class: center, middle, inverse, title-slide .title[ # LECTURE 12: model selection ] .subtitle[ ## FANR 6750 (Experimental design) ] .author[ ### <br/><br/><br/>Fall 2023 ] --- class: inverse # outline #### 1) Goals of statistical inference <br/> -- #### 2) Exploration <br/> -- #### 3) Inference <br/> -- #### 4) Prediction -- #### 5) Example --- # motivation #### As scientists, we usually have more than one hypothesis (or none at all!) <br/> -- #### Consequently, we usually want to evaluate more than one model <br/> -- #### **Model selection** is the process of choosing which model is most supported by our data <br/> -- #### Model selection is one of the most highly debated (and confusing) topics we will cover this semester --- # model selection approaches **Comparison of 2 (nested) models** - Likelihood-ratio test **Stepwise procedures** - Forward/backward/stepwise selection **Information-theortic approaches** - Akaike’s Information Criterion (AIC) **Cross-validation** - Leave-one-out - K-fold **Out-of-sample validation** - Compare predictions to new data --- # questions <br/> #### How do we know which model is best? <br/> -- #### Are any of them any good? <br/> -- #### What is a good model? <br/> -- ### The answers to these questions usually depend on *what* we want our models to do --- # goals of modeling ### Exploration - Identify potential relationships between variables - **Generate** hypotheses -- ### Inference - **Test** a priori hypotheses -- ### Prediction - **Predict** response variable outside of observed data (usually to unobserved locations or future times) -- #### The same data **cannot** be used for both exploration and inference --- # approaches to model selection #### The most appropriate approach to model selection depends on what you want the model to do -- #### The first step in any analysis is to determine what your goal is (exploration, inference, or prediction) - Confusion about model selection often comes from not defining a clear goal - Sometimes the goal will not be obvious --- class: middle, center, inverse # inference --- # inference #### Inference is a central objective of science - Using data to evaluate support for a hypothesis -- </br> `$$\Large E(y_i) = \beta_0 + \beta_1 x1_i + \beta_2 x2_i$$` </br> -- - Are coefficients ( `\(\beta_0\)`, `\(\beta_1\)`, `\(\beta_2\)` ) non-zero? -- - Are coefficients positive or negative? -- - Which covariate ( `\(x1\)` or `\(x2\)` ) has a larger effect on `\(y\)`? -- #### The concepts we've learned this semester regarding null hypothesis testing and effect sizes are used for inference --- # inference #### For a well-designed study focused on inference, null hypothesis testing from a single model may be sufficient -- #### The main risk when conducting inference is Type I error - For any single study/data set, significant results may be to due to sampling error -- #### As a field, inference is strengthed by replication and validation -- #### For a single study, risk of spurious results can be reduced by formulating good hypotheses - Covariates should be selected based on strong *a priori* reason to believe they influence response variable - Where do *a priori* hypotheses come from? Theory (ideally) or... --- class: middle, center, inverse # exploration --- # exploration **Exploratory studies are a key part of the scientific process** - Help identify *potential* relationships between variables - Particularly common in observational studies with lots of covariates - e.g., which weather covariates influence abundance of wildlife species? -- **The main risk for exploratory analyses is detecting spurious relationships** - Often desirable to "cast a wide net" for potential predictors (don't want to miss something important), but including more predictors increases risk of Type I error -- **Part of the [replication crisis](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1182327/) stems from treating exploratory analyses as if they are testing hypotheses** - Evidence of a relationship should be a treated as a proposed hypothesis, which requires testing via **new** data sets (and ideally manipulative experiments) --- # exploration Key approaches to exploration: - data visualization - pairwise correlations - stepwise model selection - model fitting + multiple comparisons -- None of these approaches is particularly robust and all carry high risk of detecting spurious relationships -- But if the **stated** modeling objective is exploration, that's not necessiarily a problem -- - Report all steps used to screen/identify covariates - Avoid statements that could be interpreted as confirmatory - Include appropriate caveats and highlight areas for further research --- class: middle, center, inverse # prediction --- # prediction #### Historically, exploration and inference have been the primary focus in ecological research - Out-sized emphasis on null hypothesis testing + very complex systems - More prominent in other fields (meteorology, economics, political science) -- #### Prediction is increasingly used to: - Forecast ecological systems (e.g., population viability analysis) - Test theory - Aid decision making (e.g., adaptive management) --- # prediction </br> `$$\Large E(y_i) = \beta_0 + \beta_1 x1_i + \beta_2 x2_i$$` -- - *Inference* focuses on `\(\beta_0\)`, `\(\beta_1\)`, and `\(\beta_2\)` -- - *Prediction* focuses on `\(E(y_i)\)` </br> -- **Question**: Does a model that provides the best predictions also provide the best inference? -- **Answer**: No! Because: - Models that predict well might include many correlated but unimportant covariates - Models that provide reliable inference may not include covariates relevant to prediction --- # prediction </br> ### In general, we seek models that are as simple as possible (but not more so) </br> -- ### Why do we want simplicity? --- # fit and over-fit #### `\(\large R^2\)` is a measure of model fit - how much variation in the response variable is explained by the predictors? -- #### Questions - Does the addition of a new predictor variable always increase `\(R^2\)`? -- - Do we want the model with the highest `\(R^2\)`? -- - What is the harm in adding "extra" predictor variables? <br/> -- #### Overly-complicated models don’t predict well. They are too specific to a particular dataset. --- # fit and over-fit <br/> <img src="12_model_selection_files/figure-html/unnamed-chunk-1-1.png" width="504" style="display: block; margin: auto;" /> --- # prediction #### Predictive ability is assessed by comparing *predicted* values to *observed* values - But we can't use the same values for both prediction and validation -- #### Ideally, we compare predictions to *out of sample* values - Predict future values, gather data, compare values (e.g., [waterfowl management](https://onlinelibrary.wiley.com/doi/full/10.1002/ece3.5836)) <img src="fig/mallard_prediction.jpg" width="280px" height="225px" style="display: block; margin: auto;" /> --- # prediction #### Predictive ability is assessed by comparing *predicted* values to *observed* values - But we can't use the same values for both prediction and validation #### Ideally, we compare predictions to *out of sample* values - Predict future values, gather data, compare values (e.g., [waterfowl management](https://onlinelibrary.wiley.com/doi/full/10.1002/ece3.5836)) - Divide data into "training" and "testing" (possibly repeated multiple times, i.e. *cross validation*) -- #### Predictive accuracy can also be approximated using *information-theoretic* approaches --- class: middle, inverse, center # aic --- # aic #### Minus twice the (maximized) log-likelihood plus two times the number of parameters `$$\Large AIC = -2L(\hat{\theta}, y) + 2K$$` -- #### Or, when ordinary least squares (OLS) is used for estimation, AIC is based on the residual sums-of-squares (RSS): `$$\Large AIC = n \log(RSS/n) + 2K$$` -- #### The key is to recognize that > AIC = measure of fit + complexity penalty <br/> -- AIC is asymtotically equivalent to leave-one-out cross-validation --- # aic in practice 1) Select a set of candidate models -- 2) Fit the models to the data (maximize the likelihood or minimize the RSS) -- 3) Compute the AIC of each model -- 4) Rank the models by AIC (lower AIC is better) -- 5) Compute the difference in AIC scores between the best model, and every other model `$$\large \Delta_i = AIC_i - AIC_{min}$$` -- 6) Compute the Akaike weight of each model: `$$\large w_i = \frac{e^{-0.5
\Delta_i}}{\sum_i e^{-0.5 \Delta_i}}$$` -- 7) A model with `\(w = 0.6\)` is twice as likely to be the best model in the set as a model with `\(w = 0.3\)` --- # presentation of results <br/> <table> <thead> <tr> <th style="text-align:center;"> Model </th> <th style="text-align:center;"> RSS </th> <th style="text-align:center;"> K </th> <th style="text-align:center;"> AIC </th> <th style="text-align:center;"> \(\Delta_{AIC}\) </th> <th style="text-align:center;"> w </th> </tr> </thead> <tbody> <tr> <td style="text-align:center;"> 1 </td> <td style="text-align:center;"> 300 </td> <td style="text-align:center;"> 2 </td> <td style="text-align:center;"> 113.8 </td> <td style="text-align:center;"> 0.0 </td> <td style="text-align:center;"> 0.98 </td> </tr> <tr> <td style="text-align:center;"> 2 </td> <td style="text-align:center;"> 320 </td> <td style="text-align:center;"> 3 </td> <td style="text-align:center;"> 122.3 </td> <td style="text-align:center;"> 8.4 </td> <td style="text-align:center;"> 0.02 </td> </tr> <tr> <td style="text-align:center;"> 3 </td> <td style="text-align:center;"> 330 </td> <td style="text-align:center;"> 3 </td> <td style="text-align:center;"> 125.4 </td> <td style="text-align:center;"> 11.5 </td> <td style="text-align:center;"> 0.00 </td> </tr> <tr> <td style="text-align:center;"> 4 </td> <td style="text-align:center;"> 330 </td> <td style="text-align:center;"> 5 </td> <td style="text-align:center;"> 129.4 </td> <td style="text-align:center;"> 15.5 </td> <td style="text-align:center;"> 0.00 </td> </tr> </tbody> </table> ??? Residual sum of squares (RSS) replaced by log likelihood if using maximum likelihood estimation --- # small sample size adjustment <br/> <br/> #### The last term is the “bias adjustment term” `$$\Large AIC_c = n \log(RSS/n) + 2K + \frac{2K(K + 1)}{n - K - 1}$$` --- # notes about aic #### AIC is not a test -- #### AIC is a relative measure. You can’t compare the AICs of models fit to different datasets -- #### AIC tells you about the *relative* predictive ability of the model set, not the *absolute* predictive ability - There will always be a model with the lowest AIC. But all of the models in the set could be terrible -- #### Because AIC is based on predictive ability, it will be more likely to select unimportant/spurious covariates than NHST - Very common to get different conclusions about variable importance between NHST and AIC - Recognizing that they are measuring different things can help clarify confusion - Best to avoid mixing NHST and AIC --- # summary The right approach to model selection depends on the goal of your study -- If the goal is exploration: - Use an approach that can identify potential relationships and consider adjustments for multiple comparisons - Recognize that the biggest risk is identifying spurious relationships - Report your procedures and interpret your results with caution (don't pretend your testing hypotheses!) -- If the goal is inference: - Model selection may not be necessary, especially for manipulative experiments - Select covariates based on strong *a priori* hypotheses and then use NHST - **DO NOT USE THE SAME DATA FOR BOTH EXPLORATION AND INFERENCE** -- If the goal is prediction - Use model selection approaches meant for prediction (AIC, cross validation) - Focus on minimizing error - Be careful when interpreting relationships between response and predictors --- # looking ahead <br/> ### **Next time**: Random effects <br/> ### **Reading**: [Fieberg chp. 18.4-18.6](https://statistics4ecologists-v1.netlify.app/mixed#what-are-random-effects-mixed-effect-models-and-when-should-they-be-used)