clustDRM (version 0.1-0)

plotDoseResponseData: plot dose-response curves

Description

function to plot dose-response curves with the possibility of adding lines indicating average response per dose levels. Also, provided a pattern for the dose-response curve, it can estimate the expected mean values per dose level for the given pattern and add them to the plot.

Usage

plotDoseResponseData(inputDataset, dose, response, ID, subjectID,
  xlab = "Dose", ylab = "Response", addMean = TRUE,
  drcPattern = NULL)

Arguments

inputDataset

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

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.

subjectID

single input as the same type as given ID column with the ID of the subject to plot.

xlab

single string with default "dose", the label on x axis.

ylab

single string with default "response", the label on y axis.

addMean

logical variable indicating whether mean values (connecting with lines) should be plotted or not.

drcPattern

single string showing the idetified pattern using clustering algorithms. The default is NULL. In such case, no extra line will be added to the plot regarding the estimated means via the identified pattern.

Value

make a plot.

Details

with addMean = TRUE, a line will be added to the plot, connecting the averaged response per dose level. But when a pattern is provided for the dose-response curve via drcPattern, then a line will be added to the data with the means estimated assuming the identified pattern. If both addMEan = TRUE and drcPattern != NULL, then two lines will be added to the plot. The line in purplish-colored with cross signs as points is the averaged response value per dose level, and the bluish-colored line with circled cross signs as points represents the estimated mean based on the pattern.

Examples

# NOT RUN {
## gnerating data, a sample of size 20
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)
}
## plotting dose response relation
plotDoseResponseData(generatedData, 2, 3, 1, 2)
## transforming it for clustering
plotDoseResponseData(generatedData, 2, 3, 1, 2, 
addMean = FALSE, 
		drcPattern = "increasing")

# }