wlasso {svyVarSel} | R Documentation |
This function allows as to fit LASSO prediction (linear or logistic) models to complex survey data, considering sampling weights in the estimation process and selects the lambda that minimizes the error based on different replicating weights methods.
wlasso(
data = NULL,
col.y = NULL,
col.x = NULL,
cluster = NULL,
strata = NULL,
weights = NULL,
design = NULL,
family = c("gaussian", "binomial"),
lambda.grid = NULL,
method = c("dCV", "JKn", "bootstrap", "subbootstrap", "BRR", "split", "extrapolation"),
k = 10,
R = 1,
B = 200,
dCV.sw.test = FALSE,
train.prob = 0.7,
method.split = c("dCV", "bootstrap", "subbootstrap"),
print.rw = FALSE
)
data |
A data frame with information about the response variable and covariates, as well as sampling weights and strata and cluster indicators. It could be |
col.y |
A numeric value indicating the number of the column in which information on the response variable can be found or a character string indicating the name of that column. |
col.x |
A numeric vector indicating the numbers of the columns in which information on the covariates can be found or a vector of character strings indicating the names of these columns. |
cluster |
A character string indicating the name of the column with cluster identifiers. It could be |
strata |
A character string indicating the name of the column with strata identifiers. It could be |
weights |
A character string indicating the name of the column with sampling weights. It could be |
design |
An object of class |
family |
A character string indicating the family to fit LASSO models. Choose between |
lambda.grid |
A numeric vector indicating a grid for penalization parameters. The default option is |
method |
A character string indicating the method to be applied to define replicate weights. Choose between one of these: |
k |
A numeric value indicating the number of folds to be defined. Default is |
R |
A numeric value indicating the number of times the sample is partitioned. Default is |
B |
A numeric value indicating the number of bootstrap resamples. Default is |
dCV.sw.test |
A logical value indicating the method for estimating the error for |
train.prob |
A numeric value between 0 and 1, indicating the proportion of clusters (for the method |
method.split |
A character string indicating the way in which replicate weights should be defined in the |
print.rw |
A logical value. If |
The output object of the function wlasso()
is an object of class wlasso
. This object is a list containing 4 or 5 elements, depending on the value set to the argument print.rw
. Below we describe the contents of these elements:
lambda
: A list containing information of two elements:
grid
: A numeric vector indicating all the values considered for the tuning parameter.
min
: A numeric value indicating the value of the tuning parameter that minimizes the average error (i.e., selected optimal tuning parameter).
error
: A list containing information of two elements:
average
: A numeric vector indicating the average error corresponding to each tuning parameter.
all
: A numeric matrix indicating the error of each test set for each tuning parameter.
model
: A list containing information of two elements in relation to the fitted models. Note that all these models are fitted considering the whole data set (and not uniquely the training sets).
grid
: A list with the information about the models fitted for each of the tuning parameters considered (i.e., all the values in the lambda$grid
object):
a0
: a numeric vector of model intercepts across the whole grid of tuning parameters (hence, of the same length as lambda$grid
).
beta
: a matrix of regression coefficients corresponding to all the considered covariates across the whole grid of tuning parameters (the number of rows is equal to the number of covariates considered and the number of columns to the length of lambda$grid
).
df
: a numeric vector of the degrees of freedom (i.e., the number of coefficients different from zero) across the whole grid of tuning parameters (hence, of the same length as lambda$grid
).
min
: A list with the information about the model fitted considering uniquely the tuning parameter that minimizes the error in the training models (i.e., the optimal tuning parameter selected between the elements in lambda$grid
):
a0
: a numeric value indicating the intercept value of the selected model.
beta
: a matrix of regression coefficients corresponding to all the considered covariates for the selected tuning parameters (the number of rows is equal to the number of covariates considered and the number of columns is one).
df
: a numeric value indicating the degrees of freedom (i.e., the number of coefficients different from zero) of the selected model.
data.rw
: A data frame containing the original data set and the replicate weights added to define training and test sets. Only included in the output object if print.rw=TRUE
.
call
: an object containing the information about the way in which the function has been run.
data(simdata_lasso_binomial)
mcv <- wlasso(data = simdata_lasso_binomial,
col.y = "y", col.x = 1:50,
family = "binomial",
cluster = "cluster", strata = "strata", weights = "weights",
method = "dCV", k=10, R=1)
# Or equivalently:
mydesign <- survey::svydesign(ids=~cluster, strata = ~strata, weights = ~weights,
nest = TRUE, data = simdata_lasso_binomial)
mcv <- wlasso(col.y = "y", col.x = 1:50, design = mydesign,
family = "binomial",
method = "dCV", k=10, R=1)