Description Usage Arguments Details Value References See Also Examples
Fit lasso (or elasticnet) penalized logistic regression for a Filebacked Big Matrix. Covariables can be added (/!\ penalized by default /!\).
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22  big_spLogReg(
X,
y01.train,
ind.train = rows_along(X),
ind.col = cols_along(X),
covar.train = NULL,
base.train = NULL,
pf.X = NULL,
pf.covar = NULL,
alphas = 1,
power_scale = 1,
power_adaptive = 0,
K = 10,
ind.sets = NULL,
nlambda = 200,
nlam.min = 50,
n.abort = 10,
dfmax = 50000,
warn = TRUE,
ncores = 1,
...
)

X 
An object of class FBM. 
y01.train 
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. 
ind.col 
An optional vector of the column indices that are used. If not specified, all columns are used. Don't use negative indices. 
covar.train 
Matrix of covariables to be added in each model to correct
for confounders (e.g. the scores of PCA), corresponding to 
base.train 
Vector of base predictions. Model will be learned starting from these predictions. This can be useful if you want to previously fit a model with largeeffect variables that you don't want to penalize. 
pf.X 
A multiplicative factor for the penalty applied to each coefficient.
If supplied, 
pf.covar 
Same as 
alphas 
The elasticnet mixing parameter that controls the relative contribution from the lasso (l1) and the ridge (l2) penalty. The penalty is defined as αβ_1 + (1α)/2β_2^2.

power_scale 
When using lasso (alpha = 1), penalization to apply that
is equivalent to scaling genotypes dividing by (standard deviation)^power_scale.
Default is 1 and corresponding to standard scaling. Using 0 would correspond
to using unscaled variables and using 0.5 is Pareto scaling. If you e.g. use

power_adaptive 
Multiplicative penalty factor to apply to variables
in the form of 1 / m_j^power_adaptive, where m_j is the marginal statistic
for variable j. Default is 0, which effectively disables this option.
If you e.g. use 
K 
Number of sets used in the CrossModel Selection and Averaging
(CMSA) procedure. Default is 
ind.sets 
Integer vectors of values between 
nlambda 
The number of lambda values. Default is 
nlam.min 
Minimum number of lambda values to investigate. Default is 
n.abort 
Number of lambda values for which prediction on the validation
set must decrease before stopping. Default is 
dfmax 
Upper bound for the number of nonzero coefficients. Default is

warn 
Whether to warn if some models may not have reached a minimum.
Default is 
ncores 
Number of cores used. Default doesn't use parallelism. You may use nb_cores. 
... 
Arguments passed on to

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 CrossModel 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 loglikelihood is determined,
the vector of coefficients corresponding to the previous vector of scores is chosen.
The K
resulting vectors of coefficients are then averaged into one final
vector of coefficients.
Return an object of class big_sp_list
(a list of length(alphas)
x K
) that has 3 methods predict
, summary
and plot
.
Tibshirani, R., Bien, J., Friedman, J., Hastie, T., Simon, N., Taylor, J. and Tibshirani, R. J. (2012), Strong rules for discarding predictors in lassotype problems. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 74: 245266. doi: 10.1111/j.14679868.2011.01004.x.
Zeng, Y., and Breheny, P. (2017). The biglasso Package: A Memory and ComputationEfficient Solver for Lasso Model Fitting with Big Data in R. doi: 10.32614/RJ2021001.
Privé, F., Aschard, H., and Blum, M. G.B. (2019). Efficient implementation of penalized regression for genetic risk prediction. Genetics, 212: 6574. doi: 10.1534/genetics.119.302019.
glmnet
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32  set.seed(2)
# simulating some data
N < 230
M < 730
X < FBM(N, M, init = rnorm(N * M, sd = 5))
y01 < as.numeric((rowSums(X[, 1:10]) + 2 * rnorm(N)) > 0)
covar < matrix(rnorm(N * 3), N)
ind.train < sort(sample(nrow(X), 150))
ind.test < setdiff(rows_along(X), ind.train)
# fitting model for multiple lambdas and alphas
test < big_spLogReg(X, y01[ind.train], ind.train = ind.train,
covar.train = covar[ind.train, ],
alphas = c(1, 0.1), K = 3, warn = FALSE)
# peek at the models
plot(test)
summary(test, sort = TRUE)
summary(test, sort = TRUE)$message
# prediction for other data > only the best alpha is used
summary(test, best.only = TRUE)
pred < predict(test, X, ind.row = ind.test, covar.row = covar[ind.test, ])
AUC(pred, y01[ind.test])
library(ggplot2)
qplot(pred, fill = as.logical(y01[ind.test]),
geom = "density", alpha = I(0.4)) +
labs(fill = "Case?") +
theme_bigstatsr() +
theme(legend.position = c(0.52, 0.8))

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