/~ubarron/tags/predictive-microbiology/ Recent content in Predictive microbiology on Ursula Gonzales Barron Hugo -- gohugo.io en-us © {year} Sun, 28 Apr 2019 21:13:14 -0500 /~ubarron/tutorial/rcode/2019-04-28-gompertzmodel/ Sun, 28 Apr 2019 21:13:14 -0500 /~ubarron/tutorial/rcode/2019-04-28-gompertzmodel/ Using the data set “gomp1.csv”, find the parameters of the reparameterised Gompertz model. \[\begin{equation} y= y_0 + (y_{max} -y_0)*exp(-exp(k*(lag-x)/(y_{max}-y_0) + 1) ) \end{equation}\] Import the data set. dat <- read.csv("gomp1.csv", sep=";", header=T) plot(dat$Time, dat$logN) str(dat) ## 'data.frame': 13 obs. of 2 variables: ## $ Time: int 0 2 4 6 8 10 12 14 16 18 ... ## $ logN: num -0.105 0.108 -0.111 0.734 2.453 ... The next step is define the Gompertz function. /~ubarron/tutorial/rcode/2019-04-25-firstordermodel/ Thu, 25 Apr 2019 21:13:14 -0500 /~ubarron/tutorial/rcode/2019-04-25-firstordermodel/ The data set “FirstOrder.csv” contains observations of microbial concentrations (log N) measured at different times (t) at a given environmental condition. Lets fit a first-order growth kinetics model \(log N = log N_0 + k \times t\) to the experimental data. Let’s import the “FirstOrder.csv” dataset, and observe the first five lines. dat <- read.csv("FirstOrder.csv", sep=";", header=TRUE) dat ## Time N ## 1 0 37.298 ## 2 1 56.149 ## 3 2 81. /~ubarron/publication/journal-article/cadavez2019-dynamic/ Tue, 01 Jan 2019 00:00:00 +0000 /~ubarron/publication/journal-article/cadavez2019-dynamic/