semipadd2pop_to_grouplasso2pop: Prepare inputs for grouplasso2pop function when using it to...

In gregorkb/semipadd2pop: Combine two data sets to fit semiparametric additive models

Prepare inputs for grouplasso2pop function when using it to fit a semiparametric model

Description

Prepare inputs for grouplasso2pop function when using it to fit a semiparametric model

Usage

semipadd2pop_to_grouplasso2pop(
  X1,
  nonparm1,
  X2,
  nonparm2,
  nCom,
  d1,
  d2,
  xi,
  w1 = 1,
  w2 = 1,
  w = 1,
  lambda.beta = 1,
  lambda.f = 1,
  eta.beta = 1,
  eta.f = 1
)

Arguments

X1	the matrix with the observed covariate values for data set 1 (including a column of ones for the intercept)
nonparm1	a vector indicating for which covariates a nonparametric function is to be estimated for data set 1
X2	the matrix with the observed covariate values for data set 2 (including a column of ones for the intercept)
nonparm2	a vector indicating for which covariates a nonparametric function is to be estimated for data set 2
nCom	the number of covariates to be treated as common between the two data sets: these must be arranged in the first nCom columns of the matrices X1 and X2 after the column of ones corresponding to the intercept.
d1	vector giving the dimensions the B-spline bases to be used when fitting the nonparametric effects. If a scalar is given, this dimension is used for all nonparametric effects.
d2	vector giving the dimensions the B-spline bases to be used when fitting the nonparametric effects. If a scalar is given, this dimension is used for all nonparametric effects.
xi	a tuning parameter governing the smoothness of the nonparametric estimates
w1	covariate-specific weights for different penalization among covariates in data set 1
w2	covariate-specific weights for different penalization among covariates in data set 2
w	covariate-specific weights for different penalization toward similarity for different covariates
lambda.beta	the level of sparsity penalization for the parametric effects
lambda.f	the level of sparsity penalization for the nonparametric effects
eta.beta	the level of penalization towards model similarity for parametric effects indicated to be common
eta.f	the level of penalization towards model similarity for nonparametric effects indicated to be common
response	a character string indicating the type of response. Can be "continuous", "binary", or "gt".

gregorkb/semipadd2pop documentation built on Oct. 2, 2022, 1:37 p.m.