Package chemosensors.

Usage

loadUNIMANdata(dataset)

subClasses(class)

ConcUnitsNames()

defaultDataModel()

defaultConcUnitsInt()

defaultConcUnits()

defaultConcUnitsSorption()

defaultSet()

defaultDataPackage()

defaultDataSensorModel()

defaultDataDistr()

defaultDataSensorNoiseModel()

defaultDataSorptionModel()

defaultDataDriftNoiseModel()

Arguments

dataset

Name of dataset to be loaded.

class

Class name.

Examples

# concentration matrix of 3 gas classes: A, C and AC
conc <- matrix(0, 60, 3)
conc[1:10, 1] <- 0.01 #  A0.01
conc[11:20, 3] <- 0.1 # C0.1
conc[21:30, 1] <- 0.05 #  A0.05
conc[31:40, 3] <- 1 # C1
conc[41:50, 1] <- 0.01 # A0.01C0.1
conc[41:50, 3] <- 0.1 # A0.01C0.1
conc[51:60, 1] <- 0.05 # A0.05C1
conc[51:60, 3] <- 1 # A0.05C1

conc <- conc[sample(1:nrow(conc)), ]

# sensor array of 20 sensors with parametrized noise parameters
sa <- SensorArray(nsensors=20, csd=0.1, ssd=0.1, dsd=0.1)

# get information about the array
print(sa)

 SensorArray
 - enableSorption: TRUE 
 (1) Sensor Model
 - num 1, 2, 3 ... 3 
 - beta 2 
 - 3 gases A, B, C 
 - (first) data model
 - method: ispline (type: spline)
 - sensor model: coeffNonneg TRUE
   -- coefficients (first): 3.4868, 3.4731, 4.3785 ... 4.5575 
 (2) Sorption Model
 - knum 1, 2, 3 ... 3 
 - 3 gases A, B, C 
 (3) Concentration Noise Model
 - 3 gases A, B, C 
 - csd: 0.1 
 - noise type: logconc 
 - log-factor: 1, 1, 2 
 (4) Sensor Noise Model
 - num 1, 2, 3 ... 3 
 - 3 gases A, B, C 
 - ssd: 0.1 
 - noise type: randomWalk 
 - noise-factor: 1, 1, 1, 1, 1, 1, 1, 1, 1 
 (5) Drift Noise Model
 - num 1, 2, 3 ... 3 
 drift common model
 - method: cpc 
 - ndcomp: 1 

plot(sa)



# generate the data
sdata <- predict(sa, conc)

# plot the data
plot(sa, "prediction", conc=conc, sdata=sdata, leg="top")