# Categorize input data by comparing the AIC values of the three fitted models. Categorizes the input data using the results of two model fits and chosen thresholds. ```r 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 = 1e+10, showDetails = FALSE ) ``` ## Arguments - `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 normalized 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 intensity at the last observed time points and the 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 intensity at the last observed time points and the 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 1E10, and the values are based on actual, not the normalized data.). - `showDetails`: Logical to choose if we want to see details or not. Default is "FALSE" ## Returns 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", "sigmoidal","double sigmoidal" or "ambiguous". ## Examples ```r # runif() is used here for consistency with previous versions of the sicegar package. However, # rnorm() will generate symmetric errors, producing less biased numerical parameter estimates. # We recommend errors generated with rnorm() for any simulation studies on sicegar. # 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) ```

categorize function