pred.transsmap {matrans}

Prediction for new data based on Trans-SMAP.

Description

Obtain predictions from a "trans.smap" object based on new samples.

Usage

pred.transsmap(object, newdata, bs.para, if.lm = FALSE)

Arguments

object

the output of function trans.smap.

newdata

a list containing the new observations of predictors for prediction, the components of which is named as "data.x" for parametric variables and "data.z" for nonparametric variables. Should be in accordance with the data for training object.

bs.para

a list containing the parameters for B-spline construction in function bs. Should be a vector with names "bs.df" and "bs.degree", each component of which is a vector with the same length as the number of nonparametric variables. For example, bs.para = list(bs.df=c(3,3,3), bs.degree=c(3,3,3)).

• "bs.df": degrees of freedom for each nonparametric component; The details can be referred to the arguments in function bs.

• "bs.degree": degree of the piecewise polynomial for each nonparametric component; The default is 3 for cubic splines.

if.lm

the logical variable, whether to set the target model as ordinary linear model. Default is False.

Value

a result list containing the predicted values on new data and the estimated coefficient vector.

References

Hu, X., & Zhang, X. (2023). Optimal Parameter-Transfer Learning by Semiparametric Model Averaging. Journal of Machine Learning Research, 24(358), 1-53.

See Also

trans.smap.

Examples

## correct target model setting

# generate simulation dataset
coeff0 <- cbind(
  as.matrix(c(1.4, -1.2, 1, -0.8, 0.65, 0.3)),
  as.matrix(c(1.4, -1.2, 1, -0.8, 0.65, 0.3) + 0.02),
  as.matrix(c(1.4, -1.2, 1, -0.8, 0.65, 0.3) + 0.3),
  as.matrix(c(1.4, -1.2, 1, -0.8, 0.65, 0.3))
)
whole.data <- simdata.gen(
  px = 6, num.source = 4, size = c(150, 200, 200, 150), coeff0 = coeff0,
  coeff.mis = as.matrix(c(coeff0[, 2], 1.8)), err.sigma = 0.5, rho = 0.5, size.test = 500,
  sim.set = "homo", tar.spec = "cor", if.heter = FALSE
)
data.train <- whole.data$data.train
data.test <- whole.data$data.test

# running Trans-SMAP and obtain the optimal weight vector
data.train$data.x[[2]] <- data.train$data.x[[2]][, -7]
fit.transsmap <- trans.smap(
  train.data = data.train, nfold = 5,
  bs.para = list(bs.df = rep(3, 3), bs.degree = rep(3, 3))
)
ma.weights <- fit.transsmap$weight.est

# predict for new data
pred.res <- pred.transsmap(
  object = fit.transsmap, newdata = data.test,
  bs.para = list(bs.df = rep(3, 3), bs.degree = rep(3, 3))
)
pred.val <- pred.res$predict.val
predict.risk <- sum((pred.val - data.test$data.x %*% data.test$beta.true - data.test$gz.te)^2) / 500


## misspecified target model setting

# generate simulation dataset
coeff.mis <- matrix(c(c(coeff0[, 1], 0.1), c(coeff0[, 2], 1.8)), ncol = 2)
whole.data <- simdata.gen(
  px = 6, num.source = 4, size = c(150, 200, 200, 150), coeff0 = coeff0,
  coeff.mis = coeff.mis, err.sigma = 0.5, rho = 0.5, size.test = 500,
  sim.set = "homo", tar.spec = "mis", if.heter = FALSE
)
data.train <- whole.data$data.train
data.test <- whole.data$data.test

# running Trans-SMAP and obtain the optimal weight vector
data.train$data.x[[1]] <- data.train$data.x[[1]][, -7]
data.train$data.x[[2]] <- data.train$data.x[[2]][, -7]
fit.transsmap <- trans.smap(
  train.data = data.train, nfold = 5,
  bs.para = list(bs.df = rep(3, 3), bs.degree = rep(3, 3))
)
ma.weights <- fit.transsmap$weight.est

# predict for new data
data.test.mis <- data.test
data.test.mis$data.x <- data.test.mis$data.x[, -7]
pred.res <- pred.transsmap(
  object = fit.transsmap, newdata = data.test.mis,
  bs.para = list(bs.df = rep(3, 3), bs.degree = rep(3, 3))
)
pred.val <- pred.res$predict.val
predict.risk <- sum((pred.val - data.test$data.x %*% data.test$beta.true - data.test$gz.te)^2) / 500