re-build

Author: JohnMount

README.md

@@ -144,14 +144,14 @@ dTestC <- data.frame(x=c('a','b','c',NA),z=c(10,20,30,NA))
 treatmentsC <- designTreatmentsC(dTrainC,colnames(dTrainC),'y',TRUE,
                                  verbose=FALSE)
 print(treatmentsC$scoreFrame[,c('origName', 'varName', 'code', 'rsq', 'sig', 'extraModelDegrees')])
- #    origName   varName  code          rsq        sig extraModelDegrees
- #  1        x  x_lev_NA   lev 2.960654e-01 0.09248399                 0
- #  2        x x_lev_x.a   lev 1.300057e-01 0.26490379                 0
- #  3        x x_lev_x.b   lev 6.067337e-03 0.80967242                 0
- #  4        x    x_catP  catP 1.030137e-01 0.32099590                 2
- #  5        x    x_catB  catB 1.125399e-05 0.99172381                 2
- #  6        z   z_clean clean 2.376018e-01 0.13176020                 0
- #  7        z   z_isBAD isBAD 2.960654e-01 0.09248399                 0
+ #    origName   varName  code         rsq        sig extraModelDegrees
+ #  1        x  x_lev_NA   lev 0.296065432 0.09248399                 0
+ #  2        x x_lev_x.a   lev 0.130005705 0.26490379                 0
+ #  3        x x_lev_x.b   lev 0.006067337 0.80967242                 0
+ #  4        x    x_catP  catP 0.060049677 0.44862725                 2
+ #  5        x    x_catB  catB 0.127625394 0.26932340                 2
+ #  6        z   z_clean clean 0.237601767 0.13176020                 0
+ #  7        z   z_isBAD isBAD 0.296065432 0.09248399                 0
 
 # help("prepare")
 
@@ -191,15 +191,15 @@ dTestN <- data.frame(x=c('a','b','c',NA),z=c(10,20,30,NA))
 treatmentsN = designTreatmentsN(dTrainN,colnames(dTrainN),'y',
                                 verbose=FALSE)
 print(treatmentsN$scoreFrame[,c('origName', 'varName', 'code', 'rsq', 'sig', 'extraModelDegrees')])
- #    origName   varName  code          rsq       sig extraModelDegrees
- #  1        x  x_lev_NA   lev 3.333333e-01 0.1339746                 0
- #  2        x x_lev_x.a   lev 2.500000e-01 0.2070312                 0
- #  3        x x_lev_x.b   lev 1.110223e-16 1.0000000                 0
- #  4        x    x_catP  catP 3.558824e-01 0.1184999                 2
- #  5        x    x_catN  catN 3.132648e-02 0.6750039                 2
- #  6        x    x_catD  catD 4.512437e-02 0.6135229                 2
- #  7        z   z_clean clean 2.880952e-01 0.1701892                 0
- #  8        z   z_isBAD isBAD 3.333333e-01 0.1339746                 0
+ #    origName   varName  code          rsq        sig extraModelDegrees
+ #  1        x  x_lev_NA   lev 3.333333e-01 0.13397460                 0
+ #  2        x x_lev_x.a   lev 2.500000e-01 0.20703125                 0
+ #  3        x x_lev_x.b   lev 1.110223e-16 0.99999998                 0
+ #  4        x    x_catP  catP 4.047085e-01 0.08994062                 2
+ #  5        x    x_catN  catN 2.822908e-01 0.17539581                 2
+ #  6        x    x_catD  catD 2.096931e-02 0.73225708                 2
+ #  7        z   z_clean clean 2.880952e-01 0.17018920                 0
+ #  8        z   z_isBAD isBAD 3.333333e-01 0.13397460                 0
 dTrainNTreated <- prepare(treatmentsN,dTrainN,pruneSig=1.0,scale=TRUE)
 varsN <- setdiff(colnames(dTrainNTreated),'y')
 # all input variables should be mean 0

cran-comments.md

@@ -1,16 +1,16 @@
 
 ## Test environments
 
-* using R version 3.4.0 (2017-04-21)
+* using R version 3.4.1 (2017-06-30)
 * using platform: x86_64-apple-darwin15.6.0 (64-bit)
-* win-builder (devel and release)
+* win-builder (using R Under development (unstable) (2017-09-12 r73242))
 
-## R CMD check --as-cran vtreat_0.5.32.tar.gz
 
-* using R version 3.4.0 (2017-04-21)
-* using platform: x86_64-apple-darwin15.6.0 (64-bit)
+## R CMD check --as-cran vtreat_0.6.0.tar.gz
+
 * using session charset: UTF-8
 * using option ‘--as-cran’
+* this is package ‘vtreat’ version ‘0.6.0’
 
 R CMD check results