| Title: | The Lasso for Tweedie's Compound Poisson Model Using an IRLS-BMD Algorithm |
|---|---|
| Description: | The Tweedie lasso model implements an iteratively reweighed least square (IRLS) strategy that incorporates a blockwise majorization decent (BMD) method, for efficiently computing solution paths of the (grouped) lasso and the (grouped) elastic net methods. |
| Authors: | Wei Qian <[email protected]>, Yi Yang <[email protected]>, Hui Zou <[email protected]> |
| Maintainer: | Wei Qian <[email protected]> |
| License: | GPL-2 |
| Version: | 1.2 |
| Built: | 2024-12-19 06:35:29 UTC |
| Source: | CRAN |
The motor insurance dataset is originially retrieved from the SAS Enterprise Miner database. The included dataset is generated by re-organization and transformation as described in Qian et al. (2016).
data(auto)data(auto)
This data set contains 2812 policy samples with 56 predictors. See Qian et al. (2016) for a detailed description of the generation of these predictors. The response is the aggregate claim loss (in thousand dollars). The predictors are expanded from the following original variables:
CAR_TYPE:car type, 6 categories
JOBCLASS:job class, 8 categories
MAX_EDUC:education level, 5 categories
KIDSDRIV:number of children passengers
TRAVTIME:time to travel from home to work
BLUEBOOK:car value
NPOLICY:number of policies
MVR_PTS:motor vehicle record point
AGE:driver age
HOMEKIDS:number of children at home
YOJ:years on job
INCOME:income
HOME_VAL:home value
SAMEHOME:years in current address
CAR_USE:whether the car is for commercial use
RED_CAR:whether the car color is red
REVOLKED:whether the driver's license was revoked in the past
GENDER:gender
MARRIED:whether married
PARENT1:whether a single parent
AREA:whether the driver lives in urban area
A list with the following elements:
x |
a [2812 x 56] matrix giving 2812 policy records with 56 predictors |
y |
the aggregate claim loss |
Yip, K. C. H. and Yau, K. K. W. (2005), “On Modeling Claim Frequency Data In General Insurance With Extra Zeros”, Insurance: Mathematics and Economics, 36, 153-163.
Zhang, Y (2013). “cplm: Compound Poisson Linear Models”. A vignette for R package cplm. Available from https://CRAN.R-project.org/package=cplm
Qian, W., Yang, Y., Yang, Y. and Zou, H. (2016), “Tweedie's Compound Poisson Model With Grouped Elastic Net,” Journal of Computational and Graphical Statistics, 25, 606-625.
# load HDtweedie library library(HDtweedie) # load data set data(auto) # how many samples and how many predictors ? dim(auto$x) # repsonse y auto$y# load HDtweedie library library(HDtweedie) # load data set data(auto) # how many samples and how many predictors ? dim(auto$x) # repsonse y auto$y
This function gets coefficients or makes coefficient predictions from a cross-validated HDtweedie model,
using the "cv.HDtweedie" object, and the optimal value
chosen for lambda.
## S3 method for class 'cv.HDtweedie' coef(object,s=c("lambda.1se","lambda.min"),...)## S3 method for class 'cv.HDtweedie' coef(object,s=c("lambda.1se","lambda.min"),...)
object |
fitted |
s |
value(s) of the penalty parameter |
... |
not used. Other arguments to predict. |
This function makes it easier to use the results of cross-validation to get coefficients or make coefficient predictions.
The coefficients at the requested values for lambda.
Wei Qian, Yi Yang and Hui Zou
Maintainer: Wei Qian <[email protected]>
Qian, W., Yang, Y., Yang, Y. and Zou, H. (2016), “Tweedie's Compound
Poisson Model With Grouped Elastic Net,” Journal of Computational and Graphical Statistics, 25, 606-625.
Friedman, J., Hastie, T., and Tibshirani, R. (2010), "Regularization paths for generalized
linear models via coordinate descent," Journal of Statistical Software, 33, 1.
cv.HDtweedie, and predict.cv.HDtweedie methods.
# load HDtweedie library library(HDtweedie) # load data set data(auto) # 5-fold cross validation using the lasso cv0 <- cv.HDtweedie(x=auto$x,y=auto$y,p=1.5,nfolds=5) # the coefficients at lambda = lambda.1se coef(cv0) # define group index group1 <- c(rep(1,5),rep(2,7),rep(3,4),rep(4:14,each=3),15:21) # 5-fold cross validation using the grouped lasso cv1 <- cv.HDtweedie(x=auto$x,y=auto$y,group=group1,p=1.5,nfolds=5) # the coefficients at lambda = lambda.min coef(cv1, s = cv1$lambda.min)# load HDtweedie library library(HDtweedie) # load data set data(auto) # 5-fold cross validation using the lasso cv0 <- cv.HDtweedie(x=auto$x,y=auto$y,p=1.5,nfolds=5) # the coefficients at lambda = lambda.1se coef(cv0) # define group index group1 <- c(rep(1,5),rep(2,7),rep(3,4),rep(4:14,each=3),15:21) # 5-fold cross validation using the grouped lasso cv1 <- cv.HDtweedie(x=auto$x,y=auto$y,group=group1,p=1.5,nfolds=5) # the coefficients at lambda = lambda.min coef(cv1, s = cv1$lambda.min)
Computes the coefficients at the requested values for lambda from a fitted HDtweedie object.
## S3 method for class 'HDtweedie' coef(object, s = NULL, ...)## S3 method for class 'HDtweedie' coef(object, s = NULL, ...)
object |
fitted |
s |
value(s) of the penalty parameter |
... |
not used. Other arguments to predict. |
s is the new vector at which predictions are requested. If s is not in the lambda sequence used for fitting the model, the coef function will use linear interpolation to make predictions. The new values are interpolated using a fraction of coefficients from both left and right lambda indices.
The coefficients at the requested values for lambda.
Wei Qian, Yi Yang and Hui Zou
Maintainer: Wei Qian <[email protected]>
Qian, W., Yang, Y., Yang, Y. and Zou, H. (2016), “Tweedie's Compound
Poisson Model With Grouped Elastic Net,” Journal of Computational and Graphical Statistics, 25, 606-625.
predict.HDtweedie method
# load HDtweedie library library(HDtweedie) # load data set data(auto) # fit the lasso m0 <- HDtweedie(x=auto$x,y=auto$y,p=1.5) # the coefficients at lambda = 0.01 coef(m0,s=0.01) # define group index group1 <- c(rep(1,5),rep(2,7),rep(3,4),rep(4:14,each=3),15:21) # fit grouped lasso m1 <- HDtweedie(x=auto$x,y=auto$y,group=group1,p=1.5) # the coefficients at lambda = 0.01 and 0.04 coef(m1,s=c(0.01,0.04))# load HDtweedie library library(HDtweedie) # load data set data(auto) # fit the lasso m0 <- HDtweedie(x=auto$x,y=auto$y,p=1.5) # the coefficients at lambda = 0.01 coef(m0,s=0.01) # define group index group1 <- c(rep(1,5),rep(2,7),rep(3,4),rep(4:14,each=3),15:21) # fit grouped lasso m1 <- HDtweedie(x=auto$x,y=auto$y,group=group1,p=1.5) # the coefficients at lambda = 0.01 and 0.04 coef(m1,s=c(0.01,0.04))
Does k-fold cross-validation for HDtweedie, produces a plot,
and returns a value for lambda. This function is modified based on the cv function from the glmnet package.
cv.HDtweedie(x, y, group = NULL, p, weights, lambda = NULL, pred.loss = c("deviance", "mae", "mse"), nfolds = 5, foldid, ...)cv.HDtweedie(x, y, group = NULL, p, weights, lambda = NULL, pred.loss = c("deviance", "mae", "mse"), nfolds = 5, foldid, ...)
x |
matrix of predictors, of dimension |
y |
response variable. This argument should be non-negative. |
group |
To apply the grouped lasso, it is a vector of consecutive integers describing the grouping of the coefficients (see example below). To apply the lasso, the user can ignore this argument, and the vector is automatically generated by treating each variable as a group. |
p |
the power used for variance-mean relation of Tweedie model. Default is 1.50. |
weights |
the observation weights. Default is equal weight. |
lambda |
optional user-supplied lambda sequence; default is
|
pred.loss |
loss to use for cross-validation error. Valid options are:
Default is |
nfolds |
number of folds - default is 5. Although |
foldid |
an optional vector of values between 1 and |
... |
other arguments that can be passed to HDtweedie. |
The function runs HDtweedie nfolds+1 times; the
first to get the lambda sequence, and then the remainder to
compute the fit with each of the folds omitted. The average error and standard deviation over the
folds are computed.
an object of class cv.HDtweedie is returned, which is a
list with the ingredients of the cross-validation fit.
lambda |
the values of |
cvm |
the mean cross-validated error - a vector of length
|
cvsd |
estimate of standard error of |
cvupper |
upper curve = |
cvlower |
lower curve = |
name |
a text string indicating type of measure (for plotting purposes). |
HDtweedie.fit |
a fitted |
lambda.min |
The optimal value of |
lambda.1se |
The largest value of |
Wei Qian, Yi Yang and Hui Zou
Maintainer: Wei Qian <[email protected]>
Qian, W., Yang, Y., Yang, Y. and Zou, H. (2016), “Tweedie's Compound
Poisson Model With Grouped Elastic Net,” Journal of Computational and Graphical Statistics, 25, 606-625.
HDtweedie, plot.cv.HDtweedie, predict.cv.HDtweedie, and coef.cv.HDtweedie methods.
# load HDtweedie library library(HDtweedie) # load data set data(auto) # 5-fold cross validation using the lasso cv0 <- cv.HDtweedie(x=auto$x,y=auto$y,p=1.5,nfolds=5) # define group index group1 <- c(rep(1,5),rep(2,7),rep(3,4),rep(4:14,each=3),15:21) # 5-fold cross validation using the grouped lasso cv1 <- cv.HDtweedie(x=auto$x,y=auto$y,group=group1,p=1.5,nfolds=5)# load HDtweedie library library(HDtweedie) # load data set data(auto) # 5-fold cross validation using the lasso cv0 <- cv.HDtweedie(x=auto$x,y=auto$y,p=1.5,nfolds=5) # define group index group1 <- c(rep(1,5),rep(2,7),rep(3,4),rep(4:14,each=3),15:21) # 5-fold cross validation using the grouped lasso cv1 <- cv.HDtweedie(x=auto$x,y=auto$y,group=group1,p=1.5,nfolds=5)
Fits regularization paths for lasso-type methods of the Tweedie model at a sequence of regularization parameters lambda.
HDtweedie(x, y, group = NULL, p = 1.50, weights = rep(1,nobs), alpha = 1, nlambda = 100, lambda.factor = ifelse(nobs < nvars, 0.05, 0.001), lambda = NULL, pf = sqrt(bs), dfmax = as.integer(max(group)) + 1, pmax = min(dfmax * 1.2, as.integer(max(group))), standardize = FALSE, eps = 1e-08, maxit = 3e+08)HDtweedie(x, y, group = NULL, p = 1.50, weights = rep(1,nobs), alpha = 1, nlambda = 100, lambda.factor = ifelse(nobs < nvars, 0.05, 0.001), lambda = NULL, pf = sqrt(bs), dfmax = as.integer(max(group)) + 1, pmax = min(dfmax * 1.2, as.integer(max(group))), standardize = FALSE, eps = 1e-08, maxit = 3e+08)
x |
matrix of predictors, of dimension |
y |
response variable. This argument should be non-negative. |
group |
To apply the grouped lasso, it is a vector of consecutive integers describing the grouping of the coefficients (see example below). To apply the lasso, the user can ignore this argument, and the vector is automatically generated by treating each variable as a group. |
p |
the power used for variance-mean relation of Tweedie model. Default is 1.50. |
weights |
the observation weights. Default is equal weight. |
alpha |
The elasticnet mixing parameter, with
|
nlambda |
the number of |
lambda.factor |
the factor for getting the minimal lambda in |
lambda |
a user supplied |
pf |
penalty factor, a vector in length of bn (bn is the total number of groups). Separate penalty weights can be applied to each group to allow differential shrinkage. Can be 0 for some groups, which implies no shrinkage, and results in that group always being included in the model. Default value for each entry is the square-root of the corresponding size of each group (for the lasso, it is 1 for each variable). |
dfmax |
limit the maximum number of groups in the model. Default is |
pmax |
limit the maximum number of groups ever to be nonzero. For example once a group enters the model, no matter how many times it exits or re-enters model through the path, it will be counted only once. Default is |
eps |
convergence termination tolerance. Defaults value is |
standardize |
logical flag for variable standardization, prior to fitting the model sequence. If |
maxit |
maximum number of inner-layer BMD iterations allowed. Default is 3e8. |
The sequence of models implied by lambda is fit by the IRLS-BMD algorithm. This gives a (grouped) lasso or (grouped) elasticnet regularization path for fitting the Tweedie generalized linear regression paths, by maximizing the corresponding penalized Tweedie log-likelihood. If the group argument is ignored, the function fits the lasso. Users can tweak the penalty by choosing different and penalty factor.
For computing speed reason, if models are not converging or running slow, consider increasing eps, decreasing
nlambda, or increasing lambda.factor before increasing
maxit.
An object with S3 class HDtweedie.
call |
the call that produced this object |
b0 |
intercept sequence of length |
beta |
a |
df |
the number of nonzero groups for each value of
|
dim |
dimension of coefficient matrix (ices) |
lambda |
the actual sequence of |
npasses |
total number of iterations (the most inner loop) summed over all lambda values |
jerr |
error flag, for warnings and errors, 0 if no error. |
group |
a vector of consecutive integers describing the grouping of the coefficients. |
Wei Qian, Yi Yang and Hui Zou
Maintainer: Wei Qian <[email protected]>
Qian, W., Yang, Y., Yang, Y. and Zou, H. (2016), “Tweedie's Compound
Poisson Model With Grouped Elastic Net,” Journal of Computational and Graphical Statistics, 25, 606-625.
plot.HDtweedie
# load HDtweedie library library(HDtweedie) # load auto data set data(auto) # fit the lasso m0 <- HDtweedie(x=auto$x,y=auto$y,p=1.5) # define group index group1 <- c(rep(1,5),rep(2,7),rep(3,4),rep(4:14,each=3),15:21) # fit the grouped lasso m1 <- HDtweedie(x=auto$x,y=auto$y,group=group1,p=1.5) # fit the grouped elastic net m2 <- HDtweedie(x=auto$x,y=auto$y,group=group1,p=1.5,alpha=0.7)# load HDtweedie library library(HDtweedie) # load auto data set data(auto) # fit the lasso m0 <- HDtweedie(x=auto$x,y=auto$y,p=1.5) # define group index group1 <- c(rep(1,5),rep(2,7),rep(3,4),rep(4:14,each=3),15:21) # fit the grouped lasso m1 <- HDtweedie(x=auto$x,y=auto$y,group=group1,p=1.5) # fit the grouped elastic net m2 <- HDtweedie(x=auto$x,y=auto$y,group=group1,p=1.5,alpha=0.7)
Plots the cross-validation curve, and upper and lower standard deviation
curves, as a function of the lambda values used. This function is modified based on the plot.cv function from the glmnet package.
## S3 method for class 'cv.HDtweedie' plot(x, sign.lambda, ...)## S3 method for class 'cv.HDtweedie' plot(x, sign.lambda, ...)
x |
fitted |
sign.lambda |
either plot against |
... |
other graphical parameters to plot |
A plot is produced.
Wei Qian, Yi Yang and Hui Zou
Maintainer: Wei Qian <[email protected]>
Qian, W., Yang, Y., Yang, Y. and Zou, H. (2016), “Tweedie's Compound
Poisson Model With Grouped Elastic Net,” Journal of Computational and Graphical Statistics, 25, 606-625.
Friedman, J., Hastie, T., and Tibshirani, R. (2010), “Regularization paths for generalized
linear models via coordinate descent,” Journal of Statistical Software, 33, 1.
# load HDtweedie library library(HDtweedie) # load data set data(auto) # 5-fold cross validation using the lasso cv0 <- cv.HDtweedie(x=auto$x,y=auto$y,p=1.5,nfolds=5,lambda.factor=.0005) # make a CV plot plot(cv0) # define group index group1 <- c(rep(1,5),rep(2,7),rep(3,4),rep(4:14,each=3),15:21) # 5-fold cross validation using the grouped lasso cv1 <- cv.HDtweedie(x=auto$x,y=auto$y,group=group1,p=1.5,nfolds=5,lambda.factor=.0005) # make a CV plot plot(cv1)# load HDtweedie library library(HDtweedie) # load data set data(auto) # 5-fold cross validation using the lasso cv0 <- cv.HDtweedie(x=auto$x,y=auto$y,p=1.5,nfolds=5,lambda.factor=.0005) # make a CV plot plot(cv0) # define group index group1 <- c(rep(1,5),rep(2,7),rep(3,4),rep(4:14,each=3),15:21) # 5-fold cross validation using the grouped lasso cv1 <- cv.HDtweedie(x=auto$x,y=auto$y,group=group1,p=1.5,nfolds=5,lambda.factor=.0005) # make a CV plot plot(cv1)
Produces a coefficient profile plot of the coefficient paths for a
fitted HDtweedie object.
## S3 method for class 'HDtweedie' plot(x, group = FALSE, log.l = TRUE, ...)## S3 method for class 'HDtweedie' plot(x, group = FALSE, log.l = TRUE, ...)
x |
fitted |
group |
what is on the Y-axis. Plot the norm of each group
if |
log.l |
what is on the X-axis. Plot against the log-lambda
sequence if |
... |
other graphical parameters to plot |
A coefficient profile plot is produced.
Wei Qian, Yi Yang and Hui Zou
Maintainer: Wei Qian <[email protected]>
Qian, W., Yang, Y., Yang, Y. and Zou, H. (2016), “Tweedie's Compound
Poisson Model With Grouped Elastic Net,” Journal of Computational and Graphical Statistics, 25, 606-625.
# load HDtweedie library library(HDtweedie) # load data set data(auto) # fit the lasso m0 <- HDtweedie(x=auto$x,y=auto$y,p=1.5) # make plot plot(m0) # plots the coefficients against the log-lambda sequence # define group index group1 <- c(rep(1,5),rep(2,7),rep(3,4),rep(4:14,each=3),15:21) # fit group lasso m1 <- HDtweedie(x=auto$x,y=auto$y,group=group1,p=1.5) # make plots par(mfrow=c(1,3)) plot(m1) # plots the coefficients against the log-lambda sequence plot(m1,group=TRUE) # plots group norm against the log-lambda sequence plot(m1,log.l=FALSE) # plots against the lambda sequence# load HDtweedie library library(HDtweedie) # load data set data(auto) # fit the lasso m0 <- HDtweedie(x=auto$x,y=auto$y,p=1.5) # make plot plot(m0) # plots the coefficients against the log-lambda sequence # define group index group1 <- c(rep(1,5),rep(2,7),rep(3,4),rep(4:14,each=3),15:21) # fit group lasso m1 <- HDtweedie(x=auto$x,y=auto$y,group=group1,p=1.5) # make plots par(mfrow=c(1,3)) plot(m1) # plots the coefficients against the log-lambda sequence plot(m1,group=TRUE) # plots group norm against the log-lambda sequence plot(m1,log.l=FALSE) # plots against the lambda sequence
This function makes predictions from a cross-validated HDtweedie model,
using the stored "cv.HDtweedie" object, and the optimal value
chosen for lambda.
## S3 method for class 'cv.HDtweedie' predict(object, newx, s=c("lambda.1se","lambda.min"),...)## S3 method for class 'cv.HDtweedie' predict(object, newx, s=c("lambda.1se","lambda.min"),...)
object |
fitted |
newx |
matrix of new values for |
s |
value(s) of the penalty parameter |
... |
not used. Other arguments to predict. |
This function makes it easier to use the results of cross-validation to make a prediction.
The returned object depends on the ... argument which is passed on
to the predict method for HDtweedie objects.
Wei Qian, Yi Yang and Hui Zou
Maintainer: Wei Qian <[email protected]>
Qian, W., Yang, Y., Yang, Y. and Zou, H. (2016), “Tweedie's Compound
Poisson Model With Grouped Elastic Net,” Journal of Computational and Graphical Statistics, 25, 606-625.
cv.HDtweedie, and coef.cv.HDtweedie methods.
# load HDtweedie library library(HDtweedie) # load data set data(auto) # 5-fold cross validation using the lasso cv0 <- cv.HDtweedie(x=auto$x,y=auto$y,p=1.5,nfolds=5) # predicted mean response at lambda = lambda.1se, newx = x[1,] pre = predict(cv0, newx = auto$x[1,], type = "response") # define group index group1 <- c(rep(1,5),rep(2,7),rep(3,4),rep(4:14,each=3),15:21) # 5-fold cross validation using the grouped lasso cv1 <- cv.HDtweedie(x=auto$x,y=auto$y,group=group1,p=1.5,nfolds=5) # predicted the log mean response at lambda = lambda.min, x[1:5,] pre = predict(cv1, newx = auto$x[1:5,], s = cv1$lambda.min, type = "link")# load HDtweedie library library(HDtweedie) # load data set data(auto) # 5-fold cross validation using the lasso cv0 <- cv.HDtweedie(x=auto$x,y=auto$y,p=1.5,nfolds=5) # predicted mean response at lambda = lambda.1se, newx = x[1,] pre = predict(cv0, newx = auto$x[1,], type = "response") # define group index group1 <- c(rep(1,5),rep(2,7),rep(3,4),rep(4:14,each=3),15:21) # 5-fold cross validation using the grouped lasso cv1 <- cv.HDtweedie(x=auto$x,y=auto$y,group=group1,p=1.5,nfolds=5) # predicted the log mean response at lambda = lambda.min, x[1:5,] pre = predict(cv1, newx = auto$x[1:5,], s = cv1$lambda.min, type = "link")
Similar to other predict methods, this functions predicts fitted values from a HDtweedie object.
## S3 method for class 'HDtweedie' predict(object, newx, s = NULL, type=c("response","link"), ...)## S3 method for class 'HDtweedie' predict(object, newx, s = NULL, type=c("response","link"), ...)
object |
fitted |
newx |
matrix of new values for |
s |
value(s) of the penalty parameter |
type |
type of prediction required:
|
... |
Not used. Other arguments to predict. |
s is the new vector at which predictions are requested. If s is not in the lambda sequence used for fitting the model, the predict function will use linear interpolation to make predictions. The new values are interpolated using a fraction of predicted values from both left and right lambda indices.
The object returned depends on type.
Wei Qian, Yi Yang and Hui Zou
Maintainer: Wei Qian <[email protected]>
Qian, W., Yang, Y., Yang, Y. and Zou, H. (2016), “Tweedie's Compound
Poisson Model With Grouped Elastic Net,” Journal of Computational and Graphical Statistics, 25, 606-625.
coef method
# load HDtweedie library library(HDtweedie) # load auto data set data(auto) # fit the lasso m0 <- HDtweedie(x=auto$x,y=auto$y,p=1.5) # predicted mean response at x[10,] print(predict(m0,type="response",newx=auto$x[10,])) # define group index group1 <- c(rep(1,5),rep(2,7),rep(3,4),rep(4:14,each=3),15:21) # fit the grouped lasso m1 <- HDtweedie(x=auto$x,y=auto$y,group=group1,p=1.5) # predicted the log mean response at x[1:5,] print(predict(m1,type="link",newx=auto$x[1:5,]))# load HDtweedie library library(HDtweedie) # load auto data set data(auto) # fit the lasso m0 <- HDtweedie(x=auto$x,y=auto$y,p=1.5) # predicted mean response at x[10,] print(predict(m0,type="response",newx=auto$x[10,])) # define group index group1 <- c(rep(1,5),rep(2,7),rep(3,4),rep(4:14,each=3),15:21) # fit the grouped lasso m1 <- HDtweedie(x=auto$x,y=auto$y,group=group1,p=1.5) # predicted the log mean response at x[1:5,] print(predict(m1,type="link",newx=auto$x[1:5,]))
Print the nonzero group counts at each lambda along the HDtweedie path.
## S3 method for class 'HDtweedie' print(x, digits = max(3, getOption("digits") - 3), ...)## S3 method for class 'HDtweedie' print(x, digits = max(3, getOption("digits") - 3), ...)
x |
fitted |
digits |
significant digits in printout |
... |
additional print arguments |
Print the information about the nonzero group counts at each lambda step in the HDtweedie object. The result is a two-column matrix with columns Df and Lambda. The Df column is the number of the groups that have nonzero within-group coefficients, the Lambda column is the the corresponding lambda.
a two-column matrix, the first columns is the number of nonzero group counts and the second column is Lambda.
Wei Qian, Yi Yang and Hui Zou
Maintainer: Wei Qian <[email protected]>
Qian, W., Yang, Y., Yang, Y. and Zou, H. (2016), “Tweedie's Compound
Poisson Model With Grouped Elastic Net,” Journal of Computational and Graphical Statistics, 25, 606-625.
# load HDtweedie library library(HDtweedie) # load auto data set data(auto) # fit the lasso m0 <- HDtweedie(x=auto$x,y=auto$y,p=1.5) # print out results print(m0) # define group index group1 <- c(rep(1,5),rep(2,7),rep(3,4),rep(4:14,each=3),15:21) # fit the grouped lasso m1 <- HDtweedie(x=auto$x,y=auto$y,group=group1,p=1.5) # print out results print(m1)# load HDtweedie library library(HDtweedie) # load auto data set data(auto) # fit the lasso m0 <- HDtweedie(x=auto$x,y=auto$y,p=1.5) # print out results print(m0) # define group index group1 <- c(rep(1,5),rep(2,7),rep(3,4),rep(4:14,each=3),15:21) # fit the grouped lasso m1 <- HDtweedie(x=auto$x,y=auto$y,group=group1,p=1.5) # print out results print(m1)