SensorModel-class. chemosensors 0.7.7

Usage

SensorModelNames()

defaultParSensorModel()

SensorModel(...)

Arguments

...: parameters of constructor.

Value

Character vector of model names.

List of the default parameters.

Description

Method coeffNonneg.

Class SensorModel predicts a sensor signal in response to an input concentration matrix by means of a regression model stored in slot dataModel.

Function to get model names of class SensorModel.

Function to get default constructor parameters of class SensorModel.

Constructor method of SensorModel Class.

Wrapper function SensorModel.

Details

The model explicitely assumes that the sensor response to a mixture of analytes is a sum of responses to the individual analyte components. Linear models mvr and plsr follow this assumtion in their nature.

Slots of the class:

`num`	Sensor number (`1:17`). The default value is `1`.
`gases`	Gas indices.
`ngases`	The number of gases.
`gnames`	Names of gases.
`concUnits`	Concentration units external to the model, values given in an input concentration matrix.
`concUnitsInt`	Concentration units internal for the model, values used numerically to build regression models.
`dataModel`	Data model of class `SensorDataModel` performs a regression (free of the routine on units convertion, etc).
`coeffNonneg`	Logical whether model coefficients must be non-negative. By default, `FALSE`.
`coeffNonnegTransform`	Name of transformation to convert negative model coefficients to non-negative values.
`beta`	(parameter of sensor diversity) A scaling coefficient of how different coefficients of `SensorDataModel` will be in comparision with those coefficients of the UNIMAN sensors. The default value is `2`.

Methods of the class:

`predict`	Predicts a sensor model response to an input concentration matrix.
`coef`	Extracts the coefficients of a regression model stored in slot `dataModel`.

The plot method has two types (parameter y):

`response`	(default) Shows the sensitivity curves per gas in normalized concentration units.
`predict`	Depicts input (parameter `conc`) and ouput of the model for a specified gas (parameter `gases`).

Examples


# sensor model: default initialization
sm <- SensorModel()

# get information about the model
show(sm)

 Sensor Model (num 1), beta 2, data model 'ispline'

print(sm)

 Sensor Model
 - num 1 
 - beta 2 
 - 3 gases A, B, C 
 - (first) data model
 - method: ispline (type: spline)
 - sensor model: coeffNonneg TRUE
   -- coefficients (first): 7.5816, 2.5812, 0 ... 0 


print(coef(sm)) # sensitivity coefficients

          [,1]
 [1,] 7.581598
 [2,] 2.581216
 [3,] 0.000000
 [4,] 7.194504
 [5,] 0.946807
 [6,] 0.000000
 [7,] 7.869399
 [8,] 1.551513
 [9,] 0.000000


plot(sm)  



# get available model names
model.names <- SensorModelNames()
print(model.names)

[1] "plsr"         "mvr"          "broken-stick" "ispline"     


# sensor model: custom parameters
sm <- SensorModel(num=7, model="plsr", gases=c(1, 3))

print(sm)

 Sensor Model
 - num 7 
 - beta 2 
 - 2 gases A, C 
 - (first) data model
 - method: plsr (type: mvr)
 - ncomp: 2 
 - sensor model: coeffNonneg FALSE
   -- coefficients (first): 0.186, 0.0116 


#plot(sm, uniman=TRUE) # add UNIMAN reference data (the model was build from)

# method plot
#  - plot types 'y': response, predict
sm <- SensorModel() # default sensor model

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


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

conc <- concSample(sm, "range", gases=1, n=10)
plot(sm, "predict", conc, gases=1, main="plot(sm, 'predict', conc, gases=1)")