It's been quite a while since I updated this tutorial series- better late than never? Introduction First, we determine the distribution our data comes from, or the likelihood, and any prior information is described using the prior distribution. Then we use Markov chain Monte Carlo to explore this posterior. Then we determine if we need… Continue reading Bayesian statistics part 4 – R tutorial

# Tag: programming r

## Introduction to modeling: parameter estimation in R

This code introduces how to perform parameter estimation for a system of differential equations in R. First, the necessary packages and data are imported using code previously introduced in a previous post. R code available here. Due to computational constraints, the mathematical model we are using to fit to the data includes product inhibition. If product inhibition doesn't… Continue reading Introduction to modeling: parameter estimation in R

## Introduction to modeling and coding in R

After introducing students to a simple mathematical model describing enzyme kinetics, I introduced them to coding and modeling in R. Coding is traditionally done by first describing some coding 'rules', including ending the line with semicolons or how to define a variable. Personally I found these methods ineffective and frustrating. I took a few classes… Continue reading Introduction to modeling and coding in R

## Modeling demo: photosynthesis

This demo is intended to help students understand the connection between photosynthetic pigment function, light energy, and plant growth. The relative absorption of 8 wavelength ranges can be modified to represent an absorption spectra. These absorption spectra could be simplified to include one or two specific colors, or approximate an absorption spectra obtained in lab.… Continue reading Modeling demo: photosynthesis

## Modeling demo: enzyme kinetics

When using this demo, students can alter the reaction rate, substrate, or enzyme concentrations. The simulation motivates understanding what a basic mechanistic model of enzyme kinetics would consist of. After running a simulation, the predicted dynamics of the product and substrate are plotted. The model behind the simulation is: $latex \frac{dS}{dt} = - r*E*S\\ \frac{dE}{dt} =… Continue reading Modeling demo: enzyme kinetics

## Computational Plant Science – Clustering in R Tutorial

Last week was the Plantae Seminar "Computational Plant Science - Science at the Interface of Math, Computer Science, and Plant Biology with Alexander Bucksch": Dr. Bucksch mentions using two clustering techniques - B splines and K-means clustering. I've discussed K-means clustering in a previous post to analyze predictors of success in Settlers of Catan. B splines… Continue reading Computational Plant Science – Clustering in R Tutorial

## Analysis of the predictors of Pokemon strength

My presentation on the best predictors of Pokemon strength (as measured by the sum of Pokemon statistics) were analyzed using clustering methods.