## ----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)