unable to parse xml file in R - r

here is the file that need to parse and eventually convert in dataframe-
<?xml version="1.0" encoding="UTF-8"?>
-<message hash="fb73481d3f3d2b9a70733d69268de71c84f151f8" type="xml" sessionid="https" connector_id="4510010" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<comment/>
-<drive utc_timestamp="2015-09-03T00:09:01.362058" report_name="HTTP Logging to RSP" device_type="ACS 800" sw_appl="ASARF012" sw_type="AS7R7322" serial_number="-">
-<signals timezone="UTC +00:00">
<signal unit="rpm" scale="1" timestamp="2015-09-03T00:07:28.006" name="02_02_01_speed_ref_2" value="0"/>
<signal unit="%" scale="1" timestamp="2015-09-03T00:07:28.511" name="02_01_05_torque" value="0"/>
<signal unit="C" scale="1" timestamp="2015-09-03T00:07:45.352" name="02_01_32_pp_3_temp" value="0"/>
<signal unit="C" scale="1" timestamp="2015-09-03T00:07:46.203" name="02_01_33_pp_4_temp" value="0"/>
<signal unit="C" scale="1" timestamp="2015-09-03T00:07:48.155" name="02_01_35_motor_1_temp" value="0"/>
<signal unit="C" scale="1" timestamp="2015-09-03T00:07:48.911" name="02_01_36_motor_2_temp" value="0"/>
<signal unit="C" scale="1" timestamp="2015-09-03T00:07:52.723" name="02_01_37_motor_temp_est" value="29.999969"/>
<signal unit="" scale="1" timestamp="2015-09-03T00:07:53.638" name="02_03_18_alarm_word_5" value="0"/>
<signal unit="" scale="1" timestamp="2015-09-03T00:07:54.747" name="02_03_19_int_init_fault" value="0"/>
<signal unit="" scale="1" timestamp="2015-09-03T00:07:56.884" name="02_03_11_follower_mcw" value="0"/>
<signal unit="" scale="1" timestamp="2015-09-03T00:07:58.405" name="02_03_13_aux_status_word_3" value="1030"/>
<signal unit="" scale="1" timestamp="2015-09-03T00:07:59.806" name="02_03_14_aux_status_word_4" value="0"/>
<signal unit="" scale="1" timestamp="2015-09-03T00:08:00.485" name="02_03_16_alarm_word_4" value="0"/>
</signals>
</drive>
</message>
I have tried multiple options (XML package is used) but I am not able to convert it into a dataframe/structured form.
## method 1
result <- xmlParse(file = "test.xml")
print(result)
# method 2
xmldataframe <- xmlToDataFrame("test.xml")
print(xmldataframe)
# method 3
xmldoc <- xmlParse(file = test.xml)
rootNode <- xmlRoot(xmldoc)
xmlSApply(rootNode,function(x) xmlSApply(x, xmlValue))
cd.catalog <- data.frame(t(data),row.names=NULL)
none of results/xmldataframe/cd.catalog has dataframe/list in structured form that i can use for further analysis.


Hope you are looking for something like this:
library(xml2)
library(dplyr)
xml_doc <- read_xml("test.xml")
df <- xml_doc %>%
xml_find_all("//signal") %>%
xml_attrs() %>%
unlist() %>%
matrix(ncol=5, byrow=T) %>%
as.data.frame(stringsAsFactors=FALSE)
colnames(df) <- c('unit','scale','timestamp','name','value')
df

Related

How to build XML file with package PMML using mlr?

I want to convert a logistic model built by the mlr-package directly into a XML-file using the package pmml. The problem is that the model.learner built by the mlr wrapper doesn't include the model link in the list, like it is in the normal stats::glm function. So here is an example:
library(dplyr)
library(titanic)
library(pmml)
library(ParamHelpers)
library(mlr)
Titanic_data = select(titanic_train, Survived, Pclass, Sex, Age)
Titanic_data$Survived = as.factor(Titanic_data$Survived)
Titanic_data$Sex = as.factor(Titanic_data$Sex)
Titanic_data$Pclass = as.factor(Titanic_data$Pclass)
Titanic_data = na.omit(Titanic_data)
lrn <- makeLearner("classif.logreg", predict.type = "prob")
task = makeClassifTask(data = Titanic_data, target = "Survived", positive = "1")
model = train(lrn, task)
model_glm = glm(Survived ~ ., data = Titanic_data, family = "binomial")
str(model$learner.model) # list of 29
str(model_glm) # list of 30
As you can see, the structure of both models is a list of different elements and they are all the same, beside the fact that the model is missing in the wrapper. Therefore I get an error message using pmml:
pmml(model_glm)
# Error in pmml.glm(model$learner.model) : object 'model.link' not found
The one built by stats::glm is working:
pmml(model)
<PMML version="4.4" xmlns="http://www.dmg.org/PMML-4_4" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.dmg.org/PMML-4_4 http://www.dmg.org/pmml/v4-4/pmml-4-4.xsd">
<Header copyright="Copyright (c) 2020 TBeige" description="Generalized Linear Regression Model">
<Extension name="user" value="TBeige" extender="SoftwareAG PMML Generator"/>
<Application name="SoftwareAG PMML Generator" version="2.3.1"/>
<Timestamp>2020-05-12 09:50:15</Timestamp>
</Header>
<DataDictionary numberOfFields="4">
<DataField name="Survived" optype="categorical" dataType="string">
<Value value="0"/>
<Value value="1"/>
</DataField>
<DataField name="Pclass" optype="categorical" dataType="string">
<Value value="1"/>
<Value value="2"/>
<Value value="3"/>
</DataField>
<DataField name="Sex" optype="categorical" dataType="string">
<Value value="female"/>
<Value value="male"/>
</DataField>
<DataField name="Age" optype="continuous" dataType="double"/>
</DataDictionary>
<GeneralRegressionModel modelName="General_Regression_Model" modelType="generalizedLinear" functionName="classification" algorithmName="glm" distribution="binomial" linkFunction="logit">
<MiningSchema>
<MiningField name="Survived" usageType="predicted" invalidValueTreatment="returnInvalid"/>
<MiningField name="Pclass" usageType="active" invalidValueTreatment="returnInvalid"/>
<MiningField name="Sex" usageType="active" invalidValueTreatment="returnInvalid"/>
<MiningField name="Age" usageType="active" invalidValueTreatment="returnInvalid"/>
</MiningSchema>
<Output>
<OutputField name="Probability_1" targetField="Survived" feature="probability" value="1" optype="continuous" dataType="double"/>
<OutputField name="Predicted_Survived" feature="predictedValue" optype="categorical" dataType="string"/>
</Output>
<ParameterList>
<Parameter name="p0" label="(Intercept)"/>
<Parameter name="p1" label="Pclass2"/>
<Parameter name="p2" label="Pclass3"/>
<Parameter name="p3" label="Sexmale"/>
<Parameter name="p4" label="Age"/>
</ParameterList>
<FactorList>
<Predictor name="Pclass"/>
<Predictor name="Sex"/>
</FactorList>
<CovariateList>
<Predictor name="Age"/>
</CovariateList>
<PPMatrix>
<PPCell value="2" predictorName="Pclass" parameterName="p1"/>
<PPCell value="3" predictorName="Pclass" parameterName="p2"/>
<PPCell value="male" predictorName="Sex" parameterName="p3"/>
<PPCell value="1" predictorName="Age" parameterName="p4"/>
</PPMatrix>
<ParamMatrix>
<PCell targetCategory="1" parameterName="p0" df="1" beta="3.77701265255885"/>
<PCell targetCategory="1" parameterName="p1" df="1" beta="-1.30979926778885"/>
<PCell targetCategory="1" parameterName="p2" df="1" beta="-2.58062531749203"/>
<PCell targetCategory="1" parameterName="p3" df="1" beta="-2.52278091988034"/>
<PCell targetCategory="1" parameterName="p4" df="1" beta="-0.0369852655754339"/>
</ParamMatrix>
</GeneralRegressionModel>
</PMML>
Any idea how I can use mlr and creating a xml find using pmml?

The problem seems to be inside pmml
From pmml::pmml.glm:
if (model$call[[1]] == "glm") {
model.type <- model$family$family
model.link <- model$family$link
}
else {
model.type <- "unknown"
}
In the mlr model we have
model$learner.model$call[[1]]
# stats::glm
So you can just hack
model$learner.model$call[[1]] = "glm"
and then
pmml(model$learner.model)
works.
To be honest it seems to be weird code in the pmml package.

Build a table from XML data file using R language

I am new learner in R Programming,i have sample xml file as shown below
<Attribute ID="GroupSEO" MultiValued="false" ProductMode="Property" FullTextIndexed="false" ExternallyMaintained="false" Derived="false" Mandatory="false">
<Name>Group SEO Name</Name>
<Validation BaseType="text" MinValue="" MaxValue="" MaxLength="1024" InputMask=""/>
<DimensionLink DimensionID="Language"/>
<MetaData>
<Value AttributeID="Attribute-Group-Order">1</Value>
<Value AttributeID="Enterprise-Label">NAV-GR-SEONAME</Value>
<Value ID="#NAMED" AttributeID="Attribute-Group-Name">#NAMED</Value>
<Value AttributeID="Enterprise-Description">Navigation Group SEO Name</Value>
<Value AttributeID="Attribute-Order">3</Value>
</MetaData>
<AttributeGroupLink AttributeGroupID="HTCategorizationsNavigation"/>
<AttributeGroupLink AttributeGroupID="HTDigitalServicesModifyClassifications"/>
<UserTypeLink UserTypeID="ENT-Group"/>
<UserTypeLink UserTypeID="NAVGRP"/>
<UserTypeLink UserTypeID="ENT-SubCategory"/>
<UserTypeLink UserTypeID="ENT-Category"/>
i want to convert this into data frame using R language.My expected output is
## FullTextIndexed MultiValued ProductMode ExternallyMaintained Derived Mandatory Attribute-Group-Order Enterprise-Description UserTypeID
1 false false Property false false false 1 Navigation group seo name ENT-Group,ENT-Category,..
i have searched the internet but couldn't find a solution to my problem.
I got a code from internet
library("XML")
library("methods")
setwd("E:/Project")
xmldata<-xmlToDataFrame("Sample.xml")
print(xmldata)
but when i execute the code i get the below error
Error in `[<-.data.frame`(`*tmp*`, i, names(nodes[[i]]), value = c(Name = "You YoutubeLink7 (URL)", :
duplicate subscripts for columns
In addition: Warning message:
In names(x) == varNames :
longer object length is not a multiple of shorter object length
> print(xmldata)
Error in print(xmldata) : object 'xmldata' not found
could anyone help me know about what the error means and also a solution to my problem,sorry for the formatting issue.
Thanks in advance for the solution.
Thanks

With a correct xml data (attribute tag at the end of the file).
<?xml version="1.0" encoding="UTF-8"?>
<Attribute ID="GroupSEO" MultiValued="false" ProductMode="Property" FullTextIndexed="false" ExternallyMaintained="false" Derived="false" Mandatory="false">
<Name>Group SEO Name</Name>
<Validation BaseType="text" MinValue="" MaxValue="" MaxLength="1024" InputMask=""/>
<DimensionLink DimensionID="Language"/>
<MetaData>
<Value AttributeID="Attribute-Group-Order">1</Value>
<Value AttributeID="Enterprise-Label">NAV-GR-SEONAME</Value>
<Value ID="#NAMED" AttributeID="Attribute-Group-Name">#NAMED</Value>
<Value AttributeID="Enterprise-Description">Navigation Group SEO Name</Value>
<Value AttributeID="Attribute-Order">3</Value>
</MetaData>
<AttributeGroupLink AttributeGroupID="HTCategorizationsNavigation"/>
<AttributeGroupLink AttributeGroupID="HTDigitalServicesModifyClassifications"/>
<UserTypeLink UserTypeID="ENT-Group"/>
<UserTypeLink UserTypeID="NAVGRP"/>
<UserTypeLink UserTypeID="ENT-SubCategory"/>
<UserTypeLink UserTypeID="ENT-Category"/>
</Attribute>
Then we use xpath to get all we need. Change the path to your xml file in the htmlParse step.
library(XML)
data=htmlParse("C:/Users/.../yourxmlfile.xml")
fulltextindexed=xpathSApply(data,"normalize-space(//attribute/#fulltextindexed)")
multivalued=xpathSApply(data,"normalize-space(//attribute/#multivalued)")
productmode=xpathSApply(data,"normalize-space(//attribute/#productmode)")
externallymaintained=xpathSApply(data,"normalize-space(//attribute/#externallymaintained)")
derived=xpathSApply(data,"normalize-space(//attribute/#derived)")
mandatory=xpathSApply(data,"normalize-space(//attribute/#mandatory)")
attribute.group.order=xpathSApply(data,"//value[#attributeid='Attribute-Group-Order']",xmlValue)
enterprise.description=xpathSApply(data,"//value[#attributeid='Enterprise-Description']",xmlValue)
user.type.id=paste(xpathSApply(data,"//usertypelink/#usertypeid"),collapse = "|")
df=data.frame(fulltextindexed,multivalued,productmode,externallymaintained,derived,mandatory,attribute.group.order,enterprise.description,user.type.id)
Result :

Using tidyverse and xml2
DATA
data <- read_xml('<Attribute ID="GroupSEO" MultiValued="false" ProductMode="Property" FullTextIndexed="false" ExternallyMaintained="false" Derived="false" Mandatory="false">
<Name>Group SEO Name</Name>
<Validation BaseType="text" MinValue="" MaxValue="" MaxLength="1024" InputMask=""/>
<DimensionLink DimensionID="Language"/>
<MetaData>
<Value AttributeID="Attribute-Group-Order">1</Value>
<Value AttributeID="Enterprise-Label">NAV-GR-SEONAME</Value>
<Value ID="#NAMED" AttributeID="Attribute-Group-Name">#NAMED</Value>
<Value AttributeID="Enterprise-Description">Navigation Group SEO Name</Value>
<Value AttributeID="Attribute-Order">3</Value>
</MetaData>
<AttributeGroupLink AttributeGroupID="HTCategorizationsNavigation"/>
<AttributeGroupLink AttributeGroupID="HTDigitalServicesModifyClassifications"/>
<UserTypeLink UserTypeID="ENT-Group"/>
<UserTypeLink UserTypeID="NAVGRP"/>
<UserTypeLink UserTypeID="ENT-SubCategory"/>
<UserTypeLink UserTypeID="ENT-Category"/>
</Attribute>')
CODE
#For attribute tag
Attributes <- xml_find_all(data, "//Attribute")
Attributes <- Attributes %>%
map(xml_attrs) %>%
map_df(~as.list(.))
#find AttributeID nodes
nodes <- xml_find_all(data, "//Value")
AGO <- nodes[xml_attr(nodes, "AttributeID")=="Attribute-Group-Order"]
Attributes["Attribute-Group-Order"] <- xml_text(AGO)
ED <- nodes[xml_attr(nodes, "AttributeID")=="Enterprise-Description"]
Attributes["Enterprise-Description"] <- xml_text(ED)
#UserTypelink tags
UserTypeLink <- xml_find_all(data, "//UserTypeLink")
UserTypeLink <- UserTypeLink %>%
map(xml_attrs) %>%
map_df(~as.list(.)) %>%
mutate(UserTypeID=map_chr(UserTypeID, ~toString(UserTypeID, .x))) %>%
filter(row_number()==1)
#Final output
do.call("cbind", list(Attributes,UserTypeLink))

How to parse a complex xml in Rinto a dataframe?

I want to parse a nested xml file with the layout below in R and load it into a dataframe. I tried using several eays including the xml and xml2 packages but could not get it to work.
<?xml version="1.0" encoding="UTF-8"?>
<Targets>
<Target TYPE="myserver.mgmt.Metric" NAME="metric1">
<Attribute NAME="name" VALUE="metric1"></Attribute>
<Attribute NAME="Value" VALUE="2.4"></Attribute>
<Attribute NAME="collectionTime" VALUE="1525118288000"></Attribute>
<Attribute NAME="State" VALUE="normal"></Attribute>
<Attribute NAME="ObjectName" VALUE="obj1"></Attribute>
<Attribute NAME="ValueHistory" VALUE="5072"></Attribute>
</Target>
...
<Target TYPE="myserver.mgmt.Metric" NAME="metric999">
<Attribute NAME="name" VALUE="metric999"></Attribute>
<Attribute NAME="Value" VALUE="60.35"></Attribute>
<Attribute NAME="collectionTime" VALUE="1525118288000"></Attribute>
<Attribute NAME="State" VALUE="normal"></Attribute>
<Attribute NAME="ObjectName" VALUE="obj1"></Attribute>
<Attribute NAME="ValueHistory" VALUE="9550"></Attribute>
</Target>
</Targets>
The final outcome I am looking to get is:
name Value collectionTime State ObjectName ValueHistory
metric1 2.4 1525118288000 normal obj1 5072
metric2 60.35 1525118288000 normal obj2 9550
Any help is appreciated.

We can make use of XML with tidyverse
library(XML)
library(tidyverse)
lst1 <- getNodeSet(xml1, path = "//Target")
map_df(seq_along(lst1), ~
XML:::xmlAttrsToDataFrame(lst1[[.x]]) %>%
mutate_all(as.character) %>%
deframe %>%
as.list %>%
as_tibble) %>%
mutate_all(type.convert, as.is = TRUE)
# A tibble: 2 x 6
# name Value collectionTime State ObjectName ValueHistory
# <chr> <dbl> <dbl> <chr> <chr> <int>
#1 metric1 2.4 1525118288000 normal obj1 5072
#2 metric999 60.4 1525118288000 normal obj1 9550
data
xml1 <- xmlParse('<?xml version="1.0" encoding="UTF-8"?>
<Targets>
<Target TYPE="myserver.mgmt.Metric" NAME="metric1">
<Attribute NAME="name" VALUE="metric1"></Attribute>
<Attribute NAME="Value" VALUE="2.4"></Attribute>
<Attribute NAME="collectionTime" VALUE="1525118288000"></Attribute>
<Attribute NAME="State" VALUE="normal"></Attribute>
<Attribute NAME="ObjectName" VALUE="obj1"></Attribute>
<Attribute NAME="ValueHistory" VALUE="5072"></Attribute>
</Target>
<Target TYPE="myserver.mgmt.Metric" NAME="metric999">
<Attribute NAME="name" VALUE="metric999"></Attribute>
<Attribute NAME="Value" VALUE="60.35"></Attribute>
<Attribute NAME="collectionTime" VALUE="1525118288000"></Attribute>
<Attribute NAME="State" VALUE="normal"></Attribute>
<Attribute NAME="ObjectName" VALUE="obj1"></Attribute>
<Attribute NAME="ValueHistory" VALUE="9550"></Attribute>
</Target>
</Targets>
')

Converting PMML representing Multinomial Logistic Regression back into R coefficients

My most recent assignment has been to parse PMML back into R models. (I've searched extensively, and there is no library that does this conversion for you.) I'm trying to convert PMML containing a Multinomial Logistic Regression back into an R model, but I don't know how to convert any of the coefficients held in the PMML document to the coefficients held by the R model.
The PMML is below:
<?xml version="1.0"?>
<PMML version="4.2" xmlns="http://www.dmg.org/PMML-4_2" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.dmg.org/PMML-4_2 http://www.dmg.org/v4-2/pmml-4-2.xsd">
<Header copyright="Copyright (c) 2014 hlin117" description="Generalized Linear Regression Model">
<Extension name="user" value="hlin117" extender="Rattle/PMML"/>
<Application name="Rattle/PMML" version="1.4"/>
<Timestamp>2014-06-23 13:04:17</Timestamp>
</Header>
<DataDictionary numberOfFields="13">
<DataField name="audit.train$TARGET_Adjusted" optype="continuous" dataType="double"/>
<DataField name="ID" optype="continuous" dataType="double"/>
<DataField name="Age" optype="continuous" dataType="double"/>
<DataField name="Employment" optype="categorical" dataType="string">
<Value value="Consultant"/>
<Value value="Private"/>
<Value value="PSFederal"/>
<Value value="PSLocal"/>
<Value value="PSState"/>
<Value value="SelfEmp"/>
<Value value="Volunteer"/>
</DataField>
<DataField name="Education" optype="categorical" dataType="string">
<Value value="Associate"/>
<Value value="Bachelor"/>
<Value value="College"/>
<Value value="Doctorate"/>
<Value value="HSgrad"/>
<Value value="Master"/>
<Value value="Preschool"/>
<Value value="Professional"/>
<Value value="Vocational"/>
<Value value="Yr10"/>
<Value value="Yr11"/>
<Value value="Yr12"/>
<Value value="Yr1t4"/>
<Value value="Yr5t6"/>
<Value value="Yr7t8"/>
<Value value="Yr9"/>
</DataField>
<DataField name="Marital" optype="categorical" dataType="string">
<Value value="Absent"/>
<Value value="Divorced"/>
<Value value="Married"/>
<Value value="Married-spouse-absent"/>
<Value value="Unmarried"/>
<Value value="Widowed"/>
</DataField>
<DataField name="Occupation" optype="categorical" dataType="string">
<Value value="Cleaner"/>
<Value value="Clerical"/>
<Value value="Executive"/>
<Value value="Farming"/>
<Value value="Home"/>
<Value value="Machinist"/>
<Value value="Military"/>
<Value value="Professional"/>
<Value value="Protective"/>
<Value value="Repair"/>
<Value value="Sales"/>
<Value value="Service"/>
<Value value="Support"/>
<Value value="Transport"/>
</DataField>
<DataField name="Income" optype="continuous" dataType="double"/>
<DataField name="Gender" optype="categorical" dataType="string">
<Value value="Female"/>
<Value value="Male"/>
</DataField>
<DataField name="Deductions" optype="continuous" dataType="double"/>
<DataField name="Hours" optype="continuous" dataType="double"/>
<DataField name="IGNORE_Accounts" optype="categorical" dataType="string">
<Value value="Canada"/>
<Value value="China"/>
<Value value="Columbia"/>
<Value value="Cuba"/>
<Value value="Ecuador"/>
<Value value="England"/>
<Value value="Fiji"/>
<Value value="Germany"/>
<Value value="Greece"/>
<Value value="Guatemala"/>
<Value value="Hong"/>
<Value value="Hungary"/>
<Value value="India"/>
<Value value="Indonesia"/>
<Value value="Iran"/>
<Value value="Ireland"/>
<Value value="Italy"/>
<Value value="Jamaica"/>
<Value value="Japan"/>
<Value value="Malaysia"/>
<Value value="Mexico"/>
<Value value="NewZealand"/>
<Value value="Nicaragua"/>
<Value value="Philippines"/>
<Value value="Poland"/>
<Value value="Portugal"/>
<Value value="Scotland"/>
<Value value="Singapore"/>
<Value value="Taiwan"/>
<Value value="UnitedStates"/>
<Value value="Vietnam"/>
<Value value="Yugoslavia"/>
</DataField>
<DataField name="RISK_Adjustment" optype="continuous" dataType="double"/>
</DataDictionary>
<GeneralRegressionModel modelName="General_Regression_Model" modelType="generalizedLinear" functionName="regression" algorithmName="glm" distribution="binomial" linkFunction="logit">
<MiningSchema>
<MiningField name="audit.train$TARGET_Adjusted" usageType="predicted"/>
<MiningField name="ID" usageType="active"/>
<MiningField name="Age" usageType="active"/>
<MiningField name="Employment" usageType="active"/>
<MiningField name="Education" usageType="active"/>
<MiningField name="Marital" usageType="active"/>
<MiningField name="Occupation" usageType="active"/>
<MiningField name="Income" usageType="active"/>
<MiningField name="Gender" usageType="active"/>
<MiningField name="Deductions" usageType="active"/>
<MiningField name="Hours" usageType="active"/>
<MiningField name="IGNORE_Accounts" usageType="active"/>
<MiningField name="RISK_Adjustment" usageType="active"/>
</MiningSchema>
<Output>
<OutputField name="Predicted_audit.train$TARGET_Adjusted" feature="predictedValue"/>
</Output>
<ParameterList>
<Parameter name="p0" label="(Intercept)"/>
<Parameter name="p1" label="ID"/>
<Parameter name="p2" label="Age"/>
<Parameter name="p3" label="EmploymentPrivate"/>
<Parameter name="p4" label="EmploymentPSFederal"/>
<Parameter name="p5" label="EmploymentPSLocal"/>
<Parameter name="p6" label="EmploymentPSState"/>
<Parameter name="p7" label="EmploymentSelfEmp"/>
<Parameter name="p8" label="EmploymentVolunteer"/>
<Parameter name="p9" label="EducationBachelor"/>
<Parameter name="p10" label="EducationCollege"/>
<Parameter name="p11" label="EducationDoctorate"/>
<Parameter name="p12" label="EducationHSgrad"/>
<Parameter name="p13" label="EducationMaster"/>
<Parameter name="p14" label="EducationPreschool"/>
<Parameter name="p15" label="EducationProfessional"/>
<Parameter name="p16" label="EducationVocational"/>
<Parameter name="p17" label="EducationYr10"/>
<Parameter name="p18" label="EducationYr11"/>
<Parameter name="p19" label="EducationYr12"/>
<Parameter name="p20" label="EducationYr1t4"/>
<Parameter name="p21" label="EducationYr5t6"/>
<Parameter name="p22" label="EducationYr7t8"/>
<Parameter name="p23" label="EducationYr9"/>
<Parameter name="p24" label="MaritalDivorced"/>
<Parameter name="p25" label="MaritalMarried"/>
<Parameter name="p26" label="MaritalMarried-spouse-absent"/>
<Parameter name="p27" label="MaritalUnmarried"/>
<Parameter name="p28" label="MaritalWidowed"/>
<Parameter name="p29" label="OccupationClerical"/>
<Parameter name="p30" label="OccupationExecutive"/>
<Parameter name="p31" label="OccupationFarming"/>
<Parameter name="p32" label="OccupationHome"/>
<Parameter name="p33" label="OccupationMachinist"/>
<Parameter name="p34" label="OccupationMilitary"/>
<Parameter name="p35" label="OccupationProfessional"/>
<Parameter name="p36" label="OccupationProtective"/>
<Parameter name="p37" label="OccupationRepair"/>
<Parameter name="p38" label="OccupationSales"/>
<Parameter name="p39" label="OccupationService"/>
<Parameter name="p40" label="OccupationSupport"/>
<Parameter name="p41" label="OccupationTransport"/>
<Parameter name="p42" label="Income"/>
<Parameter name="p43" label="GenderMale"/>
<Parameter name="p44" label="Deductions"/>
<Parameter name="p45" label="Hours"/>
<Parameter name="p46" label="IGNORE_AccountsChina"/>
<Parameter name="p47" label="IGNORE_AccountsColumbia"/>
<Parameter name="p48" label="IGNORE_AccountsCuba"/>
<Parameter name="p49" label="IGNORE_AccountsEcuador"/>
<Parameter name="p50" label="IGNORE_AccountsEngland"/>
<Parameter name="p51" label="IGNORE_AccountsFiji"/>
<Parameter name="p52" label="IGNORE_AccountsGermany"/>
<Parameter name="p53" label="IGNORE_AccountsGreece"/>
<Parameter name="p54" label="IGNORE_AccountsGuatemala"/>
<Parameter name="p55" label="IGNORE_AccountsHong"/>
<Parameter name="p56" label="IGNORE_AccountsHungary"/>
<Parameter name="p57" label="IGNORE_AccountsIndia"/>
<Parameter name="p58" label="IGNORE_AccountsIndonesia"/>
<Parameter name="p59" label="IGNORE_AccountsIran"/>
<Parameter name="p60" label="IGNORE_AccountsIreland"/>
<Parameter name="p61" label="IGNORE_AccountsItaly"/>
<Parameter name="p62" label="IGNORE_AccountsJamaica"/>
<Parameter name="p63" label="IGNORE_AccountsJapan"/>
<Parameter name="p64" label="IGNORE_AccountsMalaysia"/>
<Parameter name="p65" label="IGNORE_AccountsMexico"/>
<Parameter name="p66" label="IGNORE_AccountsNewZealand"/>
<Parameter name="p67" label="IGNORE_AccountsNicaragua"/>
<Parameter name="p68" label="IGNORE_AccountsPhilippines"/>
<Parameter name="p69" label="IGNORE_AccountsPoland"/>
<Parameter name="p70" label="IGNORE_AccountsPortugal"/>
<Parameter name="p71" label="IGNORE_AccountsScotland"/>
<Parameter name="p72" label="IGNORE_AccountsSingapore"/>
<Parameter name="p73" label="IGNORE_AccountsTaiwan"/>
<Parameter name="p74" label="IGNORE_AccountsUnitedStates"/>
<Parameter name="p75" label="IGNORE_AccountsVietnam"/>
<Parameter name="p76" label="IGNORE_AccountsYugoslavia"/>
<Parameter name="p77" label="RISK_Adjustment"/>
</ParameterList>
<FactorList>
<Predictor name="Employment"/>
<Predictor name="Education"/>
<Predictor name="Marital"/>
<Predictor name="Occupation"/>
<Predictor name="Gender"/>
<Predictor name="IGNORE_Accounts"/>
</FactorList>
<CovariateList>
<Predictor name="ID"/>
<Predictor name="Age"/>
<Predictor name="Income"/>
<Predictor name="Deductions"/>
<Predictor name="Hours"/>
<Predictor name="RISK_Adjustment"/>
</CovariateList>
<PPMatrix>
<PPCell value="1" predictorName="ID" parameterName="p1"/>
<PPCell value="1" predictorName="Age" parameterName="p2"/>
<PPCell value="Private" predictorName="Employment" parameterName="p3"/>
<PPCell value="PSFederal" predictorName="Employment" parameterName="p4"/>
<PPCell value="PSLocal" predictorName="Employment" parameterName="p5"/>
<PPCell value="PSState" predictorName="Employment" parameterName="p6"/>
<PPCell value="SelfEmp" predictorName="Employment" parameterName="p7"/>
<PPCell value="Volunteer" predictorName="Employment" parameterName="p8"/>
<PPCell value="Bachelor" predictorName="Education" parameterName="p9"/>
<PPCell value="College" predictorName="Education" parameterName="p10"/>
<PPCell value="Doctorate" predictorName="Education" parameterName="p11"/>
<PPCell value="HSgrad" predictorName="Education" parameterName="p12"/>
<PPCell value="Master" predictorName="Education" parameterName="p13"/>
<PPCell value="Preschool" predictorName="Education" parameterName="p14"/>
<PPCell value="Professional" predictorName="Education" parameterName="p15"/>
<PPCell value="Vocational" predictorName="Education" parameterName="p16"/>
<PPCell value="Yr10" predictorName="Education" parameterName="p17"/>
<PPCell value="Yr11" predictorName="Education" parameterName="p18"/>
<PPCell value="Yr12" predictorName="Education" parameterName="p19"/>
<PPCell value="Yr1t4" predictorName="Education" parameterName="p20"/>
<PPCell value="Yr5t6" predictorName="Education" parameterName="p21"/>
<PPCell value="Yr7t8" predictorName="Education" parameterName="p22"/>
<PPCell value="Yr9" predictorName="Education" parameterName="p23"/>
<PPCell value="Divorced" predictorName="Marital" parameterName="p24"/>
<PPCell value="Married" predictorName="Marital" parameterName="p25"/>
<PPCell value="Married-spouse-absent" predictorName="Marital" parameterName="p26"/>
<PPCell value="Unmarried" predictorName="Marital" parameterName="p27"/>
<PPCell value="Widowed" predictorName="Marital" parameterName="p28"/>
<PPCell value="Clerical" predictorName="Occupation" parameterName="p29"/>
<PPCell value="Executive" predictorName="Occupation" parameterName="p30"/>
<PPCell value="Farming" predictorName="Occupation" parameterName="p31"/>
<PPCell value="Home" predictorName="Occupation" parameterName="p32"/>
<PPCell value="Machinist" predictorName="Occupation" parameterName="p33"/>
<PPCell value="Military" predictorName="Occupation" parameterName="p34"/>
<PPCell value="Professional" predictorName="Occupation" parameterName="p35"/>
<PPCell value="Protective" predictorName="Occupation" parameterName="p36"/>
<PPCell value="Repair" predictorName="Occupation" parameterName="p37"/>
<PPCell value="Sales" predictorName="Occupation" parameterName="p38"/>
<PPCell value="Service" predictorName="Occupation" parameterName="p39"/>
<PPCell value="Support" predictorName="Occupation" parameterName="p40"/>
<PPCell value="Transport" predictorName="Occupation" parameterName="p41"/>
<PPCell value="1" predictorName="Income" parameterName="p42"/>
<PPCell value="Male" predictorName="Gender" parameterName="p43"/>
<PPCell value="1" predictorName="Deductions" parameterName="p44"/>
<PPCell value="1" predictorName="Hours" parameterName="p45"/>
<PPCell value="China" predictorName="IGNORE_Accounts" parameterName="p46"/>
<PPCell value="Columbia" predictorName="IGNORE_Accounts" parameterName="p47"/>
<PPCell value="Cuba" predictorName="IGNORE_Accounts" parameterName="p48"/>
<PPCell value="Ecuador" predictorName="IGNORE_Accounts" parameterName="p49"/>
<PPCell value="England" predictorName="IGNORE_Accounts" parameterName="p50"/>
<PPCell value="Fiji" predictorName="IGNORE_Accounts" parameterName="p51"/>
<PPCell value="Germany" predictorName="IGNORE_Accounts" parameterName="p52"/>
<PPCell value="Greece" predictorName="IGNORE_Accounts" parameterName="p53"/>
<PPCell value="Guatemala" predictorName="IGNORE_Accounts" parameterName="p54"/>
<PPCell value="Hong" predictorName="IGNORE_Accounts" parameterName="p55"/>
<PPCell value="Hungary" predictorName="IGNORE_Accounts" parameterName="p56"/>
<PPCell value="India" predictorName="IGNORE_Accounts" parameterName="p57"/>
<PPCell value="Indonesia" predictorName="IGNORE_Accounts" parameterName="p58"/>
<PPCell value="Iran" predictorName="IGNORE_Accounts" parameterName="p59"/>
<PPCell value="Ireland" predictorName="IGNORE_Accounts" parameterName="p60"/>
<PPCell value="Italy" predictorName="IGNORE_Accounts" parameterName="p61"/>
<PPCell value="Jamaica" predictorName="IGNORE_Accounts" parameterName="p62"/>
<PPCell value="Japan" predictorName="IGNORE_Accounts" parameterName="p63"/>
<PPCell value="Malaysia" predictorName="IGNORE_Accounts" parameterName="p64"/>
<PPCell value="Mexico" predictorName="IGNORE_Accounts" parameterName="p65"/>
<PPCell value="NewZealand" predictorName="IGNORE_Accounts" parameterName="p66"/>
<PPCell value="Nicaragua" predictorName="IGNORE_Accounts" parameterName="p67"/>
<PPCell value="Philippines" predictorName="IGNORE_Accounts" parameterName="p68"/>
<PPCell value="Poland" predictorName="IGNORE_Accounts" parameterName="p69"/>
<PPCell value="Portugal" predictorName="IGNORE_Accounts" parameterName="p70"/>
<PPCell value="Scotland" predictorName="IGNORE_Accounts" parameterName="p71"/>
<PPCell value="Singapore" predictorName="IGNORE_Accounts" parameterName="p72"/>
<PPCell value="Taiwan" predictorName="IGNORE_Accounts" parameterName="p73"/>
<PPCell value="UnitedStates" predictorName="IGNORE_Accounts" parameterName="p74"/>
<PPCell value="Vietnam" predictorName="IGNORE_Accounts" parameterName="p75"/>
<PPCell value="Yugoslavia" predictorName="IGNORE_Accounts" parameterName="p76"/>
<PPCell value="1" predictorName="RISK_Adjustment" parameterName="p77"/>
</PPMatrix>
<ParamMatrix>
<PCell parameterName="p0" df="1" beta="-12.0199804097759"/>
<PCell parameterName="p1" df="1" beta="3.62329433275629e-08"/>
<PCell parameterName="p2" df="1" beta="0.0380676635766761"/>
<PCell parameterName="p3" df="1" beta="0.756901134378277"/>
<PCell parameterName="p4" df="1" beta="0.375762595900717"/>
<PCell parameterName="p5" df="1" beta="0.50309824514625"/>
<PCell parameterName="p6" df="1" beta="0.470897191210805"/>
<PCell parameterName="p7" df="1" beta="-2.10284542055317"/>
<PCell parameterName="p8" df="1" beta="-15.5455611068614"/>
<PCell parameterName="p9" df="1" beta="0.0997435072074993"/>
<PCell parameterName="p10" df="1" beta="-1.22905386951777"/>
<PCell parameterName="p11" df="1" beta="-6.76667195830752"/>
<PCell parameterName="p12" df="1" beta="-1.01297363710822"/>
<PCell parameterName="p13" df="1" beta="-0.340407862763258"/>
<PCell parameterName="p14" df="1" beta="-15.8841924243017"/>
<PCell parameterName="p15" df="1" beta="3.18173392385448"/>
<PCell parameterName="p16" df="1" beta="-0.569821531302005"/>
<PCell parameterName="p17" df="1" beta="-3.3033217141108"/>
<PCell parameterName="p18" df="1" beta="-0.430994461878221"/>
<PCell parameterName="p19" df="1" beta="-17.0972305473487"/>
<PCell parameterName="p20" df="1" beta="-15.929168040244"/>
<PCell parameterName="p21" df="1" beta="-17.7483980280451"/>
<PCell parameterName="p22" df="1" beta="-16.1514804898207"/>
<PCell parameterName="p23" df="1" beta="-10.3889654044557"/>
<PCell parameterName="p24" df="1" beta="-0.690592385956069"/>
<PCell parameterName="p25" df="1" beta="2.53630505787246"/>
<PCell parameterName="p26" df="1" beta="1.41541804527502"/>
<PCell parameterName="p27" df="1" beta="1.49491086815453"/>
<PCell parameterName="p28" df="1" beta="0.174099244312997"/>
<PCell parameterName="p29" df="1" beta="1.01865424623088"/>
<PCell parameterName="p30" df="1" beta="1.73213477081248"/>
<PCell parameterName="p31" df="1" beta="-1.80877402327631"/>
<PCell parameterName="p32" df="1" beta="-12.4454410582178"/>
<PCell parameterName="p33" df="1" beta="-0.417346874910574"/>
<PCell parameterName="p34" df="1" beta="-12.475145396564"/>
<PCell parameterName="p35" df="1" beta="1.45214141089004"/>
<PCell parameterName="p36" df="1" beta="1.64050123149924"/>
<PCell parameterName="p37" df="1" beta="0.134775653612853"/>
<PCell parameterName="p38" df="1" beta="0.948585540443075"/>
<PCell parameterName="p39" df="1" beta="0.144171863863442"/>
<PCell parameterName="p40" df="1" beta="0.789971116324262"/>
<PCell parameterName="p41" df="1" beta="0.842781801750256"/>
<PCell parameterName="p42" df="1" beta="-9.63129083571953e-07"/>
<PCell parameterName="p43" df="1" beta="-0.52313575926474"/>
<PCell parameterName="p44" df="1" beta="0.00125611277933667"/>
<PCell parameterName="p45" df="1" beta="0.0109489183058056"/>
<PCell parameterName="p46" df="1" beta="-2.86790934232277"/>
<PCell parameterName="p47" df="1" beta="-10.4586048958891"/>
<PCell parameterName="p48" df="1" beta="-11.8078344468555"/>
<PCell parameterName="p49" df="1" beta="-8.15369086351991"/>
<PCell parameterName="p50" df="1" beta="-15.1509749621394"/>
<PCell parameterName="p51" df="1" beta="-12.6588234930477"/>
<PCell parameterName="p52" df="1" beta="7.44342418994783"/>
<PCell parameterName="p53" df="1" beta="-8.80415604321149"/>
<PCell parameterName="p54" df="1" beta="-0.909551298634999"/>
<PCell parameterName="p55" df="1" beta="3.21333791872318"/>
<PCell parameterName="p56" df="1" beta="-9.7080063371067"/>
<PCell parameterName="p57" df="1" beta="-9.94640566996892"/>
<PCell parameterName="p58" df="1" beta="-7.34469543656762"/>
<PCell parameterName="p59" df="1" beta="-10.1375079207868"/>
<PCell parameterName="p60" df="1" beta="4.03786237290128"/>
<PCell parameterName="p61" df="1" beta="-9.95289672035589"/>
<PCell parameterName="p62" df="1" beta="-11.2800534550324"/>
<PCell parameterName="p63" df="1" beta="-8.5259456003378"/>
<PCell parameterName="p64" df="1" beta="-11.1183864482514"/>
<PCell parameterName="p65" df="1" beta="-3.17790587178398"/>
<PCell parameterName="p66" df="1" beta="7.62183148791729"/>
<PCell parameterName="p67" df="1" beta="-9.29840834254978"/>
<PCell parameterName="p68" df="1" beta="5.87739404847556"/>
<PCell parameterName="p69" df="1" beta="-11.0988711939497"/>
<PCell parameterName="p70" df="1" beta="-5.78171399043641"/>
<PCell parameterName="p71" df="1" beta="-11.009822161619"/>
<PCell parameterName="p72" df="1" beta="-7.98831399897464"/>
<PCell parameterName="p73" df="1" beta="-14.2857685874083"/>
<PCell parameterName="p74" df="1" beta="4.89065048867447"/>
<PCell parameterName="p75" df="1" beta="-2.21686920486685"/>
<PCell parameterName="p76" df="1" beta="-10.0494769160447"/>
<PCell parameterName="p77" df="1" beta="0.0044395180546043"/>
</ParamMatrix>
</GeneralRegressionModel>
</PMML>
The coefficients held by the R model are below:
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -5.779e+00 1.108e+04 -0.001 0.999584
ID 3.922e-08 6.187e-08 0.634 0.526164
Age 2.705e-02 1.388e-02 1.949 0.051314 .
EmploymentPrivate 1.087e+00 6.774e-01 1.605 0.108550
EmploymentPSFederal 1.155e+00 1.050e+00 1.101 0.271105
EmploymentPSLocal 1.262e+00 8.811e-01 1.432 0.152036
EmploymentPSState 8.151e-01 1.011e+00 0.806 0.420221
EmploymentSelfEmp 2.217e-01 9.859e-01 0.225 0.822066
EmploymentVolunteer -1.667e+01 1.075e+04 -0.002 0.998764
EducationBachelor 4.297e-01 7.768e-01 0.553 0.580154
EducationCollege -1.234e+00 8.393e-01 -1.470 0.141592
EducationDoctorate 1.604e+00 1.697e+00 0.945 0.344690
EducationHSgrad -5.332e-01 7.613e-01 -0.700 0.483661
EducationMaster -3.705e-01 1.117e+00 -0.332 0.740081
EducationPreschool -1.306e+01 3.588e+03 -0.004 0.997096
EducationProfessional 1.600e+00 1.251e+00 1.279 0.200733
EducationVocational -3.887e-01 1.023e+00 -0.380 0.703998
EducationYr10 -2.121e+00 1.897e+00 -1.118 0.263626
EducationYr11 -3.222e-01 1.294e+00 -0.249 0.803322
EducationYr12 -4.786e+00 1.235e+01 -0.388 0.698298
EducationYr1t4 -1.588e+01 4.174e+03 -0.004 0.996965
EducationYr5t6 -1.779e+01 2.356e+03 -0.008 0.993976
EducationYr7t8 -1.659e+01 1.951e+03 -0.009 0.993214
EducationYr9 -1.672e+01 2.680e+03 -0.006 0.995022
MaritalDivorced -6.700e-01 8.277e-01 -0.809 0.418238
MaritalMarried 2.269e+00 5.238e-01 4.332 1.48e-05 ***
MaritalMarried-spouse-absent 1.299e+00 1.385e+00 0.938 0.348362
MaritalUnmarried 1.570e+00 9.025e-01 1.740 0.081926 .
MaritalWidowed 7.018e-01 1.209e+00 0.581 0.561438
OccupationClerical 1.060e+00 1.224e+00 0.866 0.386731
OccupationExecutive 1.851e+00 1.138e+00 1.627 0.103649
OccupationFarming 1.189e-01 1.530e+00 0.078 0.938065
OccupationHome -1.296e+01 6.601e+03 -0.002 0.998434
OccupationMachinist 2.869e-01 1.299e+00 0.221 0.825190
OccupationMilitary -1.318e+01 1.075e+04 -0.001 0.999022
OccupationProfessional 1.589e+00 1.187e+00 1.339 0.180656
OccupationProtective 1.099e+00 1.622e+00 0.678 0.497935
OccupationRepair 1.641e-01 1.204e+00 0.136 0.891597
OccupationSales 7.170e-01 1.205e+00 0.595 0.551929
OccupationService -5.600e-02 1.348e+00 -0.042 0.966858
OccupationSupport 8.431e-01 1.348e+00 0.626 0.531515
OccupationTransport 3.488e-01 1.242e+00 0.281 0.778911
Income 1.442e-06 3.112e-06 0.463 0.643050
GenderMale 1.510e-01 5.361e-01 0.282 0.778254
Deductions 1.476e-03 4.109e-04 3.593 0.000327 ***
Hours 2.116e-02 1.433e-02 1.476 0.139922
IGNORE_AccountsChina -2.048e+01 1.867e+04 -0.001 0.999125
IGNORE_AccountsColumbia -2.085e+01 1.294e+04 -0.002 0.998715
IGNORE_AccountsCuba -1.942e+01 1.544e+04 -0.001 0.998997
IGNORE_AccountsEcuador -1.701e+01 1.544e+04 -0.001 0.999121
IGNORE_AccountsEngland -1.418e+01 1.109e+04 -0.001 0.998980
IGNORE_AccountsGermany -4.952e-02 1.108e+04 0.000 0.999996
IGNORE_AccountsGreece -1.645e+01 1.544e+04 -0.001 0.999150
IGNORE_AccountsGuatemala -2.767e+00 1.459e+04 0.000 0.999849
IGNORE_AccountsHong -3.325e+00 1.557e+04 0.000 0.999830
IGNORE_AccountsIndia -1.506e+01 1.110e+04 -0.001 0.998918
IGNORE_AccountsIndonesia -1.692e+01 1.225e+04 -0.001 0.998897
IGNORE_AccountsIreland -3.329e+00 1.108e+04 0.000 0.999760
IGNORE_AccountsItaly -1.663e+01 1.304e+04 -0.001 0.998982
IGNORE_AccountsJamaica -2.174e+01 2.163e+04 -0.001 0.999198
IGNORE_AccountsJapan -1.577e+01 1.544e+04 -0.001 0.999185
IGNORE_AccountsMalaysia -1.903e+01 1.206e+04 -0.002 0.998741
IGNORE_AccountsMexico -9.440e+00 1.108e+04 -0.001 0.999320
IGNORE_AccountsNewZealand 1.773e-01 1.562e+04 0.000 0.999991
IGNORE_AccountsNicaragua -1.786e+01 1.200e+04 -0.001 0.998812
IGNORE_AccountsPhilippines -9.526e-01 1.108e+04 0.000 0.999931
IGNORE_AccountsPoland -1.878e+01 1.544e+04 -0.001 0.999030
IGNORE_AccountsPortugal -1.432e+00 1.557e+04 0.000 0.999927
IGNORE_AccountsSingapore -1.778e+01 1.225e+04 -0.001 0.998842
IGNORE_AccountsTaiwan -1.922e+01 1.259e+04 -0.002 0.998782
IGNORE_AccountsUnitedStates -2.519e+00 1.108e+04 0.000 0.999819
IGNORE_AccountsVietnam -1.984e+01 1.250e+04 -0.002 0.998734
IGNORE_AccountsYugoslavia -1.774e+01 1.544e+04 -0.001 0.999083
RISK_Adjustment 3.802e-03 6.819e-04 5.575 2.47e-08 ***
(The script in R that generated this GLM model and the corresponding PMML is below:
library(pmml)
auditDF <- read.csv("http://rattle.togaware.com/audit.csv")
auditDF <- na.omit(auditDF)
target <- auditDF$TARGET_Adjusted
N <- length(target); M <- N - 500
i.train <- sample(N, M)
audit.train <- auditDF[i.train,]
audit.test <- auditDF[-i.train,]
glm.model <- glm(audit.train$TARGET_Adjusted ~ ., data = audit.train, family = "binomial")
glm.pmml <- pmml(glm.model, name = "glm model", data = trainDF)
xmlFile <- file.path(getwd(), "audit-glm.xml")
saveXML(glm.pmml, xmlFile)
Source: http://blog.revolutionanalytics.com/2011/03/predicting-r-models-with-pmml.html)

I suppose that depends on exactly what you want to do with the model once you have it back into R. At one point i helped someone create a pseudo-gml object that knew the coefficients for the variables and could be used with predict(). Many other functions required the fill dataset to be present.
If that may be of possible interested to you. The function is called makeglm.R. You will want to copy and paste just that function into your R session. But it will be necessary to transform your data first. Here are some helper functions to do just that.
getdata <- function(xml, ns=attr(xml,"ns")) {
names<-xpathSApply(xml, "//d:DataField/#name", namespaces = ns)
vals<-xpathApply(xml, "//d:DataField", function(x) {
if(xmlGetAttr(x, "optype")=="categorical") {
levels<-xpathSApply(x, "Value/#value")
factor(character(0), levels=levels)
} else if (xmlGetAttr(x, "optype")=="continuous"){
numeric(0)
}
}, namespaces = ns)
names(vals)<-names
as.data.frame(vals)
}
getformula <- function(xml, ns=attr(xml,"ns")) {
resp<-xpathSApply(xml, "//d:MiningField[#usageType=\"predicted\"]/#name",
namespaces = ns)
covar<-xpathSApply(xml, "//d:MiningField[#usageType=\"active\"]/#name",
namespaces = ns)
fmc<-paste(paste(resp, collapse=" + "), "~", paste(covar, collapse=" + "))
as.formula(fmc)
}
getestimates <- function(xml, ns=attr(xml,"ns")) {
betas <- setNames(as.numeric(xpathSApply(xml, "//d:PCell/#beta", namespaces = ns)),
xpathSApply(xml, "//d:PCell/#parameterName", namespaces = ns))
numericparam <- unname(xpathSApply(xml, "//d:CovariateList/d:Predictor/#name", namespaces = ns))
factorparam <- unname(xpathSApply(xml, "//d:FactorList/d:Predictor/#name", namespaces = ns))
values <- do.call(rbind, Map(function(x,y,z) data.frame(p=x, val=y, pred=z, stringsAsFactors=F),
unname(xpathSApply(xml,"//d:PPCell/#parameterName", namespaces = ns)),
xpathSApply(xml, "//d:PPCell/#value", namespaces = ns),
xpathSApply(xml, "//d:PPCell/#predictorName", namespaces = ns)))
lf<-Map(function(x) {
vv <- values[values$pred==x, ]
setNames(betas[vv$p], vv$val)
}, factorparam)
ln<-Map(function(x) {
vv <- values[values$pred==x, ]
unname(betas[vv$p])
}, numericparam)
estimates<-c(lf,ln)
intercept<-getNodeSet(xml,"//d:Parameter[#label=\"(Intercept)\"]", namespaces = ns)
if(length(intercept)) {
estimates<-c(unname(betas[xmlGetAttr(intercept[[1]],"name")]), estimates)
}
estimates
}
I'm not at all familiar with the PMML format but i put these together based on your sample document. I tried extracting all the correct information needed to build a formula, data.frame stub, and parameter estimates from the data in order to use the makeglm() function. Once you've loaded that function and these helper functions, you can run
library(XML)
mypmml <- xmlParse("pmml.xml")
attr(mypmml, "ns")<-"d"
dd <- getdata(mypmml)
ff <- getformula(mypmml)
ee <- getestimates(mypmml)
do.call(makeglm, c(list(ff, family="binomial", data=dd), ee))
to actually run the function. This will return a glm object that you can use with predict(). I did have to change one thing in your sample data. For some reason you had the table name as part of the formula in the glm model
glm(audit.train$TARGET_Adjusted ~ ., data = audit.train, ...)
rather than
glm(TARGET_Adjusted ~ ., data = audit.train, ...)
which can cause problems. So I just took out the "audit.train$" from the xml file before I read it in. It maybe possible to more error checking, but I wasn't even sure if this is ultimately what you were after.

Outputting clustering information (dendogram) in R in a human readable format like XML or JSON

I wanto export the clustering information visualized in a dendogram in a human readable format (XML, JSON) with distances and the nodes and children nodes.

Have a look at the pmml package for a XML-based representation.
require(pmml)
# a 2-dimensional example
x <- rbind(matrix(rnorm(100, sd = 0.3), ncol = 2),
matrix(rnorm(100, mean = 1, sd = 0.3), ncol = 2))
colnames(x) <- c("x", "y")
cl <- kmeans(x, 2)
plot(x, col = cl$cluster)
pmml(cl, centers = cl$centers)
<PMML version="4.1" xmlns="http://www.dmg.org/PMML-4_1" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.dmg.org/PMML-4_1 http://www.dmg.org/v4-1/pmml-4-1.xsd">
<Header copyright="Copyright (c) 2013 edisz" description="KMeans cluster model">
<Extension name="user" value="edisz" extender="Rattle/PMML"/>
<Application name="Rattle/PMML" version="1.4"/>
<Timestamp>2013-11-11 15:02:46</Timestamp>
</Header>
<DataDictionary numberOfFields="2">
<DataField name="x" optype="continuous" dataType="double"/>
<DataField name="y" optype="continuous" dataType="double"/>
</DataDictionary>
<ClusteringModel modelName="KMeans_Model" functionName="clustering" algorithmName="KMeans: Hartigan and Wong" modelClass="centerBased" numberOfClusters="2">
<MiningSchema>
<MiningField name="x"/>
<MiningField name="y"/>
</MiningSchema>
<Output>
<OutputField name="predictedValue" feature="predictedValue"/>
<OutputField name="clusterAffinity_1" feature="clusterAffinity" value="1"/>
<OutputField name="clusterAffinity_2" feature="clusterAffinity" value="2"/>
</Output>
<ComparisonMeasure kind="distance">
<squaredEuclidean/>
</ComparisonMeasure>
<ClusteringField field="x" compareFunction="absDiff"/>
<ClusteringField field="y" compareFunction="absDiff"/>
<Cluster name="1" size="49" id="1">
<Array n="2" type="real">1.08242766097448 0.970387920586825</Array>
</Cluster>
<Cluster name="2" size="51" id="2">
<Array n="2" type="real">0.0261601744749776 0.0786776972701963</Array>
</Cluster>
</ClusteringModel>
</PMML>
But I don't know if this is more human-readable...

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