doublesigmoidalFitFormula {sicegar}

Double Sigmoidal Formula

Description

Calculates intensities using the double-sigmoidal model fit and the parameters (maximum, final asymptote intensity, slope1Param, midpoint1Param, slope2Param, and mid point distance).

Usage

doublesigmoidalFitFormula(
  x,
  finalAsymptoteIntensityRatio,
  maximum,
  slope1Param,
  midPoint1Param,
  slope2Param,
  midPointDistanceParam
)

Arguments

x

the "time" (time) column of the dataframe

finalAsymptoteIntensityRatio

This is the ratio between asymptote intensity and maximum intensity of the fitted curve.

maximum

the maximum intensity that the double sigmoidal function reach.

slope1Param

the slope parameter of the sigmoidal function at the steppest point in the exponential phase of the viral production.

midPoint1Param

the x axis value of the steppest point in the function.

slope2Param

the slope parameter of the sigmoidal function at the steppest point in the lysis phase. i.e when the intensity is decreasing.

midPointDistanceParam

the distance between the time of steppest increase and steppest decrease in the intensity data. In other words the distance between the x axis values of arguments of slope1Param and slope2Param.

Value

Returns the predicted intensities for the given time points with the double-sigmoidal fitted parameters for the double sigmoidal fit.

Examples

time <- seq(3, 24, 0.1)

#simulate intensity data and add noise
noise_parameter <- 0.2
intensity_noise <- stats::runif(n = length(time), min = 0, max = 1) * noise_parameter
intensity <- 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 <- normalizeData(dataInput)
parameterVector <- doublesigmoidalFitFunction(normalizedInput, tryCounter = 2)


#Check the results
if(parameterVector$isThisaFit){
 intensityTheoretical <-
       doublesigmoidalFitFormula(
               time,
               finalAsymptoteIntensityRatio = parameterVector$finalAsymptoteIntensityRatio_Estimate,
               maximum = parameterVector$maximum_Estimate,
               slope1Param = parameterVector$slope1Param_Estimate,
               midPoint1Param = parameterVector$midPoint1Param_Estimate,
               slope2Param = parameterVector$slope2Param_Estimate,
               midPointDistanceParam = parameterVector$midPointDistanceParam_Estimate)

 comparisonData <- cbind(dataInput, intensityTheoretical)

 require(ggplot2)
 ggplot(comparisonData) +
   geom_point(aes(x = time, y = intensity)) +
   geom_line(aes(x = time, y = intensityTheoretical)) +
   expand_limits(x = 0, y = 0)
   }

if(!parameterVector$isThisaFit){
  print(parameterVector)
  }