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Wed Mar 2 14:48:07 2016 UTC (2 years, 11 months ago) by variani
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new Rd produced by roxygen2
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ChemosensorsClass.R, R/SorptionModelClass.R, R/SorptionModelClassMethods.R
\title{Method knum.}





\S4method{initialize}{SorptionModel}(.Object, gases = "numeric",
  gnames = "character", concUnits = "character",
  concUnitsSorption = "character", datasetSorptionModel = "character",
  pck = "character", knum = "numeric", srdata, Qequal = "logic",
  Knorm = "logic", Kmin = "numeric", Kmax = "numeric",
  alpha = "numeric", ...)

\item{...}{parameters of constructor.}
List of the default parameters.
Method knum.

Method concUnitsSorption.

Class \code{\link{SorptionModel}} controls the the amount of gas absorbed 
by the sensor, that emulates the non-linearity nature intrinsic for the polymeric sensors.

Function to get default constructor parameters of class \code{\link{SorptionModel}}.

Constructor method of SorptionModel Class.

Wrapper function SorptionModel.
The model is based on the extended Langmuir isotherm for a multi-component gas mixture,
and has two parameters per analyte, \code{Q} denotes the sorption capacity and
\code{K} stands for the sorption affinity.

Slots of the class:
  \code{knum} \tab  \cr
  \code{sorptionModel} \tab List of model parameters 'K' and 'Q'. \cr
Slots of the class:
  \code{knum} \tab Sensor number that encodes a UNIMAN sorption profile (\code{1:17}). The default value is \code{1}. \cr
  \code{gases} \tab Gas indices. \cr
  \code{ngases} \tab The number of gases. \cr
  \code{gnames} \tab Names of gases. \cr
  \code{concUnits} \tab Concentration units external to the model, values given in an input concentration matrix. \cr
  \code{concUnitsInt} \tab Concentration units internal for the model, values used numerically to evaluate the Langmuir relation. \cr
  \code{sorptionModel} \tab A list that contains the Langmuir parameters. \cr
  \code{srdata} \tab The reference data of Lanmuir parameters from UNIMAN dataset (see \code{\link{UNIMANsorption}}). \cr
  \code{alpha} \tab (parameter of sensor non-linearity in mixtures) A scaling coefficient of non-linearity 
    induced via the affinity parameter \code{K}. The default value is \code{2.25}. \cr

Methods of the class:
  \code{predict} \tab Predicts a model response to an input concentration matrix. \cr

The \code{plot} method has three types (parameter \code{y}):
  \code{response} \tab (default) Shows a modeled trasnformation of concentration profile per analyte. \cr
  \code{data} \tab  Shows the reference data from UNIMAN dataset. \cr
  \code{predict} \tab  Depicts input and ouput of the model for all analytes. \cr
We introduce a single parameter \code{alpha} of the model to control the level of non-linearity
simulated by the Langmuir isotherm. 
This parameter \code{alpha} defines a normalization across the 17 UNIMAN sorption profiles from dataset \code{\link{UNIMANsorption}},
scaling \code{K} values based on other two parameters \code{Kmin} (default value \code{1} and \code{Kmax} (default value \code{150}).
Normalization can be disable by setting parameter \code{Knorm} to \code{FALSE},
that results in usage of the sorption \code{K} parameters, equal to UNIMAN ones.
# sorption model: default initialization
sm <- SorptionModel()

# get information about the model


# model: custom parameters
# almost linear model
sm <- SorptionModel(alpha=0.01, gases=c(1, 3)) 
plot(sm, main="Almost linear model, non-linearity 0.01")

# non-linear model
sm <- SorptionModel(alpha=0.3, gases=c(1, 3)) 
plot(sm, main="Non-linear model, non-linearity 0.5")

# saturated model
sm <- SorptionModel(alpha=0.5, gases=c(1, 3)) 
plot(sm, main="Saturated model, non-linearity 1")

# model with UNIMAN sorption parameters
sm <- SorptionModel(Knorm=FALSE, gases=c(1, 3)) 
plot(sm, main="Model with UNIMAN parameters (no normalization)")

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

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

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

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