semipadd2pop: Compute semiparametric binary-response regression model with...

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

Compute semiparametric binary-response regression model with 2 data sets while penalizing dissimilarity

Description

Compute semiparametric binary-response regression model with 2 data sets while penalizing dissimilarity

Usage

semipadd2pop(
  Y1,
  X1,
  nonparm1,
  Y2,
  X2,
  nonparm2,
  response,
  rho1,
  rho2,
  w1,
  w2,
  w,
  nCom,
  d1,
  d2,
  xi,
  lambda.beta,
  lambda.f,
  eta.beta,
  eta.f,
  tol = 1e-04,
  maxiter = 500,
  plot_obj = FALSE
)

Arguments

Y1	the response data from data set 1
nonparm1	a vector indicating for which covariates a nonparametric function is to be estimated for data set 1
Y2	the response data from data set 2
nonparm2	a vector indicating for which covariates a nonparametric function is to be estimated for data set 2
response	a character string indicating the type of response. Can be "continuous", "binary", or "gt"
rho1	weight placed on the first data set
rho2	weight placed on the second data set
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
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
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
tol	a convergence criterion
maxiter	the maximum allowed number of iterations
XX1	the matrix with the observed covariate values for data set 1 (including a column of ones for the intercept)
XX2	the matrix with the observed covariate values for data set 2 (including a column of ones for the intercept)
return_obj	a logical indicating whether the value of the objection function should be recorded after every step of the algorithm

Value

Returns the estimator of the semiparametric additive model

Examples

data <- get_semipadd2pop_data(n1 = 500, n2 = 600, response = "binary")

semipadd2pop.out <- semipadd2pop(Y1 = data$Y1,
                                 X1 = data$X1,
                                 nonparm1 = data$nonparm1,
                                 Y2 = data$Y2,
                                 X2 = data$X2,
                                 nonparm2 = data$nonparm2,
                                 response = "binary",
                                 rho1 = 1,
                                 rho2 = 1,
                                 nCom = data$nCom,
                                 d1 = 25,
                                 d2 = 15,
                                 xi = .5,
                                 w1 = 1,
                                 w2 = 1,
                                 w = 1,
                                 lambda.beta = .01,
                                 lambda.f = .01,
                                 eta.beta = .1,
                                 eta.f = .1,
                                 tol = 1e-3,
                                 maxiter = 500)


plot_semipadd2pop(semipadd2pop.out,
                  true.functions = list(f1 = data$f1,
                                        f2 = data$f2,
                                        X1 = data$X1,
                                        X2 = data$X2))

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