SensorModelNames() defaultParSensorModel() SensorModel(...)

- ...
- parameters of constructor.

Character vector of model names.

List of the default parameters.

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.

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` ). |

# 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 ... 0print(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,] 0.000000plot(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)")

`UNIMANshort`