Nothing
## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(echo = TRUE)
## ----install packages, echo=FALSE, warning=FALSE, results='hide',message=FALSE----
###*****************************
# INITIAL COMMANDS TO RESET THE SYSTEM
seedNo=14159
set.seed(seedNo)
###*****************************
###*****************************
require("sicegar")
require("dplyr")
require("ggplot2")
require("cowplot")
###*****************************
## ----fig.height=4, fig.width=4------------------------------------------------
time <- seq(3, 24, 0.5)
noise_parameter <- 0.2
intensity_noise <- runif(n = length(time), min = 0, max = 1) * noise_parameter
intensity <- doublesigmoidalFitFormula(time,
finalAsymptoteIntensityRatio = .3,
maximum = 4,
slope1Param = 1,
midPoint1Param = 7,
slope2Param = 1,
midPointDistanceParam = 8)
intensity <- intensity+intensity_noise
dataInput <- data.frame(time, intensity)
ggplot(dataInput, aes(time, intensity)) + geom_point() + theme_bw()
## -----------------------------------------------------------------------------
fitObj <- fitAndCategorize(dataInput,
threshold_minimum_for_intensity_maximum = 0.3,
threshold_intensity_range = 0.1,
threshold_t0_max_int = 0.05)
## ----fig.height=4, fig.width=8------------------------------------------------
# Double-sigmoidal fit with parameter related lines
fig_a <- figureModelCurves(dataInput = fitObj$normalizedInput,
sigmoidalFitVector = fitObj$sigmoidalModel,
showParameterRelatedLines = TRUE)
fig_b <- figureModelCurves(dataInput = fitObj$normalizedInput,
doubleSigmoidalFitVector = fitObj$doubleSigmoidalModel,
showParameterRelatedLines = TRUE)
plot_grid(fig_a, fig_b, ncol = 2) # function from the cowplot package
## -----------------------------------------------------------------------------
fitObj$decisionProcess$decision # final decision
## -----------------------------------------------------------------------------
names(fitObj)
## ----The results of the double_sigmoidal fit----------------------------------
str(fitObj$summaryVector)
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