See the package documentation for details. Install with the R commands:
install.packages("devtools")
devtools::install_github("gregorkb/semipadd2pop")
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))
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))
