transreg: Penalised regression with multiple sets of prior effects

In transreg: Penalised Regression with Multiple Sets of Prior Effects ('Transfer Learning')

Penalised regression with multiple sets of prior effects

Description

Implements penalised regression with multiple sets of prior effects

Usage

transreg(
  y,
  X,
  prior,
  family = "gaussian",
  alpha = 1,
  foldid = NULL,
  nfolds = 10,
  scale = "iso",
  stack = "sim",
  sign = FALSE,
  switch = FALSE,
  select = TRUE,
  track = FALSE,
  parallel = FALSE
)

Arguments

y	target: vector of length n (see family)
X	features: matrix with n rows (samples) and p columns (features)
prior	prior coefficients: matrix with p rows (features) and k columns (sources of co-data)
family	character "gaussian" (y: real numbers), "binomial" (y: 0s and 1s), or "poisson" (y: non-negative integers);
alpha	elastic net mixing parameter (0=ridge, 1=lasso): number between 0 and 1
foldid	fold identifiers: vector of length n with entries from 1 to 'nfolds'
nfolds	number of folds: positive integer
scale	character "exp" for exponential calibration or "iso" for isotonic calibration
stack	character "sta" (standard stacking) or "sim" (simultaneous stacking)
sign	sign discovery procedure: logical (experimental argument)
switch	choose between positive and negative weights for each source: logical
select	select from sources: logical
track	show intermediate output (messages and plots): logical
parallel	logical (see cv.glmnet)

Details

* n: sample size * p: number of features * k: number of sources

Value

Returns an object of class 'transreg'. Rather than accessing its slots (see list below), it is recommended to use methods like [coef.transreg()] and [predict.transreg()].

* slot 'base': Object of class 'glmnet'. Regression of outcome on features (without prior effects), with 1 + p estimated coefficients (intercept + features).

* slot 'meta.sta': 'NULL' or object of class 'glmnet'. Regression of outcome on cross-validated linear predictors from prior effects and estimated effects, with 1 + k + 2 estimated coefficients (intercept + sources of co-data + lambda_min and lambda_1se).

* slot 'meta.sim': 'NULL' or object of class 'glmnet'. Regression of outcome on meta-features (cross-validated linear predictors from prior effects) and original features, with 1 + k + p estimated coefficients (intercept + sources of co-data + features).

* slot 'prior.calib': Calibrated prior effects. Matrix with p rows and k columns.

* slot 'data': Original data. List with slots 'y', 'X' and 'prior' (see arguments).

* slot 'info': Information on call. Data frame with entries n, p, k, 'family', 'alpha', 'scale' and 'stack' (see details and arguments).

References

Armin Rauschenberger, Zied Landoulsi, Mark A. van de Wiel, and Enrico Glaab (2023). "Penalised regression with multiple sets of prior effects". Bioinformatics 39(12):btad680. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1093/bioinformatics/btad680")}. (Click here to access PDF.)

See Also

Methods for objects of class [transreg] include coef and predict.

Examples

#--- simulation ---
n <- 100; p <- 500
X <- matrix(rnorm(n=n*p),nrow=n,ncol=p)
beta <- rnorm(p)*rbinom(n=p,size=1,prob=0.2)
prior1 <- beta + rnorm(p)
prior2 <- beta + rnorm(p)
y_lin <- X %*% beta
y_log <- 1*(y_lin > 0)

#--- single vs multiple priors ---
one <- transreg(y=y_lin,X=X,prior=prior1)
two <- transreg(y=y_lin,X=X,prior=cbind(prior1,prior2))
weights(one)
weights(two)


#--- linear vs logistic regression ---
lin <- transreg(y=y_lin,X=X,prior=prior1,family="gaussian")
log <- transreg(y=y_log,X=X,prior=prior1,family="binomial")
hist(predict(lin,newx=X)) # predicted values
hist(predict(log,newx=X)) # predicted probabilities

#--- ridge vs lasso penalisation ---
ridge <- transreg(y=y_lin,X=X,prior=prior1,alpha=0)
lasso <- transreg(y=y_lin,X=X,prior=prior1,alpha=1)
# initial coefficients (without prior)
plot(x=coef(ridge$base)[-1]) # dense
plot(x=coef(lasso$base)[-1]) # sparse
# final coefficients (with prior)
plot(x=coef(ridge)$beta) # dense
plot(x=coef(lasso)$beta) # not sparse

#--- exponential vs isotonic calibration ---
exp <- transreg(y=y_lin,X=X,prior=prior1,scale="exp")
iso <- transreg(y=y_lin,X=X,prior=prior1,scale="iso")
plot(x=prior1,y=exp$prior.calib)
plot(x=prior1,y=iso$prior.calib)

#--- standard vs simultaneous stacking ---
prior <- c(prior1[1:250],rep(0,250))
sta <- transreg(y=y_lin,X=X,prior=prior,stack="sta")
sim <- transreg(y=y_lin,X=X,prior=prior,stack="sim")
plot(x=coef(sta$base)[-1],y=coef(sta)$beta)
plot(x=coef(sim$base)[-1],y=coef(sim)$beta)

transreg documentation built on June 10, 2025, 5:14 p.m.