sicegar (version 0.2.4)

sigmoidalFitFormula: sigmoidalFitFormula

Description

Calculates intesities for given time points (x) by using sigmoidal fit model and parameters (maximum, slopeParam, and midpoint).

Usage

sigmoidalFitFormula(x, maximum, slopeParam, midPoint)

Arguments

x

the "time" (time) column of the dataframe.

maximum

the maximum intensity that the sigmoidal function can reach while time approaches infinity.

slopeParam

the slope parameter of the sigmoidal function at the steppest point.

midPoint

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

Value

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

Examples

# NOT RUN {
time <- seq(3, 24, 0.5)

#simulate intensity data and add noise
noise_parameter <- 0.1
intensity_noise <- stats::runif(n = length(time), min = 0, max = 1) * noise_parameter
intensity <- sigmoidalFitFormula(time, maximum = 4, slopeParam = 1, midPoint = 8)
intensity <- intensity + intensity_noise

dataInput <- data.frame(intensity = intensity, time = time)
normalizedInput <- normalizeData(dataInput)
parameterVector <- sigmoidalFitFunction(normalizedInput, tryCounter = 2)

#Check the results
if(parameterVector$isThisaFit){
 intensityTheoretical <- sigmoidalFitFormula(time,
                                             maximum = parameterVector$maximum_Estimate,
                                             slopeParam = parameterVector$slopeParam_Estimate,
                                             midPoint = parameterVector$midPoint_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)
}



# }