https://drewtyre.rbind.io/classes/ Recent content in Classes on EnTyrely Too Much Hugo -- gohugo.io en-us © 2019-2020 Andrew Tyre Sun, 01 Jan 2017 00:00:00 +0000 https://drewtyre.rbind.io/classes/computer-setup/ Thu, 02 Nov 2017 00:00:00 +0000 https://drewtyre.rbind.io/classes/computer-setup/ R Download and install the R base system for your operating system. I assume you use the Rstudio Desktop system to work with the base system. You have to scroll down to find the installer for your operating system. When installing these you can accept all the default options. You should also install package tidyverse. While connected to the internet, start up RStudio, go to the console prompt at the > type https://drewtyre.rbind.io/classes/esa2018_workshop/ Thu, 02 Nov 2017 00:00:00 +0000 https://drewtyre.rbind.io/classes/esa2018_workshop/ My goal is to take everyone through the process of importing a dataset, visualizing it, running a simple analysis (e.g. linear regression/ANOVA), examining the results and getting a publication quality report out at the end. Setup Before the workshop, you need to install some software on your computer. You should also install packages lme4, lsmeans, lmerTest. I will also use package pander to make nice tables, but this is optional. https://drewtyre.rbind.io/classes/nres222/ Tue, 01 Aug 2017 00:00:00 +0000 https://drewtyre.rbind.io/classes/nres222/ This is the syllabus. Statement on Diversity I want our classroom to be a safe and inclusive place for all. Every individual is valued, has the capability to succeed and has something to offer the class. While our class may only be a temporary solace from the fear, suffering or depression you may be facing in your day as a result of racism, homophobia, transphobia, sexism, legal/immigration status, political belief, or any other discrimination, know that I am always here for you. https://drewtyre.rbind.io/classes/nres222/syllabus/ Tue, 01 Aug 2017 00:00:00 +0000 https://drewtyre.rbind.io/classes/nres222/syllabus/ Instructor Dr. Drew Tyre Office: Hardin Hall Rm 416 Phone: 402-472-4054 E-Mail: atyre2@unl.edu https://drewtyre.rbind.io Graduate Teaching Assistants: Kelly Willemessens (kelly.willemssens@huskers.unl.edu) Amber MacInnis (thesocialblowfly@gmail.com) You can also contact us through Canvas. Office hours: We will not post regular office hours, but we encourage you to make an appointment by email with either Dr. Tyre or your TA. You can also ask questions on the Canvas discussion boards opened for that purpose. https://drewtyre.rbind.io/classes/nres222/week_1/lab_1/ Sat, 20 Aug 2016 00:00:00 +0000 https://drewtyre.rbind.io/classes/nres222/week_1/lab_1/ The goal of this week’s lab is just getting your feet wet, learning how to use R. Like all labs, I expect you to submit an R Markdown file that I can compile to html. You should assume any data files are in a subdirectory called “data”. Your file should not echo the code used. Answer the numbered questions in text, referring to graphical and tabular output as needed. You may use base graphics or ggplot2, according to your preference. https://drewtyre.rbind.io/classes/nres222/nine_mile_ws/ Sat, 09 Feb 2019 00:00:00 +0000 https://drewtyre.rbind.io/classes/nres222/nine_mile_ws/ Introduction The goal of this week’s lab is to analyze the species area curve data from 9 Mile Prairie, and get more experience making figures and calculating summary statistics in R. Like all labs, I expect you to submit an R Markdown file that I can compile to html. You should assume any data files are in a subdirectory called “data”. Your file should not echo the code used. In the handout, there may be several code blocks making the same graph multiple times with slight tweaks. https://drewtyre.rbind.io/classes/nres803/ Tue, 01 Aug 2017 00:00:00 +0000 https://drewtyre.rbind.io/classes/nres803/ We will cover a broad sample of statistical topics relevant to ecologists; the intent is to provide an introduction enabling student to intelligently follow a seminar or scientific paper utilizing these methods. Depending on the needs and interests of the students we will enable students to conduct analyses on their own for a subset of topics. Across all topics we will emphasize modern methods of model selection/multi-model inference and relevant study design questions. https://drewtyre.rbind.io/classes/nres803/readiness/ Tue, 01 Aug 2017 00:00:00 +0000 https://drewtyre.rbind.io/classes/nres803/readiness/ The following figures and outputs are designed to evaluate your readiness to take NRES 803. Type your answers into a TEXT file (NOT a word document!) using a text editor or something like notepad (Windows) or TextEdit (OS/X) or using the RStudio IDE. Copy the question into the file and place your answer underneath it. Save it in a file with the name “yourlastname_is_ready.txt”. Interpreting graphical and statistical output The RIKZ dataset from the textbook has data on species occurence on 9 sandy beaches along the North Sea. https://drewtyre.rbind.io/classes/nres803/syllabus/ Tue, 01 Aug 2017 00:00:00 +0000 https://drewtyre.rbind.io/classes/nres803/syllabus/ Instructor Dr. Drew Tyre Office: Hardin Hall Rm 416 Phone: 402-472-4054 E-Mail: atyre2@unl.edu https://drewtyre.rbind.io Time: Lectures & Lab: online (see below for details) Office hours: M 10-12pm, W 10-12pm, F 2-5pm or by appointment Index Technical Requirements Description Prerequisites Learning Objectives Instructor’s Role Textbooks Hardware and software requirements Assessment/Grading Course Schedule Course Policies Students with Disabilities HELP (Click on the areas above for more detail) https://drewtyre.rbind.io/classes/nres803/computer-setup/ Tue, 01 Aug 2017 00:00:00 +0000 https://drewtyre.rbind.io/classes/nres803/computer-setup/ NRES 803 students will need their own computers set up with R and RStudio by the start of classes R Download and install the R base system. I recommend you use the Rstudio Desktop system to work with the base system. When installing you can accept all the default options. You should also install package tidyverse. I am providing all the data and some code for the course in a source package NRES803 which you can download from the files link in Canvas. https://drewtyre.rbind.io/classes/nres803/week_1/ Mon, 21 Aug 2017 00:00:00 +0000 https://drewtyre.rbind.io/classes/nres803/week_1/ Posting introductions Go to the course discussion board on Canvas, enter the “introductions” discussion , click “Create thread”, and introduce yourself! What are you doing at NU? Please also post a photograph of yourself. This is mostly for my benefit. Otherwise, we meet on campus somewhere and I’ve no idea why you know me but I don’t know you! Linear Model Review I This week you will focus on reviewing the concept of the linear model. https://drewtyre.rbind.io/classes/nres803/week_1/lab_1/ Wed, 19 Aug 2020 00:00:00 +0000 https://drewtyre.rbind.io/classes/nres803/week_1/lab_1/ The goal of this week’s lab is just getting your feet wet, remembering how to use R, what a linear model is and getting some answers out. Like all labs, I expect you to submit a compressed RStudio project folder. You should assume any data files are in a subdirectory called “data”. Answer the numbered questions in text, referring to graphical and tabular output as needed. You may use base graphics or ggplot2, according to your preference. https://drewtyre.rbind.io/classes/nres803/week_1/fittinglines_example/ Sat, 20 Aug 2016 00:00:00 +0000 https://drewtyre.rbind.io/classes/nres803/week_1/fittinglines_example/ This example from Gelman and Nolan 2002 Teaching Statistics: A bag of tricks, chapter 4. # now make each figure and the corresponding r lm() output x = c(1,2,3) y = x plot(x,y,xlab="",ylab="",bty="n",xlim=c(0,3),ylim=c(0,3)) lm.1 = lm(y~x) abline(lm.1) summary(lm.1) ## Warning in summary.lm(lm.1): essentially perfect fit: summary may be ## unreliable ## ## Call: ## lm(formula = y ~ x) ## ## Residuals: ## 1 2 3 ## 0 0 0 ## ## Coefficients: ## Estimate Std. https://drewtyre.rbind.io/classes/nres803/week_2/ Mon, 21 Aug 2017 00:00:00 +0000 https://drewtyre.rbind.io/classes/nres803/week_2/ Linear Model Review II This week you will continue to review the concept of the linear model. This week will also see the first online paper discussion. View videos by Wednesday, 10 am. Evaluating Assumptions. Limits of the linear regression (not what it sounds like!). Paper discussions board by Tuesday week 3. Read Guthery and Bingham (2007) (See Files | Readings on Canvas for pdf). https://drewtyre.rbind.io/classes/nres803/week_2/lab_2/ Tue, 25 Aug 2020 00:00:00 +0000 https://drewtyre.rbind.io/classes/nres803/week_2/lab_2/ This week will give you further practice with making plots, estimating linear models in R, and getting predicted values from those fitted models. Create a new project in RStudio named “yourlastname_lab2”. Submit the compressed project directory, which should include an R Markdown file that can be compiled directly to html without errors. The answers to the numbered questions should be in plain text. The code should be in R code chunks. https://drewtyre.rbind.io/classes/nres803/week_2/homework_2/ Tue, 01 Aug 2017 00:00:00 +0000 https://drewtyre.rbind.io/classes/nres803/week_2/homework_2/ The data bleaching are in package NRES803. These data are derived from a paper on the regional severity of coral bleaching events triggered by high ocean temperatures (McWilliams et al, 2005). The first column (SST) is Sea Surface Temperature “anomalies” – that is, the difference between the average sea surface temperature for the Caribbean between Aug and October, and the long run average from 1961 to 1990. Positive values represent years in which the water was warmer than average; negative values represent cool years. https://drewtyre.rbind.io/classes/nres803/week_3/ Mon, 21 Aug 2017 00:00:00 +0000 https://drewtyre.rbind.io/classes/nres803/week_3/ Model Selection – Week 3 In this week we’ll begin talking about model selection and inference using information theoretic methods. Monday is Labour Day! No Class Before Wednesday’s online session Read MBILS chapters 3 and 4. View: Introduction to model selection Likelihood KL information AIC differences videos In Wednesday’s online session we will go over Exercise 1 from MBILS Chapter 4 Friday Do Lab #3. https://drewtyre.rbind.io/classes/nres803/week_3/lab_3/ Wed, 02 Sep 2020 00:00:00 +0000 https://drewtyre.rbind.io/classes/nres803/week_3/lab_3/ In this week’s lab exercise we will first go through calculating the AIC values “by hand”, and then using a addon package MuMIn. Doing the calculations by hand serves three purposes: 1) you practice using R!, 2) you gain a deeper understanding of how much work the add-on package is doing for you, and 3) sometimes the add-on package won’t work for a new class of model, and if you can do it by hand you won’t be stuck. https://drewtyre.rbind.io/classes/nres803/week_4/ Mon, 21 Aug 2017 00:00:00 +0000 https://drewtyre.rbind.io/classes/nres803/week_4/ Model Selection – Week 4 In this week we’ll look at how model selection methods lead us to the idea of multimodel inference. Monday optional online help session 10-11, read, watch videos, post your pre-proposal. Post pre-proposal in discussion board by Tuesday 11:59 pm, Week 5. Before Wednesday’s online session Read MBILS chapter 5. View: Multi-model inference In Wednesday’s online session we will go over Exercises 1 & 2 from MBILS Chapter 5 https://drewtyre.rbind.io/classes/nres803/week_3/lab_4/ Tue, 01 Aug 2017 00:00:00 +0000 https://drewtyre.rbind.io/classes/nres803/week_3/lab_4/ We’re going to use the same dataset that you generated last week for this week’s exercise. Last week we looked at choosing a best model. This week we’re going to model average the coefficients and predictions from the models, and make “partial dependence plots”. As before, we’ll first do each step by hand, then with AICcmodavg. Below I’ve assumed you still have all the objects created in week 3 lab. https://drewtyre.rbind.io/classes/nres803/week_5/ Mon, 21 Aug 2017 00:00:00 +0000 https://drewtyre.rbind.io/classes/nres803/week_5/ Generalized Linear Models In this week we’ll look at breaking the assumptions that the error distribution is normal and the relationship between E(Y) and BX is linear. Learning Objectives Use the inverse link function to interpret coefficients in a GLM. Recognize binomial and Poisson distributed responses Evaluate the goodness of fit of a GLM by examining residual plots and the residual deviance. Monday Optional online help session 10-11, read, watch videos, homework https://drewtyre.rbind.io/classes/nres803/week_5/lab_5/ Tue, 01 Aug 2017 00:00:00 +0000 https://drewtyre.rbind.io/classes/nres803/week_5/lab_5/ In this exercise we’ll practice fitting glms and making predictions. The data is hanson_birds in package NRES803. This data set was collected by Andrea Hanson during her MSc. project in the School of Natural Resources. She sampled 14 transects in CRP fields scattered across SE Nebraska 4 times throughout the summer. She measured Visual Obstruction Readings (VOR) at 12 spots along each transect; these were averaged to obtain a single number representing vegetation structure within the CRP field. https://drewtyre.rbind.io/classes/nres803/week_6/ Mon, 21 Aug 2017 00:00:00 +0000 https://drewtyre.rbind.io/classes/nres803/week_6/ Generalized Additive Models In this week we’ll look at breaking the assumption that the covariates are linearly related to the inverse link of E(Y). Learning Objectives Detect violations of the linear assumption in residual plots Use a penalized smooth spline term to fit an arbitrary non-linear function Decide what type of model to use based on the properties of the data Monday Here’s a handout that I call putting it all together. https://drewtyre.rbind.io/classes/nres803/week_6/lab_6/ Tue, 01 Aug 2017 00:00:00 +0000 https://drewtyre.rbind.io/classes/nres803/week_6/lab_6/ In this lab we’re going to use a subset of data from Sikkink et al. (2007; Chapter 26 in AED) on grassland species richness in Yellowstone National park. The data were measured on 8 different transects in 8 years between 1958 and 2002. Not every transect was measured in every year, so the intervals between samples varies from 4 to 11 years in duration. According to Zuur et al. (2009) the Richness variable is the Beta diversity - the number of species unique to that site. https://drewtyre.rbind.io/classes/nres803/week_6/homework_6/ Sat, 09 Feb 2019 00:00:00 +0000 https://drewtyre.rbind.io/classes/nres803/week_6/homework_6/ The discipline of landscape ecology frequently postulates that the spatial pattern of habitat is important, in addition to local characteristics such as patch area, vegetation type, and climate. Westphal et al (2003) analyzed data from the South Australian Bird Atlas using a series of landscape pattern metrics estimated at 3 spatial scales. They concluded that landscape structure had a positive effect on many bird species. However, this dataset was never designed to be analyzed using logistic regression, and consequently their conclusions were somewhat weak, and badly compromised by model selection uncertainty. https://drewtyre.rbind.io/classes/nres803/week_8/ Mon, 21 Aug 2017 00:00:00 +0000 https://drewtyre.rbind.io/classes/nres803/week_8/ Putting it all together Reinforcing and reviewing all the bits we’ve done up to now. Learning Objectives Monday Here’s a handout that I call putting it all together. I’m not sure when the best time is give you this handout, but now seems to be a good time. In the first part, which deals with deciding what sort of models to fit, when you get to step 6 just answer NO and you’ll be good. https://drewtyre.rbind.io/classes/nres803/week_8/lab_8/ Thu, 07 Sep 2017 00:00:00 +0000 https://drewtyre.rbind.io/classes/nres803/week_8/lab_8/ We’ll play with a few datasets from Mick Crawley’s “The R Book” 1 to see how to identify when a GAM can be useful, and when to stick with a GLM. The first dataset is of population sizes of Soay Sheep. library(NRES803) library(tidyverse) library(mgcv) library(broom) library(GGally) library(gridExtra) # need this to make residual plots of gam models bollocks.augment <- function(model) { r <- model.frame(model) r$.fitted <- fitted(model) r$.resid <- resid(model) r$. https://drewtyre.rbind.io/classes/nres803/week_10/ Mon, 21 Aug 2017 00:00:00 +0000 https://drewtyre.rbind.io/classes/nres803/week_10/ This week we’ll start breaking the assumption that the observations are completely independent! Learning Objectives Recognize design scenarios resulting in non-independent data Classify possible covariates as fixed or random Estimate and interpret a mixed effects model with normally distributed data. Monday Optional online help session 10-11, read, watch video, homework Wednesday Before Wednesday’s class Read AED Chapter 8 Watch mixed model video In Wednesday’s class https://drewtyre.rbind.io/classes/nres803/week_10/lab_10/ Mon, 25 Oct 2021 00:00:00 +0000 https://drewtyre.rbind.io/classes/nres803/week_10/lab_10/ Sleep Study This is a great dataset to play with. From the help documentation: The average reaction time per day for subjects in a sleep deprivation study. On day 0 the subjects had their normal amount of sleep. Starting that night they were restricted to 3 hours of sleep per night. The observations represent the average reaction time on a series of tests given each day to each subject. https://drewtyre.rbind.io/classes/nres803/week_11/ Mon, 21 Aug 2017 00:00:00 +0000 https://drewtyre.rbind.io/classes/nres803/week_11/ Learning Objectives Diagnose convergence failures Fit and interpret Generalized Linear Mixed Models Monday Finishing lab from Week 10 Before Wednesday: Read AED Chapter 23 - example of LMM Wednesday Look at GLMM output from chapter 23 and discuss GLMM model selection Friday’s Lab Week 11 Lab instructions, turn it in on Canvas. https://drewtyre.rbind.io/classes/nres803/week_11/lab_11/ Wed, 28 Oct 2020 00:00:00 +0000 https://drewtyre.rbind.io/classes/nres803/week_11/lab_11/ The first dataset we’ll look at is from Zuur et al. (2009); the data are from Vicente et al. (2005) who looked at the distribution and faecal shedding patterns of 1st stage larvae of Elaphostrongylus cervi in red deer. Here we’re interested in the presence and absence of larvae in deer as functions of size (length in cm), sex, and farm identity. library(tidyverse) library(NRES803) library(lme4) data(ecervi) head(ecervi) The average number of larvae in a fecal sample is highly skewed to the right – many zeros but also some very large values. https://drewtyre.rbind.io/classes/nres803/week_12/ Mon, 21 Aug 2017 00:00:00 +0000 https://drewtyre.rbind.io/classes/nres803/week_12/ Learning objectives Distinguish between profile and approximate confidence limits on parameters Generate bootstrap confidence limits on predictions Monday Help with Week 11 Lab Nothing to turn in this week. Work on your project. Wednesday NO CLASS – Dr. Tyre giving R seminar in Denver Friday Week 12 Lab instructions, turn it in on Canvas. https://drewtyre.rbind.io/classes/nres803/week_12/lab_12/ Sat, 09 Feb 2019 00:00:00 +0000 https://drewtyre.rbind.io/classes/nres803/week_12/lab_12/ This week we’ll continue to look at Linear and Generalized linear mixed effects models, emphasizing how to get confidence intervals on the predictions. We’ll start with something easy - a linear mixed effects model with the sleepstudy data. library(tidyverse) library(lme4) ## notice conflicts with tidyr library(broom) library(boot) ssBase <- ggplot(sleepstudy, aes(x = Days, y = Reaction)) + geom_point(aes(color = Subject)) + scale_color_discrete() + labs(x = "Days with no sleep", y = "Reaction time [ms]") ssBase + geom_smooth(method = "lm") + facet_wrap(~Subject) + guides(color = FALSE) So there’s the basic data, and now we fit a mixed model with both the intercept and Days varying across subjects. https://drewtyre.rbind.io/classes/nres803/week_13/ Mon, 21 Aug 2017 00:00:00 +0000 https://drewtyre.rbind.io/classes/nres803/week_13/ Learning objectives Estimate parameters of a non-linear model Distinguish between linear and non-linear models Monday Help with Week 12 Lab Wednesday Three videos to watch. This is the data, in case you want to follow along. What makes it non-linear? Fitting a species area curve Maximizing the likelihood Questions about non-linear models. Friday Week 13 Lab instructions, turn it in on Canvas. https://drewtyre.rbind.io/classes/nres803/week_13/lab_13/ Tue, 10 Nov 2020 00:00:00 +0000 https://drewtyre.rbind.io/classes/nres803/week_13/lab_13/ In this exercise you will fit a non-linear model to some data simulated from information in Cousens (1985) paper on wheat yields in competition with barley. Unfortunately Cousens only reported the mean values and the residual sums of squares from the non-linear fits. So I have attempted to simulate data that has (at least approximately) the right means and variances. The script that simulates the data can be downloaded here, and the necessary data files are here and here.