试图让 gbm2sas 包工作
Trying to get gbm2sas package to work
我正在试验 R 包 gbm2sas 和 gbm。
我正在尝试创建一个 gbm 模型对象（使用 gbm() 函数）并生成将实现该模型的 SAS 代码（使用 gbm2sas() 函数）。我无法让它工作。我收到以下错误。
这是我的 R 代码：
library(gbm)
library(gbm2sas)
data(iris)
iris$setosaFlag = (iris$Species == "setosa")*1
iris.gbm = gbm(setosaFlag ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width,
data=iris,
dist="bernoulli",
n.tree = 3,
interaction.depth=3,
shrinkage = 0.01,
keep.data=TRUE,
verbose=TRUE,
n.cores=1)
print(iris.gbm)
pretty.gbm.tree(iris.gbm, i.tree=1)
pretty.gbm.tree(iris.gbm, i.tree=2)
pretty.gbm.tree(iris.gbm, i.tree=3)
gbm2sas(
iris.gbm, # gbm object from above
sasfile="studyGBM.R", # name to use for SAS code file
ntrees=3, # number of trees
mysasdata="sasdataset",
treeval="treevalue",
prefix="dobranch_"
)
我得到以下输出和错误：
> library(gbm)
> library(gbm2sas)
> data(iris)
> iris$setosaFlag = (iris$Species == "setosa")*1
> iris.gbm = gbm(setosaFlag ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width,
+ data=iris,
+ dist="bernoulli",
+ n.tree = 3,
+ interaction.depth=3,
+ shrinkage = 0.01,
+ keep.data=TRUE,
+ verbose=TRUE,
+ n.cores=1)
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2531 nan 0.0100 0.0096
2 1.2337 nan 0.0100 0.0093
3 1.2148 nan 0.0100 0.0082
> print(iris.gbm)
gbm(formula = setosaFlag ~ Sepal.Length + Sepal.Width + Petal.Length +
Petal.Width, distribution = "bernoulli", data = iris, n.trees = 3,
interaction.depth = 3, shrinkage = 0.01, keep.data = TRUE,
verbose = TRUE, n.cores = 1)
A gradient boosted model with bernoulli loss function.
3 iterations were performed.
There were 4 predictors of which 3 had non-zero influence.
> pretty.gbm.tree(iris.gbm, i.tree=1)
SplitVar SplitCodePred LeftNode RightNode MissingNode ErrorReduction Weight Prediction
0 2 2.4500 1 5 9 1.72800e+01 75 0.0012
1 0 5.0500 2 3 4 3.28692e-31 27 0.0300
2 -1 0.0300 -1 -1 -1 0.00000e+00 15 0.0300
3 -1 0.0300 -1 -1 -1 0.00000e+00 12 0.0300
4 -1 0.0300 -1 -1 -1 0.00000e+00 27 0.0300
5 0 6.8500 6 7 8 5.48890e-30 48 -0.0150
6 -1 -0.0150 -1 -1 -1 0.00000e+00 38 -0.0150
7 -1 -0.0150 -1 -1 -1 0.00000e+00 10 -0.0150
8 -1 -0.0150 -1 -1 -1 0.00000e+00 48 -0.0150
9 -1 0.0012 -1 -1 -1 0.00000e+00 75 0.0012
> pretty.gbm.tree(iris.gbm, i.tree=2)
SplitVar SplitCodePred LeftNode RightNode MissingNode ErrorReduction Weight Prediction
0 2 2.35000000 1 5 9 1.693529e+01 75 0.00103485
1 3 0.25000000 2 3 4 3.104314e-31 27 0.02940891
2 -1 0.02940891 -1 -1 -1 0.000000e+00 17 0.02940891
3 -1 0.02940891 -1 -1 -1 0.000000e+00 10 0.02940891
4 -1 0.02940891 -1 -1 -1 0.000000e+00 27 0.02940891
5 3 2.05000000 6 7 8 1.672221e-30 48 -0.01492556
6 -1 -0.01492556 -1 -1 -1 0.000000e+00 37 -0.01492556
7 -1 -0.01492556 -1 -1 -1 0.000000e+00 11 -0.01492556
8 -1 -0.01492556 -1 -1 -1 0.000000e+00 48 -0.01492556
9 -1 0.00103485 -1 -1 -1 0.000000e+00 75 0.00103485
> pretty.gbm.tree(iris.gbm, i.tree=3)
SplitVar SplitCodePred LeftNode RightNode MissingNode ErrorReduction Weight Prediction
0 2 2.700000000 1 5 9 1.762206e+01 75 0.003792325
1 0 5.050000000 2 3 4 1.479114e-30 32 0.028846427
2 -1 0.028846427 -1 -1 -1 0.000000e+00 20 0.028846427
3 -1 0.028846427 -1 -1 -1 0.000000e+00 12 0.028846427
4 -1 0.028846427 -1 -1 -1 0.000000e+00 32 0.028846427
5 0 6.750000000 6 7 8 8.513506e-31 43 -0.014852589
6 -1 -0.014852589 -1 -1 -1 0.000000e+00 33 -0.014852589
7 -1 -0.014852589 -1 -1 -1 0.000000e+00 10 -0.014852589
8 -1 -0.014852589 -1 -1 -1 0.000000e+00 43 -0.014852589
9 -1 0.003792325 -1 -1 -1 0.000000e+00 75 0.003792325
>
> gbm2sas(
+ iris.gbm, # gbm object from above
+ sasfile="studyGBM.R", # name to use for SAS code file
+ ntrees=3, # number of trees
+ mysasdata="sasdataset",
+ treeval="treevalue",
+ prefix="dobranch_"
+ )
Error in data[, gbmobject$var.names] :
object of type 'closure' is not subsettable
>
>
谁能指出我做错了什么？
谢谢。
错误不在你这边（尽管我会改变一些东西）。我深入研究了 gbm2sas 函数的源代码，发现它调用 var.names
的方式有问题
首先，运行 gbm2sas 函数的这个固定版本：
gbm2sas<-function(
gbmobject,
sasfile=NULL,
ntrees=NULL,
mysasdata="mysasdata",
treeval="treeval",
prefix="do_"
) {
if(is.null(ntrees)) ntrees<-gbmobject$n.trees
maxhmmt<-0
hasprefix<-prefix!="do_"
hasmysasdata<-mysasdata!="mysasdata"
hastreeval<-treeval!="treeval"
prepwords<-"data mysasdata; set mysasdata;"
if(hasmysasdata) prepwords<-gsub("mysasdata", mysasdata, prepwords)
write.table(prepwords, sasfile, row.names=FALSE, col.names=FALSE, quote=FALSE)
numtrees<-ntrees
for(treeloop in 1:numtrees) {
pgt<-pretty.gbm.tree(gbmobject,i.tree = treeloop)[1:7]
hmmt<-dim(pgt)[1]
maxhmmt<-max(maxhmmt, hmmt)
wordsa<-"do_x=0;"
for(loop in 0:(hmmt-1)) {
if(loop>0) {
wordsb<-gsub("x", loop, wordsa)
} else {
wordsb<-"do_0=1;"
}
if(hasprefix) wordsb<-gsub("do_", prefix, wordsb)
write.table(wordsb, sasfile, row.names=FALSE, col.names=FALSE, quote=FALSE, append=TRUE)
}
words0<-"if missing(V A R1) then do_V A R5=1; else do;"
words1<-"if V A R1 lt V A R2 then do_V A R3=1; else do_V A R4=1; end;"
words2<-"if V A R1 in (V A R2) then do_V A R3=1; else do_V A R4=1; end;"
words2b<-"do_V A R4=1; end;"
words3<-"end;"
if(hasprefix) {
words0<-gsub("do_", prefix, words0)
words1<-gsub("do_", prefix, words1)
words2<-gsub("do_", prefix, words2)
words2b<-gsub("do_", prefix, words2b)
words3<-gsub("do_", prefix, words3)
}
thevarnames<-gbmobject$var.names
thevarnames2 <- as.list(gbmobject$var.names)
types<-lapply(lapply(thevarnames2,class), function(i) ifelse (strsplit(i[1]," ")[1]=="ordered","ordered",i))
levels<-lapply(thevarnames2,levels)
for(loop in 1:hmmt) {
prepwords<-paste("if do_", (loop-1), ">0 then do;", sep="")
if(hasprefix) prepwords<-gsub("do_", prefix, prepwords)
write.table(prepwords, sasfile, row.names=FALSE, col.names=FALSE, quote=FALSE, append=TRUE)
splitvar<-1+as.numeric(as.vector(pgt[loop,]$SplitVar))
splitcodepred<-as.numeric(as.vector(pgt[loop,]$SplitCodePred))
leftnode<-as.numeric(as.vector(pgt[loop,]$LeftNode))
rightnode<-as.numeric(as.vector(pgt[loop,]$RightNode))
missingnode<-as.numeric(as.vector(pgt[loop,]$MissingNode))
if(splitvar>0) {
words0a<-gsub("V A R1", thevarnames[splitvar], words0)
words1a<-gsub("V A R1", thevarnames[splitvar], words1)
words2a<-gsub("V A R1", thevarnames[splitvar], words2)
words0a<-gsub("V A R5", missingnode, words0a)
words1a<-gsub("V A R3", leftnode, words1a)
words2a<-gsub("V A R3", leftnode, words2a)
words1a<-gsub("V A R4", rightnode, words1a)
words2a<-gsub("V A R4", rightnode, words2a)
words2ab<-gsub("V A R4", rightnode, words2b)
thistype<-types[[splitvar]]
leftstring<-" "
rightstring<-" "
write.table(words0a, sasfile, row.names=FALSE, col.names=FALSE, quote=FALSE, append=TRUE)
if(thistype=="numeric") {
words1a<-gsub("V A R2", splitcodepred, words1a)
write.table(words1a, sasfile, row.names=FALSE, col.names=FALSE, quote=FALSE, append=TRUE)
} else {
if(thistype=="ordered") {
splitcodepred<-ceiling(splitcodepred)
if(splitcodepred>=1) {
theleft<-c(levels[[splitvar]][1:splitcodepred], NA)
} else {
theleft<-rep(NA, 2)
}
} else {
describer<-unlist(gbmobject$c.splits[1+splitcodepred])
theleft<-c(levels[[splitvar]][describer==-1], NA)
}
logic<-!is.na(theleft)
if(sum(as.numeric(logic))>0) {
theleft<-theleft[logic]
hmmt2<-length(theleft)
leftstring<-NULL
for(loop2 in 1:hmmt2) {
leftstring<-paste(leftstring, "'", theleft[loop2], "'", sep="")
if(loop2<hmmt2) leftstring<-paste(leftstring, ", ", sep="")
}
} else {
leftstring<-"blah"
}
if(leftstring!="blah") {
words2a<-gsub("V A R2", leftstring, words2a)
write.table(words2a, sasfile, row.names=FALSE, col.names=FALSE, quote=FALSE, append=TRUE)
} else {
write.table(words2ab, sasfile, row.names=FALSE, col.names=FALSE, quote=FALSE, append=TRUE)
}
}
} else {
prepwords<-paste("treeval", treeloop, "=", splitcodepred, ";", sep="")
if(hastreeval) prepwords<-gsub("treeval", treeval, prepwords)
write.table(prepwords, sasfile, row.names=FALSE, col.names=FALSE, quote=FALSE, append=TRUE)
}
write.table(words3, sasfile, row.names=FALSE, col.names=FALSE, quote=FALSE, append=TRUE)
}
}
wordsa<-"drop do_x;"
for(loop in 0:(maxhmmt-1)) {
if(loop>0) {
wordsb<-gsub("x", loop, wordsa)
} else {
wordsb<-"drop do_0;"
}
if(hasprefix) wordsb<-gsub("do_", prefix, wordsb)
write.table(wordsb, sasfile, row.names=FALSE, col.names=FALSE, quote=FALSE, append=TRUE)
}
write.table("run;", sasfile, row.names=FALSE, col.names=FALSE, quote=FALSE, append=TRUE)
}
这也是您的代码的更新版本：
library(gbm)
library(gbm2sas)
library(dplyr)
data(iris)
iris$setosaFlag = (iris$Species == "setosa")*1
# remove '.' from variable names. SAS doesn't like anything but underscores.
iris$septal_length <- iris$Sepal.Length
iris$septal_width <- iris$Sepal.Width
iris$petal_length <- iris$Petal.Length
iris$petal_width <- iris$Petal.Width
iris <- select(iris, setosaFlag, septal_length, septal_width, petal_length, petal_width) # I don't believe that dataset can include variables that aren't included in the gbm(), it's entirely possible I'm wrong but doesn't hurt to remove them just in case
iris.gbm = gbm(setosaFlag ~ septal_length + septal_width + petal_length + petal_width,
data=iris,
dist="bernoulli",
n.tree = 3,
interaction.depth=3,
shrinkage = 0.01,
keep.data=TRUE,
verbose=TRUE,
n.cores=1)
print(iris.gbm)
pretty.gbm.tree(iris.gbm, i.tree=1)
pretty.gbm.tree(iris.gbm, i.tree=2)
pretty.gbm.tree(iris.gbm, i.tree=3)
# change your sasfile name to one that ends in .sas!!
gbm2sas(
iris.gbm, # gbm object from above
sasfile="studyGBM.R", # name to use for SAS code file
ntrees=3, # number of trees
mysasdata="sasdataset",
treeval="treevalue",
prefix="dobranch_"
)
测试一下，如果您仍然遇到任何问题，请告诉我。
我正在试验 R 包 gbm2sas 和 gbm。
我正在尝试创建一个 gbm 模型对象（使用 gbm() 函数）并生成将实现该模型的 SAS 代码（使用 gbm2sas() 函数）。我无法让它工作。我收到以下错误。
这是我的 R 代码：
library(gbm)
library(gbm2sas)
data(iris)
iris$setosaFlag = (iris$Species == "setosa")*1
iris.gbm = gbm(setosaFlag ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width,
data=iris,
dist="bernoulli",
n.tree = 3,
interaction.depth=3,
shrinkage = 0.01,
keep.data=TRUE,
verbose=TRUE,
n.cores=1)
print(iris.gbm)
pretty.gbm.tree(iris.gbm, i.tree=1)
pretty.gbm.tree(iris.gbm, i.tree=2)
pretty.gbm.tree(iris.gbm, i.tree=3)
gbm2sas(
iris.gbm, # gbm object from above
sasfile="studyGBM.R", # name to use for SAS code file
ntrees=3, # number of trees
mysasdata="sasdataset",
treeval="treevalue",
prefix="dobranch_"
)
我得到以下输出和错误：
> library(gbm)
> library(gbm2sas)
> data(iris)
> iris$setosaFlag = (iris$Species == "setosa")*1
> iris.gbm = gbm(setosaFlag ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width,
+ data=iris,
+ dist="bernoulli",
+ n.tree = 3,
+ interaction.depth=3,
+ shrinkage = 0.01,
+ keep.data=TRUE,
+ verbose=TRUE,
+ n.cores=1)
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2531 nan 0.0100 0.0096
2 1.2337 nan 0.0100 0.0093
3 1.2148 nan 0.0100 0.0082
> print(iris.gbm)
gbm(formula = setosaFlag ~ Sepal.Length + Sepal.Width + Petal.Length +
Petal.Width, distribution = "bernoulli", data = iris, n.trees = 3,
interaction.depth = 3, shrinkage = 0.01, keep.data = TRUE,
verbose = TRUE, n.cores = 1)
A gradient boosted model with bernoulli loss function.
3 iterations were performed.
There were 4 predictors of which 3 had non-zero influence.
> pretty.gbm.tree(iris.gbm, i.tree=1)
SplitVar SplitCodePred LeftNode RightNode MissingNode ErrorReduction Weight Prediction
0 2 2.4500 1 5 9 1.72800e+01 75 0.0012
1 0 5.0500 2 3 4 3.28692e-31 27 0.0300
2 -1 0.0300 -1 -1 -1 0.00000e+00 15 0.0300
3 -1 0.0300 -1 -1 -1 0.00000e+00 12 0.0300
4 -1 0.0300 -1 -1 -1 0.00000e+00 27 0.0300
5 0 6.8500 6 7 8 5.48890e-30 48 -0.0150
6 -1 -0.0150 -1 -1 -1 0.00000e+00 38 -0.0150
7 -1 -0.0150 -1 -1 -1 0.00000e+00 10 -0.0150
8 -1 -0.0150 -1 -1 -1 0.00000e+00 48 -0.0150
9 -1 0.0012 -1 -1 -1 0.00000e+00 75 0.0012
> pretty.gbm.tree(iris.gbm, i.tree=2)
SplitVar SplitCodePred LeftNode RightNode MissingNode ErrorReduction Weight Prediction
0 2 2.35000000 1 5 9 1.693529e+01 75 0.00103485
1 3 0.25000000 2 3 4 3.104314e-31 27 0.02940891
2 -1 0.02940891 -1 -1 -1 0.000000e+00 17 0.02940891
3 -1 0.02940891 -1 -1 -1 0.000000e+00 10 0.02940891
4 -1 0.02940891 -1 -1 -1 0.000000e+00 27 0.02940891
5 3 2.05000000 6 7 8 1.672221e-30 48 -0.01492556
6 -1 -0.01492556 -1 -1 -1 0.000000e+00 37 -0.01492556
7 -1 -0.01492556 -1 -1 -1 0.000000e+00 11 -0.01492556
8 -1 -0.01492556 -1 -1 -1 0.000000e+00 48 -0.01492556
9 -1 0.00103485 -1 -1 -1 0.000000e+00 75 0.00103485
> pretty.gbm.tree(iris.gbm, i.tree=3)
SplitVar SplitCodePred LeftNode RightNode MissingNode ErrorReduction Weight Prediction
0 2 2.700000000 1 5 9 1.762206e+01 75 0.003792325
1 0 5.050000000 2 3 4 1.479114e-30 32 0.028846427
2 -1 0.028846427 -1 -1 -1 0.000000e+00 20 0.028846427
3 -1 0.028846427 -1 -1 -1 0.000000e+00 12 0.028846427
4 -1 0.028846427 -1 -1 -1 0.000000e+00 32 0.028846427
5 0 6.750000000 6 7 8 8.513506e-31 43 -0.014852589
6 -1 -0.014852589 -1 -1 -1 0.000000e+00 33 -0.014852589
7 -1 -0.014852589 -1 -1 -1 0.000000e+00 10 -0.014852589
8 -1 -0.014852589 -1 -1 -1 0.000000e+00 43 -0.014852589
9 -1 0.003792325 -1 -1 -1 0.000000e+00 75 0.003792325
>
> gbm2sas(
+ iris.gbm, # gbm object from above
+ sasfile="studyGBM.R", # name to use for SAS code file
+ ntrees=3, # number of trees
+ mysasdata="sasdataset",
+ treeval="treevalue",
+ prefix="dobranch_"
+ )
Error in data[, gbmobject$var.names] :
object of type 'closure' is not subsettable
>
>
谁能指出我做错了什么？
谢谢。
错误不在你这边（尽管我会改变一些东西）。我深入研究了 gbm2sas 函数的源代码，发现它调用 var.names
的方式有问题首先，运行 gbm2sas 函数的这个固定版本：
gbm2sas<-function(
gbmobject,
sasfile=NULL,
ntrees=NULL,
mysasdata="mysasdata",
treeval="treeval",
prefix="do_"
) {
if(is.null(ntrees)) ntrees<-gbmobject$n.trees
maxhmmt<-0
hasprefix<-prefix!="do_"
hasmysasdata<-mysasdata!="mysasdata"
hastreeval<-treeval!="treeval"
prepwords<-"data mysasdata; set mysasdata;"
if(hasmysasdata) prepwords<-gsub("mysasdata", mysasdata, prepwords)
write.table(prepwords, sasfile, row.names=FALSE, col.names=FALSE, quote=FALSE)
numtrees<-ntrees
for(treeloop in 1:numtrees) {
pgt<-pretty.gbm.tree(gbmobject,i.tree = treeloop)[1:7]
hmmt<-dim(pgt)[1]
maxhmmt<-max(maxhmmt, hmmt)
wordsa<-"do_x=0;"
for(loop in 0:(hmmt-1)) {
if(loop>0) {
wordsb<-gsub("x", loop, wordsa)
} else {
wordsb<-"do_0=1;"
}
if(hasprefix) wordsb<-gsub("do_", prefix, wordsb)
write.table(wordsb, sasfile, row.names=FALSE, col.names=FALSE, quote=FALSE, append=TRUE)
}
words0<-"if missing(V A R1) then do_V A R5=1; else do;"
words1<-"if V A R1 lt V A R2 then do_V A R3=1; else do_V A R4=1; end;"
words2<-"if V A R1 in (V A R2) then do_V A R3=1; else do_V A R4=1; end;"
words2b<-"do_V A R4=1; end;"
words3<-"end;"
if(hasprefix) {
words0<-gsub("do_", prefix, words0)
words1<-gsub("do_", prefix, words1)
words2<-gsub("do_", prefix, words2)
words2b<-gsub("do_", prefix, words2b)
words3<-gsub("do_", prefix, words3)
}
thevarnames<-gbmobject$var.names
thevarnames2 <- as.list(gbmobject$var.names)
types<-lapply(lapply(thevarnames2,class), function(i) ifelse (strsplit(i[1]," ")[1]=="ordered","ordered",i))
levels<-lapply(thevarnames2,levels)
for(loop in 1:hmmt) {
prepwords<-paste("if do_", (loop-1), ">0 then do;", sep="")
if(hasprefix) prepwords<-gsub("do_", prefix, prepwords)
write.table(prepwords, sasfile, row.names=FALSE, col.names=FALSE, quote=FALSE, append=TRUE)
splitvar<-1+as.numeric(as.vector(pgt[loop,]$SplitVar))
splitcodepred<-as.numeric(as.vector(pgt[loop,]$SplitCodePred))
leftnode<-as.numeric(as.vector(pgt[loop,]$LeftNode))
rightnode<-as.numeric(as.vector(pgt[loop,]$RightNode))
missingnode<-as.numeric(as.vector(pgt[loop,]$MissingNode))
if(splitvar>0) {
words0a<-gsub("V A R1", thevarnames[splitvar], words0)
words1a<-gsub("V A R1", thevarnames[splitvar], words1)
words2a<-gsub("V A R1", thevarnames[splitvar], words2)
words0a<-gsub("V A R5", missingnode, words0a)
words1a<-gsub("V A R3", leftnode, words1a)
words2a<-gsub("V A R3", leftnode, words2a)
words1a<-gsub("V A R4", rightnode, words1a)
words2a<-gsub("V A R4", rightnode, words2a)
words2ab<-gsub("V A R4", rightnode, words2b)
thistype<-types[[splitvar]]
leftstring<-" "
rightstring<-" "
write.table(words0a, sasfile, row.names=FALSE, col.names=FALSE, quote=FALSE, append=TRUE)
if(thistype=="numeric") {
words1a<-gsub("V A R2", splitcodepred, words1a)
write.table(words1a, sasfile, row.names=FALSE, col.names=FALSE, quote=FALSE, append=TRUE)
} else {
if(thistype=="ordered") {
splitcodepred<-ceiling(splitcodepred)
if(splitcodepred>=1) {
theleft<-c(levels[[splitvar]][1:splitcodepred], NA)
} else {
theleft<-rep(NA, 2)
}
} else {
describer<-unlist(gbmobject$c.splits[1+splitcodepred])
theleft<-c(levels[[splitvar]][describer==-1], NA)
}
logic<-!is.na(theleft)
if(sum(as.numeric(logic))>0) {
theleft<-theleft[logic]
hmmt2<-length(theleft)
leftstring<-NULL
for(loop2 in 1:hmmt2) {
leftstring<-paste(leftstring, "'", theleft[loop2], "'", sep="")
if(loop2<hmmt2) leftstring<-paste(leftstring, ", ", sep="")
}
} else {
leftstring<-"blah"
}
if(leftstring!="blah") {
words2a<-gsub("V A R2", leftstring, words2a)
write.table(words2a, sasfile, row.names=FALSE, col.names=FALSE, quote=FALSE, append=TRUE)
} else {
write.table(words2ab, sasfile, row.names=FALSE, col.names=FALSE, quote=FALSE, append=TRUE)
}
}
} else {
prepwords<-paste("treeval", treeloop, "=", splitcodepred, ";", sep="")
if(hastreeval) prepwords<-gsub("treeval", treeval, prepwords)
write.table(prepwords, sasfile, row.names=FALSE, col.names=FALSE, quote=FALSE, append=TRUE)
}
write.table(words3, sasfile, row.names=FALSE, col.names=FALSE, quote=FALSE, append=TRUE)
}
}
wordsa<-"drop do_x;"
for(loop in 0:(maxhmmt-1)) {
if(loop>0) {
wordsb<-gsub("x", loop, wordsa)
} else {
wordsb<-"drop do_0;"
}
if(hasprefix) wordsb<-gsub("do_", prefix, wordsb)
write.table(wordsb, sasfile, row.names=FALSE, col.names=FALSE, quote=FALSE, append=TRUE)
}
write.table("run;", sasfile, row.names=FALSE, col.names=FALSE, quote=FALSE, append=TRUE)
}
这也是您的代码的更新版本：
library(gbm)
library(gbm2sas)
library(dplyr)
data(iris)
iris$setosaFlag = (iris$Species == "setosa")*1
# remove '.' from variable names. SAS doesn't like anything but underscores.
iris$septal_length <- iris$Sepal.Length
iris$septal_width <- iris$Sepal.Width
iris$petal_length <- iris$Petal.Length
iris$petal_width <- iris$Petal.Width
iris <- select(iris, setosaFlag, septal_length, septal_width, petal_length, petal_width) # I don't believe that dataset can include variables that aren't included in the gbm(), it's entirely possible I'm wrong but doesn't hurt to remove them just in case
iris.gbm = gbm(setosaFlag ~ septal_length + septal_width + petal_length + petal_width,
data=iris,
dist="bernoulli",
n.tree = 3,
interaction.depth=3,
shrinkage = 0.01,
keep.data=TRUE,
verbose=TRUE,
n.cores=1)
print(iris.gbm)
pretty.gbm.tree(iris.gbm, i.tree=1)
pretty.gbm.tree(iris.gbm, i.tree=2)
pretty.gbm.tree(iris.gbm, i.tree=3)
# change your sasfile name to one that ends in .sas!!
gbm2sas(
iris.gbm, # gbm object from above
sasfile="studyGBM.R", # name to use for SAS code file
ntrees=3, # number of trees
mysasdata="sasdataset",
treeval="treevalue",
prefix="dobranch_"
)
测试一下，如果您仍然遇到任何问题，请告诉我。