doublesigmoidalFitFormula_h0: Double Sigmoidal Formula with h0

In sicegar: Analysis of Single-Cell Viral Growth Curves

Double Sigmoidal Formula with h0

Description

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

Usage

doublesigmoidalFitFormula_h0(
  x,
  finalAsymptoteIntensityRatio,
  maximum,
  slope1Param,
  midPoint1Param,
  slope2Param,
  midPointDistanceParam,
  h0
)

Arguments

x	the "time" column of the dataframe
finalAsymptoteIntensityRatio	this is the ratio between the final asymptote intensity and maximum intensity of the fitted curve.
maximum	the maximum intensity that the double sigmoidal function reaches.
slope1Param	the slope parameter of the sigmoidal function at the steepest point in the exponential phase of the viral production.
midPoint1Param	the x axis value of the steepest point in the function.
slope2Param	the slope parameter of the sigmoidal function at the steepest point in the lysis phase. i.e when the intensity is decreasing.
midPointDistanceParam	the distance between the time of steepest increase and steepest decrease in the intensity data. In other words the distance between the x axis values of arguments of slope1Param and slope2Param.
h0	the lower asymptote (baseline) intensity

Value

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

Examples

# 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.
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_h0(time,
                                      finalAsymptoteIntensityRatio = .3,
                                      maximum = 4,
                                      slope1Param = 1,
                                      midPoint1Param = 7,
                                      slope2Param = 1,
                                      midPointDistanceParam = 8,
                                      h0 = 1)
intensity <- intensity + intensity_noise

dataInput <- data.frame(intensity = intensity, time = time)
normalizedInput <- normalizeData(dataInput)
parameterVector <- doublesigmoidalFitFunction_h0(normalizedInput, tryCounter = 1)


#Check the results
# doublesigmoidalFitFunction_h0() is run on the startList param values (because 'tryCounter = 1')
# use multipleFitFunction() for multiple random starts in order to optimize
if(parameterVector$isThisaFit){
 intensityTheoretical <-
       doublesigmoidalFitFormula_h0(
               time,
               finalAsymptoteIntensityRatio = parameterVector$finalAsymptoteIntensityRatio_Estimate,
               maximum = parameterVector$maximum_Estimate,
               slope1Param = parameterVector$slope1Param_Estimate,
               midPoint1Param = parameterVector$midPoint1Param_Estimate,
               slope2Param = parameterVector$slope2Param_Estimate,
               midPointDistanceParam = parameterVector$midPointDistanceParam_Estimate,
               h0 = parameterVector$h0_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)
  }

sicegar documentation built on Nov. 16, 2025, 1:07 a.m.