Description Usage Arguments Details Value Families Regularization sequences References See Also Examples
Fit a generalized linear model regularized with the SLOPE (Sorted LOne Penalized Estimation) norm, which applies a decreasing λ (penalty sequence) to the coefficient vector (β) after having sorted it in decreasing order according to its absolute values.
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  owl(
x,
y,
family = c("gaussian", "binomial", "multinomial", "poisson"),
intercept = TRUE,
standardize_features,
center = !inherits(x, "sparseMatrix"),
scale = c("l2", "l1", "sd", "none"),
sigma = NULL,
lambda = c("gaussian", "bh", "oscar"),
lambda_min_ratio = if (n < p) 0.01 else 1e04,
n_sigma = 100,
q = 0.1 * min(1, n/p),
screening = TRUE,
tol_dev_change = 1e05,
tol_dev_ratio = 0.995,
tol_abs = 1e05,
tol_rel = 1e04,
max_variables = n * m,
max_passes = 1e+06,
tol_rel_gap = 1e05,
tol_infeas = 0.001,
diagnostics = FALSE,
verbosity = 0
)

x 
the feature matrix, which can be either a dense matrix of the standard matrix class, or a sparse matrix inheriting from Matrix::sparseMatrix Data frames will be converted to matrices internally. 
y 
the response. For Gaussian models this must be numeric; for binomial models, it can be a factor. 
family 
response type. See Families for details. 
intercept 
whether to fit an intercept 
standardize_features 
(deprecated) 
center 
whether to center predictors or not by their mean. Defaults to true if dense matrix, false otherwise. 
scale 
type of scaling to apply to predictors, 
sigma 
scale of lambda sequence 
lambda 
either a character vector indicating the method used to construct the lambda path or a vector with length equal to the number of coefficients in the model 
lambda_min_ratio 
smallest value for 
n_sigma 
length of regularization path 
q 
shape of lambda sequence 
screening 
whether the strong rule for SLOPE be used to screen variables for inclusion 
tol_dev_change 
the regularization path is stopped if the fractional change in deviance falls below this value. Note that this is automatically set to 0 if a sigma is manually entered 
tol_dev_ratio 
the regularization path is stopped if the deviance ratio 1  deviance/(null deviance) is above this threshold 
tol_abs 
absolute tolerance criterion for ADMM solver (used for Gaussian dense designs) 
tol_rel 
relative tolerance criterion for ADMM solver (used for Gaussian dense designs) 
max_variables 
criterion for stopping the path in terms of the maximum number of unique, nonzero coefficients in absolute value in model 
max_passes 
maximum number of passes for optimizer 
tol_rel_gap 
stopping criterion for the duality gap 
tol_infeas 
stopping criterion for the level of infeasibility 
diagnostics 
should diagnostics be saved for the model fit (timings, primal and dual objectives, and infeasibility) 
verbosity 
level of verbosity for displaying output from the program. Setting this to 1 displays basic information on the path level, 2 a little bit more information on the path level, and 3 displays information from the solver. 
owl()
tries to minimize the following composite objective, given
in Lagrangian form.
f(β) + σ ∑ λ β(j),
where f(β) is a smooth, convex function of β, whereas
the second part is the SLOPE norm, which is convex but nonsmooth.
In ordinary leastsquares regression, for instance,
f(β) is simply the squared norm of the leastsquares residuals.
See section Families for specifics regarding the various types of
f(β) (model families) that are allowed in owl()
.
By default, owl()
fits a path of lambda
sequences, starting from
the null (interceptonly) model to an almost completely unregularized
model. The path will end prematurely, however, if the criteria
related to any of the
arguments tol_dev_change
, tol_dev_ratio
, or max_variables
are reached before the path is complete. This means that unless these
arguments are modified, the path is not guaranteed to be of
length n_sigma
.
An object of class "Owl"
with the following slots:
coefficients 
a threedimensional array of the coefficients from the model fit, including the intercept if it was fit. There is one row for each coefficient, one column for each target (dependent variable), and one slice for each penalty. 
nonzeros 
a threedimensional boolean array indicating whether a coefficient was zero or not 
lambda 
the lambda vector that when multiplied by a value in 
sigma 
the vector of sigma, indicating the scale of the lambda vector 
class_names 
a character vector giving the names of the classes for binomial and multinomial families 
passes 
the number of passes the solver took at each path 
violations 
the number of violations of the screening rule 
active_sets 
a list where each element indicates the indices of the coefficients that were active at that point in the regularization path 
unique 
the number of unique predictors (in absolute value) 
deviance_ratio 
the deviance ratio (as a fraction of 1) 
null_deviance 
the deviance of the null (interceptonly) model 
family 
the name of the family used in the model fit 
diagnostics 
a 
call 
the call used for fitting the model 
Gaussian
The Gaussian model (Ordinary Least Squares) minimizes the following objective.
y  Xβ_2^2
Binomial
The binomial model (logistic regression) has the following objective.
∑ log(1+ exp( y_i x_i^T β))
with y in {1, 1}.
Poisson
In poisson regression, we use the following objective.
∑ (y_i(x_i^Tβ + α)  exp(x_i^Tβ + α))
Multinomial
In multinomial regression, we minimize the fullrank objective
∑(y_ik(x_i^Tβ_k + α_k)  log(∑ exp(x_i^Tβ_k + α_k)))
with y_{ik} being the element in a n by (m1) matrix, where m is the number of classes in the response.
There are multiple ways of specifying the lambda
sequence
in owl()
. It is, first of all, possible to select the sequence manually by
using a nonincreasing
numeric vector as argument instead of a character.
If all lambda
are the same value, this will
lead to the ordinary lasso penalty. The greater the differences are between
consecutive values along the sequence, the more clustering behavior
will the model exhibit. Note, also, that the scale of the λ
vector makes no difference if sigma = NULL
, since sigma
will be
selected automatically to ensure that the model is completely sparse at the
beginning and almost unregularized at the end. If, however, both
sigma
and lambda
are manually specified, both of the scales will
matter.
Instead of choosing the sequence manually, one of the following automatically generated sequences may be chosen.
BH (Benjamini–Hochberg)
If lambda = "bh"
, the sequence used is that referred to
as λ^(BH) by Bogdan et al, which sets
λ according to
λ_i = Φ^1(1  iq/(2p)),
where Φ^1 is the quantile function for the standard
normal distribution and q is a parameter that can be
set by the user in the call to owl()
.
Gaussian
This penalty sequence is related to BH, such that
λ_i = λ^(BH)_i √{1 + w(i1) * cumsum(λ^2)_i},
where w(k) = 1/(nk1). We let λ_1 = λ^(BH)_1 and adjust the sequence to make sure that it's nonincreasing. Note that if p is large relative to n, this option will result in a constant sequence, which is usually not what you would want.
OSCAR
This sequence comes from Bondell and Reich and is a linearly nonincreasing sequence such that
λ_i = q(p  i) + 1.
Bogdan, M., van den Berg, E., Sabatti, C., Su, W., & Candès, E. J. (2015). SLOPE – adaptive variable selection via convex optimization. The Annals of Applied Statistics, 9(3), 1103–1140. https://doi.org/10/gfgwzt
Bondell, H. D., & Reich, B. J. (2008). Simultaneous Regression Shrinkage, Variable Selection, and Supervised Clustering of Predictors with OSCAR. Biometrics, 64(1), 115–123. JSTOR. https://doi.org/10.1111/j.15410420.2007.00843.x
plot.Owl()
, plotDiagnostics()
, score()
, predict.Owl()
,
trainOwl()
, coef.Owl()
, print.Owl()
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  # Gaussian response, default lambda sequence
fit < owl(bodyfat$x, bodyfat$y)
# Binomial response, BHtype lambda sequence
fit < owl(heart$x, heart$y, family = "binomial", lambda = "bh")
# Poisson response, OSCARtype lambda sequence
fit < owl(abalone$x,
abalone$y,
family = "poisson",
lambda = "oscar",
q = 0.4)
# Multinomial response, custom sigma and lambda
m < length(unique(wine$y))  1
p < ncol(wine$x)
sigma < 0.005
lambda < exp(seq(log(2), log(1.8), length.out = p*m))
fit < owl(wine$x,
wine$y,
family = "multinomial",
lambda = lambda,
sigma = sigma)

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