@@ -1,10 +1,28 @@

 #' CoGAPS Matrix Factorization Algorithm

 #' 

 #' @details calls the C++ MCMC code and performs Bayesian

-#'matrix factorization returning the two matrices that reconstruct

-#'the data matrix

+#'  matrix factorization returning the two matrices that reconstruct

+#'  the data matrix

 #' @param D data matrix

 #' @param S uncertainty matrix (std devs for chi-squared of Log Likelihood)

+#' @param nFactor number of patterns (basis vectors, metagenes), which must be

+#'  greater than or equal to the number of rows of FP

+#' @param nEquil number of iterations for burn-in

+#' @param nSample number of iterations for sampling

+#' @param nOutputs how often to print status into R by iterations

+#' @param nSnapshots the number of individual samples to capture

+#' @param alphaA sparsity parameter for A domain

+#' @param alphaP sparsity parameter for P domain

+#' @param maxGibbmassA limit truncated normal to max size

+#' @param maxGibbmassP limit truncated normal to max size

+#' @param seed a positive seed is used as-is, while any negative seed tells

+#'  the algorithm to pick a seed based on the current time

+#' @param messages display progress messages

+#' @param singleCellRNASeq indicates if the data is single cell RNA-seq data

+#' @param whichMatrixFixed character to indicate whether A or P matric contains

+#'  the fixed patterns

+#' @param fixedPatterns matrix of fixed values in either A or P matrix

+#' @param checkpointInterval time (in seconds) between creating a checkpoint

 #' @return list with A and P matrix estimates

 #' @importFrom methods new

 #' @export

@@ -63,6 +81,9 @@ CoGapsFromCheckpoint <- function(D, S, path)

 }

 #' Display Information About Package Compilation

+#'

+#' @details displays information about how the package was compiled, i.e. which

+#'  compiler/version was used, which compile time options were enabled, etc...

 #' @export

 displayBuildReport <- function()

 {

@@ -1,8 +1,8 @@

 #' Plot of Residuals

 #'

 #' @details calculate residuals and produce heatmap

-#' @param Amean matrix of mean values for A from GAPS

-#' @param Pmean matrix of mean values for P from GAPS

+#' @param AMean_Mat matrix of mean values for A from GAPS

+#' @param PMean_Mat matrix of mean values for P from GAPS

 #' @param D original data matrix run through GAPS

 #' @param S original standard deviation matrix run through GAPS

 #' @export

@@ -14,6 +14,39 @@ CoGAPS(D, S, nFactor = 7, nEquil = 1000, nSample = 1000,

 \item{D}{data matrix}

 \item{S}{uncertainty matrix (std devs for chi-squared of Log Likelihood)}

+

+\item{nFactor}{number of patterns (basis vectors, metagenes), which must be

+greater than or equal to the number of rows of FP}

+

+\item{nEquil}{number of iterations for burn-in}

+

+\item{nSample}{number of iterations for sampling}

+

+\item{nOutputs}{how often to print status into R by iterations}

+

+\item{nSnapshots}{the number of individual samples to capture}

+

+\item{alphaA}{sparsity parameter for A domain}

+

+\item{alphaP}{sparsity parameter for P domain}

+

+\item{maxGibbmassA}{limit truncated normal to max size}

+

+\item{maxGibbmassP}{limit truncated normal to max size}

+

+\item{seed}{a positive seed is used as-is, while any negative seed tells

+the algorithm to pick a seed based on the current time}

+

+\item{messages}{display progress messages}

+

+\item{singleCellRNASeq}{indicates if the data is single cell RNA-seq data}

+

+\item{whichMatrixFixed}{character to indicate whether A or P matric contains

+the fixed patterns}

+

+\item{fixedPatterns}{matrix of fixed values in either A or P matrix}

+

+\item{checkpointInterval}{time (in seconds) between creating a checkpoint}

 }

 \value{

 list with A and P matrix estimates

@@ -23,7 +56,7 @@ CoGAPS Matrix Factorization Algorithm

 }

 \details{

 calls the C++ MCMC code and performs Bayesian

-matrix factorization returning the two matrices that reconstruct

-the data matrix

+ matrix factorization returning the two matrices that reconstruct

+ the data matrix

 }

@@ -9,4 +9,8 @@ displayBuildReport()

 \description{

 Display Information About Package Compilation

 }

+\details{

+displays information about how the package was compiled, i.e. which

+ compiler/version was used, which compile time options were enabled, etc...

+}

@@ -7,13 +7,13 @@

 residuals(AMean_Mat, PMean_Mat, D, S)

 }

 \arguments{

-\item{D}{original data matrix run through GAPS}

+\item{AMean_Mat}{matrix of mean values for A from GAPS}

-\item{S}{original standard deviation matrix run through GAPS}

+\item{PMean_Mat}{matrix of mean values for P from GAPS}

-\item{Amean}{matrix of mean values for A from GAPS}

+\item{D}{original data matrix run through GAPS}

-\item{Pmean}{matrix of mean values for P from GAPS}

+\item{S}{original standard deviation matrix run through GAPS}

 }

 \description{

 Plot of Residuals

@@ -198,11 +198,11 @@ static Rcpp::List runCogaps(GapsInternalState &state)

 // [[Rcpp::export]]

 Rcpp::List cogaps_cpp(const Rcpp::NumericMatrix &D,

-const Rcpp::NumericMatrix &S, unsigned nFactor, unsigned nEquil, unsigned nEquilCool,

-unsigned nSample, unsigned nOutputs, unsigned nSnapshots, float alphaA,

-float alphaP, float maxGibbmassA, float maxGibbmassP, int seed, bool messages,

-bool singleCellRNASeq, char whichMatrixFixed, const Rcpp::NumericMatrix &FP,

-unsigned checkpointInterval)

+const Rcpp::NumericMatrix &S, unsigned nFactor, unsigned nEquil,

+unsigned nEquilCool, unsigned nSample, unsigned nOutputs, unsigned nSnapshots,

+float alphaA, float alphaP, float maxGibbmassA, float maxGibbmassP, int seed,

+bool messages, bool singleCellRNASeq, char whichMatrixFixed,

+const Rcpp::NumericMatrix &FP, unsigned checkpointInterval)

 {

     // set seed

     uint32_t seedUsed = static_cast<uint32_t>(seed);

@@ -215,9 +215,9 @@ unsigned checkpointInterval)

     gaps::random::setSeed(seedUsed);

     // create internal state from parameters and run from there

-    GapsInternalState state(D, S, nFactor, nEquil, nEquilCool, nSample, nOutputs, nSnapshots,

-        alphaA, alphaP, maxGibbmassA, maxGibbmassP, seed, messages,

-        singleCellRNASeq, whichMatrixFixed, FP, checkpointInterval);

+    GapsInternalState state(D, S, nFactor, nEquil, nEquilCool, nSample,

+        nOutputs, nSnapshots, alphaA, alphaP, maxGibbmassA, maxGibbmassP, seed,

+        messages, singleCellRNASeq, whichMatrixFixed, FP, checkpointInterval);

     return runCogaps(state);

 }