enm.proast61: PROAST61.5 functionalities

In GTsiliki/IntPROAST: Dose-response modelling for continuous data

Description

Implementation of a continuous variable dose-response PROAST61.5 model scenario.

Usage

enm.proast61(dataset, predictionFeature, parameters)

Arguments

dataset	list of 2 objects, datasetURI:= character sring, code name of dataset, dataEntry:= data frame with 2 columns
predictionFeature	character string specifying which is the prediction feature in dataEntry
parameters	a list of one object called indiVariable to indicate the 'dose' variable.

Details

Currently regression, exponential and Hill models are included for continuous data. For more details please see the PROAST61.5 manual file.

Value

A List

singleCalculations	A print out of the PROAST report in screen.
arrayCalculations	A pmml data frame with likelihood values for all models
figures	Three graphs as produced by the PROAST 61.3 software

Note

Extra notes coming soon

Author(s)

Georgia Tsiliki

References

http://www.rivm.nl/en/

See Also

http://www.rivm.nl/en/

Examples

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

data("dat1")

responseF<- "https://apps.ideaconsult.net/ambit2/feature/22137"
doseF<- list(indiVariable=c("https://apps.ideaconsult.net/ambit2/feature/22127"))

#.ypos<- 95
example1<- enm.proast61(dat1,responseF,doseF) 

GTsiliki/IntPROAST documentation built on June 2, 2019, 5:54 p.m.