version 0.2-2

DESCRIPTION

@@ -2,22 +2,23 @@ Package: vcrpart
 Type: Package
 Title: Tree-Based Varying Coefficient Regression for Generalized Linear
         and Ordinal Mixed Models
-Version: 0.2-1
-Date: 2014-09-10
+Version: 0.2-2
+Date: 2014-10-24
 Authors@R: c(
   person("Reto", "Buergin", role = c("aut", "cre", "cph"), email = "rbuergin@gmx.ch"),
   person("Gilbert", "Ritschard", role = c("ctb", "ths"), 
          email = "gilbert.ritschard@unige.ch"))
 Maintainer: Reto Buergin <rbuergin@gmx.ch>
-Description: Recursive partitioning algorithm for varying coefficient generalized linear models and ordinal 2-stage linear mixed models. Special features are coefficient-wise partitioning, non-varying coefficients and partitioning of time-varying variables in longitudinal ordinal regression.
+Description: Recursive partitioning for varying coefficient generalized linear models and ordinal linear mixed models. Special features are coefficient-wise partitioning, non-varying coefficients and partitioning of time-varying variables in longitudinal regression.
 License: GPL (>= 2)
 Depends: R (>= 3.1.0), parallel, partykit
 Imports: stats, grid, graphics, methods, nlme, rpart, numDeriv, ucminf,
         zoo, sandwich, strucchange
+URL: http://vcrpart.wordpress.com
 LazyLoad: yes
 NeedsCompilation: yes
-Packaged: 2014-09-09 22:27:25 UTC; rbuergin
+Packaged: 2014-11-07 17:28:59 UTC; reto
 Author: Reto Buergin [aut, cre, cph],
   Gilbert Ritschard [ctb, ths]
 Repository: CRAN
-Date/Publication: 2014-09-10 00:51:30
+Date/Publication: 2014-11-08 02:13:19

MD5

@@ -1,20 +1,21 @@
-1cbd34b3510d90cfb1a6848b44abb82b *DESCRIPTION
-9fa1ee1a037664dca6310ec341ced692 *NAMESPACE
-c9cf28f3456a5cf3c5d84783d4c6f7bb *NEWS
+6d24e99c18c914344785bf01d1b715a6 *DESCRIPTION
+dfae833697182c8bb7f3b3572b2413e6 *NAMESPACE
+4ad832a72530f1d077cd0296dbcdecb7 *NEWS
 8b40e51cf24df0eb99503a7070b84a93 *R/AAA.R
-76803808590893cc4202d5c78b2b8ada *R/AllGeneric.R
-ed5c8359901f28c78724ea61ac731f3b *R/fvcm.R
+b70bc37834d2f3b8c94d9d1c0020c614 *R/AllGeneric.R
+91d50efd23854dea04fca307a8c9ce97 *R/fvcm.R
 80b84163397dcda539b20d9ab51c56e7 *R/import.R
-38166df78924be2eecfa73a0df50500d *R/olmm-methods.R
+0801b64fedea58af67c400f8b0e15bd2 *R/olmm-methods.R
 7e4d28175b9184bf3a0992401f277072 *R/olmm-utils.R
-47d508ee6eda573318986badf2bc07b1 *R/olmm.R
+ecdcf046dcbb466c4cf29bc7e9efaf8a *R/olmm.R
 b6c73716e8ea10810b0ef419e666217c *R/otsplot.R
-93374105b3b9b55f260ebe19155d9d8b *R/tvcm-cv.R
-5ded623facdc6ee8b637444a5328c3d2 *R/tvcm-methods.R
-535373d36b06c8ce69666be8656a398f *R/tvcm-plot.R
-556205fc2616156b58239d61f6ae926f *R/tvcm-utils.R
-db0d8050384fd821d8f3f31acc56b14c *R/tvcm.R
-0ecd155512121e5553df3af06a0a1fc2 *R/utils.R
+2af1d39305eedf8fc4128b14307e1317 *R/tvcm-cv.R
+32a6d478af156ed579d5909795d5cc98 *R/tvcm-methods.R
+d7de761ae5514253233d0540ddd85ec5 *R/tvcm-plot.R
+3d45fc591fdde7ffc882b8add1448436 *R/tvcm-utils.R
+efa265167aaa866b67a1acd78df5a859 *R/tvcm.R
+759f474cb091fa4062bec1eaabfdd8de *R/utils.R
+8562fccdfc9dc113b72f8eb8df66bf57 *data/PL.RData
 a2c9a87cf50549fd5802cbb6a88d281a *data/movie.RData
 72890e1f368da6d42d9cf9414c5396e0 *data/poverty.RData
 ea62be4e5d10df5bc683725a7f7935f6 *data/schizo.RData
@@ -23,28 +24,33 @@ ea62be4e5d10df5bc683725a7f7935f6 *data/schizo.RData
 cb803decf95c39567f648e1e5e0ddb45 *data/vcrpart_2.RData
 3ddf5ac69dc37253946b83c5bfd5fca7 *data/vcrpart_3.RData
 c2c0ec5bc767a17c65b372996eb094bc *inst/CITATION
+3bc5b4abcf89156967fbf807a9125ee6 *man/PL.Rd
 02a3675b72ff9f699d45dab0183780b8 *man/fvcm-methods.Rd
-1bd0959c572019011ab4c537f9f86919 *man/fvcm.Rd
+2ad931e91f7922156d825b32e68df10a *man/fvcm.Rd
 edb080c605c0dacb43ae037d6199abd4 *man/movie.Rd
 f66f99d711ddf32948fbf9b39c63b888 *man/olmm-control.Rd
-b57f0746e38cad4b16bfe592ec7c60ac *man/olmm-gefp.Rd
-6ee7950a48737e3b9ef60d8ba4990989 *man/olmm-methods.Rd
+e7cc5a4ab52c84577dd32c4c04219ef1 *man/olmm-gefp.Rd
+c0635745a839f49a8ffaa66ffcacdb65 *man/olmm-methods.Rd
 e58a118afbf3ff05498f9e5bbc73e893 *man/olmm-predict.Rd
 c914ee5c769aacd190558f0b967f1072 *man/olmm-summary.Rd
-fedfc37145b8e3764bef8589ad05496c *man/olmm.Rd
+99fd9612846185c8b253368ad62640bc *man/olmm.Rd
 913e87ac8d1158061b59644190ef8984 *man/otsplot.Rd
-58735e7359b9794d34d5c78eb0c07aa4 *man/poverty.Rd
+b5d023f2e25d796fc013667de0bf0c2b *man/poverty.Rd
 198b4f1a33689dc49700c02f14e42510 *man/schizo.Rd
-061e82789901fc9517c35f3d44daa35c *man/tvcm-control.Rd
-2a47a463b75faddfdbaacdd763760b9e *man/tvcm-cv.Rd
-33dc39a7f1d534bbd8b26a5a89b05f1e *man/tvcm-methods.Rd
-20100f8ce3bbcb0ca761531b0705dee8 *man/tvcm-plot.Rd
-1676e4649643b03e5b50cf0b79988d6d *man/tvcm.Rd
+830a40f120259f6c1e30cce2f06a224e *man/tvcglm.Rd
+fc4d871d97b9315c31c720307d4b481c *man/tvcm-control.Rd
+5b0157aea2925e83c7325814864fad14 *man/tvcm-cv.Rd
+c2434053aa241bce6f70d969bf385e25 *man/tvcm-methods.Rd
+0fa2459d031af80f1b4dbd7d3d1da797 *man/tvcm-plot.Rd
+395961700e672d7ba7f7901564e83892 *man/tvcm.Rd
+8c644871c09cae7e14ca6ad204a0dee3 *man/tvcolmm.Rd
 158d7c7045232f33bd35bf5c39dcedcf *man/vcrpart-demo.Rd
 a14ef1287c994bf09c716cc21d50517f *man/vcrpart-formula.Rd
 1e0af65b11fb043bd18f7982291d076f *src/Makevars
-490c20f360ddf1fa209c81bbf51ba1de *src/init.c
-04dd4cbf292548702d5590e2475aec35 *src/olmm.c
+e0ee3aca34161fb42f8fffa717fc6c3e *src/init.c
+62783432fffd5da456a3bb3e61c2be35 *src/olmm.c
 1e5f560c59e4ea73b51fa72c057166ec *src/olmm.h
-e5dcd67c462566773294a1e5829ef188 *src/utils.c
-5d30135987641736d67d08d3c140c86e *src/utils.h
+0af72621bd0a3d74afc3a3a0220f92c4 *src/tvcm.c
+e8b96835ed09462a3fef4b1f581279cd *src/tvcm.h
+79abdf7fd5ae53f7c77a6db8453d3408 *src/utils.c
+643b41eb70ee7ca5689f176f7d82b795 *src/utils.h

NAMESPACE

@@ -49,16 +49,23 @@ export(
   predecor_control,
   estfun.olmm,
   gefp.olmm,
-  tvcm_control, 
+  tvcm_control,
+  tvcolmm_control,
+  tvcglm_control,
   tvcm,
   tvcolmm,
   tvcglm, 
   fvcm,
   fvcolmm,
   fvcglm,
   fvcm_control,
+  fvcolmm_control,
+  fvcglm_control,
   folds_control)
 
+## Exported methods for 'glm' class
+S3method(fixef, glm)
+
 ## Exported methods for 'fvcm' class
 S3method(fitted, fvcm)
 S3method(predict, fvcm)
@@ -109,6 +116,7 @@ S3method(print, otsplot)
 ## Exported methods for 'tvcm' class
 S3method(coef, tvcm)
 S3method(coefficients, tvcm)
+S3method(depth, tvcm)
 S3method(cvloss, tvcm)
 S3method(plot, cvloss.tvcm)
 S3method(print, cvloss.tvcm)
@@ -135,3 +143,4 @@ S3method(splitpath, tvcm)
 S3method(print, splitpath.tvcm)
 S3method(summary, tvcm)
 S3method(weights, tvcm)
+S3method(width, tvcm)

NEWS

@@ -1,9 +1,44 @@
+Changes in Version 0.2-2
+
+    o    Added seed option to 'tvcm_control'.
+
+    o    The new implementation clearly distinguishes between the two 
+         functions 'tvcolmm' and 'tvcolmm' with separate help files. 
+	 The general function 'tvcm' is still available.
+
+    o    Added convenience function 'tvcolmm_control' and 
+         'tvglm_control'.
+
+    o    Improvement for 'panel_coef': Points and lines surpassing
+         the boxes are now suppressed.
+
+    o    Added variable centering as default for split selection.
+
+    o    Redefinition of tuning parameters for 'tvcm'. The main 
+         tuning parameter is now 'cp'. See the help of 'tvcm' and 
+         'tvcm_control' for details. 
+
+    o    Added 'nimpute' argument for 'tvcm_control'.
+
+    o    Added detail section to the help page of 'tvcm_control'
+
+    o    Removed AIC table from 'print.tvcm' (AIC and BIC seem not
+         relevant measures for models fitted by 'tvcm').
+
+    o    Added 'PL' data set.
+
+    o    Removed bug for numeric estimation of covariance of 'olmm'
+         objects.
+
+    o    Added 'depth' and 'width' methods.
+
+
 Changes in Version 0.2-1
 
-    o   First CRAN release.
+    o    First CRAN release.
 
-    o   'tvcm' and 'fvcm' allow for multiple 'vc' terms, i.e. 
-        coefficient-specific partitions
+    o    'tvcm' and 'fvcm' allow for multiple 'vc' terms, i.e. 
+         coefficient-specific partitions
 
     o    Complete revision of syntaxes, argument names and  default 
          parameters. R commands for the former version 0.1-14 are 

R/AllGeneric.R

@@ -35,6 +35,8 @@ cvloss <- function(object, ...) UseMethod("cvloss")
 
 extract <- function(object, ...) UseMethod("extract")
 
+fixef.glm <- function(object, ...) coef(object)
+
 neglogLik2 <- function(object, ...) UseMethod("neglogLik2")
 
 neglogLik2.default <- function(object, ...)

R/fvcm.R

@@ -1,7 +1,7 @@
 ##' -------------------------------------------------------- #
 ##' Author:          Reto Buergin
 ##' E-Mail:          reto.buergin@unige.ch, rbuergin@gmx.ch
-##' Date:            2014-09-07
+##' Date:            2014-10-14
 ##'
 ##' Description:
 ##' Random forests and bagging for the 'tvcm' algorithm.
@@ -22,6 +22,9 @@
 ##' - set 'ptry', 'vtry' and 'ntry' automatically (see Hastie)
 ##'
 ##' Last modifications:
+##' 2014-10-14: found bug in predict.tvcm: now the 'coefi'
+##'             matrices are ordered by the column names of
+##'             'coef'.
 ##' 2014-09-07: - improvment of predict.tvcm function
 ##'               - treated bugs for 'type = "coef"'
 ##'               - deal with ordinal responses in cases not
@@ -33,7 +36,7 @@
 ##' 2014-08-05: - changed specification for folds
 ##' -------------------------------------------------------- #
 
-fvcolmm <- function(..., family = cumulative(), control = fvcm_control()) {
+fvcolmm <- function(..., family = cumulative(), control = fvcolmm_control()) {
   mc <- match.call()
   mc[[1L]] <- as.name("fvcm")
   mc$fit <- "olmm"
@@ -43,7 +46,17 @@ fvcolmm <- function(..., family = cumulative(), control = fvcm_control()) {
 }
 
 
-fvcglm <- function(..., family, control = fvcm_control()) {
+fvcolmm_control <- function(maxstep = 10, folds = folds_control("subsampling", 5),
+                            ptry = 1, ntry = 1, vtry = 5, alpha = 1.0, ...) {
+
+  mc <- match.call()
+  mc[[1L]] <- as.name("fvcm_control")
+  mc$sctest <- TRUE
+  return(eval.parent(mc))
+}
+
+
+fvcglm <- function(..., family, control = fvcglm_control()) {
   mc <- match.call()
   mc[[1L]] <- as.name("fvcm")
   mc$fit <- "glm"
@@ -53,6 +66,15 @@ fvcglm <- function(..., family, control = fvcm_control()) {
 }
 
 
+fvcglm_control <- function(maxstep = 10, folds = folds_control("subsampling", 5),
+                           ptry = 1, ntry = 1, vtry = 5, mindev = 0, ...) {
+
+  mc <- match.call()
+  mc[[1L]] <- as.name("fvcm_control")
+  return(eval.parent(mc))
+}
+
+
 fvcm <- function(..., control = fvcm_control()) {
 
   mc <- match.call()
@@ -110,7 +132,7 @@ fvcm <- function(..., control = fvcm_control()) {
 
 fvcm_control <- function(maxstep = 10, folds = folds_control("subsampling", 5),
                          ptry = 1, ntry = 1, vtry = 5,
-                         alpha = 1.0, maxoverstep = Inf, ...) {
+                         alpha = 1.0, mindev = 0.0, ...) {
 
   ## modify the 'papply' argument
   mc <- match.call()
@@ -123,7 +145,7 @@ fvcm_control <- function(maxstep = 10, folds = folds_control("subsampling", 5),
   ## combine the parameter to a list and disble cross validation and pruning 
   call <- list(maxstep = maxstep, folds = folds,
                ptry = ptry, ntry = ntry, vtry = vtry,
-               alpha = alpha, maxoverstep = Inf,
+               alpha = alpha, mindev = mindev,
                papply = papply, cv = FALSE, prune = FALSE)
   call <- appendDefArgs(call, list(...))
 
@@ -346,7 +368,7 @@ predict.fvcm <- function(object, newdata = NULL,
     coefi <- predict(object, newdata = newdata, type = "coef",
                      ranef = FALSE, na.action = na.pass, ...)
     if (!is.matrix(coefi)) coefi <- matrix(coefi, nrow = nrow(newdata))
-
+    
     ## acount for skipped categories
     if (object$info$fit == "olmm" && ncol(coefi) < ncol(coef)) {        
         subsiCols <- table(mf[folds[, i] > 0, yName]) > 0L
@@ -366,6 +388,9 @@ predict.fvcm <- function(object, newdata = NULL,
         colnames(coefi) <- colnamesi          
     }
 
+    ## order columns of coefi
+    coefi <- coefi[, intersect(colnames(coef), colnames(coefi)), drop = FALSE]
+
     ## index matrix for valid entries
     subsi <- subs    
     if (oob) subsi[folds[,i] > 0L, ] <- FALSE