categorize: Categorize input data by comparing the AIC values of the...
In sicegar: Analysis of Single-Cell Viral Growth Curves

Description Usage Arguments Value Examples

Catagorizes the input data using the results of two model fitsand chosen thresholds.

categorize(
  parameterVectorSigmoidal,
  parameterVectorDoubleSigmoidal,
  threshold_intensity_range = 0.1,
  threshold_minimum_for_intensity_maximum = 0.3,
  threshold_bonus_sigmoidal_AIC = 0,
  threshold_sm_tmax_IntensityRatio = 0.85,
  threshold_dsm_tmax_IntensityRatio = 0.75,
  threshold_AIC = -10,
  threshold_t0_max_int = 0.05,
  showDetails = FALSE
)

`parameterVectorSigmoidal`	Output of the sigmoidalFitFunction.
`parameterVectorDoubleSigmoidal`	Output of the doublesigmoidalFitFunction.
`threshold_intensity_range`	Minimum for intensity range, i.e. it is the lower limit for the allowed difference between the maximum and minimum of the intensities (Default is 0.1, and the values are based on actual, not the rescaled data.).
`threshold_minimum_for_intensity_maximum`	Minimum allowed value for intensity maximum. (Default is 0.3, and the values are based on actual, not the rescaled data.).
`threshold_bonus_sigmoidal_AIC`	Bonus AIC points for sigmoidal fit. Negative values help the sigmoidal model to win. Only helps in competition between sigmoidal and double sigmoidal fit at decision step "9", i.e. if none of the models fail in any of the tests and stay as a candidate until the last step (Default is 0).
`threshold_sm_tmax_IntensityRatio`	The threshold for the minimum intensity ratio between the last observed time points intensity and theoretical maximum intensity of the sigmoidal curve. If the value is below the threshold, then the data can not be represented with the sigmoidal model. (Default is 0.85)
`threshold_dsm_tmax_IntensityRatio`	The threshold for the minimum intensity ratio between the last observed time points intensity and maximum intensity of the double sigmoidal curve. If the value is above the threshold, then the data can not be represented with the double sigmoidal model. (Default is 0.75)
`threshold_AIC`	Maximum AIC values in order to have a meaningful fit (Default is -10).
`threshold_t0_max_int`	Maximum allowed intensity of the fitted curve at time is equal to zero (t=0). (Default is 0.05, and the values are based on actual, not the rescaled data.).
`showDetails`	Logical to chose if we want to see details or not. Default is "FALSE"

The returned object contains extensive information about the decision process, but the key component is the decision variable. The decision variable can be one of the following four; "no_signal", "infection","infection&lysis" or "ambugious".

# Example 1 with double sigmoidal data
time=seq(3, 24, 0.1)

#simulate intensity data and add noise
noise_parameter <- 0.2
intensity_noise <- runif(n = length(time), min = 0, max = 1) * noise_parameter
intensity <- sicegar::doublesigmoidalFitFormula(time,
                                               finalAsymptoteIntensityRatio = .3,
                                               maximum = 4,
                                               slope1Param = 1,
                                               midPoint1Param = 7,
                                               slope2Param = 1,
                                               midPointDistanceParam = 8)
intensity <- intensity + intensity_noise

dataInput <- data.frame(intensity = intensity, time = time)
normalizedInput <- sicegar::normalizeData(dataInput,
                                         dataInputName = "sample001")


# Fit sigmoidal model
sigmoidalModel <- sicegar::multipleFitFunction(dataInput = normalizedInput,
                                              model = "sigmoidal",
                                              n_runs_min = 20,
                                              n_runs_max = 500,
                                              showDetails = FALSE)

# Fit double sigmoidal model
doubleSigmoidalModel <- sicegar::multipleFitFunction(dataInput = normalizedInput,
                                                    model = "doublesigmoidal",
                                                    n_runs_min = 20,
                                                    n_runs_max = 500,
                                                    showDetails = FALSE)


# Calculate additional parameters
sigmoidalModel <- sicegar::parameterCalculation(sigmoidalModel)
doubleSigmoidalModel <- sicegar::parameterCalculation(doubleSigmoidalModel)

outputCluster <- sicegar::categorize(parameterVectorSigmoidal = sigmoidalModel,
                                    parameterVectorDoubleSigmoidal = doubleSigmoidalModel)

utils::str(outputCluster)