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We have set up a BaSTA Users mailing list so users can ask questions or provide comments, suggestions or criticism that can help us improve the package. The core of the model is a Monte Carlo Markov Chain (MCMC) algorithm that combines Metropolis sampling for survival parameters and latent states (i.e. After running these diagnostics, BaSTA uses a dynamic procedure to find adequate jump standard deviations for mortality parameters.
Allows testing four different mortality functions (Exponential, Gompertz, Weibull and logistic) and to extend the model to Makeham or bathtub shapes (Gompertz 1925, Siler 1979, Cox and Oakes 1984, Pletcher 1999).
Evaluates the effects of time-independent categorical and continuous covariates on survival. Runs multiple simulations either in parallel (using package snowfall; Knaus 2010) or in series. Calculates basic diagnostics on MCMC performance such as parameter update rates and serial autocorrelation. Finds jump standard deviations automatically through adaptive independent Metropolis (Roberts and Rosenthal 2009). In this case, BaSTA runs a single simulation for 11,000 iterations with a burn-in of 1001 steps. Argument parallel makes use of package snowfall (Knaus 2010), which allows BaSTA to run all 4 simulations in parallel, reducing computing time by a quarter of the time it would take to run each simulation one after the other.
Additional arguments such as model or shape can be used to test different functional forms for the mortality functions (Fig.
We recomend users to test different models, shapes and covariate structures and use the measures of model fit (i.e.
To visualize the result, the user only needs to type either out or, for additional information, function summary(out), which prints to the screen the relevant information such as the call of the model, the coefficients with standard errors and lower and upper bounds, if the model converged and if so, the value of model fit (DIC, which can be used for model selection; Fig. To visually assess convergence, the resulting traces for the parameters can be plotted by typing on the R console plot(out) (Fig.
In case the survival and mortality plots become too crowded, which can happen if several categorical covariates are evaluated, function plot() can be modified using arguments xlim, which reduces the range of ages over which the plots are produced, and argument noCI, which eliminates credible intervals and only plots the mean expected trajectories (Fig. In case models are not working properly or you have any doubts or problems, please contact us by registering to the BaSTA Users mailing list.
We have put together a set of functions that allow BaSTA users to run and compare several (or all) of the models featured in the package.
The main function, called MultiBaSTA() uses the basic arguments as function basta() but also allows users to specify which models and shapes they wish to compare. To modify the basic BaSTA parameters, such as the number of iterations, the number of runs, etc., just use the same arguments you would use in function basta(). Also, it is possible to print and save the coefficients of any of the functions with the built-in function coef(). Fixed a bug when calculating the mean age at death that made mortality at the initial ages equal to 0 for some life tables. Changed the range of ages plotted in the surival and mortality plots to show the curves for ages such that S(x) > 0.01, this is, until only 1% of the cohort is still alive. Fixed bug that over-estimated ages at death and underestimated recapture probabilities with proportional hazards models.

Fixed a bug that prevented to calculate quantiles for predicted mortality and survival when only continuous covariates were included. Fixed a bug that prevented to plot all the traces from proportional hazard parameters in the same plotting window. Fixed an issue when assigning times of birth and death that prevented the model to estimate deaths between ages 0 and 1. Improved the plot() function to allow zooming to different ranges of ages when plotting survival and mortality and to plot these trajectories with or without credible intervals. Update jump routine for multiple simulations runs before the main analysis, this improves convergence and consistency between multiple simulations. Function summary() ouputs the basic information, which can be stored instead of the main output.
Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization.
The point was that the variability in actual cdf values (or their complement) that are very close to 1 or 0 would be less than values near 0.5 (or really, anywhere not so close to the bounds), because the ones near the bounds cannot move beyond the bounds. Not the answer you're looking for?Browse other questions tagged survival exponential cdf or ask your own question. Is there a bias in top tier PhD admissions at top tier universities to favor those with undergraduate degrees from other top tier universities? How can I use sed or ex to replace a block (multi-line code) with new block of text (code)? If time travel is possible in the future, no matter how distant, why haven't they come back to tell us? BaSTA is based on a model developed by Colchero and Clark (2012), which extends inference from parameter estimates to the estimation of unknown (i.e. This procedure runs before the core of the analysis is performed, keeping the user from having to find these jump sd's by trial and error. As we mentioned above, convergence can only be estimated by running more than one simulation. 9 that include the density plots for the different parameters as well as the resulting survival and mortality profiles. Thus, to print only the coefficients from the model with lowest DIC, only type coef(multiOut) to the console. To view models not in consecutive order, only specify their ranking as show in the DIC table aftet typing multiOut in the R console. This upgrade greatly reduces the number of iterations needed for convergence and the time required to run the analysis. However, I then realised I would need to check the probability that the aforementioned random variable is greater than another random variable, which is normally distributed, and for that I would need both to be distributions.
BaSTA model output for sex differences in Soay sheep mortality using a Siler survival model (Colchero & Clark 2012).
Also, you can see below a video hosted by the journal Methods in Ecology and Evolution where we explain the rational behind the package and some general applications. The main function performs several diagnostics on the user's inputs such as checking that the data is consistent with the model's requirements and verifies that the number of iterations, the burnin sequence and the thinning gap are consistent (Fig.

After apropriate jumps are found, multiple MCMC simulations can be ran either in parallel or in series. This vignette provides information on how to setup the dataset in an appropriate format for BaSTA and steps to run a range of analyses. 4 we show examples on which effects can be tested with a combination of a categorical and a continuous covariates. When running multiple simulations, we strongly recomend to use the routine that updates jump sd's. In case the model does not converge, make sure that there are not too many missing records for younger individuals.
Example of an output from a Weibull model with bathtub shape on a kestrel (Falco tinnunculus) dataset (Jones et al.
Survival probability and mortality trajectories for male (M) and female (F) kestrels (Falco tinnunculus).
Zoom in on the survival probability and mortality trajectories for male (M) and female (F) kestrels (Falco tinnunculus).
However, I then realised I would need to check the Probability that the random variable here is greater than another random variable, which is Normally Distributed and for that I would need both to be distributions.
I can generate any number of distributions whose survival function has the characteristics you describe - relatively easily.
The new value would perhaps be better used as an out of sample check first to make sure things are behaving as they should. The package also allows testing the effect of categorical and continuous individual covariates on mortality and survival (for an example see Fig.
The left panel shows posterior distributions for the survival and recapture parameters while the right panel shows the resulting survival probabilities and the mortality rates for males and females. Here we provide a very general overview on the different analyses that can be performed with BaSTA and the types of outputs that the user should expect to find.
This will make the analysis sligthly longer, but will greatly increase the chances of getting convergence from the first try. This is commonly an issue with studies on birds, for which many juvenile individuals do not return to the breeding grounds when they reach maturity.
I realise the CDF must always be increasing, so would it be possible to have a graph increasing with the x values increasing from 0.008 to 0.002?
As we mentioned above, BaSTA allows users to test a range of models and functional forms for the mortality function (Fig.
To fix this without loosing information, BaSTA provides argument minAge that can be used to specify this minimum age.

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