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grep rough audit - static analysis tool
v2.8 written by @Wireghoul
=================================[justanotherhacker.com]===
r-cran-rstanarm-2.19.3/vignettes/jm.Rmd-93-
r-cran-rstanarm-2.19.3/vignettes/jm.Rmd:94:where $\boldsymbol{x}^T_{ijm}(t)$ and $\boldsymbol{z}^T_{ijm}(t)$ are both row-vectors of covariates (which likely include some function of time, for example a linear slope, cubic splines, or polynomial terms) with associated vectors of fixed and individual-specific parameters $\boldsymbol{\beta}_m$ and $\boldsymbol{b}_{im}$, respectively, and $g_m$ is some known link function. The distribution and link function are allowed to differ over the $M$ longitudinal submodels. We let the vector $\boldsymbol{\beta} = \{ \boldsymbol{\beta}_m ; m = 1,...,M\}$ denote the collection of population-level parameters across the $M$ longitudinal submodels. We assume that the dependence across the different longitudinal submodels (i.e. the correlation between the different longitudinal biomarkers) is captured through a shared multivariate normal distribution for the individual-specific parameters; that is, we assume
r-cran-rstanarm-2.19.3/vignettes/jm.Rmd-95-
##############################################
r-cran-rstanarm-2.19.3/vignettes/jm.Rmd-105-
r-cran-rstanarm-2.19.3/vignettes/jm.Rmd:106:We assume that we also observe an event time $T_i = \mathsf{min} \left( T^*_i , C_i \right)$ where $T^*_i$ denotes the so-called "true" event time for individual $i$ (potentially unobserved) and $C_i$ denotes the censoring time. We define an event indicator $d_i = I(T^*_i \leq C_i)$. We then model the hazard of the event using a parametric proportional hazards regression model of the form
r-cran-rstanarm-2.19.3/vignettes/jm.Rmd-107-
##############################################
r-cran-rstanarm-2.19.3/vignettes/jm.Rmd-117-
r-cran-rstanarm-2.19.3/vignettes/jm.Rmd:118:where $h_i(t)$ is the hazard of the event for individual $i$ at time $t$, $h_0(t; \boldsymbol{\omega})$ is the baseline hazard at time $t$ given parameters $\boldsymbol{\omega}$, $\boldsymbol{w}^T_i(t)$ is a row-vector of individual-specific covariates (possibly time-dependent) with an associated vector of regression coefficients $\boldsymbol{\gamma}$ (log hazard ratios), $f_{mq}(.)$ are a set of known functions for $m=1,...,M$ and $q=1,...,Q_m$, and the $\alpha_{mq}$ are regression coefficients (log hazard ratios).
r-cran-rstanarm-2.19.3/vignettes/jm.Rmd-119-
##############################################
r-cran-rstanarm-2.19.3/vignettes/jm.Rmd-129-
r-cran-rstanarm-2.19.3/vignettes/jm.Rmd:130:where $H_i(t) = \int_{s=0}^t h_i(s) ds$ is the cumulative hazard for individual $i$.
r-cran-rstanarm-2.19.3/vignettes/jm.Rmd-131-
##############################################
r-cran-rstanarm-2.19.3/vignettes/jm.Rmd-247-
r-cran-rstanarm-2.19.3/vignettes/jm.Rmd:248:where $\boldsymbol{y}_i = \{ y_{ijm}(t); j = 1,...,n_i, m = 1,...,M \}$ denotes the collection of longitudinal biomarker data for individual $i$ and $\boldsymbol{\theta}$ denotes all remaining population-level parameters in the model.
r-cran-rstanarm-2.19.3/vignettes/jm.Rmd-249-
##############################################
r-cran-rstanarm-2.19.3/vignettes/jm.Rmd-264-
r-cran-rstanarm-2.19.3/vignettes/jm.Rmd:265:where $\sum_{j=1}^{n_{im}} \log p(y_{ijm} \mid \boldsymbol{b}_{i}, \boldsymbol{\theta})$ is the log likelihood for the $m^{th}$ longitudinal submodel, $\log p(T_i, d_i \mid \boldsymbol{b}_{i}, \boldsymbol{\theta})$ is the log likelihood for the event submodel, $\log p(\boldsymbol{b}_{i} \mid \boldsymbol{\theta})$ is the log likelihood for the distribution of the group-specific parameters (i.e. random effects), and $\log p(\boldsymbol{\theta})$ represents the log likelihood for the joint prior distribution across all remaining unknown parameters.^[We refer the reader to the priors [vignette](priors.html) for a discussion of the possible prior distributions.]
r-cran-rstanarm-2.19.3/vignettes/jm.Rmd-266-
##############################################
r-cran-rstanarm-2.19.3/vignettes/jm.Rmd-279-
r-cran-rstanarm-2.19.3/vignettes/jm.Rmd:280:where $w_q$ and $s_q$, respectively, are the standardised weights and locations ("abscissa") for quadrature node $q$ $(q=1,...,Q)$ [17]. The default for the `stan_jm` modelling function is to use $Q=15$ quadrature nodes, however if the user wishes, they can choose between $Q=15$, $11$, or $7$ quadrature nodes (specified via the `qnodes` argument).
r-cran-rstanarm-2.19.3/vignettes/jm.Rmd-281-
##############################################
r-cran-rstanarm-2.19.3/vignettes/jm.Rmd-312-
r-cran-rstanarm-2.19.3/vignettes/jm.Rmd:313:Note that for simplicity we have ignored the implicit conditioning on covariates; $\boldsymbol{x}_{im}(t)$ and $\boldsymbol{z}_{im}(t)$, for $m = 1,...,M$, and $\boldsymbol{w}_{i}(t)$. Since individual $i$ is included in the training data, it is easy for us to approximate these posterior predictive distributions by drawing from $p(y^{*}_{im}(t) \mid \boldsymbol{\theta}^{(l)}, \boldsymbol{b}_i^{(l)})$ and $p(S^{*}_i(t) \mid \boldsymbol{\theta}^{(l)}, \boldsymbol{b}_i^{(l)})$ where $\boldsymbol{\theta}^{(l)}$ and $\boldsymbol{b}_i^{(l)}$ are the $l^{th}$ $(l = 1,...,L)$ MCMC draws from the joint posterior distribution $p(\boldsymbol{\theta}, \boldsymbol{b}_i \mid \mathcal{D})$.
r-cran-rstanarm-2.19.3/vignettes/jm.Rmd-314-
##############################################
r-cran-rstanarm-2.19.3/vignettes/jm.Rmd-412-
r-cran-rstanarm-2.19.3/vignettes/jm.Rmd:413:We can obtain draws for $\boldsymbol{\tilde{b}}_k$ in the same manner as for the individual-specific parameters $\boldsymbol{b}_i$. That is, at the $l^{th}$ iteration of the MCMC sampler we draw $\boldsymbol{\tilde{b}}_k^{(l)}$ and store it^[These random draws from the posterior distribution of the group-specific parameters are stored each time a joint model is estimated using `stan_glmer`, `stan_mvmer`, or `stan_jm`; they are saved under an ID value called `"_NEW_"`]. However, individual $k$ did not provide any contribution to the training data and so we are effectively taking random draws from the posterior distribution for the individual-specific parameters. We are therefore effectively marginalising over the distribution of the group-specific coefficients when we obtain predictions using the draws $\boldsymbol{\tilde{b}}_k^{(l)}$ fro $l = 1,\dots,L$. In other words, we are predicting for a new individual whom we have no information except that they are drawn from the same population as the $i = 1,...,N$ individuals in the training data. Because these predictions will incorporate all the uncertainty associated with between-individual variation our 95% credible intervals are likely to be very wide. These types of marginal predictions can be obtained using the `posterior_traj` and `posterior_survfit` functions by providing prediction data and specifying `dynamic = FALSE`; see the examples provided below.
r-cran-rstanarm-2.19.3/vignettes/jm.Rmd-414-
##############################################
r-cran-rstanarm-2.19.3/vignettes/jm.Rmd-448-
r-cran-rstanarm-2.19.3/vignettes/jm.Rmd:449:where $S_i^*(t)$ is the predicted survival probability for individual $i$ ($i = 1,\dots,N^{pred}$ at time $t$, and $N^{pred}$ is the number of individuals included in the prediction dataset. We refer to these predictions as *standardised survival probabilities*.
r-cran-rstanarm-2.19.3/vignettes/jm.Rmd-450-
##############################################
r-cran-rstanarm-2.19.3/vignettes/jm.Rmd-617-
r-cran-rstanarm-2.19.3/vignettes/jm.Rmd:618:Predicted individual-specific biomarker values can be obtained using either the `posterior_traj` or `posterior_predict` function. The `posterior_traj` is preferable, because it can be used to obtain the biomarker values at a series of evenly spaced time points between baseline and the individual's event or censoring time by using the default `interpolate = TRUE` option. Whereas, the `posterior_predict` function only provides the predicted biomarker values at the observed time points, or the time points in the new data. Predicting the biomarker values at a series of evenly spaced time points can be convenient because they can be easily used for plotting the longitudinal trajectory. Moreover, by default the `posterior_traj` returns a data frame with variables corresponding to the individual ID, the time, the predicted mean biomarker value, the limits for the 95% credible interval (i.e. uncertainty interval for the predicted mean biomarker value), and limits for the 95% prediction interval (i.e. uncertainty interval for a predicted biomarker data point), where the level for the uncertainty intervals can be changed via the `prob` argument. Conversely, the `posterior_predict` function returns an $S$ by $N$ matrix of predictions where $S$ is the number of posterior draws and $N$ is the number of prediction time points (note that this return type can also be obtained for `posterior_traj` by specifying the argument `return_matrix = TRUE`).
r-cran-rstanarm-2.19.3/vignettes/jm.Rmd-619-
##############################################
r-cran-rstanarm-2.19.3/vignettes/jm.Rmd-801-
r-cran-rstanarm-2.19.3/vignettes/jm.Rmd:802:In this example we show how a standardised survival curve can be obtained, where the $i = 1,...,N^{pred}$ individuals used in generating the standardised survival curve are the same individuals that were used in estimating the model. We will obtain the survival curve for the multivariate joint model estimated in an earlier example (`mod3`). The `standardise = TRUE` argument to `posterior_survfit` specifies that we want to obtain individual-specific predictions of the survival curve and then average these. Because, in practical terms, we need to obtain survival probabilities at time $t$ for each individual and then average them we want to explicitly specify the values of $t$ we want to use (and the same values of $t$ will be used for individuals). We specify the values of $t$ to use via the `times` argument; here we will predict the standardised survival curve at time 0 and then for convenience we can just specify `extrapolate = TRUE` (which is the default anyway) which will mean we automatically predict at 10 evenly spaced time points between 0 and the maximum event or censoring time.
r-cran-rstanarm-2.19.3/vignettes/jm.Rmd-803-
##############################################
r-cran-rstanarm-2.19.3/vignettes/binomial.Rmd-143-In the plot above the blue bars correspond to the
r-cran-rstanarm-2.19.3/vignettes/binomial.Rmd:144:`r sum(rstanarm::wells$switch == 1)` residents who said they switched wells and darker bars show the distribution of `dist100` for the
r-cran-rstanarm-2.19.3/vignettes/binomial.Rmd:145:`r sum(rstanarm::wells$switch == 0)` residents who didn't switch. As we would expect, for the
r-cran-rstanarm-2.19.3/vignettes/binomial.Rmd-146-residents who switched wells, the distribution of `dist100` is more concentrated
##############################################
r-cran-rstanarm-2.19.3/vignettes/count.Rmd-37-
r-cran-rstanarm-2.19.3/vignettes/count.Rmd:38:where $\lambda = E(y | \mathbf{x}) = g^{-1}(\eta)$ and $\eta = \alpha +
r-cran-rstanarm-2.19.3/vignettes/count.Rmd-39-\mathbf{x}^\top \boldsymbol{\beta}$ is a linear predictor. For the Poisson
##############################################
r-cran-rstanarm-2.19.3/vignettes/aov.Rmd-69-syntax popularized by the __lme4__ package, `y ~ 1 + (1|group)` represents a
r-cran-rstanarm-2.19.3/vignettes/aov.Rmd:70:likelihood where $\mu_j = \alpha + \alpha_j$ and $\alpha_j$ is normally
r-cran-rstanarm-2.19.3/vignettes/aov.Rmd-71-distributed across the $J$ groups with mean zero and some unknown standard
##############################################
r-cran-rstanarm-2.19.3/vignettes/priors.Rmd-104-is $\mathsf{exponential}(1)$. However, as a result of the automatic rescaling,
r-cran-rstanarm-2.19.3/vignettes/priors.Rmd:105:the actual scale used was `r fr2(priors$prior_aux$adjusted_scale)`.
r-cran-rstanarm-2.19.3/vignettes/priors.Rmd-106-
##############################################
r-cran-rstanarm-2.19.3/vignettes/priors.Rmd-112-scales actually used were
r-cran-rstanarm-2.19.3/vignettes/priors.Rmd:113:`r fr2(priors$prior$adjusted_scale[1])` and
r-cran-rstanarm-2.19.3/vignettes/priors.Rmd:114:`r fr2(priors$prior$adjusted_scale[2])`.
r-cran-rstanarm-2.19.3/vignettes/priors.Rmd-115-
##############################################
r-cran-rstanarm-2.19.3/vignettes/priors.Rmd-117-and standard deviation $10$, but in this case the standard deviation was
r-cran-rstanarm-2.19.3/vignettes/priors.Rmd:118:adjusted to `r fr2(priors$prior_intercept$adjusted_scale[1])`. There is also a
r-cran-rstanarm-2.19.3/vignettes/priors.Rmd-119-note in parentheses informing you that the prior applies to the intercept after
##############################################
r-cran-rstanarm-2.19.3/R/loo.R-627- }
r-cran-rstanarm-2.19.3/R/loo.R:628: fit_j_call$subset <- eval(fit_j_call$subset)
r-cran-rstanarm-2.19.3/R/loo.R:629: fit_j_call$data <- eval(fit_j_call$data)
r-cran-rstanarm-2.19.3/R/loo.R-630- if (!is.null(getCall(x)$offset)) {
##############################################
r-cran-rstanarm-2.19.3/R/posterior_predict.R-201- y <- eval(formula(object)[[2L]], newdata)
r-cran-rstanarm-2.19.3/R/posterior_predict.R:202: strata <- as.factor(eval(object$call$strata, newdata))
r-cran-rstanarm-2.19.3/R/posterior_predict.R-203- formals(object$family$linkinv)$g <- strata
##############################################
r-cran-rstanarm-2.19.3/R/posterior_predict.R-211- if (is.null(newdata)) ppargs$strata <- model.frame(object)[,"(weights)"]
r-cran-rstanarm-2.19.3/R/posterior_predict.R:212: else ppargs$strata <- eval(object$call$strata, newdata)
r-cran-rstanarm-2.19.3/R/posterior_predict.R-213- ppargs$strata <- as.factor(ppargs$strata)
##############################################
r-cran-rstanarm-2.19.3/R/pp_data.R-98- if (!missing(newdata) && (!is.null(offset) || !is.null(object$call$offset))) {
r-cran-rstanarm-2.19.3/R/pp_data.R:99: offset <- try(eval(object$call$offset, newdata), silent = TRUE)
r-cran-rstanarm-2.19.3/R/pp_data.R-100- if (!is.numeric(offset)) offset <- NULL
##############################################
r-cran-rstanarm-2.19.3/R/loo-kfold.R-168- }
r-cran-rstanarm-2.19.3/R/loo-kfold.R:169: fit_k_call$cores <- eval(fit_k_call$cores)
r-cran-rstanarm-2.19.3/R/loo-kfold.R:170: fit_k_call$subset <- eval(fit_k_call$subset)
r-cran-rstanarm-2.19.3/R/loo-kfold.R:171: fit_k_call$data <- eval(fit_k_call$data)
r-cran-rstanarm-2.19.3/R/loo-kfold.R:172: fit_k_call$offset <- eval(fit_k_call$offset)
r-cran-rstanarm-2.19.3/R/loo-kfold.R-173-
##############################################
r-cran-rstanarm-2.19.3/R/posterior_linpred.R-124- y <- eval(formula(object)[[2L]], newdata)
r-cran-rstanarm-2.19.3/R/posterior_linpred.R:125: strata <- as.factor(eval(object$call$strata, newdata))
r-cran-rstanarm-2.19.3/R/posterior_linpred.R-126- formals(g)$g <- strata
##############################################
r-cran-rstanarm-2.19.3/R/log_lik.R-262- } else if (is_clogit(object)) {
r-cran-rstanarm-2.19.3/R/log_lik.R:263: if (has_newdata) strata <- eval(object$call$strata, newdata)
r-cran-rstanarm-2.19.3/R/log_lik.R-264- else strata <- model.frame(object)[,"(weights)"]
##############################################
r-cran-rstanarm-2.19.3/R/stan_clogit.R-108- mf$data <- data
r-cran-rstanarm-2.19.3/R/stan_clogit.R:109: err <- try(eval(mf$weights, data, enclos = NULL), silent = TRUE)
r-cran-rstanarm-2.19.3/R/stan_clogit.R-110- if (inherits(err, "try-error")) {
##############################################
r-cran-rstanarm-2.19.3/inst/doc/jm.Rmd-93-
r-cran-rstanarm-2.19.3/inst/doc/jm.Rmd:94:where $\boldsymbol{x}^T_{ijm}(t)$ and $\boldsymbol{z}^T_{ijm}(t)$ are both row-vectors of covariates (which likely include some function of time, for example a linear slope, cubic splines, or polynomial terms) with associated vectors of fixed and individual-specific parameters $\boldsymbol{\beta}_m$ and $\boldsymbol{b}_{im}$, respectively, and $g_m$ is some known link function. The distribution and link function are allowed to differ over the $M$ longitudinal submodels. We let the vector $\boldsymbol{\beta} = \{ \boldsymbol{\beta}_m ; m = 1,...,M\}$ denote the collection of population-level parameters across the $M$ longitudinal submodels. We assume that the dependence across the different longitudinal submodels (i.e. the correlation between the different longitudinal biomarkers) is captured through a shared multivariate normal distribution for the individual-specific parameters; that is, we assume
r-cran-rstanarm-2.19.3/inst/doc/jm.Rmd-95-
##############################################
r-cran-rstanarm-2.19.3/inst/doc/jm.Rmd-105-
r-cran-rstanarm-2.19.3/inst/doc/jm.Rmd:106:We assume that we also observe an event time $T_i = \mathsf{min} \left( T^*_i , C_i \right)$ where $T^*_i$ denotes the so-called "true" event time for individual $i$ (potentially unobserved) and $C_i$ denotes the censoring time. We define an event indicator $d_i = I(T^*_i \leq C_i)$. We then model the hazard of the event using a parametric proportional hazards regression model of the form
r-cran-rstanarm-2.19.3/inst/doc/jm.Rmd-107-
##############################################
r-cran-rstanarm-2.19.3/inst/doc/jm.Rmd-117-
r-cran-rstanarm-2.19.3/inst/doc/jm.Rmd:118:where $h_i(t)$ is the hazard of the event for individual $i$ at time $t$, $h_0(t; \boldsymbol{\omega})$ is the baseline hazard at time $t$ given parameters $\boldsymbol{\omega}$, $\boldsymbol{w}^T_i(t)$ is a row-vector of individual-specific covariates (possibly time-dependent) with an associated vector of regression coefficients $\boldsymbol{\gamma}$ (log hazard ratios), $f_{mq}(.)$ are a set of known functions for $m=1,...,M$ and $q=1,...,Q_m$, and the $\alpha_{mq}$ are regression coefficients (log hazard ratios).
r-cran-rstanarm-2.19.3/inst/doc/jm.Rmd-119-
##############################################
r-cran-rstanarm-2.19.3/inst/doc/jm.Rmd-129-
r-cran-rstanarm-2.19.3/inst/doc/jm.Rmd:130:where $H_i(t) = \int_{s=0}^t h_i(s) ds$ is the cumulative hazard for individual $i$.
r-cran-rstanarm-2.19.3/inst/doc/jm.Rmd-131-
##############################################
r-cran-rstanarm-2.19.3/inst/doc/jm.Rmd-247-
r-cran-rstanarm-2.19.3/inst/doc/jm.Rmd:248:where $\boldsymbol{y}_i = \{ y_{ijm}(t); j = 1,...,n_i, m = 1,...,M \}$ denotes the collection of longitudinal biomarker data for individual $i$ and $\boldsymbol{\theta}$ denotes all remaining population-level parameters in the model.
r-cran-rstanarm-2.19.3/inst/doc/jm.Rmd-249-
##############################################
r-cran-rstanarm-2.19.3/inst/doc/jm.Rmd-264-
r-cran-rstanarm-2.19.3/inst/doc/jm.Rmd:265:where $\sum_{j=1}^{n_{im}} \log p(y_{ijm} \mid \boldsymbol{b}_{i}, \boldsymbol{\theta})$ is the log likelihood for the $m^{th}$ longitudinal submodel, $\log p(T_i, d_i \mid \boldsymbol{b}_{i}, \boldsymbol{\theta})$ is the log likelihood for the event submodel, $\log p(\boldsymbol{b}_{i} \mid \boldsymbol{\theta})$ is the log likelihood for the distribution of the group-specific parameters (i.e. random effects), and $\log p(\boldsymbol{\theta})$ represents the log likelihood for the joint prior distribution across all remaining unknown parameters.^[We refer the reader to the priors [vignette](priors.html) for a discussion of the possible prior distributions.]
r-cran-rstanarm-2.19.3/inst/doc/jm.Rmd-266-
##############################################
r-cran-rstanarm-2.19.3/inst/doc/jm.Rmd-279-
r-cran-rstanarm-2.19.3/inst/doc/jm.Rmd:280:where $w_q$ and $s_q$, respectively, are the standardised weights and locations ("abscissa") for quadrature node $q$ $(q=1,...,Q)$ [17]. The default for the `stan_jm` modelling function is to use $Q=15$ quadrature nodes, however if the user wishes, they can choose between $Q=15$, $11$, or $7$ quadrature nodes (specified via the `qnodes` argument).
r-cran-rstanarm-2.19.3/inst/doc/jm.Rmd-281-
##############################################
r-cran-rstanarm-2.19.3/inst/doc/jm.Rmd-312-
r-cran-rstanarm-2.19.3/inst/doc/jm.Rmd:313:Note that for simplicity we have ignored the implicit conditioning on covariates; $\boldsymbol{x}_{im}(t)$ and $\boldsymbol{z}_{im}(t)$, for $m = 1,...,M$, and $\boldsymbol{w}_{i}(t)$. Since individual $i$ is included in the training data, it is easy for us to approximate these posterior predictive distributions by drawing from $p(y^{*}_{im}(t) \mid \boldsymbol{\theta}^{(l)}, \boldsymbol{b}_i^{(l)})$ and $p(S^{*}_i(t) \mid \boldsymbol{\theta}^{(l)}, \boldsymbol{b}_i^{(l)})$ where $\boldsymbol{\theta}^{(l)}$ and $\boldsymbol{b}_i^{(l)}$ are the $l^{th}$ $(l = 1,...,L)$ MCMC draws from the joint posterior distribution $p(\boldsymbol{\theta}, \boldsymbol{b}_i \mid \mathcal{D})$.
r-cran-rstanarm-2.19.3/inst/doc/jm.Rmd-314-
##############################################
r-cran-rstanarm-2.19.3/inst/doc/jm.Rmd-412-
r-cran-rstanarm-2.19.3/inst/doc/jm.Rmd:413:We can obtain draws for $\boldsymbol{\tilde{b}}_k$ in the same manner as for the individual-specific parameters $\boldsymbol{b}_i$. That is, at the $l^{th}$ iteration of the MCMC sampler we draw $\boldsymbol{\tilde{b}}_k^{(l)}$ and store it^[These random draws from the posterior distribution of the group-specific parameters are stored each time a joint model is estimated using `stan_glmer`, `stan_mvmer`, or `stan_jm`; they are saved under an ID value called `"_NEW_"`]. However, individual $k$ did not provide any contribution to the training data and so we are effectively taking random draws from the posterior distribution for the individual-specific parameters. We are therefore effectively marginalising over the distribution of the group-specific coefficients when we obtain predictions using the draws $\boldsymbol{\tilde{b}}_k^{(l)}$ fro $l = 1,\dots,L$. In other words, we are predicting for a new individual whom we have no information except that they are drawn from the same population as the $i = 1,...,N$ individuals in the training data. Because these predictions will incorporate all the uncertainty associated with between-individual variation our 95% credible intervals are likely to be very wide. These types of marginal predictions can be obtained using the `posterior_traj` and `posterior_survfit` functions by providing prediction data and specifying `dynamic = FALSE`; see the examples provided below.
r-cran-rstanarm-2.19.3/inst/doc/jm.Rmd-414-
##############################################
r-cran-rstanarm-2.19.3/inst/doc/jm.Rmd-448-
r-cran-rstanarm-2.19.3/inst/doc/jm.Rmd:449:where $S_i^*(t)$ is the predicted survival probability for individual $i$ ($i = 1,\dots,N^{pred}$ at time $t$, and $N^{pred}$ is the number of individuals included in the prediction dataset. We refer to these predictions as *standardised survival probabilities*.
r-cran-rstanarm-2.19.3/inst/doc/jm.Rmd-450-
##############################################
r-cran-rstanarm-2.19.3/inst/doc/jm.Rmd-617-
r-cran-rstanarm-2.19.3/inst/doc/jm.Rmd:618:Predicted individual-specific biomarker values can be obtained using either the `posterior_traj` or `posterior_predict` function. The `posterior_traj` is preferable, because it can be used to obtain the biomarker values at a series of evenly spaced time points between baseline and the individual's event or censoring time by using the default `interpolate = TRUE` option. Whereas, the `posterior_predict` function only provides the predicted biomarker values at the observed time points, or the time points in the new data. Predicting the biomarker values at a series of evenly spaced time points can be convenient because they can be easily used for plotting the longitudinal trajectory. Moreover, by default the `posterior_traj` returns a data frame with variables corresponding to the individual ID, the time, the predicted mean biomarker value, the limits for the 95% credible interval (i.e. uncertainty interval for the predicted mean biomarker value), and limits for the 95% prediction interval (i.e. uncertainty interval for a predicted biomarker data point), where the level for the uncertainty intervals can be changed via the `prob` argument. Conversely, the `posterior_predict` function returns an $S$ by $N$ matrix of predictions where $S$ is the number of posterior draws and $N$ is the number of prediction time points (note that this return type can also be obtained for `posterior_traj` by specifying the argument `return_matrix = TRUE`).
r-cran-rstanarm-2.19.3/inst/doc/jm.Rmd-619-
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r-cran-rstanarm-2.19.3/inst/doc/jm.Rmd-801-
r-cran-rstanarm-2.19.3/inst/doc/jm.Rmd:802:In this example we show how a standardised survival curve can be obtained, where the $i = 1,...,N^{pred}$ individuals used in generating the standardised survival curve are the same individuals that were used in estimating the model. We will obtain the survival curve for the multivariate joint model estimated in an earlier example (`mod3`). The `standardise = TRUE` argument to `posterior_survfit` specifies that we want to obtain individual-specific predictions of the survival curve and then average these. Because, in practical terms, we need to obtain survival probabilities at time $t$ for each individual and then average them we want to explicitly specify the values of $t$ we want to use (and the same values of $t$ will be used for individuals). We specify the values of $t$ to use via the `times` argument; here we will predict the standardised survival curve at time 0 and then for convenience we can just specify `extrapolate = TRUE` (which is the default anyway) which will mean we automatically predict at 10 evenly spaced time points between 0 and the maximum event or censoring time.
r-cran-rstanarm-2.19.3/inst/doc/jm.Rmd-803-
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r-cran-rstanarm-2.19.3/inst/doc/binomial.Rmd-143-In the plot above the blue bars correspond to the
r-cran-rstanarm-2.19.3/inst/doc/binomial.Rmd:144:`r sum(rstanarm::wells$switch == 1)` residents who said they switched wells and darker bars show the distribution of `dist100` for the
r-cran-rstanarm-2.19.3/inst/doc/binomial.Rmd:145:`r sum(rstanarm::wells$switch == 0)` residents who didn't switch. As we would expect, for the
r-cran-rstanarm-2.19.3/inst/doc/binomial.Rmd-146-residents who switched wells, the distribution of `dist100` is more concentrated
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r-cran-rstanarm-2.19.3/inst/doc/count.Rmd-37-
r-cran-rstanarm-2.19.3/inst/doc/count.Rmd:38:where $\lambda = E(y | \mathbf{x}) = g^{-1}(\eta)$ and $\eta = \alpha +
r-cran-rstanarm-2.19.3/inst/doc/count.Rmd-39-\mathbf{x}^\top \boldsymbol{\beta}$ is a linear predictor. For the Poisson
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r-cran-rstanarm-2.19.3/inst/doc/aov.Rmd-69-syntax popularized by the __lme4__ package, `y ~ 1 + (1|group)` represents a
r-cran-rstanarm-2.19.3/inst/doc/aov.Rmd:70:likelihood where $\mu_j = \alpha + \alpha_j$ and $\alpha_j$ is normally
r-cran-rstanarm-2.19.3/inst/doc/aov.Rmd-71-distributed across the $J$ groups with mean zero and some unknown standard
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r-cran-rstanarm-2.19.3/inst/doc/priors.Rmd-104-is $\mathsf{exponential}(1)$. However, as a result of the automatic rescaling,
r-cran-rstanarm-2.19.3/inst/doc/priors.Rmd:105:the actual scale used was `r fr2(priors$prior_aux$adjusted_scale)`.
r-cran-rstanarm-2.19.3/inst/doc/priors.Rmd-106-
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r-cran-rstanarm-2.19.3/inst/doc/priors.Rmd-112-scales actually used were
r-cran-rstanarm-2.19.3/inst/doc/priors.Rmd:113:`r fr2(priors$prior$adjusted_scale[1])` and
r-cran-rstanarm-2.19.3/inst/doc/priors.Rmd:114:`r fr2(priors$prior$adjusted_scale[2])`.
r-cran-rstanarm-2.19.3/inst/doc/priors.Rmd-115-
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r-cran-rstanarm-2.19.3/inst/doc/priors.Rmd-117-and standard deviation $10$, but in this case the standard deviation was
r-cran-rstanarm-2.19.3/inst/doc/priors.Rmd:118:adjusted to `r fr2(priors$prior_intercept$adjusted_scale[1])`. There is also a
r-cran-rstanarm-2.19.3/inst/doc/priors.Rmd-119-note in parentheses informing you that the prior applies to the intercept after
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r-cran-rstanarm-2.19.3/debian/tests/run-unit-test-6-if [ "$AUTOPKGTEST_TMP" = "" ] ; then
r-cran-rstanarm-2.19.3/debian/tests/run-unit-test:7: AUTOPKGTEST_TMP=`mktemp -d /tmp/${debname}-test.XXXXXX`
r-cran-rstanarm-2.19.3/debian/tests/run-unit-test-8- trap "rm -rf $AUTOPKGTEST_TMP" 0 INT QUIT ABRT PIPE TERM