Fit lasso penalized linear regression path for a Filebacked Big Matrix. Covariates can be added to correct for confounders.
big_spLinReg(X, y.train, ind.train = rows_along(X),
ind.col = cols_along(X), covar.train = NULL, ncores = 1, ...)A FBM.
Vector of responses, corresponding to ind.train.
An optional vector of the row indices that are used, for the training part. If not specified, all rows are used. Don't use negative indices.
An optional vector of the column indices that are used. If not specified, all columns are used. Don't use negative indices.
Matrix of covariables to be added in each model to correct
for confounders (e.g. the scores of PCA), corresponding to ind.train.
Default is NULL and corresponds to only adding an intercept to each model.
Number of cores used. Default doesn't use parallelism. You may use nb_cores.
Arguments passed on to COPY_biglasso_main
The elastic-net mixing parameter that controls the relative
contribution from the lasso (l1) and the ridge (l2) penalty. The penalty is
defined as $$ \alpha||\beta||_1 + (1-\alpha)/2||\beta||_2^2.$$
alpha = 1 is the lasso penalty and alpha in between 0
(1e-4) and 1 is the elastic-net penalty. Default is 1. You can
pass multiple values, and only one will be used (optimized by grid-search).
The smallest value for lambda, as a fraction of
lambda.max. Default is .0001 if the number of observations is larger than
the number of variables and .001 otherwise.
The number of lambda values. Default is 200.
Convergence threshold for inner coordinate descent.
The algorithm iterates until the maximum change in the objective after any
coefficient update is less than eps times the null deviance.
Default value is 1e-5.
Maximum number of iterations. Default is 1000.
Upper bound for the number of nonzero coefficients. Default is
50e3 because, for large data sets, computational burden may be
heavy for models with a large number of nonzero coefficients.
Return warning messages for failures to converge and model
saturation? Default is FALSE.
Number of sets used in the Cross-Model Selection and Averaging
(CMSA) procedure. Default is 10.
Integer vectors of values between 1 and K specifying
which set each index of the training set is in. Default randomly assigns
these values.
Whether to return coefficients for all alpha and lambda
values. Default is FALSE and returns only coefficients which maximize
prediction on the validation sets.
Minimum number of lambda values to investigate. Default is 50.
Number of lambda values for which prediction on the validation
set must decrease before stopping. Default is 10.
Return an object of class big_sp_best_list (a list of K elements),
which has a method predict that can compute K vectors of predictions,
which could be combined with e.g. rowMeans. See details.
This is a modified version of one function of
package biglasso.
It adds the possibility to train models with covariables and use many
types of FBM (not only double ones).
Yet, it only corresponds to screen = "SSR" (Sequential Strong Rules).
Also, to remove the choice of the lambda parameter, we introduce the Cross-Model Selection and Averaging (CMSA) procedure:
This function separates the training set in K folds (e.g. 10).
In turn,
each fold is considered as an inner validation set and the others (K - 1) folds form an inner training set,
the model is trained on the inner training set and the corresponding predictions (scores) for the inner validation set are computed,
the vector of scores which maximizes log-likelihood is determined,
the vector of coefficients corresponding to the previous vector of scores is chosen.
The K resulting vectors of coefficients can then be combined into one
vector (see get_beta) or you can just combine the predictions
(e.g. using predict followed by rowMeans).
Tibshirani, R., Bien, J., Friedman, J., Hastie, T., Simon, N., Taylor, J. and Tibshirani, R. J. (2012), Strong rules for discarding predictors in lasso-type problems. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 74: 245<U+2013>266. https://doi.org/10.1111/j.1467-9868.2011.01004.x.
Zeng, Y., and Breheny, P. (2016). The biglasso Package: A Memory- and Computation-Efficient Solver for Lasso Model Fitting with Big Data in R. arXiv preprint arXiv:1701.05936. https://arxiv.org/abs/1701.05936.
# NOT RUN {
set.seed(1)
# simulating some data
N <- 230
M <- 730
X <- FBM(N, M, init = rnorm(N * M, sd = 5))
y <- rowSums(X[, 1:5]) + rnorm(N)
covar <- matrix(rnorm(N * 3), N)
ind.train <- sort(sample(nrow(X), 150))
ind.test <- setdiff(rows_along(X), ind.train)
test <- big_spLinReg(X, y[ind.train], ind.train = ind.train,
covar.train = covar[ind.train, ])
preds <- predict(test, X, ind.row = ind.test, covar.row = covar[ind.test, ])
plot(preds, y[ind.test], pch = 20); abline(0, 1, col = "red")
# }
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