Description Usage Arguments Value Note Author(s) Examples
Function to generate data that can be used to test Forward stagewise / Penalized Regression techniques. Currently marginally Gaussian and Poisson responses are possible.
Function is provided to allow the user simple data generation as
sgee
functions were designed for.
Various parameters controlling
aspects such as the response correlation, the covariate group
structure, the marginal response distribution, and the signal to
noise ratio for marginally gaussian responses are
provided to allow a great deal of specificity over the kind of data
that is generated.
1 2 3 4 
numClusters 
Number of clusters to be generated. 
clusterSize 
Size of each cluster. 
clusterRho 
Correlation parameter for response. 
clusterCorstr 
String indicating cluster Correlation structure.
Parameter is fed to 
yVariance 
Optional scalar value specifying the marginal response
variance; overrides 
xVariance 
Scalar value indicating marginal variance of the covariates. 
numGroups 
Number of covariate groups to be generated. Default
behavior is to generate groups of size 1 (effectively no groups).
If covariate groups are desired, 
groupSize 
Size of each group. 
groupRho 
Within group correlation parameter. 
beta 
Vector of coefficient values used to generate response. 
numMainEffects 
An integer indicating that the first

family 
Marginal response family; currently 
SNR 
Scalar value that allows fixing the signal to noise ratio as defined as the ratio of the (observed) variance in the linear predictor to the variance of the response conditioned on the covariates. 
intercept 
Scalar value indicating the true intercept value. 
List containing the generated response, y
, the generated
covariates, x
, a vector identifying the responses clusters,
clusterID
, and a vector identifying the covariate groups,
groupID
.
Function is ued to generate both the desired covariate structure and
the desired response structure. To generate poisson responses, functions
from the R package coupla
are used.
Current implementation of interactions overwrites any previous grouping structure; that is the number of groups becomes p and the group sizes are set to 1.
Gregory Vaughan
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27  ## A resonse variance can be given,
dat1 < genData(numClusters = 10,
clusterSize = 4,
clusterRho = .5,
clusterCorstr = "exchangeable",
yVariance = 1,
xVariance = 1,
numGroups = 5,
groupSize = 4,
groupRho = .5,
beta = c(rep(1,8), rep(0,12)),
family = gaussian(),
intercept = 1)
## or the signal to noise ratio can be fixed
dat2 < genData(numClusters = 10,
clusterSize = 4,
clusterRho = .5,
clusterCorstr = "exchangeable",
xVariance = 1,
numGroups = 5,
groupSize = 4,
groupRho = .5,
beta = c(rep(1,8), rep(0,12)),
family = poisson(),
SNR = 10,
intercept = 1)

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.