Experimental data analysis Lecture 3: Confidence intervals. Dodo Das
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1 Experimental data analysis Lecture 3: Confidence intervals Dodo Das
2 Review of lecture 2 Nonlinear regression - Iterative likelihood maximization Levenberg-Marquardt algorithm (Hybrid of steepest descent and Gauss-Newton) Stochastic optimization - MCMC, Simulated annealing.
3 Demo 3: Nonlinear regression in MATLAB Objective: Using a Hill function to model dose-response data.
4 Demo 3: Nonlinear regression in MATLAB Need the statistics toolbox which contains the function nlinfit i) Write a MATLAB function to compute the Hill function for a given set of parameter values.
5 Demo 3: Nonlinear regression in MATLAB ii) Choose some model parameters to simulate data [or collect data from experiments]
6 Demo 3: Nonlinear regression in MATLAB iii) Pick an initial guess and call nlinfit
7 Demo 3: Nonlinear regression in MATLAB
8 Demo 3: Nonlinear regression in MATLAB
9 Demo 3: Nonlinear regression in MATLAB Convergence can be sensitive to the initial guess.
10 Demo 3: Nonlinear regression in MATLAB Convergence can be sensitive to the initial guess.
11 Demo 3: Nonlinear regression in MATLAB Compute and plot residuals
12 Parameter confidence intervals Question: What does a 95% confidence interval mean? eg: Say, a best fit parameter estimate is â = 1, and we have estimated the 95% CI to be [0.5, 1.5]. How can we interpret this result?
13 Parameter confidence intervals Question: What does a 95% confidence interval mean? eg: Say, a best fit parameter estimate is â = 1, and we have estimated the 95% CI to be [0.5, 1.5]. How can we interpret this result? If we repeat our experiment and the fitting procedure many times, 95% of the times the true (but unknown) parameter value will lie within this CI.
14 Computing parameter confidence intervals Two approaches: 1. Asymptotic confidence intervals: Based on an analytical approximation. 2. Bootstrap confidence intervals: Computational technique based on resampling the errors.
15 Asymptotic confidence intervals Use a local approximation of the SSR landscape to estimate the curvature (covariance matrix). Assuming that the errors are normally distributed, this covariance matrix can be used to compute a confidence interval around each parameter.
16 Demo 4: Computing asymptotic CIs in MATLAB Use the nlparci command in the Statistics toolbox
17 Demo 4: Computing asymptotic CIs in MATLAB Use the nlparci command in the Statistics toolbox
18 Bootstrapping: The principle A computational approach that addresses the following question: Given a limited number of observations, how can we estimate some quantity, eg: mean, median etc. for the population from which the observations are drawn? If we resample from the observations, we can, in some sense, simulate the population distribution.
19 Bootstrapping in practice for nonlinear regression. 1. Use the nonlinear least squares regression to determine the best-fit estimates of the model parameters, and the predicted model response (y i ) predicted at each value of the independent variable x i. 2. Calculate the residuals ɛ i =(y i ) observed (y i ) predicted at each of the N data points. 3. Resample the residuals with replacement to generate a new set of residuals {ɛ i }.Whatthismeansisthatwe generate a new set of N residuals where each of N values is one of the original residuals chosen with equal probability. Typically, some of the original residuals will be chosen more than once, while some will not be chosen at all. For example, say we have three data points and we calculatetheresidualstobe0.1,-0.2and 0.3. Then some possible sets of resampled residuals are: {-0.2, 0.1, -0.2}, {0.1, 0.3, -0.2}, {0.3, -0.2, -0.2}, and so on. 4. Add the resampled residuals to the predicted response to generate a bootstrap data set, {x i,y i } = {x i, (y i ) predicted + ɛ i } 5. Treat the bootstrap dataset as an independent replicate experiment, and fit it to the model to calculate new estimates of model parameters. 6. Repeat steps 3-5 many times - typically 500 to 1000 times - each time generating a new bootstrap data set, and fitting it to the model. Store the resulting best-fit parameter estimates.theseindependentestimatesconstitute asamplefromthebootstrapdistributionofthemodelparameters. 7. For each parameter, calculate the standard deviation of the bootstrap sample. This standard deviation is the estimated bootstrap standard error for that parameter. 8. To calculate the 95% bootstrap CIs, compute the 97.5 th and the 2.5 th percentile values of each parameter from the bootstrap distributions. (There are other prescriptions for calculating bootstrap CIs, but this one is the simplest.)
20 Demo 5: Estimating bootstrap CIs in MATLAB
21 Demo 5: Estimating bootstrap CIs in MATLAB
22 Demo 5: Estimating bootstrap CIs in MATLAB
23 Demo 5: Estimating bootstrap CIs in MATLAB
24 Comparing fits from two different experiments A Which paramters are different between the two datasets?
25 Bootstrap-based hypothesis testing Emax is not (the 95% CI of the difference distribution crosses 0), but EC50 and n are different.
26 Friday How to pick the best model from a set of proposed models? Bias-variance tradeoff F-test Akaike s information criterion (AIC)
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