clustDRM (version 0.1-0)

inputDataMaker: Creating suitable inputData for clustering of the dose-response curve patterns

Description

function to create needed information as the input of the functions to cluster dose-response cruve patterns.

Usage

inputDataMaker(dose, response, ID, inputDataset)

Arguments

dose

either a single string or a scalar, indicating the name of the dose column or its index.

response

either a single string or a scalar, indicating the name of the response column or its index.

ID

either a single string or a scalar, indicating the name of the ID column or its index.

inputDataset

a data frame containing the input dataset, it should at least include dose, response, and ID

Value

a list with the following elements:

inputDataset: includes the ID (first column), and the response for all doses with their replications for each subject as rows. doseLevels: unique dose levels numReplicatrions: number of replicatios per each unique dose level. colsData: the index of columns with responses. colID: the index of ID column.

Details

Note that the output of this function can be feed into the function for clustering dose-response curve patterns.

Examples

# NOT RUN {
 
## gnerating data
set.seed(11)
doses2Use <-  c(0, 5, 20)
numRep2Use <- c(3, 3, 3)
generatedData <- cbind(rep(1,sum(numRep2Use)), 
MCPMod::genDFdata("logistic",c(5, 3, 10, 0.05), 
doses2Use, numRep2Use, 1), 
		matrix(rnorm(1*sum(numRep2Use)), sum(numRep2Use), 1))
colnames(generatedData) <- c("ID", "dose", "response", "x1")
for (iGen in 2:15){
	genData0 <- cbind(rep(iGen,sum(numRep2Use)), 
MCPMod::genDFdata("logistic",c(5, 3, 10, 0.05), 
doses2Use, numRep2Use, 1), 
			matrix(rnorm(1*sum(numRep2Use)), sum(numRep2Use), 1))
	colnames(genData0) <- c("ID", "dose", "response", "x1")
	generatedData <- rbind(generatedData, genData0)
}
## transforming it for clustering
toInput <- inputDataMaker(2, 3, 1, generatedData)

# }