data_gen {cvmaPLFAM} | R Documentation |
Simulated data
Description
Simulate sample data for illustration, including a M0
-column design matrix of scalar predictors,
a 100
-column matrix of the functional predictor, a one-column vector of mu
, a one-column vector of Y
,
and a one-column vector of testY
.
Usage
data_gen(R, K, n, ntest, M0, typ, design)
Arguments
R |
A scalar of value ranging from |
K |
A scalar. The number of replications. |
n |
A scalar. The sample size of training data. |
ntest |
A scalar. The sample size of test data. |
M0 |
A scalar. True dimension of scalar predictors. |
typ |
A scalar of value |
design |
A scalar of value |
Value
A list
of K
simulated training data sets and K
simulated test data sets. Each data set is of matrix
type,
whose first M0
columns corresponds to the design matrix of scalar predictors, followed by the
recording/measurement matrix of the functional predictor, and vectors mu
, Y
.
Examples
library(MASS)
# Example: Design 1 in simulation study
set.seed(22)
data1 <- data_gen(R = 0.6, K = 2, n = 10, ntest = 5, M0 = 4, typ = 1, design = 1)
str(data1)
# List of 4
#$ : num [1:10, 1:106] -0.501 -1.266 -0.564 -0.563 -0.395 ...
#$ : num [1:10, 1:106] -1.207 -0.089 -0.782 0.123 0.66 ...
#$ : num [1:5, 1:106] 0.816 0.679 0.816 -0.563 -1.367 ...
#$ : num [1:5, 1:106] -0.089 -0.785 0.899 -0.785 -0.445 ...
# Example: Design 2 in simulation study
data_gen(R = 0.3, K = 3, n = 10, ntest = 5, M0 = 20, typ = 1, design = 2)
# Example: Design 3 in simulation study
data_gen(R = 0.9, K = 5, n = 20, ntest = 10, M0 = 4, typ = 2, design = 3)