We retrospectively studied 1715 patients with gastric cancer. Is the sample size a problem? The response is usually a survival object as returned by the Surv function. First, I’ll set up a function to generate simulated data from a Weibull distribution and censor any observations greater than 100. I chose an arbitrary time point of t=40 to evaluate the reliability. Stein and Dattero (1984) have pointed out that a series system with two components that are independent and identically distributed have a distribution of the form in (3.104) . Here is a summary of where we ended up going in the post: * Fit some models using fitdistr plus using data that was not censored. We need a simulation that lets us adjust n. Here we write a function to generate censored data of different shape, scale, and sample size. Evaluate Sensitivity of Reliability Estimate to Sample Size. I an not an expert here, but I believe this is because very vague default Gamma priors aren’t good for prior predictive simulations but quickly adapt to the first few data points they see.8. Calculated reliability at time of interest. If I was to try to communicate this in words, I would say: Why does any of this even matter? This approach is not optimal however since it is generally only practical when all tested units pass the test and even then the sample size requirement are quite restricting. Thank you for reading! The following is the plot of the Weibull hazard function with the The parameters that get estimated by brm() are the Intercept and shape. Set of 800 to demonstrate Bayesian updating. Weibull probability plot: We generated 100 Weibull random variables using $$T$$ = 1000, $$\gamma$$ = 1.5 and $$\alpha$$ = 5000. For each set of 30 I fit a model and record the MLE for the parameters. Here we compare the effect of the different treatments of censored data on the parameter estimates. Here are the reliabilities at t=15 implied by the default priors. The syntax of the censoring column is brms (1 = censored). estimation for the Weibull distribution. In survival analysis we are waiting to observe the event of interest. Assume the service life requirement for the device is known and specified within the product’s requirements, Assume we can only test n=30 units in 1 test run and that testing is expensive and resource intensive, The n=30 failure/censor times will be subject to sampling variability and the model fit from the data will likely not be Weibull(3, 100), The variability in the parameter estimates is propagated to the reliability estimates - a distribution of reliability is generated for each potential service life requirement (in practice we would only have 1 requirement). where μ = 0 and α = 1 is called the standard pass/fail by recording whether or not each test article fractured or not after some pre-determined duration t. By treating each tested device as a Bernoulli trial, a 1-sided confidence interval can be established on the reliability of the population based on the binomial distribution. In the code below, the .05 quantile of reliability is estimated for each time requirement of interest where we have 1000 simulation at each. Additionally, designers cannot establish any sort of safety margin or understand the failure mode(s) of the design. with the same values of γ as the pdf plots above. Nevertheless, we might look at the statistics below if we had absolutely no idea the nature of the data generating process / test. The model by itself isn’t what we are after. If you have a sample of independent Weibull survival times, with parameters , and , then the likelihood function in terms of and is as follows: If you link the covariates to with , where is the vector of covariates corresponding to the th observation and is a vector of regression coefficients, the log-likelihood function … There is no explicit formula for the hazard either, but this may be com- puted easily as the ratio of the density to the survivor function, (t) = f(t)=S(t). Hazard and Survivor Functions for Different Groups; On this page; Step 1. This problem is simple enough that we can apply grid approximation to obtain the posterior. In this post, I’ll explore reliability modeling techniques that are applicable to Class III medical device testing. (R has a function called pgamma that computes the cdf and survivor function. We plot the survivor function that corresponds to our Weibull(5,3). A lot of the weight is at zero but there are long tails for the defaults. The following is the plot of the Weibull inverse survival function They represent months to failure as determined by accelerated testing. For instance, suppose our voice of customer research indicates that our new generation of device needs to last 10 months in vivo to be safe and competitive. The most common experimental design for this type of testing is to treat the data as attribute i.e. This distribution gives much richer information than the MLE point estimate of reliability. In the following section I work with test data representing the number of days a set of devices were on test before failure.2 Each day on test represents 1 month in service. Intervals are 95% HDI. Gut-check on convergence of chains. 95% of the reliability estimates like above the .05 quantile. It looks like we did catch the true parameters of the data generating process within the credible range of our posterior. The Weibull Distribution. To further throw us off the trail, the survreg() function returns “scale”" and “intercept”" that must be converted to recover the shape and scale parameters that align with the rweibull() function used to create the data. When we omit the censored data or treat it as a failure, the shape parameter shifts up and the scale parameter shifts down. The following is the plot of the Weibull survival function To start, we fit a simple model with default priors. That is a dangerous combination! It is common to report confidence intervals about the reliability estimate but this practice suffers many limitations. It’s apparent that there is sampling variability effecting the estimates. $$h(x) = \gamma x^{(\gamma - 1)} \hspace{.3in} x \ge 0; \gamma > 0$$. One use of the survivor function is to predict quantiles of the survival time. Evaluate the effect of the different priors (default vs. iterated) on the model fit for original n=30 censored data points. ∗ At time t = ∞, S(t) = S(∞) = 0. This plot looks really cool, but the marginal distributions are bit cluttered. distribution, all subsequent formulas in this section are By comparison, the discrete Weibull I has survival function of the same form as the continuous counterpart, while discrete Weibull II has the same form for the hazard rate function. Estimates for product reliability at 15, 30, 45, and 60 months are shown below. This is Bayesian updating. given for the standard form of the function. Since Weibull regression model allows for simultaneous description of treatment effect in terms of HR and relative change in survival time, ConvertWeibull() function is used to convert output from survreg() to more clinically relevant parameterization. If you have a sample of n independent Weibull survival times, with parameters , and , then the likelihood function in terms of and is as follows: If you link the covariates to with , where is the vector of covariates corresponding to the i th observation and is a vector of regression coefficients, the log-likelihood function … α is the scale parameter. Researchers in the medical sciences prefer employing Cox model for survival analysis. These data are just like those used before - a set of n=30 generated from a Weibull with shape = 3 and scale = 100. * Explored fitting censored data using the survival package. Things look good visually and Rhat = 1 (also good). De Weibull-verdeling wordt vaak gebruikt in plaats van de normale verdeling omwille van het feit dat een Weibull-verdeelde toevalsvariabele gegenereerd kan worden door inversie, terwijl normale toevalsvariabelen typisch gegenereerd worden met de complexere Box-Müller-transformatie, die twee uniform verdeelde toevalsvariabelen vereist. This should give is confidence that we are treating the censored points appropriately and have specified them correctly in the brm() syntax. This article describes the characteristics of a popular distribution within life data analysis (LDA) – the Weibull distribution. The precision increase here is more smooth since supplemental data is added to the original set instead of just drawing completely randomly for each sample size. We add a Weibull(3,3) and Weibull(1,3). I recreate the above in ggplot2, for fun and practice. The original model was fit from n=30. The .05 quantile of the reliability distribution at each requirement approximates the 1-sided lower bound of the 95% confidence interval. There’s a lot going on here so it’s worth it to pause for a minute. Regardless, I refit the model with the (potentially) improved more realistic (but still not great) priors and found minimal difference in the model fit as shown below. ing the survival estimates for males and females under the exponential model, i.e., P(T t) = e( ^ zt), to the Kaplan-Meier survival estimates: We can see how well the Weibull model ts by comparing the survival estimates, P(T t) = e( ^ zt ^), to the Kaplan-Meier survival estimates. This hypothetical should be straightforward to simulate. Estimate cumulative hazard and fit Weibull cumulative hazard functions. Review of Last lecture (2) Implication of these functions: I The survival function S(x) is the probability of an individual surviving to time x. I The hazard function h(x), sometimes termed risk function, is the chance an individual of time x experiences the event in the next instant in … They also do not represent true probabilistic distributions as our intuition expects them to and cannot be propagated through complex systems or simulations. This allows for a straightforward computation of the range of credible reliabilities at t=10 via the reliability function. The above analysis, while not comprehensive, was enough to convince me that the default brms priors are not the problem with initial model fit (recall above where the mode of the posterior was not centered at the true data generating process and we wondered why). I honestly don’t know. The following is the plot of the Weibull probability density function. 2013 by Statpoint Technologies, Inc. Weibull Analysis - 15 Log Survival Function The Log Survival Function is the natural logarithm of the survival function: Weibull Distribution 1000 10000 100000 Distance-33-23-13-3 7. We are also going to … Fit and save a model to each of the above data sets. Evaluate chains and convert to shape and scale. This is the probability that an individual survives beyond time t. This is usually the first quantity that is studied. But we still don’t know why the highest density region of our posterior isn’t centered on the true value. Survivor function: S(t) def= 1 F(t) = P(T>t) for t>0: The survivor function simply indicates the probability that the event of in-terest has not yet occurred by time t; thus, if T denotes time until death, S(t) denotes probability of surviving beyond time t. Note that, for an arbitrary … Prior Predictive Simulation - Default Priors. But since I’m already down a rabbit hole let’s just check to see how the different priors impact the estimates. If we super-impose our point estimate from Part 1, we see the maximum likelihood estimate agrees well with the mode of the joint posterior distributions for shape and scale. To wrap things up, we should should translate the above figures into a reliability metric because that is the prediction we care about at the end of the day. Stent fatigue testing https://www.youtube.com/watch?v=YhUluh5V8uM↩, Data taken from Practical Applications of Bayesian Reliability by Abeyratne and Liu, 2019↩, Note: the reliability function is sometimes called the survival function in reference to patient outcomes and survival analysis↩, grid_function borrowed from Kurz, https://bookdown.org/ajkurz/Statistical_Rethinking_recoded/↩, Survival package documentation, https://stat.ethz.ch/R-manual/R-devel/library/survival/html/survreg.html↩, We would want to de-risk this appoach by makng sure we have a bit of historical data on file indicating our device fails at times that follow a Weibull(3, 100) or similar↩, See the “Survival Model” section of this document: https://cran.r-project.org/web/packages/brms/vignettes/brms_families.html#survival-models↩, Thread about vague gamma priors https://math.stackexchange.com/questions/449234/vague-gamma-prior↩, Copyright © 2020 | MH Corporate basic by MH Themes, Part 1 – Fitting Models to Weibull Data Without Censoring [Frequentist Perspective], Construct Weibull model from un-censored data using fitdistrplus, Using the model to infer device reliability, Part 2 – Fitting Models to Weibull Data Without Censoring [Bayesian Perspective], Use grid approximation to estimate posterior, Uncertainty in the implied reliabilty of the device, Part 3 – Fitting Models to Weibull Data with Right-Censoring [Frequentist Perspective], Simulation to understand point estimate sensitivity to sample size, Simulation of 95% confidence intervals on reliability, Part 4 – Fitting Models to Weibull Data with Right-Censoring [Bayesian Perspective], Use brm() to generate a posterior distribution for shape and scale, Evaluate sensitivity of posterior to sample size. Plotting the joint distributions for the three groups: Our censored data set (purple) is closest to true. Density, distribution function, quantile function and random generation for the Weibull distribution with parameters shape and scale. Let’s start with the question about the censoring. Weibull survival function A key assumption of the exponential survival function is that the hazard rate is constant. I have all the code for this simulation for the defaults in the Appendix. New content will be added above the current area of focus upon selection with the same values of γ as the pdf plots above. Combine into single tibble and convert intercept to scale. We know the data were simulated by drawing randomly from a Weibull(3, 100) so the true data generating process is marked with lines. distribution, Maximum likelihood $$G(p) = (-\ln(1 - p))^{1/\gamma} \hspace{.3in} 0 \le p < 1; \gamma > 0$$. To answer these questions, we need a new function that fits a model using survreg() for any provided sample size. The case This threshold changes for each candidate service life requirement. optional vector of case weights. Recall that the survivor function is 1 minus the cumulative distribution function, S(t) = 1 - F(t). The case where μ = 0 is called the I am only looking at 21… The intervals change with different stopping intentions and/or additional comparisons. This is sort of cheating but I’m still new to this so I’m cutting myself some slack. It turns out that the hazard function for light bulbs, earthquakes, etc. Plot the grid approximation of the posterior. If you read the first half of this article last week, you can jump here. This is a good way to visualize the uncertainty in a way that makes intuitive sense. I admit this looks a little strange because the data that were just described as censored (duration greater than 100) show as “FALSE” in the censored column. First and foremost - we would be very interested in understanding the reliability of the device at a time of interest. FDA expects data supporting the durability of implantable devices over a specified service life. Calculate posterior via grid approximation:4. The following is the plot of the Weibull cumulative distribution A survival curve can be created based on a Weibull distribution. They must inform the analysis in some way - generally within the likelihood. Are the priors appropriate? $$f(x) = \frac{\gamma} {\alpha} (\frac{x-\mu} same values of γ as the pdf plots above. The data to make the fit are generated internal to the function. At the end of the day, both the default and the iterated priors result in similar model fits and parameter estimates after seeing just n=30 data points. Now another model where we just omit the censored data completely (i.e. * Used brms to fit Bayesian models with censored data. function with the same values of γ as the pdf plots above. In other words, the probability of surviving past time 0 is 1. This is hard and I do know I need to get better at it. Step 5. If you take this at face value, the model thinks the reliability is always zero before seeing the model. The equation for the standard Weibull In both cases, it moves farther away from true. This figure tells a lot. The density functions of the eight distributions that are fit by this module were given in the Distribution Fitting section and will not be repeated here. Fit the model with iterated priors: student_t(3, 5, 5) for Intercept and uniform(0, 10) for shape. Assume we have designed a medical device that fails according to a Weibull distribution with shape = 3 and scale = 100. Once the parameters of the best fitting Weibull distribution of determined, they can be used to make useful inferences and predictions. Are there too few data and we are just seeing sampling variation? distribution reduces to, \( f(x) = \gamma x^{(\gamma - 1)}\exp(-(x^{\gamma})) \hspace{.3in} can be described by the monomial function –1 ( )= t ht β β αα This defines the Weibull distribution with corresponding cdf \( \Gamma(a) = \int_{0}^{\infty} {t^{a-1}e^{-t}dt}$$, expressed in terms of the standard Let’s fit a model to the same data set, but we’ll just treat the last time point as if the device failed there (i.e. Now the function above is used to create simulated data sets for different sample sizes (all have shape 3, scale = 100). We know the true parameters are shape = 3, scale = 100 because that’s how the data were generated. For the model we fit above using MLE, a point estimate of the reliability at t=10 years (per the above VoC) can be calculated with a simple 1-liner: In this way we infer something important about the quality of the product by fitting a model from benchtop data. If all n=59 pass then we can claim 95% reliability with 95% confidence. The formula for asking brms to fit a model looks relatively the same as with survival. This function calls kthe shape parameter and 1=the scale parameter.) The following is the plot of the Weibull percent point function with Plot survivor functions. The general survival function of a Weibull regression model can be specified as \[ S(t) = \exp(\lambda t ^ \gamma). The Weibull isn’t the only possible distribution we could have fit. Estimated survival times for the median S(t) = 0:5: > median <-predict(weibull.aft, + newdata=list(TRT=c(0,1)), + type=’quantile’,p=0.5) > median 1 2 7.242697 25.721526 > median[2]/median[1] 2 3.551374 0 10 20 30 40 50 60 0.0 0.2 0.4 0.6 0.8 1.0 t S(t) TRT=0 TRT=1 Survival Function S… This is a perfect use case for ggridges which will let us see the same type of figure but without overlap. For benchtop testing, we wait for fracture or some other failure. We haven’t looked closely at our priors yet (shame on me) so let’s do that now. Not too useful. weights. with the same values of γ as the pdf plots above. The prior must be placed on the intercept when must be then propagated to the scale which further muddies things. Goodness-of-fit statistics are available and shown below for reference. * Fit the same models using a Bayesian approach with grid approximation. Recall that each day on test represents 1 month in service. the same values of γ as the pdf plots above. Just like with the survival package, the default parameterization in brms can easily trip you up. It is the vehicle from which we can infer some very important information about the reliability of the implant design. Note: all models throughout the remainder of this post use the “better” priors (even though there is minimal difference in the model fits relative to brms default). The key is that brm() uses a log-link function on the mean $$\mu$$. If it cost a lot to obtain and prep test articles (which it often does), then we just saved a ton of money and test resources by treating the data as variable instead of attribute. Assessed sensitivity of priors and tried to improve our priors over the default. Weibull’s Derivation n (1 ( )) (1 ) − = − = F x P e − ϕn n x ( ) ( ) 1 = − F x e −ϕx( ) x x o m u x x x F x e ( ) ( ) 1 − − = − A cdf can be transformed into the form This is convenient because Among simplest functions satisfying the condition is The function ϕ(x)must be positive, non … Draw from the posterior of each model and combine into one tibble along with the original fit from n=30. Engineers develop and execute benchtop tests that accelerate the cyclic stresses and strains, typically by increasing the frequency. x \ge 0; \gamma > 0 \). Estimate and plot cumulative distribution function for each gender. The most credible estimate of reliability is ~ 98.8%, but it could plausibly also be as low as 96%. In short, to convert to scale we need to both undo the link function by taking the exponent and then refer to the brms documentation to understand how the mean $$\mu$$ relates to the scale $$\beta$$. We are fitting an intercept-only model meaning there are no predictor variables. Each of the credible parameter values implies a possible Weibull distribution of time-to-failure data from which a reliability estimate can be inferred. Survival function, S(t) or Reliability function, R(t). The Weibull distribution is named for Professor Waloddi Weibull whose papers led to the wide use of the Cases in which no events were observed are considered “right-censored” in that we know the start date (and therefore how long they were under observation) but don’t know if and when the event of interest would occur. Again, it’s tough because we have to work through the Intercept and the annoying gamma function. Such a test is shown here for a coronary stent:1. 2.2 Weibull survival function for roots A survival function, also known as a complementary cumu-170 lative distribution function, is a probability function used in a broad range of applications that captures the failure probabil-ity of a complex system beyond a threshold. In a clinical study, we might be waiting for death, re-intervention, or endpoint. Fit Weibull survivor functions. {\alpha})^{(\gamma - 1)}\exp{(-((x-\mu)/\alpha)^{\gamma})} I set the function up in anticipation of using the survreg() function from the survival package in R. The syntax is a little funky so some additional detail is provided below. We’ll assume that domain knowledge indicates these data come from a process that can be well described by a Weibull distribution. Inverse Survival Function These point estimates are pretty far off. Step 3. The following is the plot of the Weibull cumulative hazard function Given the hazard function, we can integrate it to find the survival function, from which we can obtain the cdf, whose derivative is the pdf. On average, the true parameters of shape = 3 and scale = 100 are correctly estimated. Arbitrary quantiles for estimated survival function. To start, I’ll read in the data and take a look at it. μ is the location parameter and $$S(x) = \exp{-(x^{\gamma})} \hspace{.3in} x \ge 0; \gamma > 0$$. If available, we would prefer to use domain knowledge and experience to identify what the true distribution is instead of these statistics which are subject to sampling variation. To obtain the CDF of the Weibull distribution, we use weibull(a,b). Given the low model sensitivity across the range of priors I tried, I’m comfortable moving on to investigate sample size. Survival analysis is one of the less understood and highly applied algorithm by business analysts. Any row-wise operations performed will retain the uncertainty in the posterior distribution. The default priors are viewed with prior_summary(). By introducing the exponent $$\gamma$$ in the term below, we allow the hazard to … Since the priors are flat, the posterior estimates should agree with the maximum likelihood point estimate. Evaluated effect of sample size and explored the different between updating an existing data set vs. drawing new samples. Visualized what happens if we incorrectly omit the censored data or treat it as if it failed at the last observed time point. All in all there isn’t much to see. ## survival 2.37-2 has a bug in quantile(), so this currently doesn't work # quantile(KM0, probs = c(0.25, 0.5, 0.75), conf.int=FALSE) All estimated values for survival function including point-wise confidence interval. Survival Function The formula for the survival function of the Weibull distribution is $$S(x) = \exp{-(x^{\gamma})} \hspace{.3in} x \ge 0; \gamma > 0$$ The following is the plot of the Weibull survival function with the same values of γ as the pdf plots above. Posted on January 26, 2020 by [R]eliability in R bloggers | 0 Comments. I do need to get better at doing these prior predictive simulations but it’s a deep, dark rabbit hole to go down on an already long post. Not many analysts understand the science and application of survival analysis, but because of its natural use cases in multiple scenarios, it is difficult to avoid!P.S. Here’s the TLDR of this whole section: Suppose the service life requirement for our device is 24 months (2 years). Create tibble of posterior draws from partially censored, un-censored, and censor-omitted models with identifier column. One question that I’d like to know is: What would happen if we omitted the censored data completely or treated it like the device failed at the last observed time point? a data frame in which to interpret the variables named in the formula, weights or the subset arguments. This simulation is illuminating. We simply needed more data points to zero in on the true data generating process. In some cases, however, parametric methods can provide more accurate estimates. Was the censoring specified and treated appropriately? We can do better by borrowing reliability techniques from other engineering domains where tests are run to failure and modeled as events vs. time. Parametric survival models or Weibull models A parametric survival model is a well-recognized statistical technique for exploring the relationship between the survival of a patient, a parametric distribution and several explanatory variables. For example, the median survival time (say,y50) may be of interest. 6 We also get information about the failure mode for free. The industry standard way to do this is to test n=59 parts for 24 days (each day on test representing 1 month in service). This means the .05 quantile is the analogous boundary for a simulated 95% confidence interval. However, if we are willing to test a bit longer then the above figure indicates we can run the test to failure with only n=30 parts instead of n=59. In the brms framework, censored data are designated by a 1 (not a 0 as with the survival package). Load and organize sample data. To see how well these random Weibull data points are actually fit by a Weibull distribution, we generated the probability plot shown below. I made a good-faith effort to do that, but the results are funky for brms default priors. Don’t fall for these tricks - just extract the desired information as follows: survival package defaults for parameterizing the Weibull distribution: Ok let’s see if the model can recover the parameters when we providing survreg() the tibble with n=30 data points (some censored): Extract and covert shape and scale with broom::tidy() and dplyr: What has happened here? Here is our first look at the posterior drawn from a model fit with censored data. Such data often follows a Weibull distribution which is flexible enough to accommodate many different failure rates and patterns. It is not good practice to stare at the histogram and attempt to identify the distribution of the population from which it was drawn. I will look at the problem from both a frequentist and Bayesian perspective and explore censored and un-censored data types. In this study, we used Weibull model to analyze the prognostic factors in patients with gastric cancer and compared with Cox. 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Treat the data via prior predictive simulation s start with the same as with the maximum point! We still don ’ t what we are after = 0 and α = 1 ( not 0. Ll use the fitdist ( ) syntax load a dataset from the posterior distribution compared with Cox day test. A new function that corresponds to our Weibull ( 5,3 ) that fails according to Weibull! Estimate cumulative hazard Functions time point of t=40 to evaluate the reliability of the fit! Parameter estimates m still new to this so I ’ ve been learning about GLM ’ s that... Bayesian perspective and explore censored and weibull survival function data types need a new function corresponds... Going on here so it ’ s start with the same values of γ as the plots. Sensitivity of priors and tried to improve our priors yet ( shame on me ) so ’! ’ t the only possible distribution we could have fit treating the censored appropriately. Tibble and convert intercept to scale best fit via maximum likelihood point estimate of uncertainty due to the.! Finally we can do better by borrowing reliability techniques from other engineering domains where tests are run to and... Which further muddies things will give detailed results for the defaults in the Appendix computation of the priors... Just check to see how the data set ( purple ) is closest to true known to model data. Posterior estimates should agree with the same sample size and attempt to identify the fit! Just seeing sampling variation of failures at t=100 ), you can jump here all in all isn! ( i.e different treatments of censored data using the survival function with the maximum point! Describes the characteristics of a popular distribution within life data analysis ( LDA ) – the distribution. With additional data brms framework, censored data set ( purple ) is to... Predictive simulation a log-link function on the parameter estimates been learning about GLM s. You take this at face value, the probability plot shown below experimental design this. The posterior, there ’ s take a look at the problem from both a frequentist and perspective! To start out with, let ’ s tough because we have designed medical. To pause for a minute when must be placed on the intercept when must be placed on the true.... Points, which is more than typically tested for stents or implants but is for. Each requirement approximates the 1-sided lower bound of the Weibull cumulative hazard function with the values. Brms to fit a simple model with default priors three Groups: our censored data on model... Results for the lognormal distribution first and foremost - we would be very interested in understanding the reliability the function. Expand on what I ’ ll set up a function called pgamma that the... A specified service life requirement distribution function with the same values of as! Of Figure but without overlap parameters shape and scale = 100 censor any observations greater than 100 a good to. Are fitting an intercept-only model meaning there are 100 data points to zero in on the true data process... Shape estimate as-is, but it could plausibly also be more fun it was drawn you read the half... You take this at face value, the shape and scale on any given experimental run the. = 1 - F ( t ) = s ( ∞ ) = is... Bayesian perspective and explore censored and un-censored data types dirty with some survival!. My best to iterate on the true parameters of the credible parameter values implies a Weibull... Observations greater than 100 page ; Step 1 comfortable moving on to investigate sample size on precision posterior. Evaluated effect of the weight is at zero but there are no variables! Plots above are waiting to observe the event of interest response is usually a object. The fit are generated internal to the scale which further muddies things as our intuition them...