README.md
In mvs: Methods for High-Dimensional Multi-View Learning

R-package `mvs`

Methods for high-dimensional multi-view learning based on the multi-view stacking (MVS) framework. Data have a multi-view structure when features comprise different ‘views’ of the same observations. For example, the different views may comprise omics, imaging or electronic health records. Package mvs provides functions to fit stacked penalized logistic regression (StaPLR) models, which are a special case of multi-view stacking (MVS). Additionally, mvs generalizes the StaPLR model to settings with a Gaussian or Poisson outcome distribution, and to hierarchical multi-view structures with more than two levels. For more information about the StaPLR and MVS methods, see Van Loon, Fokkema, Szabo, & De Rooij (2020) and Van Loon et al. (2022).

The current stable release can be installed directly from CRAN:

utils::install.packages("mvs")

The current development version can be installed from GitLab using package devtools:

devtools::install_gitlab("wsvanloon/mvs@develop")

The two main functions are StaPLR() (alias staplr), which fits penalized and stacked penalized regression models models with up to two levels, and MVS() (alias mvs), which fits multi-view stacking models with >= 2 levels. Objects returned by either function have associated coef and predict methods.

Example: `StaPLR`

library("mvs")

Generate 1000 observations with four two-feature views with varying within- and between-view correlation:

set.seed(012)
n <- 1000
cors <- seq(0.1, 0.7, 0.1)
X <- matrix(NA, nrow=n, ncol=length(cors)+1)
X[ , 1] <- rnorm(n)
for (i in 1:length(cors)) {
  X[ , i+1] <- X[ , 1]*cors[i] + rnorm(n, 0, sqrt(1-cors[i]^2))
}
beta <- c(1, 0, 0, 0, 0, 0, 0, 0)
eta <- X %*% beta
p <- exp(eta)/(1+exp(eta))
y <- rbinom(n, 1, p)

Fit StaPLR:

view_index <- rep(1:(ncol(X)/2), each=2)
set.seed(012)
fit <- StaPLR(X, y, view_index)

Extract coefficients at the view level:

coefs <- coef(fit)
coefs$meta

## 5 x 1 sparse Matrix of class "dgCMatrix"
##                    s1
## (Intercept) -2.345398
## V1           4.693861
## V2           .       
## V3           .       
## V4           .

We see that the only the first view has been selected. The data was generated so that only the first feature (from the first view) was a true predictor, but it was also substantially correlated with features from other views (see cor(X)), most strongly with the features from the fourth view.

Extract coefficients at the base level:

coefs$base

## [[1]]
## 3 x 1 sparse Matrix of class "dgCMatrix"
##                      s1
## (Intercept) -0.05351035
## V1           0.86273113
## V2           0.09756006
## 
## [[2]]
## 3 x 1 sparse Matrix of class "dgCMatrix"
##                        s1
## (Intercept) -6.402186e-02
## V1           1.114585e-38
## V2           1.156060e-38
## 
## [[3]]
## 3 x 1 sparse Matrix of class "dgCMatrix"
##                      s1
## (Intercept) -0.06875322
## V1           0.26176566
## V2           0.35602028
## 
## [[4]]
## 3 x 1 sparse Matrix of class "dgCMatrix"
##                      s1
## (Intercept) -0.03101978
## V1           0.27605205
## V2           0.39234018

We see that the first feature has the strongest effect on the predicted outcome, with a base-level regression coefficient of 0.86. The features in views two, three and four all have zero effect, since the meta-level coefficients for these views are zero.

Compute predictions:

new_X <- matrix(rnorm(16), nrow=2)
predict(fit, new_X)

##      lambda.min
## [1,]  0.8698197
## [2,]  0.1819153

By default, the predictions are made using the values of the penalty parameters which minimize the cross-validation error (lambda.min).

As StaPLR was developed in the context of binary classification problems, the default outcome distribution is family = "binomial". Other outcome distributions (e.g., Gaussian, Poisson) can be modeled by specifying, e.g., family = "gaussian" or family = "poisson".
A generalization of stacked penalized (logistic) regression to three or more hierarchical levels is implemented in function MVS (alias mvs).
Model relaxation (as used in, e.g., the relaxed lasso) can be applied using argument relax, which can be either either a logical vector of length levels specifying whether model relaxation should be employed at each level, or a single TRUE or FALSE to enable or disable relaxing across all levels.
Adaptive weights (as used in, e.g., the adaptive lasso) can be applied using argument adaptive, which is either a logical vector of length levels specifying whether adaptive weights should be employed at each level, or a single TRUE or FALSE to enable or disable adaptive weights across all levels. Note that using adaptive weights is generally only sensible if alpha > 0 (i.e., if there is at least some amount of L1 regularization). Adaptive weights are initialized using ridge regression as described in Van Loon, Fokkema, Szabo, & De Rooij (2024).

In a two-level StaPLR model, the meta-level regression coefficient of each view can be used as a measure of that view’s importance. Since, by default, the view specific predictions are all between 0 and 1, these regression coefficients are effectively on the same scale. However, in hierarchical StaPLR/MVS models with more than two levels, it may be hard to deduce view importance based purely on regression coefficients since these coefficients may correspond to different sub-models at different levels of the hierarchy. For hierarchical StaPLR/MVS models the minority report measure (MRM) (Van Loon et al. (2022)) can be calculated using MRM() (alias mrm). The MRM quantifies how much the prediction of the complete stacked model changes as the view-specific prediction of view i changes from a (default value 0) to b (default value 1), while the other predictions are kept constant (the recommended value for this constant being the mean of the outcome variable). For technical details see Van Loon et al. (2022).

In practice, it is likely that not all views were measured for all observations. Broadly, there are three ways for dealing with this situation:

Remove any observations with missing data.
Impute missing values at the base (feature) level.
Impute missing values at the meta (cross-validated prediction) level.

The first approach is wasteful, and the second one may be very computationally intensive if there are many features. Assuming the missing views are missing completely at random, we recommend to impute missing values at the meta level (Van Loon, Fokkema, De Vos, et al. (2024)). This is implemented in mvs through the na.action argument. The following options are available:

fail causes mvs to stop whenever it detects missing values (the default).
pass ‘propagates’ the missing values through to the prediction level, but does not perform imputation.
mean performs meta-level (unconditional) mean imputation.
mice performs meta-level predictive mean matching. It requires the R package mice to be installed.
missForestperforms meta-level missForest imputation. It requires the R package missForest to be installed.

For more information about meta-level imputation see Van Loon, Fokkema, De Vos, et al. (2024).

Van Loon, W., De Vos, F., Fokkema, M., Szabo, B., Koini, M., Schmidt, R., & De Rooij, M. (2022). Analyzing hierarchical multi-view MRI data with StaPLR: An application to Alzheimer’s disease classification. Frontiers in Neuroscience, 16, 830630. https://doi.org/10.3389/fnins.2022.830630

Van Loon, W., Fokkema, M., De Vos, F., Koini, M., Schmidt, R., & De Rooij, M. (2024). Imputation of missing values in multi-view data. Information Fusion, 111, 102524. https://doi.org/10.1016/j.inffus.2024.102524

Van Loon, W., Fokkema, M., Szabo, B., & De Rooij, M. (2020). Stacked penalized logistic regression for selecting views in multi-view learning. Information Fusion, 61, 113–123. https://doi.org/10.1016/j.inffus.2020.03.007

Van Loon, W., Fokkema, M., Szabo, B., & De Rooij, M. (2024). View selection in multi-view stacking: Choosing the meta-learner. Advances in Data Analysis and Classification. https://doi.org/10.1007/s11634-024-00587-5