WinBUGS Examples .


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We hone the WinBUGS dialect arrangement utilizing a basic case of evaluating a mean and difference of 20 perceptions utilizing a diffuse earlier for the mean and change (accuracy). At the end of the day, we would prefer not to get any earlier data.. In WinBUGS we indicate ordinary conveyances as far as their mean and accuracy. The accuracy that is typically spoken to by tau is 1/sigma^2.WinBUGS requ
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WinBUGS Examples "I admit that I have been a visually impaired mole, however it is ideal to learn intelligence late than never to learn it by any means." Sherlock Holmes The Man with the Twisted Lip James B. Elsner Department of Geography, Florida State University Portions of this introduction are brought from Getting Started with WinBUGS, Gillian Raab, June 1999.

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We rehearse the WinBUGS dialect design utilizing a straightforward case of evaluating a mean and difference of 20 perceptions utilizing a diffuse earlier for the mean and change (accuracy). At the end of the day, we would prefer not to get any earlier data. In WinBUGS we indicate ordinary circulations as far as their mean and accuracy. The exactness that is generally spoken to by tau is 1/sigma^2. WinBUGS requires all priors to be legitimate. That is, they coordinate to a limited number. For the mean of a dispersion, the standard decision is a typical conveyance. In the event that we would prefer not to get any earlier data, then we permit the standard deviation to cover an extensive variety of qualities. The default in WinBUGS is an estimation of 1.0E-6 for the exactness. Therefore we are permitting a 95% tenable interim amongst –2,000,000 and +2,000,000. That is truly dubious, yet you can simply make it much more unclear. Priors for the exactness are more subtle. One approach to indicate the earlier on the exactness is to utilize a uniform dispersion over a settled range, which gives a Pareto conveyance for the accuracy. Another is to utilize an individual from the Gamma group of appropriations for the exactness, which is a conjugate earlier for the accuracy in an ordinary conveyance. Conjugate priors are decisions of earlier circulations that are normally associated with a specific probability ( Poisson , Gamma , and so on.). The conjugate earlier has an indistinguishable frame from the probability, aside from that the part of the "information" and the "gauge" are exchanged, and the conjugate earlier and the probability have distinctive normalizing constants.

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It is prescribed that you determine an unclear earlier for tau as Gamma(0.001,0.001) . This is the default when you compose your model with a doodle. The main parameter of the conveyance is marked an and the second is named q . The mean of the dissemination is given by aq and the change by aq ^2. We will utilize doodle. Open WinBUGS > Doodle > New… We have 20 estimations of the irregular variable x, so we have to set up a plate in our doodle.

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Doodle Quick Help

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Node Plate Edge Select Doodle > Write Code display { mean ~ dnorm(0,0.00001) # Prior on typical mean tau ~dgamma(0.01, 0.01) # Prior on ordinary exactness sd <- sqrt(1/tau) # Variance = 1/accuracy for (i in 1:N) { x[i] ~ dnorm(mean,tau)} } Notice that since we utilize tau to characterize the fluctuation, we ascertain the standard deviation from tau, with the sd being a legitimate hub. In the Code window include the information. list(N=20, x=c(98,160,136,128,130,114,123,134,128,107,123,125,129,132,154,115,126,132,136,130))

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X[i]~dnorm(mean,tau) Select Model > Specification check demonstrate load information gen inits shape parameter of gamma tau too little – can\'t test. To beat this, we can indicate an underlying incentive for tau—any sensible esteem will do, regardless of the possibility that it isn\'t right, the sampler will bring them once again into the right range after a couple tests.

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Recheck the model, stack the information, and incorporate. At that point select the list(tau=1) and snap stack inits. At that point click gen inits to create the other starting qualities (for this situation, mean). Select Inference > Sample to raise the Sample Monitor Tool. Sort mean and set, then sd and select. Select Model > Update to raise the Update Tool. Pick 1000 updates, then backpedal to the Sample Monitor Tool and select details.

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The synopsis insights from one WinBUGS run demonstrates a back mean for the mean hub of 128.0. The back sd for the mean is 3.41. The 95% solid interim for the mean is (121.2, 134.9). This is contrasted and a frequentist result utilizing the t dissemination. To see this utilizing R, sort: >mean(x)+1.96*sd(x)/sqrt(length(x)) > [1] 134.1 >mean(x)- 1.96*sd(x)/sqrt(length(x)) > [1] 121.9 The mean is 128.0 with a 95% certainty interim (121.9, 134.1). The back mean for the sd hub is 14.49, this contrasts and a frequentist consequence of 13.9. Take note of the back valid points of confinement (10.58, 20.46). Utilizing frequentist approach, we would utilize the c - squared circulation.

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The MC mistake instructs you to what degree recreation blunder adds to the instability in the estimation of the mean. This can be lessened by producing extra examples. The recreation mistakes on the 95% dependable interim qualities will be impressively bigger on account of the little numbers in the tails of the conveyance. You ought to look at the hint of every one of your examples. To do this choose the history catch on the Sample Monitor Tool. In the event that the specimens are to a great extent free, it will look exceptionally spiked demonstrating little autocorrelation between tests. You can address the subject of test autocorrelation straightforwardly by choosing the auto cor catch. Autocorrelation is not an issue in this case. In the event that autocorrelation exists, you have to produce extra specimens. The outcomes from the WinBUGS Bayesian approach are fundamentally the same as the outcomes from a frequentist approach. Why? This is a muddled examination for a straightforward issue, yet it frames the building obstructs for more mind boggling issues.

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We keep rehearsing with WinBUGS. Here is a common slightest squares relapse case. The accompanying information should be fit by direct relapse. They concern the fuel utilization (reponse variable, y) and weight (informative variable, x) of ten autos. Y[] X[] 3.4 5.5 3.8 5.9 9.1 6.5 2.2 3.3 2.6 3.6 2.9 4.6 2.0 2.9 2.7 3.6 1.9 3.1 3.4 4.9 The plot demonstrates the information alongside the OLS relapse line. We can see that there is the thing that seems, by all accounts, to be a solitary persuasive point that might be an exception.

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The relapse fit utilizing a frequentist approach is depicted by the take after rundown, as given in R.

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Alternatively, we can set up a Bayesian relapse show. Begin with Doodle. Include a plate. record: i from: 1 up to: N Add hubs. On the plate. name: Y[i] sort: stochastic thickness: dnorm mean: mu[i] exactness tau name: mu[i] sort: sensible thickness: character esteem: a+b*X[i] name: X[i] sort: steady Off the plate. name: a sort: stochastic thickness: dnorm mean: 0.0 accuracy 1.0E-6 name: b sort: stochastic thickness: dnorm mean: 0.0 exactness 1.0E-6

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Add hubs. Off the plate. name: tau sort: stochastic thickness: dgamma shape: 1.0E-3 scale: 1.0E-3 name: sd sort: consistent connection: character esteem: 1/sqrt(tau) Connect the hubs utilizing edges to create the accompanying direct relapse doodle. Select Doodle > Write Code, then include information and starting an incentive for tau.

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Use the Sample Monitor Tool to set the hub values for an and b. The select Model > Update … to produce tests. Check for autocorrelation among the examples. What do you discover? How do the outcomes contrast and the frequentist approach?

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What about the anomaly? It is positively a powerful point in the relapse. A frequentist approach may dispose of it. Established relapse comes about with that point evacuated are:

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In standard relapse we expect regularly dispersed blunders. On the off chance that we imagine that there may be anomalies, then it is ideal to expect a blunder conveyance with longer tails than the typical. The t conveyance is better, for instance. We can do this effectively in WinBUGS by supplanting the ordinary dispersion supposition on Y[] by a t circulation with some modest number of degrees of opportunity. The littler the degrees of opportunity, the more drawn out the tails. This makes the relapse strong to exceptions. Run the sampler and produce 5000 examples. It is dependably a smart thought to dispose of the initial couple of hundred or so tests so that the last arrangement has not "memory" of it\'s underlying qualities (smolder in). In the Sample Monitor Tool exchange set ask to 1001 and end to 5000. Print out the insights for the slant and catch hubs.

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Plot the follow and autcorrelation to check for relationship in the specimens. Some feeble autocorrelation in the specimens. Analyze the back thickness for the incline parameter. Contrast it with the first model. Select coda to print out the progressive specimen values. You can cut/glue or fare to another program.

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With the t dispersion, we get an altogether different outcome. The relapse is presently what we call safe relapse, implying that it is not unduly affected by anomalies. It works in light of the fact that the probability now perceives the likelihood of finding an odd point in the tails of the dissemination. You can explore different avenues regarding diverse estimations of the degrees of flexibility to perceive how the back appropriation on the slant parameter changes. How do the outcomes from the Bayesian model contrast and those from the forget it frequentist demonstrate? Examination questions: How do you pick an incentive for the degrees of opportunity in the t appropriation? Would we need to expect this is additionally a parameter with a conveyance? Display decision is a piece of the earlier, so would it be advisable for it to be picked before we see the information? Next: Hierarchical models.

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