From 6e8ccbdd1ee94af751641f8b5295bb7b80327c3d Mon Sep 17 00:00:00 2001 From: Dimitris Rizopoulos Date: Sun, 26 May 2024 10:04:23 +0200 Subject: [PATCH] updates --- R/basic_methods.R | 4 +- R/predict_funs.R | 4 +- docs/pkgdown.yml | 2 +- docs/reference/Rplot001.png | Bin 6910 -> 1011 bytes docs/reference/Rplot002.png | Bin 10716 -> 19192 bytes docs/reference/Rplot003.png | Bin 10401 -> 17699 bytes docs/reference/Rplot004.png | Bin 10777 -> 27084 bytes docs/reference/Rplot005.png | Bin 13415 -> 16737 bytes docs/reference/Rplot006.png | Bin 21025 -> 25874 bytes docs/reference/Rplot007.png | Bin 22135 -> 19852 bytes docs/reference/Rplot008.png | Bin 24089 -> 23354 bytes docs/reference/accuracy.html | 142 +++++++++++++++++------------------ docs/reference/jm.html | 6 +- docs/reference/predict.html | 79 +++++++++---------- 14 files changed, 120 insertions(+), 117 deletions(-) diff --git a/R/basic_methods.R b/R/basic_methods.R index 3f96de91..d3683660 100644 --- a/R/basic_methods.R +++ b/R/basic_methods.R @@ -695,7 +695,7 @@ predict.jm <- function (object, newdata = NULL, newdata2 = NULL, times = NULL, if (is.null(con$cores)) { n <- if (!is.data.frame(newdata)) length(unique(newdata$newdataL[[id_var]])) else length(unique(newdata[[id_var]])) - cores <- if (n > 20) 4L else 1L + con$cores <- if (n > 20) 4L else 1L } components_newdata <- get_components_newdata(object, newdata, con$n_samples, @@ -1221,7 +1221,7 @@ predict.jmList <- function (object, weights, newdata = NULL, newdata2 = NULL, if (is.data.frame(x)) attr(x, "mcmc") else x[["mcmc"]] } MCMC <- lapply(preds, extract_mcmc) - alp <- 1 - level + alp <- 1 - con$level if (is.list(MCMC[[1L]])) { n_outcomes <- length(MCMC[[1L]]) pred_ <- qs <- vector("list", n_outcomes) diff --git a/R/predict_funs.R b/R/predict_funs.R index f199310f..54412448 100644 --- a/R/predict_funs.R +++ b/R/predict_funs.R @@ -907,8 +907,8 @@ predict_Event <- function (object, components_newdata, newdata, newdata2, "maximum time to predict; redefine 'times' accordingly.") } if (times_per_id) { - g <- function (lt, tt, tm) c(lt, min(tt, tm)) - times <- mapply2(g, lt = last_times, tt = times, + gg <- function (lt, tt, tm) c(lt, min(tt, tm)) + times <- mapply2(gg, lt = last_times, tt = times, MoreArgs = list(tm = t_max)) } else { diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index 737f6efc..95e0df7e 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: 2024-05-25T18:21Z +last_built: 2024-05-26T06:56Z urls: reference: https://drizopoulos.github.io/JMbayes2/reference article: https://drizopoulos.github.io/JMbayes2/articles diff --git a/docs/reference/Rplot001.png b/docs/reference/Rplot001.png index 445665a4c806b9866174ad10b20c839e8999e43c..17a358060aed2a86950757bbd25c6f92c08c458f 100644 GIT binary patch delta 86 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time: 4 (225 subjects still at risk) #> Accounting for censoring using model-based weights #> -#> cut-off SN SP qSN qSP -#> 1 0.01 0.01305524 0.99776480 0.008649236 0.497080443 -#> 2 0.03 0.02703047 0.99576564 0.018304284 0.523634851 -#> 3 0.05 0.04879765 0.99576564 0.035943566 0.682423234 -#> 4 0.06 0.06076768 0.99325202 0.043768005 0.620425952 -#> 5 0.07 0.06076768 0.98766727 0.039421496 0.445027347 -#> 6 0.08 0.08253487 0.98766727 0.057398837 0.537522789 -#> 7 0.09 0.10430205 0.98766727 0.075541108 0.603590962 -#> 8 0.10 0.12606923 0.98766727 0.093850588 0.653142092 -#> 9 0.13 0.14477820 0.98129789 0.105000442 0.579202616 -#> 10 0.17 0.15942148 0.97947013 0.116214171 0.580070929 -#> 11 0.18 0.18118866 0.97947013 0.135058445 0.615065019 -#> 12 0.19 0.18118866 0.97388539 0.130978532 0.548016396 -#> 13 0.20 0.23145370 0.97002752 0.172617620 0.578511977 -#> 14 0.21 0.28848471 0.96790557 0.222859516 0.619934389 -#> 15 0.22 0.30213854 0.96582395 0.234054494 0.615519441 -#> 16 0.23 0.32390572 0.96023921 0.250634421 0.593355534 -#> 17 0.25 0.34567291 0.96023921 0.271170315 0.611035728 -#> 18 0.26 0.36360375 0.94808544 0.280456500 0.550739420 -#> 19 0.30 0.36360375 0.94250070 0.276822441 0.520839180 -#> 20 0.32 0.38537093 0.94250070 0.298012485 0.537952066 -#> 21 0.34 0.40713811 0.94250070 0.319418754 0.553884754 -#> 22 0.36 0.42890530 0.93691596 0.337647897 0.542131961 -#> 23 0.37 0.45067248 0.93691596 0.359592271 0.556440337 -#> 24 0.41 0.49420685 0.93691596 0.404170370 0.582532082 -#> 25 0.45 0.50563644 0.92867893 0.411471950 0.554243315 -#> 26 0.46 0.50563644 0.92309419 0.408341482 0.532329521 -#> 27 0.47 0.51426257 0.91413788 0.412414399 0.504641639 -#> 28 0.51 0.52845754 0.90661036 0.423385584 0.487495889 -#> 29 0.54 0.53186968 0.89631632 0.421267460 0.457469108 -#> 30 0.55 0.55363686 0.88514683 0.438929016 0.438218206 -#> 31 0.56 0.56055812 0.88133786 0.444525719 0.431936558 -#> 32 0.57 0.60409249 0.87575312 0.490976060 0.440889023 -#> 33 0.58 0.64762686 0.87575312 0.541711231 0.462393292 -#> 34 0.60 0.66939404 0.87575312 0.567521274 0.472536814 -#> 35 0.61 0.66939404 0.87016837 0.564992159 0.459034890 -#> 36 0.62 0.66939404 0.85899889 0.559844137 0.433477677 -#> 37 0.63 0.69619207 0.85470490 0.590677937 0.436355197 -#> 38 0.65 0.71795925 0.85470490 0.617715854 0.445908499 -#> 39 0.66 0.71795925 0.84912015 0.615398981 0.434200573 -#> 40 0.68 0.71908398 0.83823924 0.612232488 0.412964969 -#> 41 0.69 0.74085116 0.83823924 0.640071061 0.422282985 * -#> 42 0.70 0.74085116 0.83265449 0.637835477 0.411675953 -#> 43 0.71 0.74530474 0.82821239 0.641834790 0.405350586 -#> 44 0.72 0.74530474 0.82262765 0.639582178 0.395321531 -#> 45 0.73 0.74530474 0.80587342 0.632651066 0.366978548 -#> 46 0.74 0.76707192 0.80587342 0.661878597 0.376021711 -#> 47 0.75 0.77171790 0.78472646 0.659844550 0.345452060 -#> 48 0.76 0.77171790 0.77914171 0.657576847 0.337425138 -#> 49 0.77 0.77397988 0.76296783 0.654050833 0.316253369 -#> 50 0.78 0.77397988 0.75738309 0.651681318 0.309002478 -#> 51 0.79 0.77397988 0.74621361 0.646843559 0.295037797 -#> 52 0.80 0.79806496 0.74122356 0.680032502 0.298497603 -#> 53 0.81 0.82369474 0.72546035 0.712545765 0.289983657 -#> 54 0.82 0.83206421 0.69968397 0.715898095 0.265531437 -#> 55 0.83 0.85791982 0.67839395 0.750249691 0.254006591 -#> 56 0.84 0.85842846 0.66177022 0.745171223 0.238983003 -#> 57 0.85 0.86267886 0.63493703 0.742522870 0.217722197 -#> 58 0.86 0.86853589 0.59176181 0.735898000 0.187136338 -#> 59 0.87 0.87327113 0.57622248 0.738403706 0.178017748 -#> 60 0.88 0.87327113 0.54829877 0.725826961 0.160059697 -#> 61 0.89 0.87384555 0.53727666 0.721718125 0.153554871 -#> 62 0.90 0.88081208 0.46087765 0.695258163 0.114580082 -#> 63 0.91 0.90438431 0.43900179 0.740800831 0.111094391 -#> 64 0.92 0.92842931 0.42283198 0.796159436 0.110528737 -#> 65 0.93 0.92944603 0.37841489 0.776413461 0.091839944 -#> 66 0.94 0.93023762 0.33394005 0.750848645 0.074916739 -#> 67 0.95 0.99618475 0.27825826 0.982831374 0.072046335 -#> 68 0.96 0.99935537 0.18971586 0.995734090 0.045476795 -#> 69 0.97 0.99962873 0.11718434 0.996022143 0.026306256 -#> 70 0.98 0.99990340 0.02789893 0.995653157 0.005805723 +#> cut-off SN SP qSN qSP +#> 1 0.01 0.01306 0.9978 0.008649 0.497080 +#> 2 0.03 0.02703 0.9958 0.018304 0.523635 +#> 3 0.05 0.04880 0.9958 0.035944 0.682423 +#> 4 0.06 0.06077 0.9933 0.043768 0.620426 +#> 5 0.07 0.06077 0.9877 0.039421 0.445027 +#> 6 0.08 0.08253 0.9877 0.057399 0.537523 +#> 7 0.09 0.10430 0.9877 0.075541 0.603591 +#> 8 0.10 0.12607 0.9877 0.093851 0.653142 +#> 9 0.13 0.14478 0.9813 0.105000 0.579203 +#> 10 0.17 0.15942 0.9795 0.116214 0.580071 +#> 11 0.18 0.18119 0.9795 0.135058 0.615065 +#> 12 0.19 0.18119 0.9739 0.130979 0.548016 +#> 13 0.20 0.23145 0.9700 0.172618 0.578512 +#> 14 0.21 0.28848 0.9679 0.222860 0.619934 +#> 15 0.22 0.30214 0.9658 0.234054 0.615519 +#> 16 0.23 0.32391 0.9602 0.250634 0.593356 +#> 17 0.25 0.34567 0.9602 0.271170 0.611036 +#> 18 0.26 0.36360 0.9481 0.280456 0.550739 +#> 19 0.30 0.36360 0.9425 0.276822 0.520839 +#> 20 0.32 0.38537 0.9425 0.298012 0.537952 +#> 21 0.34 0.40714 0.9425 0.319419 0.553885 +#> 22 0.36 0.42891 0.9369 0.337648 0.542132 +#> 23 0.37 0.45067 0.9369 0.359592 0.556440 +#> 24 0.41 0.49421 0.9369 0.404170 0.582532 +#> 25 0.45 0.50564 0.9287 0.411472 0.554243 +#> 26 0.46 0.50564 0.9231 0.408341 0.532330 +#> 27 0.47 0.51426 0.9141 0.412414 0.504642 +#> 28 0.51 0.52846 0.9066 0.423386 0.487496 +#> 29 0.54 0.53187 0.8963 0.421267 0.457469 +#> 30 0.55 0.55364 0.8851 0.438929 0.438218 +#> 31 0.56 0.56056 0.8813 0.444526 0.431937 +#> 32 0.57 0.60409 0.8758 0.490976 0.440889 +#> 33 0.58 0.64763 0.8758 0.541711 0.462393 +#> 34 0.60 0.66939 0.8758 0.567521 0.472537 +#> 35 0.61 0.66939 0.8702 0.564992 0.459035 +#> 36 0.62 0.66939 0.8590 0.559844 0.433478 +#> 37 0.63 0.69619 0.8547 0.590678 0.436355 +#> 38 0.65 0.71796 0.8547 0.617716 0.445908 +#> 39 0.66 0.71796 0.8491 0.615399 0.434201 +#> 40 0.68 0.71908 0.8382 0.612232 0.412965 +#> 41 0.69 0.74085 0.8382 0.640071 0.422283 * +#> 42 0.70 0.74085 0.8327 0.637835 0.411676 +#> 43 0.71 0.74530 0.8282 0.641835 0.405351 +#> 44 0.72 0.74530 0.8226 0.639582 0.395322 +#> 45 0.73 0.74530 0.8059 0.632651 0.366979 +#> 46 0.74 0.76707 0.8059 0.661879 0.376022 +#> 47 0.75 0.77172 0.7847 0.659845 0.345452 +#> 48 0.76 0.77172 0.7791 0.657577 0.337425 +#> 49 0.77 0.77398 0.7630 0.654051 0.316253 +#> 50 0.78 0.77398 0.7574 0.651681 0.309002 +#> 51 0.79 0.77398 0.7462 0.646844 0.295038 +#> 52 0.80 0.79806 0.7412 0.680033 0.298498 +#> 53 0.81 0.82369 0.7255 0.712546 0.289984 +#> 54 0.82 0.83206 0.6997 0.715898 0.265531 +#> 55 0.83 0.85792 0.6784 0.750250 0.254007 +#> 56 0.84 0.85843 0.6618 0.745171 0.238983 +#> 57 0.85 0.86268 0.6349 0.742523 0.217722 +#> 58 0.86 0.86854 0.5918 0.735898 0.187136 +#> 59 0.87 0.87327 0.5762 0.738404 0.178018 +#> 60 0.88 0.87327 0.5483 0.725827 0.160060 +#> 61 0.89 0.87385 0.5373 0.721718 0.153555 +#> 62 0.90 0.88081 0.4609 0.695258 0.114580 +#> 63 0.91 0.90438 0.4390 0.740801 0.111094 +#> 64 0.92 0.92843 0.4228 0.796159 0.110529 +#> 65 0.93 0.92945 0.3784 0.776413 0.091840 +#> 66 0.94 0.93024 0.3339 0.750849 0.074917 +#> 67 0.95 0.99618 0.2783 0.982831 0.072046 +#> 68 0.96 0.99936 0.1897 0.995734 0.045477 +#> 69 0.97 0.99963 0.1172 0.996022 0.026306 +#> 70 0.98 0.99990 0.0279 0.995653 0.005806 #> tvAUC(roc) #> diff --git a/docs/reference/jm.html b/docs/reference/jm.html index eb885312..080eee98 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: 35 sec +#> time: 38 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: 1.5 min traceplot(joint_model_fit_2) @@ -789,7 +789,7 @@

Examples

#> iterations per chain: 11000 #> burn-in per chain: 1000 #> thinning: 1 -#> time: 1.7 min +#> time: 1.6 min # } diff --git a/docs/reference/predict.html b/docs/reference/predict.html index fa56c23d..3fbb2aa8 100644 --- a/docs/reference/predict.html +++ b/docs/reference/predict.html @@ -102,15 +102,12 @@

Predictions from Joint Models

# S3 method for jm
-predict(object, newdata = NULL, newdata2 = NULL, times = NULL,
-  all_times = FALSE, times_per_id = FALSE,
-  process = c("longitudinal", "event"),
-  type_pred = c("response", "link"),
-  type = c("subject_specific", "mean_subject"),
-  level = 0.95, return_newdata = FALSE, use_Y = TRUE, return_mcmc = FALSE,
-  n_samples = 200L, n_mcmc = 55L, parallel = c("snow", "multicore"),
-  cores = NULL, seed = 123L,
-  ...)
+predict(object,
+    newdata = NULL, newdata2 = NULL, times = NULL,
+    process = c("longitudinal", "event"),
+    type_pred = c("response", "link"),
+    type = c("subject_specific", "mean_subject"),
+    control = NULL, ...)
 
 # S3 method for predict_jm
 plot(x, x2 = NULL, subject = 1, outcomes = 1,
@@ -126,15 +123,12 @@ 

Predictions from Joint Models

...) # S3 method for jmList -predict(object, weights, newdata = NULL, newdata2 = NULL, - times = NULL, all_times = FALSE, times_per_id = FALSE, - process = c("longitudinal", "event"), - type_pred = c("response", "link"), - type = c("subject_specific", "mean_subject"), - level = 0.95, return_newdata = FALSE, - return_mcmc = FALSE, n_samples = 200L, n_mcmc = 55L, - parallel = c("snow", "multicore"), - cores = parallelly::availableCores(omit = 1L), ...)
+predict(object, + weights, newdata = NULL, newdata2 = NULL, + times = NULL, process = c("longitudinal", "event"), + type_pred = c("response", "link"), + type = c("subject_specific", "mean_subject"), + control = NULL, ...)
@@ -155,25 +149,27 @@

Arguments

a numeric vector of future times to calculate predictions.

-
all_times
-

logical; if TRUE predictions for the longitudinal outcomes are calculated for all the times -given in the times argumet, not only the ones after the last longitudinal measurement.

-

.

-
times_per_id
-

logical; if TRUE the times argument is a vector of times equal to the number of -subjects in newdata.

- -
process

for which process to calculation predictions, for the longitudinal outcomes or the event times.

+
type
+

level of predictions; only relevant when type_pred = "longitudinal". Option type = "subject_specific" combines the fixed- and random-effects parts, whereas type = "mean_subject" uses only the fixed effects.

+ +
type_pred

type of predictions; options are "response" using the inverse link function in GLMMs, and "link" that correspond to the linear predictor.

-
type
-

level of predictions; only relevant when type_pred = "longitudinal". Option type = "subject_specific" combines the fixed- and random-effects parts, whereas type = "mean_subject" uses only the fixed effects.

+
control
+

a named list of control parameters:

all_times
+

logical; if TRUE predictions for the longitudinal outcomes are calculated for all the times +given in the times argumet, not only the ones after the last longitudinal measurement.

+. + +
times_per_id
+

logical; if TRUE the times argument is a vector of times equal to the number of +subjects in newdata.

level
@@ -211,6 +207,9 @@

Arguments

seed

an integer denoting the seed.

+ +
+
x, x2

objects returned by predict.jm() with argument return_data set to TRUE.

@@ -252,7 +251,7 @@

Arguments

...
-

extra aguments; currently none is used.

+

extra aguments passed to control.

@@ -303,38 +302,42 @@

Examples

# predictions for the longitudinal outcomes using newdata predLong1 <- predict(jointFit, newdata = ND, return_newdata = TRUE) +#> Warning: first element used of 'length.out' argument +#> Error in seq_len(cores): argument must be coercible to non-negative integer # predictions for the longitudinal outcomes at future time points # from year 3 to 10 predLong2 <- predict(jointFit, newdata = ND, times = seq(t0, 10, length.out = 51), return_newdata = TRUE) +#> Warning: first element used of 'length.out' argument +#> Error in seq_len(cores): argument must be coercible to non-negative integer # predictions for the event outcome at future time points # from year 3 to 10 predSurv <- predict(jointFit, newdata = ND, process = "event", times = seq(t0, 10, length.out = 51), return_newdata = TRUE) +#> Warning: first element used of 'length.out' argument +#> Error in seq_len(cores): argument must be coercible to non-negative integer plot(predLong1) - +#> Error in eval(expr, envir, enclos): object 'predLong1' not found # for subject 25, outcomes in reverse order plot(predLong2, outcomes = 3:1, subject = 25) - - - +#> Error in eval(expr, envir, enclos): object 'predLong2' not found # prediction for the event outcome plot(predSurv) - +#> Error in eval(expr, envir, enclos): object 'predSurv' not found # combined into one plot, the first longitudinal outcome and cumulative risk plot(predLong2, predSurv, outcomes = 1) - +#> Error in eval(expr, envir, enclos): object 'predLong2' not found # the first two longitudinal outcomes plot(predLong1, predSurv, outcomes = 1:2) - +#> Error in eval(expr, envir, enclos): object 'predLong1' not found # all three longitudinal outcomes, we display survival probabilities instead # of cumulative risk, and we transform serum bilirubin to the original scale @@ -343,7 +346,7 @@

Examples

ylab_event = "Survival Probabilities", ylab_long = c("Serum Bilirubin", "Prothrombin", "Ascites"), pos_ylab_long = c(1.9, 1.9, 0.08)) - +#> Error in eval(expr, envir, enclos): object 'predLong2' not found # }