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c060

Extended Inference for Lasso and Elastic-Net Regularized Cox and Generalized Linear Models

Maintainer F. Bertrand

https://doi.org/10.32614/CRAN.package.c060

The goal of the c060 package is to provide additional functions to perform stability selection, model validation and parameter tuning for glmnet models.

Installation

You can install the released version of c060 from CRAN with:

install.packages("c060")

And the development version from GitHub with:

install.packages("devtools")
devtools::install_github("fbertran/c060")

Examples

Gaussian Stability Selection

set.seed(1234)
x=matrix(rnorm(100*1000,0,1),100,1000)
y <- x[1:100,1:1000] %*% c(rep(2,5),rep(-2,5),rep(.1,990))
res <- stabpath(y,x,weakness=1,mc.cores=2)
stabsel(res,error=0.05,type="pfer")
#> $stable
#> integer(0)
#> 
#> $lambda
#> [1] 2.02235
#> 
#> $lpos
#> [1] 8
#> 
#> $error
#> [1] 0.05
#> 
#> $type
#> [1] "pfer"

Gaussian Stability Paths

set.seed(1234)
x <- matrix(rnorm(100*1000,0,1),100,1000)
y <- x[1:100,1:1000] %*% c(rep(2,5),rep(-2,5),rep(.1,990))
res <- stabpath(y,x,weakness=1,mc.cores=2)
plot(res)
#> Error in t.default(x$x): argument is not a matrix

Binomial Stability Paths

y=sample(1:2,100,replace=TRUE)
res <- stabpath(y,x,weakness=1,mc.cores=2,family="binomial")
plot(res)
#> Error in t.default(x$x): argument is not a matrix

Multinomial Stability Paths

y=sample(1:4,100,replace=TRUE)
res <- stabpath(y,x,weakness=1,mc.cores=2,family="multinomial")
#> Warning in mclapply(1:steps, mc.cores = mc.cores, glmnet.subset, subsets, :
#> all scheduled cores encountered errors in user code
#> Error in res[[1]]: subscript out of bounds
plot(res)
#> Error in t.default(x$x): argument is not a matrix

Poisson Stability Paths

N=100; p=1000
nzc=5
x=matrix(rnorm(N*p),N,p)
beta=rnorm(nzc)
f = x[,seq(nzc)] %*% beta
mu=exp(f)
y=rpois(N,mu)
res <- stabpath(y,x,weakness=1,mc.cores=2,family="poisson")
plot(res)
#> Error in t.default(x$x): argument is not a matrix

Cox Stability Paths

library(survival)
set.seed(10101)
N=100;p=1000
nzc=p/3
x=matrix(rnorm(N*p),N,p)
beta=rnorm(nzc)
fx=x[,seq(nzc)] %*% beta/3
hx=exp(fx)
ty=rexp(N,hx)
tcens=rbinom(n=N,prob=.3,size=1)
y=cbind(time=ty,status=1-tcens)
res <- stabpath(y,x,weakness=1,mc.cores=2,family="cox")
plot(res)
#> Error in t.default(x$x): argument is not a matrix

Example from glmnet package

set.seed(10101)
library(glmnet)
library(survival)
library(peperr)
N=1000;p=30
nzc=p/3
x=matrix(rnorm(N*p),N,p)
beta=rnorm(nzc)
fx=x[,seq(nzc)] %*% beta/3
hx=exp(fx)
ty=rexp(N,hx)
tcens=rbinom(n=N,prob=.3,size=1)# censoring indicator
y=Surv(ty,1-tcens)

EPSGO

set.seed(1010)
n=1000;p=100
nzc=trunc(p/10)
x=matrix(rnorm(n*p),n,p)
beta=rnorm(nzc)
fx= x[,seq(nzc)] %*% beta
eps=rnorm(n)*5
y=drop(fx+eps)
px=exp(fx)
px=px/(1+px)
ly=rbinom(n=length(px),prob=px,size=1)
set.seed(1011)

y - binomial

y.classes<-ifelse(y>= median(y),1, 0)
set.seed(1234)
nfolds = 10
foldid <- balancedFolds(class.column.factor=y.classes, cross.outer=nfolds)
#> 121 
#> 2 
#> 3 
#> 4 
#> 5 
#> 6 
#> 7 
#> 8 
#> 9 
#> 10
bounds <- t(data.frame(alpha=c(0, 1)))
colnames(bounds)<-c("lower","upper")
 
fit <- EPSGO(Q.func="tune.glmnet.interval", 
             bounds=bounds, 
             parms.coding="none", 
             seed = 1234, 
             show="final",
             fminlower = -100,
             x = x, y = y.classes, family = "binomial", 
             foldid = foldid,
             my.mfrow = c(4, 4),
             type.min = "lambda.1se",
             type.measure = "mse",
             verbose = FALSE)
#> ...done
#> ...done
#> ...done
#> ...done
#> ...done
#> [1] "finished? FALSE"
#> [1] "X"
#>        alpha
#> [1,] 0.04184
#>     Xtrain    Ytrain
#> 1  0.73445 0.4427822
#> 2  0.85311 0.4422716
#> 3  0.53235 0.4419218
#> 4  0.13097 0.4444470
#> 5  0.60814 0.4435479
#> 6  0.62236 0.4434463
#> 7  0.99216 0.4438921
#> 8  0.26607 0.4440248
#> 9  0.31969 0.4426631
#> 10 0.15851 0.4444240
#> 11 0.67349 0.4431161
#> 12 0.86540 0.4422268
#> 13 0.41169 0.4432506
#> 14 0.90698 0.4420846
#> 15 0.01183 0.4534574
#> 16 0.48959 0.4423172
#> 17 0.22937 0.4430704
#> 18 0.35578 0.4419665
#> 19 0.09411 0.4462451
#> 20 0.45474 0.4426950
#> 21 0.77450 0.4425921
#> ...done
#> ...done
#> ...done
#> ...done
#> ...done
#> [1] "finished? FALSE"
#> [1] "X"
#>      alpha
#> [1,]     0
#>      alpha    Ytrain
#> 1  0.73445 0.4427822
#> 2  0.85311 0.4422716
#> 3  0.53235 0.4419218
#> 4  0.13097 0.4444470
#> 5  0.60814 0.4435479
#> 6  0.62236 0.4434463
#> 7  0.99216 0.4438921
#> 8  0.26607 0.4440248
#> 9  0.31969 0.4426631
#> 10 0.15851 0.4444240
#> 11 0.67349 0.4431161
#> 12 0.86540 0.4422268
#> 13 0.41169 0.4432506
#> 14 0.90698 0.4420846
#> 15 0.01183 0.4534574
#> 16 0.48959 0.4423172
#> 17 0.22937 0.4430704
#> 18 0.35578 0.4419665
#> 19 0.09411 0.4462451
#> 20 0.45474 0.4426950
#> 21 0.77450 0.4425921
#> 22 0.04184 0.4502101
#> ...done
#> ...done
#> ...done
#> ...done
#> ...done
#> [1] "finished? FALSE"
#> [1] "X"
#>        alpha
#> [1,] 0.01859
#>      alpha    Ytrain
#> 1  0.73445 0.4427822
#> 2  0.85311 0.4422716
#> 3  0.53235 0.4419218
#> 4  0.13097 0.4444470
#> 5  0.60814 0.4435479
#> 6  0.62236 0.4434463
#> 7  0.99216 0.4438921
#> 8  0.26607 0.4440248
#> 9  0.31969 0.4426631
#> 10 0.15851 0.4444240
#> 11 0.67349 0.4431161
#> 12 0.86540 0.4422268
#> 13 0.41169 0.4432506
#> 14 0.90698 0.4420846
#> 15 0.01183 0.4534574
#> 16 0.48959 0.4423172
#> 17 0.22937 0.4430704
#> 18 0.35578 0.4419665
#> 19 0.09411 0.4462451
#> 20 0.45474 0.4426950
#> 21 0.77450 0.4425921
#> 22 0.04184 0.4502101
#> 23 0.00000 0.4563529
#> ...done
#> ...done
#> ...done
#> ...done
#> ...done
#> [1] "finished? FALSE"
#> [1] "X"
#>        alpha
#> [1,] 0.03437
#>      alpha    Ytrain
#> 1  0.73445 0.4427822
#> 2  0.85311 0.4422716
#> 3  0.53235 0.4419218
#> 4  0.13097 0.4444470
#> 5  0.60814 0.4435479
#> 6  0.62236 0.4434463
#> 7  0.99216 0.4438921
#> 8  0.26607 0.4440248
#> 9  0.31969 0.4426631
#> 10 0.15851 0.4444240
#> 11 0.67349 0.4431161
#> 12 0.86540 0.4422268
#> 13 0.41169 0.4432506
#> 14 0.90698 0.4420846
#> 15 0.01183 0.4534574
#> 16 0.48959 0.4423172
#> 17 0.22937 0.4430704
#> 18 0.35578 0.4419665
#> 19 0.09411 0.4462451
#> 20 0.45474 0.4426950
#> 21 0.77450 0.4425921
#> 22 0.04184 0.4502101
#> 23 0.00000 0.4563529
#> 24 0.01859 0.4535900
#> ...done
#> ...done
#> ...done
#> ...done
#> ...done
#> [1] "finished? FALSE"
#> [1] "X"
#>        alpha
#> [1,] 0.02538
#>      alpha    Ytrain
#> 1  0.73445 0.4427822
#> 2  0.85311 0.4422716
#> 3  0.53235 0.4419218
#> 4  0.13097 0.4444470
#> 5  0.60814 0.4435479
#> 6  0.62236 0.4434463
#> 7  0.99216 0.4438921
#> 8  0.26607 0.4440248
#> 9  0.31969 0.4426631
#> 10 0.15851 0.4444240
#> 11 0.67349 0.4431161
#> 12 0.86540 0.4422268
#> 13 0.41169 0.4432506
#> 14 0.90698 0.4420846
#> 15 0.01183 0.4534574
#> 16 0.48959 0.4423172
#> 17 0.22937 0.4430704
#> 18 0.35578 0.4419665
#> 19 0.09411 0.4462451
#> 20 0.45474 0.4426950
#> 21 0.77450 0.4425921
#> 22 0.04184 0.4502101
#> 23 0.00000 0.4563529
#> 24 0.01859 0.4535900
#> 25 0.03437 0.4498726
#> ...done
#> ...done
#> ...done
#> ...done
#> ...done
#> [1] "finished? FALSE"
#> [1] "X"
#>        alpha
#> [1,] 0.00565
#>      alpha    Ytrain
#> 1  0.73445 0.4427822
#> 2  0.85311 0.4422716
#> 3  0.53235 0.4419218
#> 4  0.13097 0.4444470
#> 5  0.60814 0.4435479
#> 6  0.62236 0.4434463
#> 7  0.99216 0.4438921
#> 8  0.26607 0.4440248
#> 9  0.31969 0.4426631
#> 10 0.15851 0.4444240
#> 11 0.67349 0.4431161
#> 12 0.86540 0.4422268
#> 13 0.41169 0.4432506
#> 14 0.90698 0.4420846
#> 15 0.01183 0.4534574
#> 16 0.48959 0.4423172
#> 17 0.22937 0.4430704
#> 18 0.35578 0.4419665
#> 19 0.09411 0.4462451
#> 20 0.45474 0.4426950
#> 21 0.77450 0.4425921
#> 22 0.04184 0.4502101
#> 23 0.00000 0.4563529
#> 24 0.01859 0.4535900
#> 25 0.03437 0.4498726
#> 26 0.02538 0.4515460
#> ...done
#> ...done
#> ...done
#> ...done
#> ...done
#> [1] "finished? FALSE"
#> [1] "X"
#>        alpha
#> [1,] 0.02255
#>      alpha    Ytrain
#> 1  0.73445 0.4427822
#> 2  0.85311 0.4422716
#> 3  0.53235 0.4419218
#> 4  0.13097 0.4444470
#> 5  0.60814 0.4435479
#> 6  0.62236 0.4434463
#> 7  0.99216 0.4438921
#> 8  0.26607 0.4440248
#> 9  0.31969 0.4426631
#> 10 0.15851 0.4444240
#> 11 0.67349 0.4431161
#> 12 0.86540 0.4422268
#> 13 0.41169 0.4432506
#> 14 0.90698 0.4420846
#> 15 0.01183 0.4534574
#> 16 0.48959 0.4423172
#> 17 0.22937 0.4430704
#> 18 0.35578 0.4419665
#> 19 0.09411 0.4462451
#> 20 0.45474 0.4426950
#> 21 0.77450 0.4425921
#> 22 0.04184 0.4502101
#> 23 0.00000 0.4563529
#> 24 0.01859 0.4535900
#> 25 0.03437 0.4498726
#> 26 0.02538 0.4515460
#> 27 0.00565 0.4550679
#> ...done
#> ...done
#> ...done
#> ...done
#> ...done
#> [1] "finished? FALSE"
#> [1] "X"
#>        alpha
#> [1,] 0.00881
#>      alpha    Ytrain
#> 1  0.73445 0.4427822
#> 2  0.85311 0.4422716
#> 3  0.53235 0.4419218
#> 4  0.13097 0.4444470
#> 5  0.60814 0.4435479
#> 6  0.62236 0.4434463
#> 7  0.99216 0.4438921
#> 8  0.26607 0.4440248
#> 9  0.31969 0.4426631
#> 10 0.15851 0.4444240
#> 11 0.67349 0.4431161
#> 12 0.86540 0.4422268
#> 13 0.41169 0.4432506
#> 14 0.90698 0.4420846
#> 15 0.01183 0.4534574
#> 16 0.48959 0.4423172
#> 17 0.22937 0.4430704
#> 18 0.35578 0.4419665
#> 19 0.09411 0.4462451
#> 20 0.45474 0.4426950
#> 21 0.77450 0.4425921
#> 22 0.04184 0.4502101
#> 23 0.00000 0.4563529
#> 24 0.01859 0.4535900
#> 25 0.03437 0.4498726
#> 26 0.02538 0.4515460
#> 27 0.00565 0.4550679
#> 28 0.02255 0.4518852
#> ...done
#> ...done
#> ...done
#> ...done
#> ...done
#> [1] "finished? FALSE"
#> [1] "X"
#>        alpha
#> [1,] 0.01515
#>      alpha    Ytrain
#> 1  0.73445 0.4427822
#> 2  0.85311 0.4422716
#> 3  0.53235 0.4419218
#> 4  0.13097 0.4444470
#> 5  0.60814 0.4435479
#> 6  0.62236 0.4434463
#> 7  0.99216 0.4438921
#> 8  0.26607 0.4440248
#> 9  0.31969 0.4426631
#> 10 0.15851 0.4444240
#> 11 0.67349 0.4431161
#> 12 0.86540 0.4422268
#> 13 0.41169 0.4432506
#> 14 0.90698 0.4420846
#> 15 0.01183 0.4534574
#> 16 0.48959 0.4423172
#> 17 0.22937 0.4430704
#> 18 0.35578 0.4419665
#> 19 0.09411 0.4462451
#> 20 0.45474 0.4426950
#> 21 0.77450 0.4425921
#> 22 0.04184 0.4502101
#> 23 0.00000 0.4563529
#> 24 0.01859 0.4535900
#> 25 0.03437 0.4498726
#> 26 0.02538 0.4515460
#> 27 0.00565 0.4550679
#> 28 0.02255 0.4518852
#> 29 0.00881 0.4553102
#> ...done
#> ...done
#> ...done
#> ...done
#> ...done
#> [1] "finished? FALSE"
#> [1] "X"
#>        alpha
#> [1,] 0.00258
#>      alpha    Ytrain
#> 1  0.73445 0.4427822
#> 2  0.85311 0.4422716
#> 3  0.53235 0.4419218
#> 4  0.13097 0.4444470
#> 5  0.60814 0.4435479
#> 6  0.62236 0.4434463
#> 7  0.99216 0.4438921
#> 8  0.26607 0.4440248
#> 9  0.31969 0.4426631
#> 10 0.15851 0.4444240
#> 11 0.67349 0.4431161
#> 12 0.86540 0.4422268
#> 13 0.41169 0.4432506
#> 14 0.90698 0.4420846
#> 15 0.01183 0.4534574
#> 16 0.48959 0.4423172
#> 17 0.22937 0.4430704
#> 18 0.35578 0.4419665
#> 19 0.09411 0.4462451
#> 20 0.45474 0.4426950
#> 21 0.77450 0.4425921
#> 22 0.04184 0.4502101
#> 23 0.00000 0.4563529
#> 24 0.01859 0.4535900
#> 25 0.03437 0.4498726
#> 26 0.02538 0.4515460
#> 27 0.00565 0.4550679
#> 28 0.02255 0.4518852
#> 29 0.00881 0.4553102
#> 30 0.01515 0.4539218
#> [1] "No changes in the last 10 iterations, break iterations"
summary(fit)
#>             Length Class      Mode     
#> fmin         1     -none-     numeric  
#> xmin         1     -none-     numeric  
#> iter         1     -none-     numeric  
#> neval        1     -none-     numeric  
#> maxevals     1     -none-     numeric  
#> seed         1     -none-     numeric  
#> bounds       2     -none-     numeric  
#> Q.func       1     -none-     character
#> points.fmin  2     data.frame list     
#> Xtrain      31     -none-     numeric  
#> Ytrain      31     -none-     numeric  
#> gp.seed     10     -none-     numeric  
#> model.list  31     -none-     list

y - multinomial: low - low 25%, middle - (25,75)-quantiles, high - larger 75%.

y.classes<-ifelse(y <= quantile(y,0.25),1, ifelse(y >= quantile(y,0.75),3, 2))
set.seed(1234)
nfolds = 10
foldid <- balancedFolds(class.column.factor=y.classes, cross.outer=nfolds)
#> 1231 
#> 2 
#> 3 
#> 4 
#> 5 
#> 6 
#> 7 
#> 8 
#> 9 
#> 10
bounds <- t(data.frame(alpha=c(0, 1)))
colnames(bounds)<-c("lower","upper")
 
fit <- EPSGO(Q.func="tune.glmnet.interval", 
             bounds=bounds, 
             parms.coding="none", 
             seed = 1234, 
             show="none",
             fminlower = -100,
             x = x, y = y.classes, family = "multinomial", 
             foldid = foldid,
             type.min = "lambda.1se",
             type.measure = "mse",
             verbose = FALSE)
#> ...done
#> ...done
#> ...done
#> ...done
#> ...done
#> [1] "finished? FALSE"
#> [1] "X"
#>       alpha
#> [1,] 0.0605
#>     Xtrain    Ytrain
#> 1  0.73445 0.5889776
#> 2  0.85311 0.5884918
#> 3  0.53235 0.5902795
#> 4  0.13097 0.5943578
#> 5  0.60814 0.5896935
#> 6  0.62236 0.5895989
#> 7  0.99216 0.5880701
#> 8  0.26607 0.5919904
#> 9  0.31969 0.5906950
#> 10 0.15851 0.5943488
#> 11 0.67349 0.5892911
#> 12 0.86540 0.5884489
#> 13 0.41169 0.5891854
#> 14 0.90698 0.5883127
#> 15 0.01183 0.6058205
#> 16 0.48959 0.5883259
#> 17 0.22937 0.5911021
#> 18 0.35578 0.5900189
#> 19 0.09411 0.5962309
#> 20 0.45474 0.5886749
#> 21 0.77450 0.5887978
#> ...done
#> ...done
#> ...done
#> ...done
#> ...done
#> [1] "finished? FALSE"
#> [1] "X"
#>        alpha
#> [1,] 0.03825
#>      alpha    Ytrain
#> 1  0.73445 0.5889776
#> 2  0.85311 0.5884918
#> 3  0.53235 0.5902795
#> 4  0.13097 0.5943578
#> 5  0.60814 0.5896935
#> 6  0.62236 0.5895989
#> 7  0.99216 0.5880701
#> 8  0.26607 0.5919904
#> 9  0.31969 0.5906950
#> 10 0.15851 0.5943488
#> 11 0.67349 0.5892911
#> 12 0.86540 0.5884489
#> 13 0.41169 0.5891854
#> 14 0.90698 0.5883127
#> 15 0.01183 0.6058205
#> 16 0.48959 0.5883259
#> 17 0.22937 0.5911021
#> 18 0.35578 0.5900189
#> 19 0.09411 0.5962309
#> 20 0.45474 0.5886749
#> 21 0.77450 0.5887978
#> 22 0.06050 0.5985227
#> ...done
#> ...done
#> ...done
#> ...done
#> ...done
#> [1] "finished? FALSE"
#> [1] "X"
#>        alpha
#> [1,] 0.07476
#>      alpha    Ytrain
#> 1  0.73445 0.5889776
#> 2  0.85311 0.5884918
#> 3  0.53235 0.5902795
#> 4  0.13097 0.5943578
#> 5  0.60814 0.5896935
#> 6  0.62236 0.5895989
#> 7  0.99216 0.5880701
#> 8  0.26607 0.5919904
#> 9  0.31969 0.5906950
#> 10 0.15851 0.5943488
#> 11 0.67349 0.5892911
#> 12 0.86540 0.5884489
#> 13 0.41169 0.5891854
#> 14 0.90698 0.5883127
#> 15 0.01183 0.6058205
#> 16 0.48959 0.5883259
#> 17 0.22937 0.5911021
#> 18 0.35578 0.5900189
#> 19 0.09411 0.5962309
#> 20 0.45474 0.5886749
#> 21 0.77450 0.5887978
#> 22 0.06050 0.5985227
#> 23 0.03825 0.5998758
#> ...done
#> ...done
#> ...done
#> ...done
#> ...done
#> [1] "finished? FALSE"
#> [1] "X"
#>      alpha
#> [1,]     0
#>      alpha    Ytrain
#> 1  0.73445 0.5889776
#> 2  0.85311 0.5884918
#> 3  0.53235 0.5902795
#> 4  0.13097 0.5943578
#> 5  0.60814 0.5896935
#> 6  0.62236 0.5895989
#> 7  0.99216 0.5880701
#> 8  0.26607 0.5919904
#> 9  0.31969 0.5906950
#> 10 0.15851 0.5943488
#> 11 0.67349 0.5892911
#> 12 0.86540 0.5884489
#> 13 0.41169 0.5891854
#> 14 0.90698 0.5883127
#> 15 0.01183 0.6058205
#> 16 0.48959 0.5883259
#> 17 0.22937 0.5911021
#> 18 0.35578 0.5900189
#> 19 0.09411 0.5962309
#> 20 0.45474 0.5886749
#> 21 0.77450 0.5887978
#> 22 0.06050 0.5985227
#> 23 0.03825 0.5998758
#> 24 0.07476 0.5974274
#> ...done
#> ...done
#> ...done
#> ...done
#> ...done
#> [1] "finished? FALSE"
#> [1] "X"
#>        alpha
#> [1,] 0.02692
#>      alpha    Ytrain
#> 1  0.73445 0.5889776
#> 2  0.85311 0.5884918
#> 3  0.53235 0.5902795
#> 4  0.13097 0.5943578
#> 5  0.60814 0.5896935
#> 6  0.62236 0.5895989
#> 7  0.99216 0.5880701
#> 8  0.26607 0.5919904
#> 9  0.31969 0.5906950
#> 10 0.15851 0.5943488
#> 11 0.67349 0.5892911
#> 12 0.86540 0.5884489
#> 13 0.41169 0.5891854
#> 14 0.90698 0.5883127
#> 15 0.01183 0.6058205
#> 16 0.48959 0.5883259
#> 17 0.22937 0.5911021
#> 18 0.35578 0.5900189
#> 19 0.09411 0.5962309
#> 20 0.45474 0.5886749
#> 21 0.77450 0.5887978
#> 22 0.06050 0.5985227
#> 23 0.03825 0.5998758
#> 24 0.07476 0.5974274
#> 25 0.00000 0.6118007
#> ...done
#> ...done
#> ...done
#> ...done
#> ...done
#> [1] "the differences in functions between 2 last iterations is small, stop iterations"
#> [1] "finished? TRUE"
#> [1] "X"
#>        alpha
#> [1,] 0.02692
#>      alpha    Ytrain
#> 1  0.73445 0.5889776
#> 2  0.85311 0.5884918
#> 3  0.53235 0.5902795
#> 4  0.13097 0.5943578
#> 5  0.60814 0.5896935
#> 6  0.62236 0.5895989
#> 7  0.99216 0.5880701
#> 8  0.26607 0.5919904
#> 9  0.31969 0.5906950
#> 10 0.15851 0.5943488
#> 11 0.67349 0.5892911
#> 12 0.86540 0.5884489
#> 13 0.41169 0.5891854
#> 14 0.90698 0.5883127
#> 15 0.01183 0.6058205
#> 16 0.48959 0.5883259
#> 17 0.22937 0.5911021
#> 18 0.35578 0.5900189
#> 19 0.09411 0.5962309
#> 20 0.45474 0.5886749
#> 21 0.77450 0.5887978
#> 22 0.06050 0.5985227
#> 23 0.03825 0.5998758
#> 24 0.07476 0.5974274
#> 25 0.00000 0.6118007
#> 26 0.02692 0.6012652
summary(fit)
#>             Length Class      Mode     
#> fmin         1     -none-     numeric  
#> xmin         1     -none-     numeric  
#> iter         1     -none-     numeric  
#> neval        1     -none-     numeric  
#> maxevals     1     -none-     numeric  
#> seed         1     -none-     numeric  
#> bounds       2     -none-     numeric  
#> Q.func       1     -none-     character
#> points.fmin  2     data.frame list     
#> Xtrain      26     -none-     numeric  
#> Ytrain      26     -none-     numeric  
#> gp.seed      6     -none-     numeric  
#> model.list  26     -none-     list

Gaussian

set.seed(1234)
x=matrix(rnorm(100*1000,0,1),100,1000)
y <- x[1:100,1:1000]%*%c(rep(2,5),rep(-2,5),rep(.1,990))

foldid <- rep(1:10,each=10)

fit <- EPSGO(Q.func="tune.glmnet.interval", 
             bounds=bounds, 
             parms.coding="none", 
             seed = 1234, 
             show="none",
             fminlower = -100,
             x = x, y = y, family = "gaussian", 
             foldid = foldid,
             type.min = "lambda.1se",
             type.measure = "mse",
             verbose = FALSE)
#> ...done
#> ...done
#> ...done
#> ...done
#> ...done
#> [1] "finished? FALSE"
#> [1] "X"
#>      alpha
#> [1,]     1
#>     Xtrain   Ytrain
#> 1  0.73445 25.59440
#> 2  0.85311 25.34047
#> 3  0.53235 26.20553
#> 4  0.13097 32.02254
#> 5  0.60814 25.93154
#> 6  0.62236 25.88775
#> 7  0.99216 25.10230
#> 8  0.26607 29.01779
#> 9  0.31969 28.01304
#> 10 0.15851 31.00683
#> 11 0.67349 25.74568
#> 12 0.86540 25.31520
#> 13 0.41169 26.83464
#> 14 0.90698 25.23994
#> 15 0.01183 38.56498
#> 16 0.48959 26.40450
#> 17 0.22937 29.94172
#> 18 0.35578 27.74582
#> 19 0.09411 32.97585
#> 20 0.45474 26.58546
#> 21 0.77450 25.49951
#> ...done
#> ...done
#> ...done
#> ...done
#> ...done
#> [1] "finished? FALSE"
#> [1] "X"
#>        alpha
#> [1,] 0.97776
#>      alpha   Ytrain
#> 1  0.73445 25.59440
#> 2  0.85311 25.34047
#> 3  0.53235 26.20553
#> 4  0.13097 32.02254
#> 5  0.60814 25.93154
#> 6  0.62236 25.88775
#> 7  0.99216 25.10230
#> 8  0.26607 29.01779
#> 9  0.31969 28.01304
#> 10 0.15851 31.00683
#> 11 0.67349 25.74568
#> 12 0.86540 25.31520
#> 13 0.41169 26.83464
#> 14 0.90698 25.23994
#> 15 0.01183 38.56498
#> 16 0.48959 26.40450
#> 17 0.22937 29.94172
#> 18 0.35578 27.74582
#> 19 0.09411 32.97585
#> 20 0.45474 26.58546
#> 21 0.77450 25.49951
#> 22 1.00000 25.09094
#> ...done
#> ...done
#> ...done
#> ...done
#> ...done
#> [1] "finished? FALSE"
#> [1] "X"
#>        alpha
#> [1,] 0.95773
#>      alpha   Ytrain
#> 1  0.73445 25.59440
#> 2  0.85311 25.34047
#> 3  0.53235 26.20553
#> 4  0.13097 32.02254
#> 5  0.60814 25.93154
#> 6  0.62236 25.88775
#> 7  0.99216 25.10230
#> 8  0.26607 29.01779
#> 9  0.31969 28.01304
#> 10 0.15851 31.00683
#> 11 0.67349 25.74568
#> 12 0.86540 25.31520
#> 13 0.41169 26.83464
#> 14 0.90698 25.23994
#> 15 0.01183 38.56498
#> 16 0.48959 26.40450
#> 17 0.22937 29.94172
#> 18 0.35578 27.74582
#> 19 0.09411 32.97585
#> 20 0.45474 26.58546
#> 21 0.77450 25.49951
#> 22 1.00000 25.09094
#> 23 0.97776 25.12374
#> ...done
#> ...done
#> ...done
#> ...done
#> ...done
#> [1] "finished? FALSE"
#> [1] "X"
#>        alpha
#> [1,] 0.98756
#>      alpha   Ytrain
#> 1  0.73445 25.59440
#> 2  0.85311 25.34047
#> 3  0.53235 26.20553
#> 4  0.13097 32.02254
#> 5  0.60814 25.93154
#> 6  0.62236 25.88775
#> 7  0.99216 25.10230
#> 8  0.26607 29.01779
#> 9  0.31969 28.01304
#> 10 0.15851 31.00683
#> 11 0.67349 25.74568
#> 12 0.86540 25.31520
#> 13 0.41169 26.83464
#> 14 0.90698 25.23994
#> 15 0.01183 38.56498
#> 16 0.48959 26.40450
#> 17 0.22937 29.94172
#> 18 0.35578 27.74582
#> 19 0.09411 32.97585
#> 20 0.45474 26.58546
#> 21 0.77450 25.49951
#> 22 1.00000 25.09094
#> 23 0.97776 25.12374
#> 24 0.95773 25.15577
#> ...done
#> ...done
#> ...done
#> ...done
#> ...done
#> [1] "finished? FALSE"
#> [1] "X"
#>        alpha
#> [1,] 0.80954
#>      alpha   Ytrain
#> 1  0.73445 25.59440
#> 2  0.85311 25.34047
#> 3  0.53235 26.20553
#> 4  0.13097 32.02254
#> 5  0.60814 25.93154
#> 6  0.62236 25.88775
#> 7  0.99216 25.10230
#> 8  0.26607 29.01779
#> 9  0.31969 28.01304
#> 10 0.15851 31.00683
#> 11 0.67349 25.74568
#> 12 0.86540 25.31520
#> 13 0.41169 26.83464
#> 14 0.90698 25.23994
#> 15 0.01183 38.56498
#> 16 0.48959 26.40450
#> 17 0.22937 29.94172
#> 18 0.35578 27.74582
#> 19 0.09411 32.97585
#> 20 0.45474 26.58546
#> 21 0.77450 25.49951
#> 22 1.00000 25.09094
#> 23 0.97776 25.12374
#> 24 0.95773 25.15577
#> 25 0.98756 25.10930
#> ...done
#> ...done
#> ...done
#> ...done
#> ...done
#> [1] "the differences in functions between 2 last iterations is small, stop iterations"
#> [1] "finished? TRUE"
#> [1] "X"
#>        alpha
#> [1,] 0.80954
#>      alpha   Ytrain
#> 1  0.73445 25.59440
#> 2  0.85311 25.34047
#> 3  0.53235 26.20553
#> 4  0.13097 32.02254
#> 5  0.60814 25.93154
#> 6  0.62236 25.88775
#> 7  0.99216 25.10230
#> 8  0.26607 29.01779
#> 9  0.31969 28.01304
#> 10 0.15851 31.00683
#> 11 0.67349 25.74568
#> 12 0.86540 25.31520
#> 13 0.41169 26.83464
#> 14 0.90698 25.23994
#> 15 0.01183 38.56498
#> 16 0.48959 26.40450
#> 17 0.22937 29.94172
#> 18 0.35578 27.74582
#> 19 0.09411 32.97585
#> 20 0.45474 26.58546
#> 21 0.77450 25.49951
#> 22 1.00000 25.09094
#> 23 0.97776 25.12374
#> 24 0.95773 25.15577
#> 25 0.98756 25.10930
#> 26 0.80954 25.42666
summary(fit) 
#>             Length Class      Mode     
#> fmin         1     -none-     numeric  
#> xmin         1     -none-     numeric  
#> iter         1     -none-     numeric  
#> neval        1     -none-     numeric  
#> maxevals     1     -none-     numeric  
#> seed         1     -none-     numeric  
#> bounds       2     -none-     numeric  
#> Q.func       1     -none-     character
#> points.fmin  2     data.frame list     
#> Xtrain      26     -none-     numeric  
#> Ytrain      26     -none-     numeric  
#> gp.seed      6     -none-     numeric  
#> model.list  26     -none-     list

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Version

Install

install.packages('c060')

Monthly Downloads

448

Version

0.4-0

License

GPL-2

Issues

2

Pull Requests

0

Stars

3

Forks

0

Maintainer

Frederic Bertrand

Last Published

September 19th, 2025

Functions in c060 (0.4-0)

EPSGO

Efficient Parameter Selection via Global Optimization
summary.intsearch

Summary method for interval search models
coef.sum.intsearch

Get coefficients for a model
complexity.glmnet

Interface for determination of penalty lambda in penalized regression model via cross-validation
aggregation.auc

Determine the area under the ROC curve for a fitted model
c060-package

c060: Extended Inference for Lasso and Elastic-Net Regularized Cox and Generalized Linear Models
balancedFolds

Function producing stratified/ balanced folds for cross validation
stabsel

function to estimate a stable set of variables
plot.sum.intsearc

Plot Summary object for interval search models
PLL.coxnet

Predictive partial log-likelihood for glmnet Cox PH model fit
stabpath

Stability path for glmnet models
plot.stabpath

function to plot a stability path
predictProb.coxnet

Extract predicted survival probabilities from a glmnet fit
tune.glmnet.interval

Wrapper function for glmnet objects.
fit.glmnet

Interface function for fitting a penalized regression model with glmnet
Plot.coef.glmnet

function to highlight the path of a pre-specified set of variables within the coefficient path
Plot.peperr.curves

Plot method for prediction error curves of a peperr object