Resampling Methods
Levi Waldron, CUNY School of Public Health
levi.waldron@sph.cuny.edu
waldronlab.github.io / waldronlab.org
June 15, 2017
Outline and introduction
- Objectives: prediction or inference?
- Cross-validation
- Bootstrap
- Permutation Test
- Monte Carlo Simulation
ISLR Chapter 5: James, G. et al. An Introduction to Statistical Learning: with Applications in R. (Springer, 2013). This book can be downloaded for free at http://www-bcf.usc.edu/~gareth/ISL/getbook.html
Why do regression?
Inference
- Questions:
- Which predictors are associated with the response?
- How are predictors associated with the response?
- Example: do dietary habits influence the gut microbiome?
- Linear regression and generalized linear models are the workhorses
- We are more interested in interpretability than accuracy
- Produce interpretable models for inference on coefficients
Bootstrap, permutation tests
Why do regression? (cont’d)
Prediction
- Questions:
- How can we predict values of \(Y\) based on values of \(X\)
- Examples: Framingham Risk Score, OncotypeDX Risk Score
- Regression methods are still workhorses, but also less-interpretable machine learning methods
- We are more interested in accuracy than interpretability
- e.g. sensitivity/specificity for binary outcome
- e.g. mean-squared prediction error for continuous outcome
Cross-validation
Cross-validation
Why cross-validation?
Under-fitting, over-fitting, and optimal fitting
K-fold cross-validation approach
- Create \(K\) “folds” from the sample of size \(n\), \(K \le n\)
- Randomly sample \(1/K\) observations (without replacement) as the validation set
- Use remaining samples as the training set
- Fit model on the training set, estimate accuracy on the validation set
- Repeat \(K\) times, not using the same validation samples
- Average validation accuracy from each of the validation sets
Variability in cross-validation
Cross-validation summary
- In prediction modeling, we think of data as training or test
- Cross-validation estimates test set error from a training set
- Training set error always decreases with more complex (flexible) models
- Test set error as a function of model flexibility tends to be U-shaped
- The low point of the U represents the optimal bias-variance trade-off, or the most appropriate amount of model flexibility
- Computationally, \(K\) models must be fitted
- 5 or 10-fold CV are popular choices
- can be repeated for smoothing (e.g. see Braga-Neto et al 2004. Is cross-validation valid for small-sample microarray classification?. Bioinformatics, 20(3), 374-380.)
Cross-validation caveats
- Be very careful of information “leakage” into test sets, e.g.:
- feature selection using all samples
- “human-loop” over-fitting
- changing your mind on accuracy measure
- try a different dataset
http://hunch.net/?p=22
Cross-validation caveats (cont’d)
- Tuning plus accuracy estimation requires nested cross-validation
- Example: high-dimensional training and test sets simulated from identical true model
- Penalized regression models tuned by 5-fold CV
- Tuning by cross-validation does not prevent over-fitting
Waldron et al.: Optimized application of penalized regression methods to diverse genomic data. Bioinformatics 2011, 27:3399–3406.
Cross-validation caveats (cont’d)
- Cross-validation estimates assume that the sample is representative of the population
Bernau C et al.: Cross-study validation for the assessment of prediction algorithms. Bioinformatics 2014, 30:i105–12.
Permutation test
Permutation test
- Classical hypothesis testing: \(H_0\) of test statistic derived from assumptions about the underlying data distribution
- e.g. \(t\), \(\chi^2\) distribution
- Permutation testing: \(H_0\) determined empirically using permutations of the data where \(H_0\) is guaranteed to be true
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)
Steps of permutation test:
- Calculate test statistic (e.g. T) in observed sample
- Permutation:
- Sample without replacement the response values (\(Y\)), using the same \(X\)
- re-compute and store the test statistic T
- Repeat R times, store as a vector \(T_R\)
- Calculate empirical p value: proportion of permutation \(T_R\) that exceed actual T
Calculating a p-value
\[
P = \frac{sum \left( abs(T_R) > abs(T) \right)+ 1}{length(T_R) + 1}
\]
- Why add 1?
- Phipson B, Smyth GK: Permutation P-values should never be zero: calculating exact P-values when permutations are randomly drawn. Stat. Appl. Genet. Mol. Biol. 2010, 9:Article39.
Calculating a False Discovery Rate
- calculate # all discoveries from unpermuted data
- estimate # false discoveries by averaging over permutations
Permutation test - pros and cons
- Pros:
- does not require distributional assumptions
- can be applied to any test statistic
- applicable to False Discovery Rate estimation
- Cons:
- less useful for small sample sizes
- in naive implementations, can get p-values of “0”
Example from (sleep) data:
- Sleep data show the effect of two soporific drugs (increase in hours of sleep compared to control) on 10 patients.
## extra group ID
## Min. :-1.600 1:10 1 :2
## 1st Qu.:-0.025 2:10 2 :2
## Median : 0.950 3 :2
## Mean : 1.540 4 :2
## 3rd Qu.: 3.400 5 :2
## Max. : 5.500 6 :2
## (Other):8
t-test for difference in mean sleep
##
## Welch Two Sample t-test
##
## data: extra by group
## t = -1.8608, df = 17.776, p-value = 0.07939
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -3.3654832 0.2054832
## sample estimates:
## mean in group 1 mean in group 2
## 0.75 2.33
Permutation test instead of t-test
set.seed(1)
permT = function(){
index = sample(1:nrow(sleep), replace=FALSE)
t.test(extra ~ group[index], data=sleep)$statistic
}
Tr = replicate(999, permT())
(sum(abs(Tr) > abs(Tactual)) + 1) / (length(Tr) + 1)
## [1] 0.079
Bootstrap
The Bootstrap
ISLR Figure 5.11: Schematic of the bootstrap
Uses of the Bootstrap
- The Bootstrap is a very general approach to estimating sampling uncertainty, e.g. standard errors
- Can be applied to a very wide range of models and statistics
- Robust to outliers and violations of model assumptions
Example: bootstrap in the sleep dataset
- We used a permutation test to estimate a p-value
- We will use bootstrap to estimate a confidence interval
t.test(extra ~ group, data=sleep)
##
## Welch Two Sample t-test
##
## data: extra by group
## t = -1.8608, df = 17.776, p-value = 0.07939
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -3.3654832 0.2054832
## sample estimates:
## mean in group 1 mean in group 2
## 0.75 2.33
Example: bootstrap in the sleep dataset
set.seed(2)
bootDiff = function(){
boot = sleep[sample(1:nrow(sleep), replace = TRUE), ]
mean(boot$extra[boot$group==1]) -
mean(boot$extra[boot$group==2])
}
bootR = replicate(1000, bootDiff())
bootR[match(c(25, 975), rank(bootR))]
## [1] -3.32083333 0.02727273
note: better to use library(boot)
Example: oral carcinoma recurrence risk
- Oral carcinoma patients treated with surgery
- Surgeon takes “margins” of normal-looking tissue around to tumor to be safe
- number of “margins” varies for each patient
- Can an oncogenic gene signature in histologically normal margins predict recurrence?
Reis PP, Waldron L, et al.: A gene signature in histologically normal surgical margins is predictive of oral carcinoma recurrence. BMC Cancer 2011, 11:437.
Example: oral carcinoma recurrence risk
- Model was trained and validated using the maximum expression of each of 4 genes from any margin
Bootstrap estimation of HR for only one margin
Example: oral carcinoma recurrence risk
From results:
Simulating the selection of only a single margin from each patient, the 4-gene signature maintained a predictive effect in both the training and validation sets (median HR = 2.2 in the training set and 1.8 in the validation set, with 82% and 99% of bootstrapped hazard ratios greater than the no-effect value of HR = 1)
Monte Carlo
What is a Monte Carlo simulation?
- “Resampling” is done from known theoretical distribution
- Simulated data are used to estimate the probability of possible outcomes
- most useful application for me is power estimation
- also used for Bayesian estimation of posterior distributions
How to conduct a Monte Carlo simulation
- Steps of a Monte Carlo simulations:
- Sample randomly from the simple distributions in each step
- Estimate the complex function for the sample
- Repeat this a large number of times
Power Calculation for a follow-up sleep study
- What sample size do we need for a future study to detect the same effect on sleep, with 90% power and \(\alpha = 0.05\)?
power.t.test(power=0.9, delta=(2.33-.75),
sd=1.9, sig.level=.05,
type="two.sample", alternative="two.sided")
##
## Two-sample t test power calculation
##
## n = 31.38141
## delta = 1.58
## sd = 1.9
## sig.level = 0.05
## power = 0.9
## alternative = two.sided
##
## NOTE: n is number in *each* group
The same calculation by Monte Carlo simulation
- Use
rnorm()
function to draw samples
- Use
t.test()
function to get a p-value
- Repeat many times, what % of p-values are less than 0.05?
R script
set.seed(1)
montePval = function(n){
group1 = rnorm(n, mean=.75, sd=1.9)
group2 = rnorm(n, mean=2.33, sd=1.9)
t.test(group1,group2)$p.value
}
sum(replicate(1000, montePval(n=32)) < 0.05) / 1000
## [1] 0.895
Summary: resampling methods
Cross-Validation |
Data is randomly divided into subsets. Results validated across sub-samples. |
Model tuning Estimation of prediction accuracy |
|
|
|
Permutation Test |
Samples of size N drawn at random without replacement. |
Hypothesis testing |
Summary: resampling methods
Bootstrap |
Samples of size N drawn at random with replacement. |
Confidence intervals, hypothesis testing |
|
|
|
Monte Carlo |
Data are sampled from a known distribution |
Power estimation, Bayesian posterior probabilities |
Links
- A built html version of this lecture is available.
- The source R Markdown is also available from Github.