SensorArray-class. chemosensors 0.7.7

Usage

defaultParSensorArray()

SensorArray(...)

Sensor(num = 1, ...)

Arguments

...: parameters of constructor.
num: Type of sensors (or UNIMAN number).

Value

List of the default parameters.

Description

Method getSensor.

Method affinity.

Class SensorArray is a extension of the class Sensor for many sensor elements.

Function to get default constructor parameters of class SensorArray.

Constructor method of SensorArray Class.

Wrapper function SensorArray.

Wrapper function Sensor

Details

The array aggregates classes ConcNoiseModel, SensorNoiseModel, SorptionModel, SensorModel and DriftNoiseModel.

In comparision to the class Sensor, slot num is a numeric vector, and class SensorArray also inherits class DriftNoiseModel.

See Sensor and DriftNoiseModel for more details.

Slots of the class:

`type`	Sensor type (not used). Default value is `polymeric`.
`idx`	Sensor index (unique ID number).
`enableSorption`	Boolean whether `SorptionModel` is enabled. Default value is `TRUE`.
`...`	Slots inherited from super-classes `ConcNoiseModel`, `SensorNoiseModel`, `SorptionModel`, `SensorModel` and `DriftNoiseModel`.

Methods of the class:

`predict`	Predicts a model response to an input concentration matrix.
`coef`	Extracts the coefficient matrix from sensors.
`csd`	Gets the concentration noise level (inherited from class `ConcNoiseModel`).
`csd<-`	Sets the concentration noise level.
`ssd`	Gets the sensor noise level (inherited from class `SensorNoiseModel`).
`ssd<-`	Sets the sensor noise level.

The plot method has the only type (parameter y):

response (default) Shows the sensitivity curves per gas in normalized concentration units.

Examples


# array: default initialization
sa <- SensorArray()

# get information about the array
show(sa)

 Sensor Array of 2 sensors, 3 gases A, B, C
 - enableSorption TRUE, enableDyn FALSE
 - Sensor Model (num 1, 2), beta 2, data model 'ispline'
 - Sorption Model (knum 1, 2), alpha 2.25
 - Concentration Noise Model (csd 0.1), noise type 'logconc'
 - Sensor Noise Model (ssd 0.1), noise type 'randomWalk'
 - Drift Noise Model (dsd 0.1), common model 'cpc'

print(sa)

 SensorArray
 - enableSorption: TRUE 
 (1) Sensor Model
 - num 1, 2 
 - beta 2 
 - 3 gases A, B, C 
 - (first) data model
 - method: ispline (type: spline)
 - sensor model: coeffNonneg TRUE
   -- coefficients (first): 3.2174, 3.8031, 4.4229 ... 4.2032 
 (2) Sorption Model
 - knum 1, 2 
 - 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 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 
 drift common model
 - method: cpc 
 - ndcomp: 1 


print(coef(sa)) # array coefficients

          [,1]     [,2]
 [1,] 3.217389 3.008966
 [2,] 3.803072 3.643202
 [3,] 4.422950 4.299156
 [4,] 5.350381 5.336516
 [5,] 2.577624 2.611796
 [6,] 0.000000 0.000000
 [7,] 3.002658 2.807784
 [8,] 3.602933 3.424680
 [9,] 4.203208 4.041576


#plot(sa)  

# model: custom parameters
sa <- SensorArray(num=1:17) # 17 UNIMAN virtual sensors
plot(sa, main="17 UNIMAN virtual sensors")



# array with quite linear sensors
sa <- SensorArray(num=15:17, alpha=0.01, model="mvr") 
print(sa)

 SensorArray
 - enableSorption: TRUE 
 (1) Sensor Model
 - num 15, 16, 17 
 - beta 2 
 - 3 gases A, B, C 
 - (first) data model
 - method: mvr (type: mvr)
 - sensor model: coeffNonneg TRUE
   -- coefficients (first): 0.4382, 0, 0.0328 
 (2) Sorption Model
 - knum 15, 16, 17 
 - 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 15, 16, 17 
 - 3 gases A, B, C 
 - ssd: 0.1 
 - noise type: randomWalk 
 - noise-factor: 1, 1, 1 
 (5) Drift Noise Model
 - num 15, 16, 17 
 drift common model
 - method: cpc 
 - ndcomp: 1 


# add UNIMAN reference data (the models were build from)
p1 <- plotResponse(sa, main="Array of more linear sensors") 
# sensor object: default initialization
s <- Sensor()

# get information about the sensor
show(s)

 Sensor Array of 1 sensors, 3 gases A, B, C
 - enableSorption TRUE, enableDyn FALSE
 - Sensor Model (num 1), beta 2, data model 'ispline'
 - Sorption Model (knum 1), alpha 2.25
 - Concentration Noise Model (csd 0.1), noise type 'logconc'
 - Sensor Noise Model (ssd 0.1), noise type 'randomWalk'
 - Drift Noise Model (dsd 0.1), common model 'cpc'

print(s)

 SensorArray
 - enableSorption: TRUE 
 (1) Sensor Model
 - num 1 
 - beta 2 
 - 3 gases A, B, C 
 - (first) data model
 - method: ispline (type: spline)
 - sensor model: coeffNonneg TRUE
   -- coefficients (first): 3.2174, 3.8031, 4.4229 ... 4.2032 
 (2) Sorption Model
 - knum 1 
 - 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 
 - 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 
 drift common model
 - method: cpc 
 - ndcomp: 1 


plot(s)  



# sensor object: custom parameters
s <- Sensor(num=5, enableSorption=FALSE) # sorption model disabled
plot(s, main="Sensor with sorption model disabled")



s <- Sensor(num=5, alpha=0.01) # amost linear sensor
plot(s, main="Almost linear sensor, non-linearity 0.01")



s <- Sensor(num=5, alpha=1) # saturated sensor
plot(s, main="Saturated sensor, non-linearity 1")



s <- Sensor(num=5, csd=0, ssd=0, dsd = 0) # noise level is set to zero
plot(s, "snoise", main="Noise-free sensor")



s <- Sensor(num=5, csd=1, ssd=1, dsd = 0) # maximum reasonable level of noise
plot(s, "snoise", main="Very noisy sensor")



# method plot
#  - plot types 'y': response, noise
s <- Sensor() # default model

plot(s, "response", main="plot(s, 'response')") 


# default plot type, i.e. 'plot(s)' does the same plotting

plot(s, "snoise", main="plot(s, 'snoise')")