Package 'permRand'

Data Randomization

Description

Randomizes aliquots to batches.

Usage

allRand(dataR, batchTot, numQC, withinN, numMatch, chkRep)

Arguments

dataR	Data for randomization.
batchTot	c(batchTot1, batchTot2) sizes of plates, just use one plate per batch, batch size inclusive of QC samples.
numQC	Number of QC samples per batch.
withinN	Number of samples away that the QC samples must be from each other.
numMatch	Number of QC samples from a single mother within a batch.
chkRep	Check if there is a repeat of the groups within the batches.

Value

A dataset with serum order randomized.

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Tests the number in each batch.

Description

Counts the number of samples that are in each of the batches.

Usage

batchCount(dataS, batchSizeT)

Arguments

dataS	Test dataset.
batchSizeT	Batch size to test for.

Value

The ID of any batch that does not contain the specified number of samples.

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Number of QC in each batch.

Description

Count how many QC samples are in each of the batches, and if it doesn't match the number specified.

Usage

countQC(dataS, QCN)

Arguments

dataS	Test dataset.
QCN	Number of QC samples per batch.

Value

The output includes any batches that does not contain the number of QC samples specified.

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Formats data

Description

The function formatRand formats the dataset for the randomization function. This function inputs serum data for both the study subjects and QC.

Usage

formatRand(QCdata, serumIDR, serumPack)

Arguments

serumIDR	Serum data with serumIDs.
serumPack	Serum data with packing lists.
QCData	QC data.

Value

A dataset that is formatted and ready for the randomization file.

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Number of single group in sequence.

Description

Tests if a large number of cases or controls are next to each other.

Usage

orderCases(dataI, betW)

Arguments

dataI	Dataset to be tested.
betW	Number of cases or controls to check if they are next to each other.

Value

The output stores if there are any cases or controls together beyond a certain specified value.

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Output labels.

Description

Creates output labels for the randomized groups.

Usage

outputLab(dataOut, blind, origP, maxRows, maxCols, newPack)

Arguments

dataOut	Dataset to be formatted for packing list.
origP	Indicator, 0/1, inclusion of the original packing location (1) or deletion of the packing location (0).
maxRows	Maximum row for the output dataset.
maxCols	Maximum column for the output dataset.
newPack	0/1 indicator to generate new packing locations.
Blind	Indicator 0/1 select if a blinded (1) or unblinded(0) packing list is to be generated.

Value

A dataset to be used for packing lists.

Examples

blind <- outputLab(dataOut=serumRand,blind=1,origP=0,maxRows=9,maxCols=9,newPack=1)
unBlindSw <- outputLab(serumSwitchP,blind=0,origP=.,maxRows=.,maxCols=.,newPack=0)

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QC Identifiers

Description

We first assign IDs linking mother/child and events using the function randTest.

Usage

randTest(dataMom, dataChild, maxAliq, nEvent)

Arguments

dataMom	The mother dataset.
dataChild	Child dataset.
maxAliq	Number of aliquots per mother aliquot.
nEvent	Number of aliquots per each of event or lab.

Value

The output of the function is a dataset with the ID links.

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Switching Generating Function

Description

Minimizes switches without completely re-randomizing the locations.

Usage

switchR(dataIn, numqc, numqcM, batchS)

Arguments

dataIn	Randomized dataset.
numqc	Number of QC samples per set.
numqcM	Numberof QC matching samples.
batchS	New batch size.

Value

A dataset with switches indicated.

Examples

serumSwitch <- switchR(dataIn=serumRand,numqc=2,numqcM=2,batchS=43)

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Sample groups within batches

Description

Tests and finds sample groups that are across batches.

Usage

testCCAcross(dataS)

Arguments

dataS

The test dataset.

Value

The output lists all batches with not enough QC sample sets or the QC samples do not come from the same mother.

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Ensures complete sets.

Description

Tests if sets are next to each other. Any sets that are not next to each other are flagged.

Usage

testPair(dataS)

Arguments

dataS

Test dataset.

Value

The output reports any sets that are separated in the "loc".

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Tests QC matches

Description

Measures that every batch has at least the specified number of matching QC sample sets in a batch.

Usage

testQCmatch(dataS, numQCs, numMatch)

Arguments

dataS	Randomized data.
numQCs	Number of QCs specified per dataset.
numMatch	Number of QC samples form a single mother within a batch.

Value

The output lists all batches with not enough QC sample sets or the QC samples do not come from the same mother.

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Test Dataset

Description

Generates test data for the randomization functions.

Usage

testRand(rowSize, colSize, studySize, expNS, numCC, QCpct, child)

Arguments

rowSize	Max row size.
colSize	Max column size.
studySize	Number of cases.
expNS	Number of aliquots per case/control.
numCC	Number of controls per case.
QCpct	Percent of QCs for number of samples.
child	Number of children per "mother' aliquot.

Value

A practice dataset.

Examples

testR <- testRand(rowSize=20,colSize=15,studySize=1000,expNS=7000,numCC=2,QCpct=0.05,child=4)

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Unique IDs

Description

Test for unique IDs.

Usage

uniqueID(testD, IDN)

Arguments

testD	Test dataset.
IDN	ID to test.

Value

Any IDs that are not unique.

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