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.
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")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
#>
#> $type
#> [1] "pfer"set.seed(1234)
x <- matrix(rnorm(100*1000,0,1),100,1000)
y <- x[1:100,1:1000] %*% c(rep(2,5),rep(-2,5),rep(.1,990))
res <- stabpath(y,x,weakness=1,mc.cores=2)
plot(res)
#> Error in t.default(x$x): argument is not a matrix
plot of chunk unnamed-chunk-5
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$x): argument is not a matrix
plot of chunk unnamed-chunk-6
y=sample(1:4,100,replace=TRUE)
res <- stabpath(y,x,weakness=1,mc.cores=2,family="multinomial")
#> Warning in mclapply(1:steps, mc.cores = mc.cores, glmnet.subset, subsets, :
#> all scheduled cores encountered errors in user code
#> Error in res[[1]]: subscript out of bounds
plot(res)
#> Error in t.default(x$x): argument is not a matrix
plot of chunk unnamed-chunk-7
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$x): argument is not a matrix
plot of chunk unnamed-chunk-8
library(survival)
set.seed(10101)
N=100;p=1000
nzc=p/3
x=matrix(rnorm(N*p),N,p)
beta=rnorm(nzc)
fx=x[,seq(nzc)] %*% beta/3
hx=exp(fx)
ty=rexp(N,hx)
tcens=rbinom(n=N,prob=.3,size=1)
y=cbind(time=ty,status=1-tcens)
res <- stabpath(y,x,weakness=1,mc.cores=2,family="cox")
plot(res)
#> Error in t.default(x$x): argument is not a matrix
plot of chunk unnamed-chunk-9
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)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.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
plot of chunk unnamed-chunk-13
#> [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- listy.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- listset.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