predictive2 {bayesSurv} | R Documentation |
This function computes predictive densities, survivor and hazard curves for specified combinations of covariates.
Firstly, either the function bayesBisurvreg
or the
function bayessurvreg2
or the function bayessurvreg3
has to be used to obtain a sample from the posterior distribution of unknown quantities.
Function predictive2.control
serves only to perform some input
checks inside the main function predictive2
.
predictive2(formula, random, obs.dim, time0, data = parent.frame(), grid, na.action = na.fail, Gspline, quantile = c(0, 0.025, 0.5, 0.975, 1), skip = 0, by = 1, last.iter, nwrite, only.aver = TRUE, predict = list(density=FALSE, Surv=TRUE, hazard=FALSE, cum.hazard=FALSE), dir = getwd(), extens = "", extens.random="_b", version=0) predictive2.control(predict, only.aver, quantile, obs.dim, time0, Gspline, n)
formula |
the same formula as that one used to sample from the
posterior distribution of unknown quantities by the function
bayesBisurvreg or bayessurvreg2 or
bayessurvreg3 . On the left hand side whichever
Surv object of a~proper length
can be used (it is ignored anyway).
REMARK: the prediction must be asked for at least two combinations of covariates. This is the restriction imposed by one of the internal functions I use. | ||
random |
the same random statement as that one used to sample from the
posterior distribution of unknown quantities by the function
bayessurvreg2 or bayessurvreg3 , not applicable if
bayesBisurvreg was used to sample from the posterior
distribution.
| ||
obs.dim |
a vector that has to be supplied if bivariate data were
used for estimation (using the function
bayesBisurvreg ). This vector has to be of the same
length as the number of covariate combinations for which the
predictive quantities are to be computed. It determines to which
dimension (1 or 2) each observation belong.
| ||
time0 |
a~vector of length Gspline$dim giving the starting
time for the survival model. It does not have to be supplied if equal
to zero (usually).
This option is used to get hazard and density functions on the
original time scale in the case that the model was
log(T - time0) = .... Note that
time0 IS NOT the starting time of doubly censored observation
since there after subtracting the onset time, the starting time is
(usually) equal to zero.
| ||
data |
optional data frame in which to interpret the variables
occuring in the formulas. Usually, you create a new
data.frame similar to that one used to obtain a sample from
the posterior distribution. In this new data.frame , put
covariate values equal to these for which predictive quantities are
to be obtained. If cluster statement was used, assign a
unique cluster identification to each observation. Response variable
and a censoring indicator may be set to arbitrary values. They are
only used in formula but are ignored for computation.
| ||
grid |
a~vector giving the grid of values where predictive
quantities are to be evaluated. The grid should normally start at some
value slightly higher than time0 .
| ||
na.action |
function to be used to handle any NA s in the
data. The user is discouraged to change a default value
na.fail .
| ||
Gspline |
a~list specifying the G-spline used for the error
distribution in the model. It is a~list with the following components:
| ||
quantile |
a vector of quantiles that are to be computed for each predictive quantity. | ||
skip |
number of rows that should be skipped at the beginning of each *.sim file with the stored sample. | ||
by |
additional thinning of the sample. | ||
last.iter |
index of the last row from *.sim files that should be
used. If not specified than it is set to the maximum available
determined according to the file mixmoment.sim .
| ||
nwrite |
frequency with which is the user informed about the
progress of computation (every nwrite th iteration count of
iterations change).
| ||
only.aver |
if TRUE only posterior predictive mean is
computed for all quantities and no quantiles.
The word of warning: with only.aver set to FALSE , all
quantities must be stored for all iterations of the MCMC to be able to
compute the quantiles. This might require quite lots of memory.
| ||
predict |
a list of logical values indicating which predictive quantities are to be computed.
Components of the list:
| ||
dir |
directory where to search for files (`mixmoment.sim', `mweight.sim', mmean.sim', gspline.sim', 'beta.sim', 'D.sim', ...) with the McMC sample. | ||
extens |
an extension used to distinguish different sampled
G-splines if more formulas were used in one MCMC simulation (e.g. with
doubly-censored data).
| ||
extens.random |
only applicable if the function
bayessurvreg3 was used to generate the MCMC sample.
This is an extension used to distinguish different sampled G-splines determining the distribution of the random intercept (under the presence of doubly-censored data).
| ||
version |
this argument indicates by which bayes*survreg* function the
chains used by bayesGspline were created. Use the following:
| ||
n |
number of covariate combinations for which the prediction will be performed. |
A list with possibly the following components (what is included depends
on the value of the arguments predict
and only.aver
):
grid |
a~vector with the grid values at which the survivor function, survivor density, hazard and cumulative hazard are computed. |
Surv |
predictive survivor functions.
A~matrix with as many columns as length(grid) and as many rows as the number of covariate combinations for which the predictive quantities were asked. One row per covariate combination. |
density |
predictive survivor densities.
The same structure as Surv component of the list.
|
hazard |
predictive hazard functions.
The same structure as Surv component of the list.
|
cum.hazard |
predictive cumulative hazard functions.
The same structure as Surv component of the list.
|
quant.Surv |
pointwise quantiles for the predictive survivor
functions.
This is a list with as many components as the number of covariate combinations. One component per covariate combination. Each component of this list is a~matrix with as many columns as length(grid) and as many rows as the length of the argument quantile . Each row of this matrix gives values of one
quantile. The rows are also labeled by the probabilities (in %) of
the quantiles.
|
quant.density |
pointwise quantiles for the predictive survivor
densities.
The same structure as quant.Surv component of the list.
|
quant.hazard |
pointwise quantiles for the predictive hazard
functions.
The same structure as quant.Surv component of the list.
|
quant.cum.hazard |
pointwise quantiles for the predictive
cumulative hazard functions.
The same structure as quant.Surv component of the list.
|
Arnošt Komárek arnost.komarek[AT]mff.cuni.cz
Komárek, A. (2006). Accelerated Failure Time Models for Multivariate Interval-Censored Data with Flexible Distributional Assumptions. PhD. Thesis, Katholieke Universiteit Leuven, Faculteit Wetenschappen.
Komárek, A. and Lesaffre, E. (2007). Bayesian accelerated failure time model with multivariate doubly-interval-censored data and flexible distributional assumptions. To appear in Journal of the American Statistical Association.
Komárek, A. and Lesaffre, E. (2006). Bayesian semi-parametric accelerated failurew time model for paired doubly interval-censored data. Statistical Modelling, 6, 3–22.
Komárek, A., Lesaffre, E., and Legrand, C. (2007). Baseline and treatment effect heterogeneity for survival times between centers using a random effects accelerated failure time model with flexible error distribution. To appear in Statistics in Medicine.
## See the description of R commands for ## the models described in ## Komarek (2006), ## Komarek and Lesaffre (2006), ## Komarek and Lesaffre (2007), ## Komarek, Lesaffre, and Legrand (2007). ## ## R commands available in the documentation ## directory of this package ## as tandmobPA.pdf, tandmobPA.R, ## tandmobCS.pdf, tandmobCS.R, ## eortc.pdf.