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--- tutorials:sensitivity-analysis-using-grolink-and-gror [2024/06/25 17:30] – [Example: Morris Screening using the sensitivity package] thomas
+++ tutorials:sensitivity-analysis-using-grolink-and-gror [2024/07/01 12:39] (current) – [Example: Morris Screening using the sensitivity package] thomas
@@ Line 3: / Line 3: @@
 ===== Prerequisites =====
 Make sure to [[:tutorials:getting-started-with-grolink-and-gror|set up GroR]]. Play a bit around with it and get a feel for how it works and what the [[https://gitlab.com/grogra/groimp-utils/rapilibrary#grolink|different functions]] do and what they return. The approach presented in the wiki here is just one way you could approach a sensitivity analysis. You will likely find your own approach for your specific model, but GroR will be the space you work in.
+==== Downloads ====
+  * {{ :tutorials:example08_prepared.zip | Prepared Example08 model}}
+  * {{ :tutorials:example08_analysis.zip | R Script of this wiki and model outputs as .rds}}
 ===== Prepare your model=====
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 ===== Example: Morris Screening using the sensitivity package =====
-The Morris Screening will be used to analyze the 5 structural plant growth parameters with regard to their importance (their main and interaction effect) on the total amount of absorbed light by leaves. This is just a random example out of the plethora of available sensitivity analysis methods. Most of the common ones are implemented in the ''sensitivity'' R-package. This is not a tutorial on ''sensitivity'', but it is very well documented (e.g. in the R help function).
+The Morris Screening will be used to analyze the 5 structural plant growth parameters with regard to their importance (their main and interaction effect) on the total amount of absorbed light by leaves. This is just a random example out of the plethora of available sensitivity analysis methods. Most of the common ones are implemented in the ''sensitivity'' R-package. This is not a tutorial on ''sensitivity'', but it is very well documented (e.g. in the R help function). It is worth noting that this example uses the so-called decoupled approach of ''sensitivity'', see ''?tell''. This basically means that the generation of the parameter set, model execution and output analysis will be done as separate steps.
 First, I generate a set of input parameters for the model:
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 </code>
-Through the use of ''future_apply'' and the way the ''multisession'' is setup, you will run one instance of GroIMP on every core of your computer. With this ''Example08'' model, this can take some time, on my 20-core machine it took around 2 minutes (and used ~5Gb RAM).
+Through the use of ''future_apply'' and the way the ''multisession'' is setup, you will run one instance of GroIMP on every core of your computer. With this ''Example08'' model, this can take some time, on my 20-core machine it took around 2 minutes (and used ~5Gb RAM). This simple parallelization could likely be optimized a lot and will not scale amazingly to e.g. a remote cluster.
 The only thing remaining is to analyze the output and plot the results: