From 0abb53efaf70a1de081f31adc9cd25b2f886ca89 Mon Sep 17 00:00:00 2001 From: Dimitris Rizopoulos Date: Sun, 26 May 2024 21:08:36 +0200 Subject: [PATCH] updates --- docs/articles/JMbayes2.html | 6 ++--- docs/articles/Multi_State_Processes.html | 2 +- docs/articles/Non_Gaussian_Mixed_Models.html | 10 ++++----- docs/articles/Recurring_Events.html | 2 +- docs/articles/Super_Learning.html | 23 ++++++++++---------- docs/articles/Time_Varying_Effects.html | 6 ++--- docs/articles/Transformation_Functions.html | 6 ++--- docs/pkgdown.yml | 2 +- docs/reference/jm.html | 6 ++--- 9 files changed, 31 insertions(+), 32 deletions(-) diff --git a/docs/articles/JMbayes2.html b/docs/articles/JMbayes2.html index 143bbf6..b96f6fd 100644 --- a/docs/articles/JMbayes2.html +++ b/docs/articles/JMbayes2.html @@ -215,7 +215,7 @@

Univariate#> iterations per chain: 3500 #> burn-in per chain: 500 #> thinning: 1 -#> time: 15 sec +#> time: 16 sec

The output of the summary() method provides some descriptive statistics of the sample at hand, followed by some fit statistics based on the marginal (random effects are integrated out @@ -338,7 +338,7 @@

Multivariate#> iterations per chain: 12000 #> burn-in per chain: 2000 #> thinning: 5 -#> time: 1.9 min +#> time: 2.1 min

The survival submodel output now contains the estimated coefficients for value(prothrombin) and value(ascites), as well as parameter estimates for all three longitudinal submodels.

@@ -440,7 +440,7 @@

Functional forms#> iterations per chain: 12000 #> burn-in per chain: 2000 #> thinning: 5 -#> time: 2 min +#> time: 2.1 min

As seen above, the functional_forms argument is a named list with elements corresponding to the longitudinal outcomes. If a longitudinal outcome is not specified in this list, then the default diff --git a/docs/articles/Multi_State_Processes.html b/docs/articles/Multi_State_Processes.html index 3bc2e00..edb355e 100644 --- a/docs/articles/Multi_State_Processes.html +++ b/docs/articles/Multi_State_Processes.html @@ -431,7 +431,7 @@

Fitting the model#> iterations per chain: 10000 #> burn-in per chain: 500 #> thinning: 1 -#> time: 2.8 min +#> time: 2.9 min

which differs from a default call to jm() by the addition of the functional_forms argument specifying that we want an “interaction” between the marker’s value and each transition, diff --git a/docs/articles/Non_Gaussian_Mixed_Models.html b/docs/articles/Non_Gaussian_Mixed_Models.html index 870e64a..a763eeb 100644 --- a/docs/articles/Non_Gaussian_Mixed_Models.html +++ b/docs/articles/Non_Gaussian_Mixed_Models.html @@ -308,7 +308,7 @@

Beta mixed models#> iterations per chain: 3500 #> burn-in per chain: 500 #> thinning: 1 -#> time: 19 sec +#> time: 20 sec
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@@ -454,7 +454,7 @@

Censored linear mixed models#> iterations per chain: 3500 #> burn-in per chain: 500 #> thinning: 1 -#> time: 14 sec +#> time: 15 sec

Students’s-t mixed models @@ -593,7 +593,7 @@

Students’s-t mixed models#> iterations per chain: 3500 #> burn-in per chain: 500 #> thinning: 1 -#> time: 14 sec

+#> time: 15 sec
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@@ -734,7 +734,7 @@

Negative binomial mixed models#> iterations per chain: 3500 #> burn-in per chain: 500 #> thinning: 1 -#> time: 25 sec +#> time: 27 sec
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@@ -876,7 +876,7 @@

Beta-binomial longitudinal outcomes #> iterations per chain: 3500 #> burn-in per chain: 500 #> thinning: 1 -#> time: 49 sec +#> time: 52 sec diff --git a/docs/articles/Recurring_Events.html b/docs/articles/Recurring_Events.html index 987ce8f..58ff8fe 100644 --- a/docs/articles/Recurring_Events.html +++ b/docs/articles/Recurring_Events.html @@ -520,7 +520,7 @@

Fitting the model#> iterations per chain: 3500 #> burn-in per chain: 500 #> thinning: 1 -#> time: 1.5 min +#> time: 1.7 min

One can find the association parameters between the underlying value of the longitudinal outcome and the recurrent and terminating event processes in the summary output as value(y):strataRec diff --git a/docs/articles/Super_Learning.html b/docs/articles/Super_Learning.html index 9f21373..2fd698b 100644 --- a/docs/articles/Super_Learning.html +++ b/docs/articles/Super_Learning.html @@ -136,7 +136,7 @@

Combined Dynamic Predictions via Super

Dimitris Rizopoulos

-

2024-05-25

+

2024-05-26

Source: vignettes/Super_Learning.Rmd @@ -365,12 +365,12 @@

Example#> Integrated Brier score per model: 0.0601 0.0575 0.0626 0.0509 0.0601 #> Weights per model: 0 0 0 1 0 #> Number of folds: 5 -

We observe that the first two models dominate the weights. We also -note that the integrated Brier score based on the combined predictions -is smaller than the integrated Brier score of each individual model. To -calculate the model weights using the expected predictive cross-entropy, -use function tvEPCE() with an almost identical call as for -the Brier score:

+

We observe that the fourth model dominates the weights. Hence, the +integrated Brier score based on the combined predictions is essentially +the integrated Brier score of this model. To calculate the model weights +using the expected predictive cross-entropy, use function +tvEPCE() with an almost identical call as for the Brier +score:

 EPCE_weights <- tvEPCE(Models_folds, newdata = CVdats$testing, 
                        Tstart = tstr, Thoriz = thor)
@@ -387,11 +387,10 @@ 
Example#> EPCE per model: 0.3545 0.348 0.3708 0.341 0.4037
 #> Weights per model: 0.0019 0.567 0.4308 0 3e-04
 #> Number of folds: 5
-

The EPCE results are similar to those from the integrated Brier -score; however, now only models M2 and M3 -share the weights. Again, we see that the EPCE based on the combined -cross-validated predictions is smaller than the EPCE based on the -cross-validated predictions of each individual model.

+

The EPCE results indicate that models M2 and +M3 share the most weight. We observe that the EPCE based on +the combined cross-validated predictions is smaller than the EPCE based +on the cross-validated predictions of each individual model.

To use these weights in practice, we must first refit the five joint models we considered in the original dataset.

 +#> time: 18 sec

To specify that the association of serum bilirubin may change over time, we include an interaction of this time-varying covariate with a natural cubic spline of time using function ns() from the @@ -289,7 +289,7 @@

Non Proportional Hazards#> iterations per chain: 6500 #> burn-in per chain: 2500 #> thinning: 1 -#> time: 36 sec +#> time: 40 sec

The spline coefficients do not have a straightforward interpretation. We, therefore, visualize the time-varying association of log serum bilirubin with the hazard of the composite event using the following diff --git a/docs/articles/Transformation_Functions.html b/docs/articles/Transformation_Functions.html index 4c9b9ae..873c572 100644 --- a/docs/articles/Transformation_Functions.html +++ b/docs/articles/Transformation_Functions.html @@ -136,7 +136,7 @@

Transformation Functions for Functional

Dimitris Rizopoulos

-

2024-05-25

+

2024-05-26

Source: vignettes/Transformation_Functions.Rmd @@ -213,7 +213,7 @@

Simplified syntax#> iterations per chain: 3500 #> burn-in per chain: 500 #> thinning: 1 -#> time: 15 sec +#> time: 17 sec

In the output, this is named value(hepatomegaly) to denote that the current value functional form is used. That is, we assume that the risk at a specific time \(t\) is associated with the value of the @@ -274,7 +274,7 @@

Transformation functions#> iterations per chain: 3500 #> burn-in per chain: 500 #> thinning: 1 -#> time: 16 sec +#> time: 19 sec

Other available functions to use in the definition of the functional_forms argument are vexp() to calculate the exponent, vlog() to calculate the natural diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index 4e1a517..f47f0c0 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -12,7 +12,7 @@ articles: Super_Learning: Super_Learning.html Time_Varying_Effects: Time_Varying_Effects.html Transformation_Functions: Transformation_Functions.html -last_built: 2024-05-26T11:35Z +last_built: 2024-05-26T17:57Z urls: reference: https://drizopoulos.github.io/JMbayes2/reference article: https://drizopoulos.github.io/JMbayes2/articles diff --git a/docs/reference/jm.html b/docs/reference/jm.html index ea9d538..7b660a3 100644 --- a/docs/reference/jm.html +++ b/docs/reference/jm.html @@ -509,7 +509,7 @@

Examples

#> iterations per chain: 11000 #> burn-in per chain: 1000 #> thinning: 1 -#> time: 36 sec +#> time: 43 sec traceplot(joint_model_fit_1) @@ -627,7 +627,7 @@

Examples

#> iterations per chain: 11000 #> burn-in per chain: 1000 #> thinning: 1 -#> time: 1.4 min +#> time: 1.6 min traceplot(joint_model_fit_2) @@ -789,7 +789,7 @@

Examples

#> iterations per chain: 11000 #> burn-in per chain: 1000 #> thinning: 1 -#> time: 1.6 min +#> time: 1.8 min # }