ubic.basecode.util.r

Class AbstractRClientTest

java.lang.Object

ubic.basecode.util.r.AbstractRClientTest

Direct Known Subclasses:

JRIClientTest, RServeClientTest

public abstract class AbstractRClientTest
extends Object

Author:

Paul

Field Summary

Fields

Modifier and Type	Field and Description
protected static org.slf4j.Logger	log

Constructor Summary

Constructors

Constructor and Description
AbstractRClientTest()

Method Summary

Modifier and Type	Method and Description
void	testAnovaA()
void	testAnovaB()
void	testAnovaC()
void	testAnovaD()
void	testAnovaE()
void	testAnovaF() One way @
void	testAssignAndRetrieveMatrix()
void	testAssignAndRetrieveMatrixB()
void	testAssignAndRetrieveMatrixC()
void	testAssignAndRetrieveMatrixD()
void	testAssignStringList()
void	testDataFrameA()
void	testDoubleArrayTwoDoubleArrayEval()
void	testDoubleTwoDoubleArrayEval()
void	testExec()
void	testExecDoubleArray()
void	testExecError()
void	testFactorAssign()
void	testFindExecutable()
void	testLimmaA() Also exercises dataFrameEval library(limma) dat<-read.table("data/testdata.txt", header=T, row.names=1) f1<-factor(c("A", "A", "A", "A", "A", "A", "B", "B", "B", "B", "B", "B")); f2<-factor(c("X", "X", "Y", "Y", "Z", "Z", "X", "X", "Y", "Y", "Z", "Z")); cov1<-c( -0.230 , 1.400, -0.210, 0.570, -0.064, 0.980 ,-0.082, -0.094, 0.630, -2.000, 0.640, -0.870); mo<-model.matrix(˜ f1 + f2 + cov1 - 1); contr<-makeContrasts(A-B, levels=mo); fit<-lmFit(dat, mo); fit<-contrasts.fit(fit, contr); fit<-eBayes(fit) res<-topTable(fit)
void	testLinearModelA() Like a two-sample t-test where the intercept is also of interest
void	testLinearModelB() With a continuous covariate as well a categorical one
void	testLinearModelC()
void	testLinearModelD() Basically a one-way anova with 4 levels in the factor
void	testListEvalA()
void	testListEvalB()
void	testLoadLibrary()
void	testLoadScript()
void	testStringListEval()
void	testStringListEvalB()
void	testTTest()
void	testTTestFail()

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

log

protected static org.slf4j.Logger log

Constructor Detail

AbstractRClientTest

public AbstractRClientTest()

Method Detail

testAnovaA

public void testAnovaA()

testAnovaB

public void testAnovaB()

testAnovaC

public void testAnovaC()

testAnovaD

public void testAnovaD()

testAnovaE

public void testAnovaE()

testAnovaF

public void testAnovaF()

One way @

testAssignAndRetrieveMatrix

public void testAssignAndRetrieveMatrix()

testAssignAndRetrieveMatrixB

public void testAssignAndRetrieveMatrixB()

testAssignAndRetrieveMatrixC

public void testAssignAndRetrieveMatrixC()

testAssignAndRetrieveMatrixD

public void testAssignAndRetrieveMatrixD()

testAssignStringList

public void testAssignStringList()

testDataFrameA

public void testDataFrameA()

testDoubleArrayTwoDoubleArrayEval

public void testDoubleArrayTwoDoubleArrayEval()

testDoubleTwoDoubleArrayEval

public void testDoubleTwoDoubleArrayEval()

testExec

public void testExec()

testExecDoubleArray

public void testExecDoubleArray()

testExecError

public void testExecError()

testFactorAssign

public void testFactorAssign()

testFindExecutable

public void testFindExecutable()
                        throws Exception

Throws:

Exception

testLimmaA

public void testLimmaA()
                throws Exception

Also exercises dataFrameEval

 library(limma)
 
 dat<-read.table("data/testdata.txt", header=T, row.names=1)
 
 f1<-factor(c("A", "A", "A", "A", "A", "A", "B", "B", "B", "B", "B", "B"));
 
 f2<-factor(c("X", "X", "Y", "Y", "Z", "Z", "X", "X", "Y", "Y", "Z", "Z"));
 
 cov1<-c( -0.230 , 1.400, -0.210, 0.570, -0.064, 0.980 ,-0.082, -0.094, 0.630, -2.000, 0.640, -0.870);
 
 mo<-model.matrix(˜ f1 + f2 + cov1 - 1);
 
 contr<-makeContrasts(A-B, levels=mo);
 
 fit<-lmFit(dat, mo);
 
 fit<-contrasts.fit(fit, contr);
 
 fit<-eBayes(fit)
 
 res<-topTable(fit)
 

Throws:

Exception

testLinearModelA

public void testLinearModelA()

Like a two-sample t-test where the intercept is also of interest. @

testLinearModelB

public void testLinearModelB()

With a continuous covariate as well a categorical one. @

testLinearModelC

public void testLinearModelC()

testLinearModelD

public void testLinearModelD()

Basically a one-way anova with 4 levels in the factor. @

testListEvalA

public void testListEvalA()
                   throws Exception

Throws:

Exception

testListEvalB

public void testListEvalB()
                   throws Exception

Throws:

Exception

testLoadLibrary

public void testLoadLibrary()

testLoadScript

public void testLoadScript()
                    throws Exception

Throws:

Exception

testStringListEval

public void testStringListEval()

testStringListEvalB

public void testStringListEvalB()

testTTest

public void testTTest()

testTTestFail

public void testTTestFail()