This simulated benchmark data follows the model with skewed normal distribution as the marginal distribution and Gaussian copula
as the dependence structure. It can be used to demonstrate the usage of methods and validation. DST
is the Data
Simulated for Training purpose; DSV
is the Data Simulated for Validation (prediction) purpose.
1 2 3 | data(data.simulation) #load data sets: DST and DSV
DST # data simulated for training
DSV # data simulated for validation
|
Each of DST
and DSV
is a list containing the following components:
obs
matrix of 100 observations (as row), with 80 timepoints for each
tp
vector of time points, with length 80
cp
vector of covariates for each subject, with length 100
pars
parameter list with mean, logvar
(matrices of 100 by 80) and shape, skew
(vectors of length 80)
corr
correlation matrix to determine the Gaussian corpula
In addition, each data set contains one specific component:
quantile
3-dimensional array with dimension c(5, 100, 80)
for the true quantiles of the quantile levels
c(.50, .80, .90, .95, .99)
; only available for the training data set DST
obs.full
fully observed observation matrix with diemsnion c(100, 80)
, in the validation data set DSV
;
obs
in DSV
contains missing values, and obs.full
can be used to measure the performance of predication at those points where missing values are observed.
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