metafuse-package: Fused Lasso Approach in Regression Coefficient Clustering
In metafuse: Fused Lasso Approach in Regression Coefficient Clustering
Description
Details
Author(s)
References
Examples
Description
Fused lasso method to cluster and estimate regression coefficients
of the same covariate across different data sets when a large number of
independent data sets are combined. Package supports Gaussian, binomial,
Poisson and Cox PH models.
Details
Simple to use. Accepts X, y, and sid (numerica data source ID for which data entry belongs to) for regression models. Returns regression coefficient estimates and clusterings patterns of coefficients across different datasets, for each covariate. Provides visualization by fusogram, a dendrogram-type of presentation of coefficient clustering pattern across data sources.
Author(s)
Lu Tang, Ling Zhou, Peter X.K. Song
Maintainer: Lu Tang <lutang@umich.edu>
References
Lu Tang, and Peter X.K. Song. Fused Lasso Approach in Regression Coefficients Clustering - Learning Parameter Heterogeneity in Data Integration. Journal of Machine Learning Research, 17(113):1-23, 2016.
Fei Wang, Lu Wang, and Peter X.K. Song. Fused lasso with the adaptation of parameter ordering in combining multiple studies with repeated measurements. Biometrics, DOI:10.1111/biom.12496, 2016.
Examples
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
33
34
35
36
37
38
39
########### generate data ###########
n <- 200 # sample size in each dataset (can also be a K-element vector)
K <- 10 # number of datasets for data integration
p <- 3 # number of covariates in X (including the intercept)
# the coefficient matrix of dimension K * p, used to specify the heterogeneous pattern
beta0 <- matrix(c(0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0, # beta_0 of intercept
0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0, # beta_1 of X_1
0.0,0.0,0.0,0.0,0.5,0.5,0.5,1.0,1.0,1.0), # beta_2 of X_2
K, p)
# generate a data set, family=c("gaussian", "binomial", "poisson", "cox")
data <- datagenerator(n=n, beta0=beta0, family="gaussian", seed=123)
# prepare the input for metafuse
y <- data$y
sid <- data$group
X <- data[,-c(1,ncol(data))]
########### run metafuse ###########
# fuse slopes of X1 (which is heterogeneous with 2 clusters)
metafuse(X=X, y=y, sid=sid, fuse.which=c(1), family="gaussian", intercept=TRUE, alpha=0,
criterion="EBIC", verbose=TRUE, plots=TRUE, loglambda=TRUE)
# fuse slopes of X2 (which is heterogeneous with 3 clusters)
metafuse(X=X, y=y, sid=sid, fuse.which=c(2), family="gaussian", intercept=TRUE, alpha=0,
criterion="EBIC", verbose=TRUE, plots=TRUE, loglambda=TRUE)
# fuse all three covariates
metafuse(X=X, y=y, sid=sid, fuse.which=c(0,1,2), family="gaussian", intercept=TRUE, alpha=0,
criterion="EBIC", verbose=TRUE, plots=TRUE, loglambda=TRUE)
# fuse all three covariates, with sparsity penalty
metafuse(X=X, y=y, sid=sid, fuse.which=c(0,1,2), family="gaussian", intercept=TRUE, alpha=1,
criterion="EBIC", verbose=TRUE, plots=TRUE, loglambda=TRUE)
# fit metafuse at a given lambda
metafuse.l(X=X, y=y, sid=sid, fuse.which=c(0,1,2), family="gaussian", intercept=TRUE,
alpha=1, lambda=0.5)
Example output
************************** verbose (start) **************************
Lambda = 0
Intercept x1 x2
1 10 1
Lambda = 1e-04
Intercept x1 x2
1 9 1
Lambda = 0.0025
Intercept x1 x2
1 8 1
Lambda = 0.0562
Intercept x1 x2
1 7 1
Lambda = 0.1259
Intercept x1 x2
1 6 1
Lambda = 0.1778
Intercept x1 x2
1 5 1
Lambda = 0.3981
Intercept x1 x2
1 4 1
Lambda = 1.122
Intercept x1 x2
1 3 1
Lambda = 1.5849
Intercept x1 x2
1 2 1
Lambda = 177.8279
Intercept x1 x2
1 1 1
************************** verbose (end) **************************
Press [enter] to continue
Press [enter] to continue
$family
[1] "gaussian"
$criterion
[1] "EBIC"
$alpha
[1] 0
$if.fuse
Intercept x1 x2
0 1 0
$betahat
Intercept x1 x2
study1 -0.009447474 -0.1067597 0.4443723
study2 -0.009447474 -0.1067597 0.4443723
study3 -0.009447474 -0.1067597 0.4443723
study4 -0.009447474 -0.1067597 0.4443723
study5 -0.009447474 -0.1067597 0.4443723
study6 -0.009447474 1.0552546 0.4443723
study7 -0.009447474 1.0552546 0.4443723
study8 -0.009447474 1.0552546 0.4443723
study9 -0.009447474 1.0552546 0.4443723
study10 -0.009447474 1.0552546 0.4443723
$betainfo
$betainfo$original_scale
Intercept x1 x2
DF 1.000000 2.0000000 1.000000
lambda_opt 2.511886 2.5118864 2.511886
lambda_fuse 0.000000 177.8279410 0.000000
friction NaN 0.9858746 NaN
$betainfo$log10_scale
Intercept x1 x2
DF 1.0000000 2.0000000 1.0000000
lambda_opt 0.5455405 0.5455405 0.5455405
lambda_fuse 0.0000000 2.2524354 0.0000000
friction NaN 0.7577997 NaN
************************** verbose (start) **************************
Lambda = 0
Intercept x1 x2
1 1 10
Lambda = 1e-04
Intercept x1 x2
1 1 9
Lambda = 0.0032
Intercept x1 x2
1 1 8
Lambda = 0.0079
Intercept x1 x2
1 1 7
Lambda = 0.0126
Intercept x1 x2
1 1 6
Lambda = 0.0282
Intercept x1 x2
1 1 5
Lambda = 0.1
Intercept x1 x2
1 1 4
Lambda = 0.2818
Intercept x1 x2
1 1 3
Lambda = 0.7943
Intercept x1 x2
1 1 2
Lambda = 2.8184
Intercept x1 x2
1 1 1
************************** verbose (end) **************************
Press [enter] to continue
Press [enter] to continue
$family
[1] "gaussian"
$criterion
[1] "EBIC"
$alpha
[1] 0
$if.fuse
Intercept x1 x2
0 0 1
$betahat
Intercept x1 x2
study1 0.02126244 0.4476072 -0.20473895
study2 0.02126244 0.4476072 -0.06921323
study3 0.02126244 0.4476072 -0.20473895
study4 0.02126244 0.4476072 -0.06921323
study5 0.02126244 0.4476072 0.52722701
study6 0.02126244 0.4476072 0.52722701
study7 0.02126244 0.4476072 0.77538250
study8 0.02126244 0.4476072 1.14221687
study9 0.02126244 0.4476072 1.14221687
study10 0.02126244 0.4476072 1.14221687
$betainfo
$betainfo$original_scale
Intercept x1 x2
DF 1.00000000 1.00000000 5.00000000
lambda_opt 0.02818383 0.02818383 0.02818383
lambda_fuse 0.00000000 0.00000000 2.81838293
friction NaN NaN 0.99000000
$betainfo$log10_scale
Intercept x1 x2
DF 1.00000000 1.00000000 5.00000000
lambda_opt 0.01207077 0.01207077 0.01207077
lambda_fuse 0.00000000 0.00000000 0.58187948
friction NaN NaN 0.97925555
************************** verbose (start) **************************
Lambda = 0
Intercept x1 x2
10 10 10
Lambda = 1e-04
Intercept x1 x2
8 9 10
Lambda = 4e-04
Intercept x1 x2
8 9 9
Lambda = 4e-04
Intercept x1 x2
7 9 9
Lambda = 6e-04
Intercept x1 x2
6 9 9
Lambda = 0.0013
Intercept x1 x2
5 8 8
Lambda = 0.0025
Intercept x1 x2
4 8 8
Lambda = 0.0028
Intercept x1 x2
4 7 8
Lambda = 0.0045
Intercept x1 x2
4 7 7
Lambda = 0.0071
Intercept x1 x2
3 6 7
Lambda = 0.01
Intercept x1 x2
3 5 6
Lambda = 0.0316
Intercept x1 x2
3 5 5
Lambda = 0.0355
Intercept x1 x2
3 4 5
Lambda = 0.0562
Intercept x1 x2
3 3 4
Lambda = 0.0708
Intercept x1 x2
2 3 4
Lambda = 0.0794
Intercept x1 x2
1 3 4
Lambda = 0.0891
Intercept x1 x2
1 2 4
Lambda = 0.1585
Intercept x1 x2
1 2 3
Lambda = 3.9811
Intercept x1 x2
1 2 2
Lambda = 5.6234
Intercept x1 x2
1 2 1
Lambda = 15.8489
Intercept x1 x2
1 1 1
************************** verbose (end) **************************
Press [enter] to continue
Press [enter] to continue
Press [enter] to continue
Press [enter] to continue
$family
[1] "gaussian"
$criterion
[1] "EBIC"
$alpha
[1] 0
$if.fuse
Intercept x1 x2
1 1 1
$betahat
Intercept x1 x2
study1 0.003334298 -0.01606108 -0.006970854
study2 0.003334298 -0.01606108 -0.006970854
study3 0.003334298 -0.01606108 -0.006970854
study4 0.003334298 -0.01606108 -0.006970854
study5 0.003334298 -0.01606108 0.513345365
study6 0.003334298 0.96328884 0.513345365
study7 0.003334298 0.96328884 0.513345365
study8 0.003334298 0.96328884 1.008996127
study9 0.003334298 0.96328884 1.008996127
study10 0.003334298 0.96328884 1.008996127
$betainfo
$betainfo$original_scale
Intercept x1 x2
DF 1.00000000 2.0000000 3.0000000
lambda_opt 0.15848932 0.1584893 0.1584893
lambda_fuse 0.07943282 15.8489319 5.6234133
friction 0.00000000 0.9900000 0.9718162
$betainfo$log10_scale
Intercept x1 x2
DF 1.00000000 2.00000000 3.00000000
lambda_opt 0.06389203 0.06389203 0.06389203
lambda_fuse 0.03319562 1.22657238 0.82108185
friction 0.00000000 0.94791010 0.92218555
************************** verbose (start) **************************
Lambda = 0
Intercept x1 x2
10 10 10
Lambda = 1e-04
Intercept x1 x2
8 9 10
Lambda = 4e-04
Intercept x1 x2
8 9 9
Lambda = 4e-04
Intercept x1 x2
7 9 9
Lambda = 6e-04
Intercept x1 x2
6 9 9
Lambda = 0.0013
Intercept x1 x2
5 8 9
Lambda = 0.0018
Intercept x1 x2
5 8 8
Lambda = 0.0025
Intercept x1 x2
4 8 8
Lambda = 0.0028
Intercept x1 x2
4 7 8
Lambda = 0.0045
Intercept x1 x2
4 7 7
Lambda = 0.0071
Intercept x1 x2
3 7 7
Lambda = 0.0079
Intercept x1 x2
3 6 7
Lambda = 0.01
Intercept x1 x2
3 5 6
Lambda = 0.0316
Intercept x1 x2
3 5 5
Lambda = 0.0398
Intercept x1 x2
3 4 5
Lambda = 0.0562
Intercept x1 x2
3 4 4
Lambda = 0.0708
Intercept x1 x2
2 4 4
Lambda = 0.0794
Intercept x1 x2
1 4 4
Lambda = 0.0891
Intercept x1 x2
1 2 4
Lambda = 0.1778
Intercept x1 x2
1 2 3
Lambda = 3.9811
Intercept x1 x2
1 2 2
Lambda = 14.1254
Intercept x1 x2
1 2 1
Lambda = 35.4813
Intercept x1 x2
1 1 1
************************** verbose (end) **************************
Press [enter] to continue
Press [enter] to continue
Press [enter] to continue
Press [enter] to continue
$family
[1] "gaussian"
$criterion
[1] "EBIC"
$alpha
[1] 1
$if.fuse
Intercept x1 x2
1 1 1
$betahat
Intercept x1 x2
study1 0.003676365 0.0000000 0.0000000
study2 0.003676365 0.0000000 0.0000000
study3 0.003676365 0.0000000 0.0000000
study4 0.003676365 0.0000000 0.0000000
study5 0.003676365 0.0000000 0.5121932
study6 0.003676365 0.9632209 0.5121932
study7 0.003676365 0.9632209 0.5121932
study8 0.003676365 0.9632209 1.0072402
study9 0.003676365 0.9632209 1.0072402
study10 0.003676365 0.9632209 1.0072402
$betainfo
$betainfo$original_scale
Intercept x1 x2
DF 1.00000000 2.0000000 3.0000000
lambda_opt 0.17782794 0.1778279 0.1778279
lambda_fuse 0.07943282 35.4813389 14.1253754
friction 0.00000000 0.9949881 0.9874107
$betainfo$log10_scale
Intercept x1 x2
DF 1.00000000 2.00000000 3.00000000
lambda_opt 0.07108185 0.07108185 0.07108185
lambda_fuse 0.03319562 1.56207077 1.17970616
friction 0.00000000 0.95449511 0.93974614
$family
[1] "gaussian"
$alpha
[1] 1
$if.fuse
Intercept x1 x2
1 1 1
$betahat
Intercept x1 x2
study1 0.003102299 0.0000000 0.0000000
study2 0.003102299 0.0000000 0.0000000
study3 0.003102299 0.0000000 0.0000000
study4 0.003102299 0.0000000 0.0000000
study5 0.003102299 0.0000000 0.5161078
study6 0.003102299 0.9561642 0.5161078
study7 0.003102299 0.9561642 0.5161078
study8 0.003102299 0.9561642 0.9667165
study9 0.003102299 0.9561642 0.9667165
study10 0.003102299 0.9561642 0.9667165
$betainfo
Intercept x1 x2
DF 1 2 3
metafuse documentation built on May 2, 2019, 2:15 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Details Author(s) References Examples
Description
Fused lasso method to cluster and estimate regression coefficients of the same covariate across different data sets when a large number of independent data sets are combined. Package supports Gaussian, binomial, Poisson and Cox PH models.
Details
Simple to use. Accepts X, y, and sid (numerica data source ID for which data entry belongs to) for regression models. Returns regression coefficient estimates and clusterings patterns of coefficients across different datasets, for each covariate. Provides visualization by fusogram, a dendrogram-type of presentation of coefficient clustering pattern across data sources.
Author(s)
Lu Tang, Ling Zhou, Peter X.K. Song
Maintainer: Lu Tang <lutang@umich.edu>
References
Lu Tang, and Peter X.K. Song. Fused Lasso Approach in Regression Coefficients Clustering - Learning Parameter Heterogeneity in Data Integration. Journal of Machine Learning Research, 17(113):1-23, 2016.
Fei Wang, Lu Wang, and Peter X.K. Song. Fused lasso with the adaptation of parameter ordering in combining multiple studies with repeated measurements. Biometrics, DOI:10.1111/biom.12496, 2016.
Examples
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 33 34 35 36 37 38 39 | ########### generate data ###########
n <- 200 # sample size in each dataset (can also be a K-element vector)
K <- 10 # number of datasets for data integration
p <- 3 # number of covariates in X (including the intercept)
# the coefficient matrix of dimension K * p, used to specify the heterogeneous pattern
beta0 <- matrix(c(0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0, # beta_0 of intercept
0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0, # beta_1 of X_1
0.0,0.0,0.0,0.0,0.5,0.5,0.5,1.0,1.0,1.0), # beta_2 of X_2
K, p)
# generate a data set, family=c("gaussian", "binomial", "poisson", "cox")
data <- datagenerator(n=n, beta0=beta0, family="gaussian", seed=123)
# prepare the input for metafuse
y <- data$y
sid <- data$group
X <- data[,-c(1,ncol(data))]
########### run metafuse ###########
# fuse slopes of X1 (which is heterogeneous with 2 clusters)
metafuse(X=X, y=y, sid=sid, fuse.which=c(1), family="gaussian", intercept=TRUE, alpha=0,
criterion="EBIC", verbose=TRUE, plots=TRUE, loglambda=TRUE)
# fuse slopes of X2 (which is heterogeneous with 3 clusters)
metafuse(X=X, y=y, sid=sid, fuse.which=c(2), family="gaussian", intercept=TRUE, alpha=0,
criterion="EBIC", verbose=TRUE, plots=TRUE, loglambda=TRUE)
# fuse all three covariates
metafuse(X=X, y=y, sid=sid, fuse.which=c(0,1,2), family="gaussian", intercept=TRUE, alpha=0,
criterion="EBIC", verbose=TRUE, plots=TRUE, loglambda=TRUE)
# fuse all three covariates, with sparsity penalty
metafuse(X=X, y=y, sid=sid, fuse.which=c(0,1,2), family="gaussian", intercept=TRUE, alpha=1,
criterion="EBIC", verbose=TRUE, plots=TRUE, loglambda=TRUE)
# fit metafuse at a given lambda
metafuse.l(X=X, y=y, sid=sid, fuse.which=c(0,1,2), family="gaussian", intercept=TRUE,
alpha=1, lambda=0.5)
|
Example output
************************** verbose (start) **************************
Lambda = 0
Intercept x1 x2
1 10 1
Lambda = 1e-04
Intercept x1 x2
1 9 1
Lambda = 0.0025
Intercept x1 x2
1 8 1
Lambda = 0.0562
Intercept x1 x2
1 7 1
Lambda = 0.1259
Intercept x1 x2
1 6 1
Lambda = 0.1778
Intercept x1 x2
1 5 1
Lambda = 0.3981
Intercept x1 x2
1 4 1
Lambda = 1.122
Intercept x1 x2
1 3 1
Lambda = 1.5849
Intercept x1 x2
1 2 1
Lambda = 177.8279
Intercept x1 x2
1 1 1
************************** verbose (end) **************************
Press [enter] to continue
Press [enter] to continue
$family
[1] "gaussian"
$criterion
[1] "EBIC"
$alpha
[1] 0
$if.fuse
Intercept x1 x2
0 1 0
$betahat
Intercept x1 x2
study1 -0.009447474 -0.1067597 0.4443723
study2 -0.009447474 -0.1067597 0.4443723
study3 -0.009447474 -0.1067597 0.4443723
study4 -0.009447474 -0.1067597 0.4443723
study5 -0.009447474 -0.1067597 0.4443723
study6 -0.009447474 1.0552546 0.4443723
study7 -0.009447474 1.0552546 0.4443723
study8 -0.009447474 1.0552546 0.4443723
study9 -0.009447474 1.0552546 0.4443723
study10 -0.009447474 1.0552546 0.4443723
$betainfo
$betainfo$original_scale
Intercept x1 x2
DF 1.000000 2.0000000 1.000000
lambda_opt 2.511886 2.5118864 2.511886
lambda_fuse 0.000000 177.8279410 0.000000
friction NaN 0.9858746 NaN
$betainfo$log10_scale
Intercept x1 x2
DF 1.0000000 2.0000000 1.0000000
lambda_opt 0.5455405 0.5455405 0.5455405
lambda_fuse 0.0000000 2.2524354 0.0000000
friction NaN 0.7577997 NaN
************************** verbose (start) **************************
Lambda = 0
Intercept x1 x2
1 1 10
Lambda = 1e-04
Intercept x1 x2
1 1 9
Lambda = 0.0032
Intercept x1 x2
1 1 8
Lambda = 0.0079
Intercept x1 x2
1 1 7
Lambda = 0.0126
Intercept x1 x2
1 1 6
Lambda = 0.0282
Intercept x1 x2
1 1 5
Lambda = 0.1
Intercept x1 x2
1 1 4
Lambda = 0.2818
Intercept x1 x2
1 1 3
Lambda = 0.7943
Intercept x1 x2
1 1 2
Lambda = 2.8184
Intercept x1 x2
1 1 1
************************** verbose (end) **************************
Press [enter] to continue
Press [enter] to continue
$family
[1] "gaussian"
$criterion
[1] "EBIC"
$alpha
[1] 0
$if.fuse
Intercept x1 x2
0 0 1
$betahat
Intercept x1 x2
study1 0.02126244 0.4476072 -0.20473895
study2 0.02126244 0.4476072 -0.06921323
study3 0.02126244 0.4476072 -0.20473895
study4 0.02126244 0.4476072 -0.06921323
study5 0.02126244 0.4476072 0.52722701
study6 0.02126244 0.4476072 0.52722701
study7 0.02126244 0.4476072 0.77538250
study8 0.02126244 0.4476072 1.14221687
study9 0.02126244 0.4476072 1.14221687
study10 0.02126244 0.4476072 1.14221687
$betainfo
$betainfo$original_scale
Intercept x1 x2
DF 1.00000000 1.00000000 5.00000000
lambda_opt 0.02818383 0.02818383 0.02818383
lambda_fuse 0.00000000 0.00000000 2.81838293
friction NaN NaN 0.99000000
$betainfo$log10_scale
Intercept x1 x2
DF 1.00000000 1.00000000 5.00000000
lambda_opt 0.01207077 0.01207077 0.01207077
lambda_fuse 0.00000000 0.00000000 0.58187948
friction NaN NaN 0.97925555
************************** verbose (start) **************************
Lambda = 0
Intercept x1 x2
10 10 10
Lambda = 1e-04
Intercept x1 x2
8 9 10
Lambda = 4e-04
Intercept x1 x2
8 9 9
Lambda = 4e-04
Intercept x1 x2
7 9 9
Lambda = 6e-04
Intercept x1 x2
6 9 9
Lambda = 0.0013
Intercept x1 x2
5 8 8
Lambda = 0.0025
Intercept x1 x2
4 8 8
Lambda = 0.0028
Intercept x1 x2
4 7 8
Lambda = 0.0045
Intercept x1 x2
4 7 7
Lambda = 0.0071
Intercept x1 x2
3 6 7
Lambda = 0.01
Intercept x1 x2
3 5 6
Lambda = 0.0316
Intercept x1 x2
3 5 5
Lambda = 0.0355
Intercept x1 x2
3 4 5
Lambda = 0.0562
Intercept x1 x2
3 3 4
Lambda = 0.0708
Intercept x1 x2
2 3 4
Lambda = 0.0794
Intercept x1 x2
1 3 4
Lambda = 0.0891
Intercept x1 x2
1 2 4
Lambda = 0.1585
Intercept x1 x2
1 2 3
Lambda = 3.9811
Intercept x1 x2
1 2 2
Lambda = 5.6234
Intercept x1 x2
1 2 1
Lambda = 15.8489
Intercept x1 x2
1 1 1
************************** verbose (end) **************************
Press [enter] to continue
Press [enter] to continue
Press [enter] to continue
Press [enter] to continue
$family
[1] "gaussian"
$criterion
[1] "EBIC"
$alpha
[1] 0
$if.fuse
Intercept x1 x2
1 1 1
$betahat
Intercept x1 x2
study1 0.003334298 -0.01606108 -0.006970854
study2 0.003334298 -0.01606108 -0.006970854
study3 0.003334298 -0.01606108 -0.006970854
study4 0.003334298 -0.01606108 -0.006970854
study5 0.003334298 -0.01606108 0.513345365
study6 0.003334298 0.96328884 0.513345365
study7 0.003334298 0.96328884 0.513345365
study8 0.003334298 0.96328884 1.008996127
study9 0.003334298 0.96328884 1.008996127
study10 0.003334298 0.96328884 1.008996127
$betainfo
$betainfo$original_scale
Intercept x1 x2
DF 1.00000000 2.0000000 3.0000000
lambda_opt 0.15848932 0.1584893 0.1584893
lambda_fuse 0.07943282 15.8489319 5.6234133
friction 0.00000000 0.9900000 0.9718162
$betainfo$log10_scale
Intercept x1 x2
DF 1.00000000 2.00000000 3.00000000
lambda_opt 0.06389203 0.06389203 0.06389203
lambda_fuse 0.03319562 1.22657238 0.82108185
friction 0.00000000 0.94791010 0.92218555
************************** verbose (start) **************************
Lambda = 0
Intercept x1 x2
10 10 10
Lambda = 1e-04
Intercept x1 x2
8 9 10
Lambda = 4e-04
Intercept x1 x2
8 9 9
Lambda = 4e-04
Intercept x1 x2
7 9 9
Lambda = 6e-04
Intercept x1 x2
6 9 9
Lambda = 0.0013
Intercept x1 x2
5 8 9
Lambda = 0.0018
Intercept x1 x2
5 8 8
Lambda = 0.0025
Intercept x1 x2
4 8 8
Lambda = 0.0028
Intercept x1 x2
4 7 8
Lambda = 0.0045
Intercept x1 x2
4 7 7
Lambda = 0.0071
Intercept x1 x2
3 7 7
Lambda = 0.0079
Intercept x1 x2
3 6 7
Lambda = 0.01
Intercept x1 x2
3 5 6
Lambda = 0.0316
Intercept x1 x2
3 5 5
Lambda = 0.0398
Intercept x1 x2
3 4 5
Lambda = 0.0562
Intercept x1 x2
3 4 4
Lambda = 0.0708
Intercept x1 x2
2 4 4
Lambda = 0.0794
Intercept x1 x2
1 4 4
Lambda = 0.0891
Intercept x1 x2
1 2 4
Lambda = 0.1778
Intercept x1 x2
1 2 3
Lambda = 3.9811
Intercept x1 x2
1 2 2
Lambda = 14.1254
Intercept x1 x2
1 2 1
Lambda = 35.4813
Intercept x1 x2
1 1 1
************************** verbose (end) **************************
Press [enter] to continue
Press [enter] to continue
Press [enter] to continue
Press [enter] to continue
$family
[1] "gaussian"
$criterion
[1] "EBIC"
$alpha
[1] 1
$if.fuse
Intercept x1 x2
1 1 1
$betahat
Intercept x1 x2
study1 0.003676365 0.0000000 0.0000000
study2 0.003676365 0.0000000 0.0000000
study3 0.003676365 0.0000000 0.0000000
study4 0.003676365 0.0000000 0.0000000
study5 0.003676365 0.0000000 0.5121932
study6 0.003676365 0.9632209 0.5121932
study7 0.003676365 0.9632209 0.5121932
study8 0.003676365 0.9632209 1.0072402
study9 0.003676365 0.9632209 1.0072402
study10 0.003676365 0.9632209 1.0072402
$betainfo
$betainfo$original_scale
Intercept x1 x2
DF 1.00000000 2.0000000 3.0000000
lambda_opt 0.17782794 0.1778279 0.1778279
lambda_fuse 0.07943282 35.4813389 14.1253754
friction 0.00000000 0.9949881 0.9874107
$betainfo$log10_scale
Intercept x1 x2
DF 1.00000000 2.00000000 3.00000000
lambda_opt 0.07108185 0.07108185 0.07108185
lambda_fuse 0.03319562 1.56207077 1.17970616
friction 0.00000000 0.95449511 0.93974614
$family
[1] "gaussian"
$alpha
[1] 1
$if.fuse
Intercept x1 x2
1 1 1
$betahat
Intercept x1 x2
study1 0.003102299 0.0000000 0.0000000
study2 0.003102299 0.0000000 0.0000000
study3 0.003102299 0.0000000 0.0000000
study4 0.003102299 0.0000000 0.0000000
study5 0.003102299 0.0000000 0.5161078
study6 0.003102299 0.9561642 0.5161078
study7 0.003102299 0.9561642 0.5161078
study8 0.003102299 0.9561642 0.9667165
study9 0.003102299 0.9561642 0.9667165
study10 0.003102299 0.9561642 0.9667165
$betainfo
Intercept x1 x2
DF 1 2 3
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.