This is the hub for accessing Simile teaching resources provided by members of the Simile Teaching Club.
Here are 4 flash videos which demonstrate how to create a simple model (createModel.swf), add in quantitative information to the model's components (quantitativeModel.swf), delete unwanted model components (deleteComponents.swf) and run the model (runningModel.swf). The model I use is the first example model in the book by Grant and Swannack, "Ecological modelling: a common-sense approach to theory and practice" John Wiley & Sons, 2008 My undergraduate and masters students find these types of videos very helpful when learning how to use Simile. The use of text balloons, rather than an audio track, allows you to scroll through the video easily, and it doesn't create noise during a large class. Any suggestions on these kinds of videos would be very welcome. Jon
Attached are the models (and output interfaces) from some of the chapters in the book by Hannon and Ruth.
Hannon, B & Ruth, M 1997 "Modelling dynamic biological systems" (Springer, New York)
Chapter 2 has some simple population growth models
Chapter 3 has some stochastic population growth models where death rate is a random variable
Chapter 4 has some population models with time lags that show chaotic behaviour
Chapter 7 has a model for a catalyzed reaction, which could describe enzyme dynamics.
Here's a pdf document which I'm going to use as the basis of a practical class to introduce Simile and how it can be used to develop and analyse a model. It is aimed at undergraduate biology/ecology students whoc will have little if any programming experience and even less mathematics.
This is the first time I've written anything like this. If people see potential pitfalls in the approach or ways to improve the description to make it clearer then I'd like to hear about it.
|Intro_to_Simile_Version 5_2009.pdf||1.45 MB|
Here are a couple of Simile models which try and show that statistical inference is largely about comparing data against the output of a null model. In statistical text books the null model is not usually mentioned explicitly. Instead the null hypothesis, and the the assmptions of a test are presented, but the null hypothesis and the assumptions are really specifying an underlying null model.
These two Simile models look at a t-test (actually a paired t-test) and a one-way ANOVA. They use the same data set, which comprises two samples, each with 20 observations.
Both tests ask the question: are the means of the two samples the same? But they have slightly different null models. For these simple statistical tests the statistical theory can be derived analytically, so the null model is often forgotten about because the results from the analytical calculations (i.e. the statistical tables, Student's t-distribution for the t-test and the F-distribution for ANOVA) already contain the null model output. But I think it is useful for teaching statistics (or modelling) to explicitly show the null model, it's role in a statistical test and how it is used to calculate the probability that the data are consistent with the null model (which is the p-value).
I'd be interested to hear if anyone finds this approach confussing, interesting, misleading, etc.
There are 5 files.
The example data is contained in sample_data.txt
The model for the t-test is in paired_ttest.sml and the output interface is in paired_ttest.shf
The model for the ANOVA is in anova.sml and the output interface is in anova.shf
When statistical tests become more complicated the null model often becomes more prominent. Perhaps the best example of this is in Approximate Bayesian Computations where a simulation model is explicitly part of the statistical methodology.
Look forward to any comments and suggestions,