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

knitr::opts_chunk$set(echo = TRUE)
library(semipadd2pop)

Using the semipadd2pop package

See the package documentation for details. Install with the R commands:

install.packages("devtools")

devtools::install_github("gregorkb/semipadd2pop")

Fitting a sparse nonparametric model to a single data set

The semipadd function fits a sparse semiparametric regression model to a single data set using user-specified tuning parameter values.

The following code generates a synthetic data set with continuous responses using the get_semipadd_data function and uses the semipadd function to fit a sparse semiparametric regression model. The plot_semipadd function plots the fitted nonparametric effects. The true effects are plotted with dashed lines.

data <- get_semipadd_data(n = 200, response = "continuous")

semipadd.out <- semipadd(Y = data$Y,
                         X = data$X,
                         nonparm = data$nonparm,
                         response = "continuous",
                         w = 1,
                         d = 20,
                         xi = 1,
                         lambda.beta = 1,
                         lambda.f = 1,
                         tol = 1e-3,
                         max.iter = 500)

plot_semipadd(semipadd.out, 
              true.functions = list( f = data$f,
                                     X = data$X))

The semipadd_cv_adapt function fits a sparse semiparametric regression model to a single data set with the tuning parameter governing sparsity chosen via crossvalidation.

The following code generates a synthetic data set with binary responses using the get_semipadd_data function and uses the semipadd_cv_adapt function to fit a sparse semiparametric regression model. The tuning parameter governing sparsity is chosen via crossvalidation. The plot_semipadd_cv_adapt function plots the fitted nonparametric effects, showing with transparent curves the fitted effects under candidate tuning parameter values which were not chosen by crossvalidation. The true effects are plotted with dashed lines.

data <- get_semipadd_data(n = 1000, response = "binary")

semipadd_cv_adapt.out <- semipadd_cv_adapt(Y = data$Y,
                                           X = data$X,
                                           nonparm = data$nonparm,
                                           response = "binary",
                                           w = 1,
                                           d = 20,
                                           xi = 1,
                                           n.lambda = 10,
                                           lambda.min.ratio = .01,
                                           lambda.max.ratio = 1,
                                           lambda.beta = 1,
                                           lambda.f = 1,
                                           tol = 1e-3,
                                           maxiter = 1000,
                                           report.prog = FALSE)

plot_semipadd_cv_adapt(semipadd_cv_adapt.out, 
                       true.functions  = list( f = data$f,
                                               X = data$X))

Combining data sets to fit two sparse semiparametric additive models

The semipadd2pop function fits sparse semiparametric regression models to two data sets which have some covariates in common. The function requires the user to choose values of the parameters governing the sparsity of the fitted models and the penalization towards similar fits of common covariate effects.

The following code generates two synthetic data sets with group testing responses using the get_semipadd2pop_data function and uses the semipadd2pop function to fit sparse semiparametric regression models. The plot_semipadd2pop function plots the fitted nonparametric effects in both data sets. The true effects are plotted with dashed lines.

data <- get_semipadd2pop_data(n1 = 1000, n2 = 800, response = "gt")

semipadd2pop.out <- semipadd2pop(Y1 = data$Y1,
                                 X1 = data$X1,
                                 nonparm1 = data$nonparm1,
                                 Y2 = data$Y2,
                                 X2 = data$X2,
                                 nonparm2 = data$nonparm2,
                                 response = "gt",
                                 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))

The semipadd2pop_cv_adapt function fits sparse semiparametric regression models to two data sets with some common covariates. It choosing values of the tuning parameters governing sparsity and penalization towards similar fitting of common effects via crossvalidation.

The following code generates two synthetic data sets with continuous responses using the get_semipadd2pop_data function and uses the semipadd2pop_cv_adapt function to fit a sparse semiparametric regression models for the two data sets. The tuning parameters governing sparsity and penalization towards similarity in the fitted effects of common covariates are chosen via crossvalidation. The plot_semipadd2pop_cv_adapt function plots the fitted nonparametric effects in each data set, showing with transparent curves the fitted effects under candidate tuning parameter values which were not chosen by crossvalidation. The true effects are plotted with dashed lines.

data <- get_semipadd2pop_data(n1 = 300, n2 = 200, response = "continuous")

semipadd2pop_cv_adapt.out <- semipadd2pop_cv_adapt(Y1 = data$Y1,
                                                   X1 = data$X1,
                                                   nonparm1 = data$nonparm1,
                                                   Y2 = data$Y2,
                                                   X2 = data$X2,
                                                   nonparm2 = data$nonparm2,
                                                   response = "continuous",
                                                   rho1 = 2,
                                                   rho2 = 1,
                                                   w1 = 1,
                                                   w2 = 1,
                                                   w = 1,
                                                   nCom = data$nCom,
                                                   d1 = 25,
                                                   d2 = 15,
                                                   xi = .5,
                                                   n.lambda = 5,
                                                   n.eta = 5,
                                                   lambda.min.ratio = 0.01,
                                                   lambda.max.ratio = 0.10,
                                                   n.folds = 5,
                                                   lambda.beta = 1,
                                                   lambda.f = 1,
                                                   eta.beta = 1,
                                                   eta.f = 1,
                                                   tol = 1e-3,
                                                   maxiter = 1000,
                                                   report.prog = FALSE)

plot_semipadd2pop_cv_adapt(semipadd2pop_cv_adapt.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.