SDModels (version 1.0.10)

SDAM: Spectrally Deconfounded Additive Models

Description

Estimate high-dimensional additive models using spectral deconfounding scheidegger2023spectralSDModels. The covariates are expanded into B-spline basis functions. A spectral transformation is used to remove bias arising from hidden confounding and a group lasso objective is minimized to enforce component-wise sparsity. Optimal number of basis functions per component and sparsity penalty are chosen by cross validation.

Usage

SDAM(
  formula = NULL,
  data = NULL,
  x = NULL,
  y = NULL,
  Q_type = "trim",
  trim_quantile = 0.5,
  q_hat = 0,
  nfolds = 5,
  cv_method = "1se",
  n_K = 4,
  n_lambda1 = 10,
  n_lambda2 = 20,
  Q_scale = TRUE,
  ind_lin = NULL,
  mc.cores = 1,
  verbose = TRUE,
  notRegularized = NULL
)

Value

An object of class `SDAM` containing the following elements:

X

The original design matrix.

p

The number of covariates in `X`.

var_names

Names of the covariates in the training data.

intercept

The intercept term of the fitted model.

K

A vector of the number of basis functions for each covariate, where 1 corresponds to a linear term. The entries of the vector will mostly by the same, but some entries might be lower if the corresponding component of X contains only few unique values.

breaks

A list of breakpoints used for the B-splines. Used to reconstruct the B-spline basis functions.

coefs

A list of coefficients for the B-spline basis functions for each component.

active

A vector of active covariates that contribute to the model.

Arguments

formula

Object of class formula or describing the model to fit of the form y ~ x1 + x2 + ... where y is a numeric response and x1, x2, ... are vectors of covariates. Interactions are not supported.

data

Training data of class data.frame containing the variables in the model.

x

Matrix of covariates, alternative to formula and data.

y

Vector of responses, alternative to formula and data.

Q_type

Type of deconfounding, one of 'trim', 'pca', 'no_deconfounding'. 'trim' corresponds to the Trim transform Cevid2020SpectralModelsSDModels as implemented in the Doubly debiased lasso Guo2022DoublyConfoundingSDModels, 'pca' to the PCA transformationPaul2008PreconditioningProblemsSDModels. See get_Q.

trim_quantile

Quantile for Trim transform, only needed for trim, see get_Q.

q_hat

Assumed confounding dimension, only needed for pca, see get_Q.

nfolds

The number of folds for cross-validation. Default is 5.

cv_method

The method for selecting the regularization parameter during cross-validation. One of "min" (minimum cv-loss) and "1se" (one-standard-error rule) Default is "1se".

n_K

The number of candidate values for the number of basis functions for B-splines. Default is 4.

n_lambda1

The number of candidate values for the regularization parameter in the initial cross-validation step. Default is 10.

n_lambda2

The number of candidate values for the regularization parameter in the second stage of cross-validation (once the optimal number of basis function K is decided, a second stage of cross-validation for the regularization parameter is performed on a finer grid). Default is 20.

Q_scale

Should data be scaled to estimate the spectral transformation? Default is TRUE to not reduce the signal of high variance covariates.

ind_lin

A vector of indices specifying which covariates to model linearly (i.e. not expanded into basis function). Default is `NULL`.

mc.cores

Number of cores to use for parallel processing, if mc.cores > 1 the cross validation is parallelized. Default is `1`. (only supported for unix)

verbose

If TRUE fitting information is shown.

notRegularized

A vector of indices specifying which covariates not to regularize. Default is `NULL`.

Author

Cyrill Scheidegger

References

See Also

get_Q, predict.SDAM, varImp.SDAM, predict_individual_fj, partDependence

Examples

set.seed(1)
X <- matrix(rnorm(10 * 5), ncol = 5)
Y <- sin(X[, 1]) -  X[, 2] + rnorm(10)
model <- SDAM(x = X, y = Y, Q_type = "trim", trim_quantile = 0.5, nfold = 2, n_K = 1)

# if we know that the first covariate one is relevant, we can also choose to not regularize it
model <- SDAM(x = X, y = Y, Q_type = "trim", trim_quantile = 0.5, nfold = 2, 
              n_K = 1, notRegularized = c(1))

# \donttest{
library(HDclassif)
data(wine)
names(wine) <- c("class", "alcohol", "malicAcid", "ash", "alcalinityAsh", "magnesium", 
                 "totPhenols", "flavanoids", "nonFlavPhenols", "proanthocyanins", 
                 "colIntens", "hue", "OD", "proline")
wine <- log(wine)

# estimate model
# do not use class in the model and restrict proline to be linear 
model <- SDAM(alcohol ~ ., wine, ind_lin = "proline", nfold = 3)

# extract variable importance
varImp(model)

# most important variable
mostImp <- names(which.max(varImp(model)))
mostImp

# predict for individual Xj
x <- seq(min(wine[, mostImp]), max(wine[, mostImp]), length.out = 100)
predJ <- predict_individual_fj(object = model, j = mostImp, x = x)

plot(x, predJ, 
     xlab = paste0("log ", mostImp), ylab = "log alcohol")

# partial dependece
plot(partDependence(model, mostImp))

# predict 
predict(model, newdata = wine[42, ])

## alternative function call
mod_none <- SDAM(x = as.matrix(wine[1:10, -c(1, 2)]), y = wine$alcohol[1:10], 
                 Q_type = "no_deconfounding", nfolds = 2, n_K = 4, 
                 n_lambda1 = 4, n_lambda2 = 8)
# }