, one hetrozygote
+	//One homozygote, one heterozygote
 	prob[2] = std::pow(0.5, selfingGenerations+1) * (1 - quadraticPower);
-	//Two hetrozygotes. We don't distinguish between the two different hetrozygote combinations
+	//Two heterozygotes. We don't distinguish between the two different heterozygote combinations
 	prob[3] = std::pow(0.5, selfingGenerations) * quadraticPower;
 	//But we might want to allow us to get back to the seperate probabilities in code that calls this function, so compute the probability for state 4 by itself. 
 	prob[4] = std::pow(0.5, selfingGenerations-1) * (quadraticPower - oneMinusTwoRPower);
 
-	//The hetrozygotes will be counted twice, so divide by two (or four if there are two hetrozygotes). 
+	//The heterozygotes will be counted twice, so divide by two (or four if there are two heterozygotes). 
 	prob[2] /= 2;
 	prob[3] /= 4;
 	prob[4] /= 4;
@@ -86,7 +86,7 @@ template<> void genotypeProbabilitiesWithIntercross<2, false>(std::array<double,
 	0.25*(-2*powOneMinus2R*powOneMinusR 
    		+ (oneMinusRSquared + oneMinusTwoRSquared*powOneMinusRSquared)*quadraticPower
    	)/(8*pow2*oneMinusRSquared);
-	//We don't distinguish between the two hetrozygote combinations
+	//We don't distinguish between the two heterozygote combinations
    	prob[3] += prob[4];
 }
 template<> void singleLocusGenotypeProbabilitiesNoIntercross<2, true>(array2<2>&data, int selfingGenerations, std::size_t nFunnels)

src/probabilities2.h

@@ -6,7 +6,7 @@ template<> struct probabilityData<2>
 public:
 	/*See Karl Bromans paper on intermediate generations. This mask converts allele encodings (0 - 2) into indices into 
 	the array of 4 different probabilities. In terms of indices, 
-	Zero = homozygote, One = other homozygote, Two = hetrozygote
+	Zero = homozygote, One = other homozygote, Two = heterozygote
 	In terms of values, see Table one of the paper. Zero = first equation of table, one = second equation, etc. Note that
 	we combine equations 4 and 5 into a single state. 
 	*/

src/probabilities4.h

@@ -6,7 +6,7 @@ template<> struct probabilityData<4>
 public:
 	/*See Karl Bromans paper on intermediate generations. This mask converts allele encodings (0 - 2) into indices into 
 	the array of 4 different probabilities. In terms of indices, 
-	Zero = homozygote, One = other homozygote, Two = hetrozygote
+	Zero = homozygote, One = other homozygote, Two = heterozygote
 	In terms of values, see Table one of the paper. Zero = first equation of table, one = second equation, etc. Note that
 	we combine equations 4 and 5 into a single state. 
 	*/

src/probabilities8.h

@@ -6,7 +6,7 @@ template<> struct probabilityData<8>
 public:
 	/*See Karl Bromans paper on intermediate generations. This mask converts allele encodings (0 - 2) into indices into 
 	the array of 4 different probabilities. In terms of indices, 
-	Zero = homozygote, One = other homozygote, Two = hetrozygote
+	Zero = homozygote, One = other homozygote, Two = heterozygote
 	In terms of values, see Table one of the paper. Zero = first equation of table, one = second equation, etc. Note that
 	we combine equations 4 and 5 into a single state. 
 	*/

src/recodeFoundersFinalsHets.cpp

@@ -52,7 +52,7 @@ void recodeFoundersFinalsHets(recodeDataStruct& inputs)
 				recodedCurrentMarkerHetData(i, 2) = recodedCurrentMarkerHetData(i, 0);
 			}
 		}
-		//The homozygotes get translated as-is, the hetrozygotes are given sequential labels afterwards. 
+		//The homozygotes get translated as-is, the heterozygotes are given sequential labels afterwards. 
 		std::sort(hetValues.begin(), hetValues.end());
 		hetValues.erase(std::unique(hetValues.begin(), hetValues.end()), hetValues.end());
 		inputs.maxAlleles = std::max(inputs.maxAlleles, (unsigned int)(nFounderAlleles + hetValues.size()));