Likelihood Methods in Ecology

1. Likelihood Methods in Ecology May 29 – June 2, 2017 Fort Collins, Colorado Instructors: Charles Canham And Patrick Martin

2. Daily Schedule • Morning • 8:30 – 9:30 Lecture • 9:30 – 10:30 Case Study and Discussion • 10:30 – 12:00 Lab • Afternoon • 1:00 – 2:00 Lecture • 2:00 – 5:00 Lab

3. Course OutlineStatistical Inference using Likelihood • Principles and practice of maximum likelihood estimation • Know your data – choosing appropriate likelihood functions • Formulate statistical models as alternate hypotheses • Find the ML estimates of the parameters of your models • Compare alternate models and choose the most parsimonious • Evaluate individual models • Advanced topics Likelihood is much more than a set of statistical methods... (it can completely change the way you ask and answer questions…)

4. Lecture 1An Introduction to Likelihood Estimation • Probability and probability density functions • Maximum likelihood estimates (versus traditional “method of moment” estimates) • Statistical inference • Classical “frequentist” statistics : Limitations and mental gyrations... • The “likelihood” alternative: Basic principles and definitions • Model comparison as a generalization of hypothesis testing

5. A simple definition of probability for discrete events... “...the ratio of the number of events of type A to the total number of all possible events (outcomes)...” The enumeration of all possible outcomes is called the sample space (S). If there are n possible outcomes in a sample space, S, and m of those are favorable for event A, then the probability of event, A is given as P{A} = m/n

6. Probability defined more generally... • Consider an outcome X from some process that has a set of possible outcomes S: • If X and S are discrete, then P{X} = X/S • If X is continuous, then the probability has to be defined in the limit: Where g(x) is a probability density function(PDF)

7. The Normal Probability Density Function (PDF) m = mean s2= variance • Properties of a PDF: • (1) 0 <prob(x)< 1 • (2) ∫ prob(x) = 1

8. Common PDFs... • For continuous data: • Normal • Lognormal • Gamma • For discrete data: • Poisson • Binomial • Multinomial • Negative Binomial See McLaughlin (1993) “A compendium of common probability distributions” in the reading list

9. Why are PDFs important? Answer: because they are used to calculate likelihood… (And in that case, they are called “likelihood functions”)

10. Statistical “Estimators” A statistical estimator is a function applied to a sample of data, and used to estimate an unknown population parameter (and an “estimate” is just the result of applying an “estimator” to a sample)

11. Properties of Estimators • Some desirable properties of “point estimators” (functions to estimate a fixed parameter) • Bias: if the average error is zero, the estimate is unbiased • Efficiency: an estimate with the minimum variance is the most efficient (note: the most efficient estimator is often biased) • Consistency: As sample size increases, the probability of the estimate being close to the parameter increases • Asymptotically normal: a consistent estimator whose distribution around the true parameter θ approaches a normal distribution with standard deviation shrinking in proportion to as the sample size n grows

12. Maximum likelihood (ML) estimates versus Method of moment (MOM) estimates Bottom line: MOM was born in the time before computers, and was OK, ML needs computing power, but has more desirable properties…

13. Doing it MOM’s way: Central Moments

14. What’s wrong with MOM’s way? • Nothing, if all you are interested in is calculating properties of your sample… • But MOM’s formulas are generally not the best way1 to infer estimates of the statistical properties of the population from which the sample was drawn… For example: Population variance (because the second central moment is a biased underestimate of the population variance) 1… in the formal terms of bias, efficiency, consistency, and asymptotic normality

15. The Maximum Likelihood alternative… Going back to PDF’s: in plain language, a PDF allows you to calculate the probability that an observation will take on a value (x), given the underlying (true?) parameters of the population

16. But there’s a problem… • The PDF defines the probability of observing an outcome (x), given that you already know the true population parameter (θ) • But we want to generate an estimate of θ, given our data (x) • And, unfortunately, the two are not identical:

17. Fisher and the concept of “Likelihood”... The “Likelihood Principle” In plain English: “The likelihood (L) of the parameter estimates (θ), given a sample (x) is proportional to the probability of observing the data, given the parameters...” {and this probability is something we can calculate, using the appropriate underlying probability model (i.e. a PDF)}

18. R.A. Fisher (1890- 1962) “Likelihood and Probability in R. A. Fisher’s Statistical Methods for Research Workers” (John Aldrich) A good summary of the evolution of Fisher’s ideas on probability, likelihood, and inference… Contains links to PDFs of Fisher’s early papers… A second page shows the evolution of his ideas through changes in successive editions of Fisher’s books… Age 22 http://www.economics.soton.ac.uk/staff/aldrich/fisherguide/prob+lik.htm

19. Calculating Likelihood and Log-Likelihood for Datasets From basic probability theory: If two events (A and B) are independent, then P(A,B) = P(A)P(B) More generally, for i = 1..nindependent observations, and a vector X of observations (xi): where is the appropriate PDF But, logarithms are easier to work with, so...

20. A simple example… A sample of 10 observations… Assume they are normally distributed, with an unknown population mean and standard deviation. What is the (log) likelihood that the mean is 4.5 and the standard deviation is 1.2?

21. Likelihood “Surfaces” The variation in likelihood for any given set of parameter values defines a likelihood “surface”... For a model with just 1 parameter, the surface is simply a curve: (aka a “likelihood profile”)

22. “Support” and “Support Limits” Log-likelihood = “Support” (Edwards 1992)

23. Another (still somewhat trivial) example… • MOM vs ML estimates of the probability of survival for a population: • Data: a quadrat in which 16 of 20 seedlings survived during a census interval. (Note that in this case, the quadrat is the unit of observation…, so sample size = 1) likelihood i.e. Given N=20, x = 16, what is p? p <- seq(0,1,0.005) lh<- dbinom(16,20,p) plot(p,lh) p[which.max(lh)] p

24. A more interesting example # Create some data (5 quadrats) N <- c(11,14,8,22,50) x <- c(8,7,5,17,35) x/N [1] 0.7272727 0.5000000 0.6250000 0.7727273 0.7000000 # Calculate the log-likelihood for each # probability of survival p <- seq(0,1,0.005) log_likelihood <- rep(0,length(p)) for (i in 1:length(p)) { log_likelihood[i] <- sum(log(dbinom(x,N,p[i]))) } # Plot the likelihood profile plot(p,log_likelihood) # What probability of survival maximizes log likelihood? p[which.max(log_likelihood)] 0.685 # How does this compare to the average of the proportions across the 5 quadrats mean(x/N) 0.665

25. Focus in on the MLE… # what is the log-likelihood of the MLE? max(log_likelihood) [1] -9.46812 • Things to note about log-likelihoods: • They should always be negative! (if not, you have a problem with your likelihood function) • The absolute magnitude of the log-likelihood increases as sample size increases

26. An example with continuous data… The normal PDF: x = observed m = mean s2= variance In R: dnorm(x, mean = 0, sd = 1, log = FALSE) > dnorm(2,2.5,1) [1] 0.3520653 > dnorm(2,2.5,1,log=T) [1] -1.043939 > Problem: Now there are TWO unknowns needed to calculate likelihood (the mean and the variance)! Solution: treat the variance just like another parameter in the model, and find the ML estimate of the variance just like you would any other parameter… (this is exactly what you’ll do in the lab this morning…)