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How To Build A Model In R

🚨 Disclaimer: This is not the final material, please check again later. 🚨

GLEON All Hands' Meeting 2022

👥 Robert Ladwig, Hilary Dugan and Paul Hanson
🕐 Oct 30th, 13:30-16:30 Boyd Center Conference Room
💬2.5 - 3 hours
💻 Please install the packages tidyverse, patchwork and reshape2 before the workshop


Want to be a modeler but don't know where to start? Do you find the word "model" scary? Wish you knew more about how to get started building models in R? If yes to any of the above, this workshop is for you. If you already have some R basics under your belt, we aim to add modeling to your R skillset. Learn what models are and how models are built, calibrated, and used for simplifying data and prediction.

Installation

You can clone or download files from this Github repository (click the green "Code" button and select the "Clone" or "Download ZIP" option). You’ll need R (version >= 3.5), a GUI of your choice (e.g., Rstudio) and these packages:

install.packages("tidyverse")
install.packages("reshape2")
install.packages("patchwork")

Part 1: Ordinary least squares (OLS) - estimating parameters in a linear regression model (timeless)

Lead: Hilary Dugan
Walkthrough of R code for estimating the parameters in a linear regression model using ordinary least squares. Parameters are derived both from formulas and optimization the sum of squared estimate of errors.

Learning goals:

  • What is a model?
  • Analytical vs numerical solutions
  • What is a parameter?
  • Fitting parameters and optimization
  • optim

Part 2: Starting from scratch with a dynamic mass balance metabolism model (~62 min)

Lead: Paul Hanson
A brief introduction to process-based time dynamic models is followed by a walkthrough of an R code example of lake metabolism and oxygen dynamics. Example code will be available for participants to play along.

Learning goals:

  • What is a dynamical model?
  • Why metabolism?
  • Coding a simple example
  • Exploring lake system behavior

Part 3: 1D transport (~ ½ hour)

Lead: Robert Ladwig
Fancy coding a one-dimensional diffusion model? We will code up the diffusion algorithm from theory using for-loops in R. Then we will play around with the model to see how parameter changes result in alternative transport scenarios.

Learning goals:

  • Understanding the one-dimensional diffusion equation
  • How can we discretize the equation?
  • How to code up the diffusion model in R and visualize the output
  • Understanding model assumptions and uncertainties

About

GLEON All Hands' Meeting 2022

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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How To Build A Model In R
🚨 Disclaimer: This is not the final material, please check again later. 🚨

GLEON All Hands' Meeting 2022
👥 Robert Ladwig, Hilary Dugan and Paul Hanson

🕐 Oct 30th, 13:30-16:30 Boyd Center Conference Room 

💬2.5 - 3 hours 

💻 Please install the packages tidyverse, patchwork and reshape2 before the workshop

Want to be a modeler but don't know where to start? Do you find the word "model" scary? Wish you knew more about how to get started building models in R? If yes to any of the above, this workshop is for you. If you already have some R basics under your belt, we aim to add modeling to your R skillset. Learn what models are and how models are built, calibrated, and used for simplifying data and prediction.
Installation
You can clone or download files from this Github repository (click the green "Code" button and select the "Clone" or "Download ZIP" option).
You’ll need R (version >= 3.5), a GUI of your choice (e.g., Rstudio) and these packages:
install.packages("tidyverse")
install.packages("reshape2")
install.packages("patchwork")

Part 1: Ordinary least squares (OLS) - estimating parameters in a linear regression model (timeless)
Lead: Hilary Dugan

Walkthrough of R code for estimating the parameters in a linear regression model using ordinary least squares. Parameters are derived both from formulas and optimization the sum of squared estimate of errors.
Learning goals:
  • What is a model?
  • Analytical vs numerical solutions
  • What is a parameter?
  • Fitting parameters and optimization
  • optim
Part 2: Starting from scratch with a dynamic mass balance metabolism model (~62 min)
Lead: Paul Hanson 

A brief introduction to process-based time dynamic models is followed by a walkthrough of an R code example of lake metabolism and oxygen dynamics. Example code will be available for participants to play along.
Learning goals:
  • What is a dynamical model?
  • Why metabolism?
  • Coding a simple example
  • Exploring lake system behavior
Part 3: 1D transport (~ ½ hour)
Lead: Robert Ladwig 

Fancy coding a one-dimensional diffusion model? We will code up the diffusion algorithm from theory using for-loops in R. Then we will play around with the model to see how parameter changes result in alternative transport scenarios.
Learning goals:
  • Understanding the one-dimensional diffusion equation
  • How can we discretize the equation?
  • How to code up the diffusion model in R and visualize the output
  • Understanding model assumptions and uncertainties
About
GLEON All Hands' Meeting 2022
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
2 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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How To Build A Model In R
🚨 Disclaimer: This is not the final material, please check again later. 🚨

GLEON All Hands' Meeting 2022
👥 Robert Ladwig, Hilary Dugan and Paul Hanson

🕐 Oct 30th, 13:30-16:30 Boyd Center Conference Room 

💬2.5 - 3 hours 

💻 Please install the packages tidyverse, patchwork and reshape2 before the workshop

Want to be a modeler but don't know where to start? Do you find the word "model" scary? Wish you knew more about how to get started building models in R? If yes to any of the above, this workshop is for you. If you already have some R basics under your belt, we aim to add modeling to your R skillset. Learn what models are and how models are built, calibrated, and used for simplifying data and prediction.
Installation
You can clone or download files from this Github repository (click the green "Code" button and select the "Clone" or "Download ZIP" option).
You’ll need R (version >= 3.5), a GUI of your choice (e.g., Rstudio) and these packages:
install.packages("tidyverse")
install.packages("reshape2")
install.packages("patchwork")

Part 1: Ordinary least squares (OLS) - estimating parameters in a linear regression model (timeless)
Lead: Hilary Dugan

Walkthrough of R code for estimating the parameters in a linear regression model using ordinary least squares. Parameters are derived both from formulas and optimization the sum of squared estimate of errors.
Learning goals:
  • What is a model?
  • Analytical vs numerical solutions
  • What is a parameter?
  • Fitting parameters and optimization
  • optim
Part 2: Starting from scratch with a dynamic mass balance metabolism model (~62 min)
Lead: Paul Hanson 

A brief introduction to process-based time dynamic models is followed by a walkthrough of an R code example of lake metabolism and oxygen dynamics. Example code will be available for participants to play along.
Learning goals:
  • What is a dynamical model?
  • Why metabolism?
  • Coding a simple example
  • Exploring lake system behavior
Part 3: 1D transport (~ ½ hour)
Lead: Robert Ladwig 

Fancy coding a one-dimensional diffusion model? We will code up the diffusion algorithm from theory using for-loops in R. Then we will play around with the model to see how parameter changes result in alternative transport scenarios.
Learning goals:
  • Understanding the one-dimensional diffusion equation
  • How can we discretize the equation?
  • How to code up the diffusion model in R and visualize the output
  • Understanding model assumptions and uncertainties
About
GLEON All Hands' Meeting 2022
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
2 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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How To Build A Model In R
🚨 Disclaimer: This is not the final material, please check again later. 🚨

GLEON All Hands' Meeting 2022
👥 Robert Ladwig, Hilary Dugan and Paul Hanson

🕐 Oct 30th, 13:30-16:30 Boyd Center Conference Room 

💬2.5 - 3 hours 

💻 Please install the packages tidyverse, patchwork and reshape2 before the workshop

Want to be a modeler but don't know where to start? Do you find the word "model" scary? Wish you knew more about how to get started building models in R? If yes to any of the above, this workshop is for you. If you already have some R basics under your belt, we aim to add modeling to your R skillset. Learn what models are and how models are built, calibrated, and used for simplifying data and prediction.
Installation
You can clone or download files from this Github repository (click the green "Code" button and select the "Clone" or "Download ZIP" option).
You’ll need R (version >= 3.5), a GUI of your choice (e.g., Rstudio) and these packages:
install.packages("tidyverse")
install.packages("reshape2")
install.packages("patchwork")

Part 1: Ordinary least squares (OLS) - estimating parameters in a linear regression model (timeless)
Lead: Hilary Dugan

Walkthrough of R code for estimating the parameters in a linear regression model using ordinary least squares. Parameters are derived both from formulas and optimization the sum of squared estimate of errors.
Learning goals:
  • What is a model?
  • Analytical vs numerical solutions
  • What is a parameter?
  • Fitting parameters and optimization
  • optim
Part 2: Starting from scratch with a dynamic mass balance metabolism model (~62 min)
Lead: Paul Hanson 

A brief introduction to process-based time dynamic models is followed by a walkthrough of an R code example of lake metabolism and oxygen dynamics. Example code will be available for participants to play along.
Learning goals:
  • What is a dynamical model?
  • Why metabolism?
  • Coding a simple example
  • Exploring lake system behavior
Part 3: 1D transport (~ ½ hour)
Lead: Robert Ladwig 

Fancy coding a one-dimensional diffusion model? We will code up the diffusion algorithm from theory using for-loops in R. Then we will play around with the model to see how parameter changes result in alternative transport scenarios.
Learning goals:
  • Understanding the one-dimensional diffusion equation
  • How can we discretize the equation?
  • How to code up the diffusion model in R and visualize the output
  • Understanding model assumptions and uncertainties
About
GLEON All Hands' Meeting 2022
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
2 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
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} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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How To Build A Model In R
🚨 Disclaimer: This is not the final material, please check again later. 🚨

GLEON All Hands' Meeting 2022
👥 Robert Ladwig, Hilary Dugan and Paul Hanson

🕐 Oct 30th, 13:30-16:30 Boyd Center Conference Room 

💬2.5 - 3 hours 

💻 Please install the packages tidyverse, patchwork and reshape2 before the workshop

Want to be a modeler but don't know where to start? Do you find the word "model" scary? Wish you knew more about how to get started building models in R? If yes to any of the above, this workshop is for you. If you already have some R basics under your belt, we aim to add modeling to your R skillset. Learn what models are and how models are built, calibrated, and used for simplifying data and prediction.
Installation
You can clone or download files from this Github repository (click the green "Code" button and select the "Clone" or "Download ZIP" option).
You’ll need R (version >= 3.5), a GUI of your choice (e.g., Rstudio) and these packages:
install.packages("tidyverse")
install.packages("reshape2")
install.packages("patchwork")

Part 1: Ordinary least squares (OLS) - estimating parameters in a linear regression model (timeless)
Lead: Hilary Dugan

Walkthrough of R code for estimating the parameters in a linear regression model using ordinary least squares. Parameters are derived both from formulas and optimization the sum of squared estimate of errors.
Learning goals:
  • What is a model?
  • Analytical vs numerical solutions
  • What is a parameter?
  • Fitting parameters and optimization
  • optim
Part 2: Starting from scratch with a dynamic mass balance metabolism model (~62 min)
Lead: Paul Hanson 

A brief introduction to process-based time dynamic models is followed by a walkthrough of an R code example of lake metabolism and oxygen dynamics. Example code will be available for participants to play along.
Learning goals:
  • What is a dynamical model?
  • Why metabolism?
  • Coding a simple example
  • Exploring lake system behavior
Part 3: 1D transport (~ ½ hour)
Lead: Robert Ladwig 

Fancy coding a one-dimensional diffusion model? We will code up the diffusion algorithm from theory using for-loops in R. Then we will play around with the model to see how parameter changes result in alternative transport scenarios.
Learning goals:
  • Understanding the one-dimensional diffusion equation
  • How can we discretize the equation?
  • How to code up the diffusion model in R and visualize the output
  • Understanding model assumptions and uncertainties
About
GLEON All Hands' Meeting 2022
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
2 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
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})();
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try {
var __m = "*";
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How To Build A Model In R
🚨 Disclaimer: This is not the final material, please check again later. 🚨

GLEON All Hands' Meeting 2022
👥 Robert Ladwig, Hilary Dugan and Paul Hanson

🕐 Oct 30th, 13:30-16:30 Boyd Center Conference Room 

💬2.5 - 3 hours 

💻 Please install the packages tidyverse, patchwork and reshape2 before the workshop

Want to be a modeler but don't know where to start? Do you find the word "model" scary? Wish you knew more about how to get started building models in R? If yes to any of the above, this workshop is for you. If you already have some R basics under your belt, we aim to add modeling to your R skillset. Learn what models are and how models are built, calibrated, and used for simplifying data and prediction.
Installation
You can clone or download files from this Github repository (click the green "Code" button and select the "Clone" or "Download ZIP" option).
You’ll need R (version >= 3.5), a GUI of your choice (e.g., Rstudio) and these packages:
install.packages("tidyverse")
install.packages("reshape2")
install.packages("patchwork")

Part 1: Ordinary least squares (OLS) - estimating parameters in a linear regression model (timeless)
Lead: Hilary Dugan

Walkthrough of R code for estimating the parameters in a linear regression model using ordinary least squares. Parameters are derived both from formulas and optimization the sum of squared estimate of errors.
Learning goals:
  • What is a model?
  • Analytical vs numerical solutions
  • What is a parameter?
  • Fitting parameters and optimization
  • optim
Part 2: Starting from scratch with a dynamic mass balance metabolism model (~62 min)
Lead: Paul Hanson 

A brief introduction to process-based time dynamic models is followed by a walkthrough of an R code example of lake metabolism and oxygen dynamics. Example code will be available for participants to play along.
Learning goals:
  • What is a dynamical model?
  • Why metabolism?
  • Coding a simple example
  • Exploring lake system behavior
Part 3: 1D transport (~ ½ hour)
Lead: Robert Ladwig 

Fancy coding a one-dimensional diffusion model? We will code up the diffusion algorithm from theory using for-loops in R. Then we will play around with the model to see how parameter changes result in alternative transport scenarios.
Learning goals:
  • Understanding the one-dimensional diffusion equation
  • How can we discretize the equation?
  • How to code up the diffusion model in R and visualize the output
  • Understanding model assumptions and uncertainties
About
GLEON All Hands' Meeting 2022
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
2 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
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})();
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How To Build A Model In R
🚨 Disclaimer: This is not the final material, please check again later. 🚨

GLEON All Hands' Meeting 2022
👥 Robert Ladwig, Hilary Dugan and Paul Hanson

🕐 Oct 30th, 13:30-16:30 Boyd Center Conference Room 

💬2.5 - 3 hours 

💻 Please install the packages tidyverse, patchwork and reshape2 before the workshop

Want to be a modeler but don't know where to start? Do you find the word "model" scary? Wish you knew more about how to get started building models in R? If yes to any of the above, this workshop is for you. If you already have some R basics under your belt, we aim to add modeling to your R skillset. Learn what models are and how models are built, calibrated, and used for simplifying data and prediction.
Installation
You can clone or download files from this Github repository (click the green "Code" button and select the "Clone" or "Download ZIP" option).
You’ll need R (version >= 3.5), a GUI of your choice (e.g., Rstudio) and these packages:
install.packages("tidyverse")
install.packages("reshape2")
install.packages("patchwork")

Part 1: Ordinary least squares (OLS) - estimating parameters in a linear regression model (timeless)
Lead: Hilary Dugan

Walkthrough of R code for estimating the parameters in a linear regression model using ordinary least squares. Parameters are derived both from formulas and optimization the sum of squared estimate of errors.
Learning goals:
  • What is a model?
  • Analytical vs numerical solutions
  • What is a parameter?
  • Fitting parameters and optimization
  • optim
Part 2: Starting from scratch with a dynamic mass balance metabolism model (~62 min)
Lead: Paul Hanson 

A brief introduction to process-based time dynamic models is followed by a walkthrough of an R code example of lake metabolism and oxygen dynamics. Example code will be available for participants to play along.
Learning goals:
  • What is a dynamical model?
  • Why metabolism?
  • Coding a simple example
  • Exploring lake system behavior
Part 3: 1D transport (~ ½ hour)
Lead: Robert Ladwig 

Fancy coding a one-dimensional diffusion model? We will code up the diffusion algorithm from theory using for-loops in R. Then we will play around with the model to see how parameter changes result in alternative transport scenarios.
Learning goals:
  • Understanding the one-dimensional diffusion equation
  • How can we discretize the equation?
  • How to code up the diffusion model in R and visualize the output
  • Understanding model assumptions and uncertainties
About
GLEON All Hands' Meeting 2022
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
2 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
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How To Build A Model In R
🚨 Disclaimer: This is not the final material, please check again later. 🚨

GLEON All Hands' Meeting 2022
👥 Robert Ladwig, Hilary Dugan and Paul Hanson

🕐 Oct 30th, 13:30-16:30 Boyd Center Conference Room 

💬2.5 - 3 hours 

💻 Please install the packages tidyverse, patchwork and reshape2 before the workshop

Want to be a modeler but don't know where to start? Do you find the word "model" scary? Wish you knew more about how to get started building models in R? If yes to any of the above, this workshop is for you. If you already have some R basics under your belt, we aim to add modeling to your R skillset. Learn what models are and how models are built, calibrated, and used for simplifying data and prediction.
Installation
You can clone or download files from this Github repository (click the green "Code" button and select the "Clone" or "Download ZIP" option).
You’ll need R (version >= 3.5), a GUI of your choice (e.g., Rstudio) and these packages:
install.packages("tidyverse")
install.packages("reshape2")
install.packages("patchwork")

Part 1: Ordinary least squares (OLS) - estimating parameters in a linear regression model (timeless)
Lead: Hilary Dugan

Walkthrough of R code for estimating the parameters in a linear regression model using ordinary least squares. Parameters are derived both from formulas and optimization the sum of squared estimate of errors.
Learning goals:
  • What is a model?
  • Analytical vs numerical solutions
  • What is a parameter?
  • Fitting parameters and optimization
  • optim
Part 2: Starting from scratch with a dynamic mass balance metabolism model (~62 min)
Lead: Paul Hanson 

A brief introduction to process-based time dynamic models is followed by a walkthrough of an R code example of lake metabolism and oxygen dynamics. Example code will be available for participants to play along.
Learning goals:
  • What is a dynamical model?
  • Why metabolism?
  • Coding a simple example
  • Exploring lake system behavior
Part 3: 1D transport (~ ½ hour)
Lead: Robert Ladwig 

Fancy coding a one-dimensional diffusion model? We will code up the diffusion algorithm from theory using for-loops in R. Then we will play around with the model to see how parameter changes result in alternative transport scenarios.
Learning goals:
  • Understanding the one-dimensional diffusion equation
  • How can we discretize the equation?
  • How to code up the diffusion model in R and visualize the output
  • Understanding model assumptions and uncertainties
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