Dynamic Hazard Models using State Space Models

Contains functions that lets you fit dynamic hazard models using state space models. The first implemented model is described in Fahrmeir (1992) and Fahrmeir (1994) . Extensions hereof are available where the Extended Kalman filter is replaced by an unscented Kalman filter. See Christoffersen (2021) for more details. Particle filters and smoothers are also supported more general state space models.


dynamichazard 0.6.2

  • The cloglog link function is added.
  • predict.ddhazard has been re-written. The output with type = "term" has changed. It now yields a list of lists. Each list contains a list for each new_data row. The time zero index is no longer included if tstart and tstop is not matched in new_data. Parallel computing is no longer supported. It likely did not yield any reduction in computation time with the previous implementation. Calls with type = "term" now uses the tstart and tstop argument and supports predictions in the future. A covariance matrix is added to the terms in the predictions.
  • A method has been added to plot the survival curve for a given observation which may have time-varying covariates.

dynamichazard 0.6.1

  • Fixed a bug in type == "VAR" in particle filters in the smoothing proposal distribution. This has a major impact for most calls.
  • Fixed a bug in type == "VAR" in particle filters where the transition from the time zero state to time one was not used in the M-step estimation. This only has a larger impact for short series.
  • Fixed a bug in method == "bootstrap_filter" where a wrong covariance matrix was used for the proposal distribution.
  • Fixed a bug in method == "AUX_normal_approx_w_particles" where a wrong covariance matrix was used for the proposal distribution.
  • Fixed a bug in the logLik.PF_clouds. The log-likelihood approximation was too high especially for the auxiliary particle filters.
  • An option is added to use a (multivariate) t-distribution as the proposal distribution in particle filters.
  • A few miscellaneous functions have been added for particle filter methods.

dynamichazard 0.6.0

  • One can fit first order Vector vector autoregression models with the particle filter.
  • The averages used in the Taylor approximation in the backward smoothing have changed so the results differ for PF_EM.
  • A bug is fixed with the ..._w_particles methods so results have changed.
  • Mode estimation is now done in the proposal distribution with more than one iteration.
  • A random and fixed argument is added to PF_EM as an alternative way to specify the random and fixed effect parts.

dynamichazard 0.5.2

  • Fixed effects is estimated faster with PF_EM and can be estimated with model = "exponential".
  • The covariance matrix in ddhazard objects are no longer degenerate (e.g., in the case where a second order random walk is used). Instead the dimension is equal to the dimension of the error term.
  • The seed argument in PF_EM has been moved from the control list. Further, there is a PF_control which should preferably be used to construct the object for the control argument of PF.
  • A more stable and parallel estimation method have been added to static_glm.
  • PF_EM uses $Q_0$ instead of $Q$ for the artificial prior and a bug have been fixed for sampling in the initial state in the backward filter. This have changed the output.
  • Fixed bug in PF_EM with seed argument. The new way to get reproducible is to call f1 <- PF_EM(...); .GlobalEnv$.Random.seed" <- f1$seed; f2 <- eval(f1$call) kinda as in simulate.lm.

dynamichazard 0.5.0

  • Fixed issues with close to equal non-integer stop and start times.
  • Re-factored code to use more generic setup for distribution families.
  • Changed EKF and UKF to use Poisson counts instead of the three different types used before for exponential distributed arrival times. This means that the model argument to ddhazard should be changed from "exp_bin", "exp_clip_time" or "exp_clip_time_w_jump" to "exponential".

dynamichazard 0.4.0

  • Parallel version of glm is used to find the first state vector.
  • Default values of ddhazard control argument is changed.
  • Particle filter and smoothers alternative are available.

dynamichazard 0.3.0

The following has been changed or added:

  • Refactored C++, R and test codes.
  • A bug have been fixed in the get_risk_obj when is_for_discrete_model = TRUE in the call. The issue was that individuals who were right censored in the middle of an interval were included despite that we do not know that they survive the entire interval. This will potentially affect the output for logit fits with ddhazard.
  • The ddhazard_boot now provides the option of different learning rates to be used rather than one if the first fit succeeds.
  • Error in computation of likelihood have been fixed.
  • Added the option to use relative change in the likelihood (not including the prior) as a convergence criteria instead of relative change in coefficients. The new option is selected by by calling ddhazard with control = list(criteria = "delta_likeli", ...). The relative change in coefficient seems "preferable" as a default since it tends to not converge when the fit is has large "odd" deviation due to a few observations. The likelihood method though stops earlier for model does not have such deviation.
  • The default for kappa in the UKF have been changed to yield a weight on the first sigma point of 0.1 rather than 0.
  • Fixed memory leak in ddhazard.
  • New example have been added to the bootstrap vignette and other minor changes have been made.
  • Added a hatvalues method for ddhazard. These described "ddhazard" vignette and examples of usage are shown the vignette "Diagnostics".
  • Added information about the residuals method and a vignette "Diagnostics" with examples of usage of the residuals function.
  • Fixed bug with default starting value with fixed effects.
  • Re-wrote the ddhazard vignette to have more a consistent notation.
  • Implemented new filter which use an series of rank-one approximation of the posterior in the correction step.
  • Global mode approximation have been added.
  • Added description of the posterior approximation methods to the "ddhazard" vignette.
  • Added section about weights to the "ddhazard" vignette.
  • Removed the rug call in the shiny app demo, fixed a bug with the simulation function for the logit model and added the computation time of the estimation to the output.
  • Inverses has been replaced by pseudo inverses. This will only have implications the matrices are rank deficient. The old option can be used by calling ddhazard with control = list(use_pinv = FALSE, ...).
  • Refactored the UKF code. This version no longer supports the exp_combined method.

dynamichazard 0.2.0

The following have been added:

  • Weights can be used in estimation. This is done by setting the weights argument when calling ddhazard.
  • Bootstrap of confidence bounds have been added. The function to bootstrap the estimates is ddhazard_boot. See the new vignette 'Bootstrap_illustration' for details.
  • S3 generic methods for print is added.
  • The method name for the variables have changed. What was previously denoted as truncated in now denoted as clipped.

Reference manual

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1.0.1 by Benjamin Christoffersen, 3 months ago


Report a bug at https://github.com/boennecd/dynamichazard/issues

Browse source code at https://github.com/cran/dynamichazard

Authors: Benjamin Christoffersen [cre, aut] , Alan Miller [cph] , Anthony Williams [cph] , Boost developers [cph] , R-core [cph]

Documentation:   PDF Manual  

Task views:

GPL-2 license

Imports parallel, Rcpp, boot

Depends on stats, graphics, utils, survival

Suggests testthat, knitr, rmarkdown, timereg, captioner, biglm, httr, mgcv, shiny, formatR, R.rsp, speedglm, dichromat, colorspace, plyr, gsl, mvtnorm, nloptr

Linking to Rcpp, RcppArmadillo

System requirements: C++11

See at CRAN