[Rsiena-commits] r98 - in pkg: RSiena RSiena/R RSiena/inst/doc RSienaTest RSienaTest/R RSienaTest/doc RSienaTest/inst/doc

[noreply at r-forge.r-project.org]

Author: ripleyrm
Date: 2010-06-08 17:14:02 +0200 (Tue, 08 Jun 2010)
New Revision: 98

Modified:
   pkg/RSiena/R/simstatsc.r
   pkg/RSiena/changeLog
   pkg/RSiena/inst/doc/s_man400.pdf
   pkg/RSienaTest/R/simstatsc.r
   pkg/RSienaTest/changeLog
   pkg/RSienaTest/doc/RSiena.bib
   pkg/RSienaTest/doc/s_man400.tex
   pkg/RSienaTest/inst/doc/s_man400.pdf
Log:
Changes to dyadic covariates and bipartite networks, also additions to bib file.

Modified: pkg/RSiena/R/simstatsc.r
===================================================================
--- pkg/RSiena/R/simstatsc.r	2010-06-04 14:22:23 UTC (rev 97)
+++ pkg/RSiena/R/simstatsc.r	2010-06-08 15:14:02 UTC (rev 98)
@@ -9,9 +9,9 @@
 # * communicating with C++. Only subsidiary routines used for maximum likelihood
 # *****************************************************************************/
 ##@simstats0c siena07 Simulation Module
-simstats0c <-function(z, x, INIT=FALSE, TERM=FALSE, initC=FALSE, data=NULL,
-                        effects=NULL, fromFiniteDiff=FALSE,
-                      profileData=FALSE, prevAns=NULL, returnDeps=FALSE)
+simstats0c <- function(z, x, INIT=FALSE, TERM=FALSE, initC=FALSE, data=NULL,
+                       effects=NULL, fromFiniteDiff=FALSE,
+                       profileData=FALSE, prevAns=NULL, returnDeps=FALSE)
 {
     if (INIT || initC)  ## initC is to initialise multiple C processes in phase3
     {
@@ -291,8 +291,7 @@
                   mymat[, 3] <- 1
                   mymat
               }, y = matorig)
-    mat2 <- do.call(rbind, tmp)   ##drop the diagonal : no, in case bipartite
-    ## mat1 <- mat1[mat1[,1] != mat1[, 2],]
+    mat2 <- do.call(rbind, tmp)
     ## add attribute of size
     attr(mat1,'nActors1') <- nrow(mat)
     attr(mat1,'nActors2') <- ncol(mat)
@@ -415,7 +414,7 @@
                         ## set to missing in raw data for distances later
                         depvar[[i]][j, ] <- NA
                         depvar[[i]][, j] <- NA
-                   }
+                    }
                 }
                 for (j in threes) ## False data is preceded and followed by real
                 {
@@ -597,7 +596,7 @@
                         networks[[i]][j, use] <- networks[[i-1]][j, use]
                         networks[[i]][use, j] <- networks[[i-1]][use, j]
                     }
-               }
+                }
                 else if (ccOption == 3)
                 {
                     depvar[j, , i] <- NA ## missing
@@ -613,7 +612,7 @@
                     depvar[use, j, i] <- 0
                     depvar[j, j, i] <- NA
                     ## carry forward already done
-                     if (i == 1)
+                    if (i == 1)
                     {
                         networks[[i]][j, use] <- 0
                         networks[[i]][use, j] <- 0
@@ -624,13 +623,16 @@
                         networks[[i]][use, j] <- networks[[i-1]][use, j]
 
                     }
-               }
+                }
                 else if (ccOption %in% c(2, 3))
                 {
                     depvar[j, , i] <- NA ## missing
                     depvar[, j, i] <- NA
                 }
             }
+        }
+        for (i in 1:observations)
+        {
             if (i < observations)
             {
                 ## recreate distances, as we have none in c++. (no longer true)
@@ -644,13 +646,12 @@
                 diag(mymat2[,,1]) <- 0
                 mydiff <- mymat2 - mymat1
                 attr(depvar, 'distance')[i] <- sum(mydiff != 0,
-                                                         na.rm = TRUE)
+                                                   na.rm = TRUE)
                 if (all(mydiff >= 0, na.rm=TRUE))
                     attr(depvar, 'uponly')[i] <- TRUE
                 if (all(mydiff <= 0, na.rm=TRUE))
                     attr(depvar, 'downonly')[i] <- TRUE
             }
-
             diag(networks[[i]]) <- 0
             edgeLists[[i]] <- createEdgeLists(networks[[i]], depvar[, , i])
         }
@@ -683,8 +684,8 @@
     compChange <- any(!is.na(thisComp))
     if (compChange)
     {
-        stop("Composition change is not yet implemented for bipartite",
-             "networks")
+      #  stop("Composition change is not yet implemented for bipartite",
+      #       "networks")
         action <- attr(compositionChange[[thisComp]], "action")
         ccOption <- attr(compositionChange[[thisComp]], "ccOption")
     }
@@ -705,6 +706,7 @@
             ## extract this matrix
             networks[[i]] <- depvar[[i]]
             nActors <- nrow(depvar[[i]])
+            nReceivers <- ncol(depvar[[i]])
             ## stop if any duplicates
             netmat <- cbind(networks[[i]]@i+1, networks[[i]]@j+1,
                             networks[[i]]@x)
@@ -713,12 +715,12 @@
                 stop("duplicate entries in sparse matrix")
             }
             ## extract missing entries
-            netmiss[[i]] <- netmat[is.na(netmat[,3]), , drop = FALSE]
+            netmiss[[i]] <- netmat[is.na(netmat[, 3]), , drop = FALSE]
             ## carry forward missing values if any
             if (i == 1) # set missings to zero
             {
                 netmat <- netmat[!is.na(netmat[,3]), ]
-                networks[[i]] <- spMatrix(nActors, nActors, netmat[, 1],
+                networks[[i]] <- spMatrix(nActors, nReceivers, netmat[, 1],
                                           netmat[, 2], netmat[,3])
             }
             else
@@ -744,6 +746,118 @@
             mat3 <- mat1[struct, , drop = FALSE]
             ## now remove the zeros from reset data
             mat1 <- mat1[!mat1[, 3] == 0, ]
+            ## do comp change
+            if (compChange)
+            {
+                ## revert to sparse matrices temporarily
+                mat1 <- spMatrix(nrow=nActors, ncol=nReceivers, i = mat1[, 1],
+                                 j=mat1[, 2], x=mat1[, 3])
+                mat2 <- spMatrix(nrow=nActors, ncol=nReceivers, i = mat2[, 1],
+                                 j=mat2[, 2], x=mat2[, 3])
+                mat3 <- spMatrix(nrow=nActors, ncol=nReceivers, i = mat3[, 1],
+                                 j=mat3[, 2], x=mat3[, 3])
+                ones <- which(action[, i] == 1)
+                twos <- which(action[, i] == 2)
+                threes <- which(action[, i] == 3)
+                for (j in ones) ## False data is not preceded by anything real
+                {
+                    if (ccOption %in% c(1, 2))
+                    {
+                        ## find missing values for this actor
+                        use <- mat2[j, ] > 0
+                        ## remove from real data (i.e. zero)
+                        mat1[j, use] <- 0
+                        ## remove from missing data
+                        mat2[j, use] <- 0
+                        ## remove from raw data for distances later
+                        depvar[[i]][j, use] <- 0 ## zero
+                    }
+                    else if (ccOption == 3)
+                    {
+                        ## add the row  to the missing data
+                        mat2[j, ] <- 1
+                        ## set to missing in raw data for distances later
+                        depvar[[i]][j, ] <- NA
+                    }
+                }
+                for (j in threes) ## False data is preceded and followed by real
+                {
+                    if (ccOption %in% c(1, 2))
+                    {
+                        ## find missing values for this actor
+                        use <- mat2[j, ] > 0
+                        ## remove these from mat2, the missing data
+                        mat2[j, use] <- 0
+                        ## carry forward
+                        if (i == 1)
+                        {
+                            ## 0 any matches from mat1, the real data
+                            mat1[j, use] <- 0
+                        }
+                        else
+                        {
+                            mat1[j, use] <- networks[[i-1]][j, use]
+                        }
+                        depvar[[i]][j, use] <- 0 ##  not missing
+                    }
+                    else if (ccOption == 3)
+                    {
+                        ## add the row to the missing data
+                        mat2[j, ] <- 1
+                        depvar[[i]][j, ] <- NA
+                    }
+                }
+                for (j in twos) ## False data is not followed by anything real
+                {
+                    if (ccOption == 1)
+                    {
+                        ## find missing values for this actor
+                        use <- mat2[j, ] > 0
+                        ## remove these from mat2, the missing data
+                        mat2[j, use] <- 0
+                        depvar[[i]][j, use] <- 0 ##  not missing
+                        ## carry forward
+                        if (i == 1)
+                        {
+                            ## 0 any matches from mat1, the real data
+                            mat1[j, use] <- 0
+                        }
+                        else
+                        {
+                            mat1[j, use] <- networks[[i-1]][j , use]
+                        }
+                    }
+                    else if (ccOption %in% c(2, 3))
+                    {
+                        ## add the row  to the missing data
+                        mat2[j, ] <- 1
+                        depvar[[i]][j, ] <- NA
+                    }
+                }
+
+                ## now revert to triplet matrices, after updating networks
+                networks[[i]] <- mat1
+                mat1 <- cbind(mat1 at i + 1, mat1 at j + 1, mat1 at x)
+                mat2 <- cbind(mat2 at i + 1, mat2 at j + 1, mat2 at x)
+                mat3 <- cbind(mat3 at i + 1, mat3 at j + 1, mat3 at x)
+                if (any (mat1[, 3] == 0) || any (mat2[, 3] == 0) ||
+                    any (mat3[, 3] == 0))
+                {
+                    stop("zero values in sparse matrices")
+                }
+                if (any (duplicated(mat1[, -3])) ||
+                    any (duplicated(mat2[, -3])) ||
+                    any (duplicated(mat3[, -3])))
+                {
+                    stop("duplicate values in sparse matrices")
+                }
+                if (any (mat1[, 1] == mat1[, 2]) ||
+                    any (mat2[, 1] == mat2[, 2]) ||
+                    any (mat3[, 1] == mat3[, 2]))
+                {
+                    stop("loop values in sparse matrices")
+                }
+            }
             ##fix up storage mode to be integer
             storage.mode(mat1) <- 'integer'
             storage.mode(mat2) <- 'integer'
@@ -789,6 +903,69 @@
         }
         for (i in 1:observations)
         {
+            ones <- which(action[, i] == 1)
+            twos <- which(action[, i] == 2)
+            threes <- which(action[, i] == 3)
+            for (j in ones) ## False data is not preceded by anything real
+            {
+                if (ccOption %in% c(1, 2))
+                {
+                    use <- is.na(depvar[j, , i])
+                    depvar[j, use, i] <- 0 ## not missing
+                    networks[[i]][j, use] <- 0 ## zero
+                }
+                else if (ccOption == 3)
+                {
+                    depvar[j, , i] <- NA ## missing
+                }
+            }
+            for (j in threes) ## False data is preceded and followed by real
+            {
+
+                if (ccOption %in% c(1, 2))
+                {
+                    use <- is.na(depvar[j, , i])
+                    depvar[j, use, i] <- 0 ##  not missing
+                    ## carry forward already done
+                    if (i == 1)
+                    {
+                        networks[[i]][j, use] <- 0
+                    }
+                    else
+                    {
+                        networks[[i]][j, use] <- networks[[i-1]][j, use]
+                    }
+                }
+                else if (ccOption == 3)
+                {
+                    depvar[j, , i] <- NA ## missing
+                }
+            }
+            for (j in twos) ## False data is not followed by anything real
+            {
+                if (ccOption == 1)
+                {
+                    use <- is.na(depvar[j, , i])
+                    depvar[j, use, i] <- 0 ##  not missing
+                    ## carry forward already done
+                    if (i == 1)
+                    {
+                        networks[[i]][j, use] <- 0
+                    }
+                    else
+                    {
+                        networks[[i]][j, use] <- networks[[i-1]][j, use]
+
+                    }
+                }
+                else if (ccOption %in% c(2, 3))
+                {
+                    depvar[j, , i] <- NA ## missing
+                }
+            }
+        }
+        for (i in 1:observations)
+        {
             if (i < observations)
             {
                 ## recreate distances, as we have none in c++. (no longer true)
@@ -885,6 +1062,7 @@
 unpackCDyad<- function(dycCovar)
 {
     sparse <- attr(dycCovar, 'sparse')
+    nodeSets <- attr(dycCovar, "nodeSet")
     if (sparse)
     {
         ## have a sparse matrix in triplet format
@@ -892,9 +1070,12 @@
         ## with 0 based indices!
         varmat <- cbind(dycCovar at i+1, dycCovar at j+1, dycCovar at x)
         ##drop the diagonal, if present - not for bipartite
-        ## varmat <- varmat[varmat[,1] != varmat[, 2],]
+        if (nodeSets[1] == nodeSets[2])
+        {
+            varmat <- varmat[varmat[,1] != varmat[, 2],]
+        }
         mat1 <- varmat
-        ##mat1[is.na(varmat[, 3]), 3] <- attr(dycCovar, "mean")
+        mat1[is.na(varmat[, 3]), 3] <- attr(dycCovar, "mean")
         mat1 <- mat1[!mat1[, 3] == 0, ]
         ## add attribute of dim
         attr(mat1,'nActors1') <- nrow(dycCovar)
@@ -908,8 +1089,12 @@
     }
     else
     {
+        if (nodeSets[1] == nodeSets[2])
+        {
+            diag(dycCovar) <- 0
+        }
         dycCovar1 <- dycCovar
-        ##dycCovar1[is.na(dycCovar1)] <- attr(dycCovar, "mean")
+        dycCovar1[is.na(dycCovar1)] <- attr(dycCovar, "mean")
         edgeLists <- createCovarEdgeList(dycCovar1, dycCovar)
     }
     ## add attribute of nodesets
@@ -929,6 +1114,7 @@
     varmats <- vector('list', observations)
     sparse <- attr(dyvCovar, 'sparse')
     means <- attr(dyvCovar, "meanp")
+    nodeSets <- attr(dyvCovar, "nodeSet")
     if (sparse)
     {
         ## have a list of sparse matrices in triplet format
@@ -938,7 +1124,10 @@
             thisvar <- dyvCovar[[i]]
             varmat <- cbind(var at i+1, var at j+1, var at x)
             ## drop the diagonal, if present no - bipartite?
-            ## varmat <- varmat[varmat[,1] != varmat[, 2],]
+            if (nodeSets[1] == nodeSets[2])
+            {
+                varmat <- varmat[varmat[,1] != varmat[, 2],]
+            }
             mat1 <- varmat
             mat1[is.na(varmat[, 3]), 3] <- means[i]
             mat1 <- mat1[!mat1[, 3] == 0, ]
@@ -956,6 +1145,10 @@
     {
         for (i in 1:(observations - 1))
         {
+            if (nodeSets[1] == nodeSets[2])
+            {
+                diag(dyvCovar[, , i]) <- 0
+            }
             thisvar <- dyvCovar[, , i]
             thisvar[is.na(thisvar) ] <- means[i]
             edgeLists[[i]] <- createCovarEdgeList(thisvar, dyvCovar[, , i])

Modified: pkg/RSiena/changeLog
===================================================================
--- pkg/RSiena/changeLog	2010-06-04 14:22:23 UTC (rev 97)
+++ pkg/RSiena/changeLog	2010-06-08 15:14:02 UTC (rev 98)
@@ -1,3 +1,16 @@
+2010-06-08 R-forge revision 97
+
+	* R/simstatsc.r: constant dyadic covariates with missing values
+	should now work correctly, varying ones do not fail, but the
+	centering and filling in of missing values for these is still to
+	be sorted out. Also treatment of bipartite nets with composition
+	change has been corrected. (Previously missings were not processed
+	correctly if a sparse-matrix format network (even after the
+	previous change!) and composition change was ignored for all
+	bipartite networks.)
+
+	* doc/RSiena.bib: additions.
+
 2010-06-04 R-forge revision 96 (RSiena mainly)
 
 	* R/sienaTimeTest.r, R/sienaeffects.r, man/sienaTimeTest.rd,

Modified: pkg/RSiena/inst/doc/s_man400.pdf
===================================================================
--- pkg/RSiena/inst/doc/s_man400.pdf	2010-06-04 14:22:23 UTC (rev 97)
+++ pkg/RSiena/inst/doc/s_man400.pdf	2010-06-08 15:14:02 UTC (rev 98)
@@ -507,12 +507,13 @@
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[TRUNCATED]

To get the complete diff run:
    svnlook diff /svnroot/rsiena -r 98