dcalasso: Divide-and-conquer method for the fitting of adaptive lasso...
In michaelyanwang/dcalasso: Divide-and-conquer adaptive lasso for big data
Description
Usage
Arguments
Value
Author(s)
References
Examples
Description
dcalasso fits adaptive lasso for big datasets using multiple linearization methods,
including one-step estimation and least square approximation. This function is able to
fit the adaptive lasso model either when the dataset is being loaded as a whole into data or when
the datasets are splitted a priori and saved into multiple rds files.
The algorithm uses a divide-and-conquer one-step estimator as the initial estimator
and uses a least square approximation to the partial likelihood, which
reduces the computation cost. The algorithm currently supports adaptive lasso with
Cox proportional hazards model with or without
time-dependent covariates. Ties in survival data analysis are handled by Efron's method.
The first half of the routine computes an initial estimator (n^1/2 consistent estimator). It first obtains a warm-start by
fitting coxph to the first subset (first random split of data or first data file indicated by data.rds) and then uses one-step
estimation with iter.os rounds to update the warm-start. The one-step estimation loops through each subset and gathering scores
and information matrices. The second half of the routine then shrinks the initial estimator using a least square approximation-based adaptive lasso step.
Usage
1
2
3
Arguments
formula
a formula specifying the model. For Cox model, the outcome should be specified as the Surv(start, stop, status) or Surv(start, status) object in the survival package.
family
For Cox model, family should be cox.ph(), or "cox.ph".
data
data frame containing all variables.
data.rds
when the dataset is too big to load as a whole into the RAM, one can specify data.rds
which are the full paths of all randomly splitted subsets of the full data, saved into multiple .rds format.
weights
a prior weights on each observation
na.action
how to handle NA
offset
an offset term with a fixed coefficient of one
lambda
tuning parameter for the adaptive lasso penalty. penalty = lambda * sum_j |beta_j|/|beta_j initial|^gamma
gamma
exponent of the adaptive penalty. penalty = lambda * sum_j |beta_j|/|beta_j initial|^gamma
K
number of division of the full dataset. It will be overwritten to length(data.rds) if data.rds is given.
iter.os
number of iterations for one-step updates
ncores
number of cores to use. The iterations will be paralleled using foreach if ncores>1.
subset
an expression indicating subset of rows of data used in model fitting
Value
coefficients.pen
adaptive lasso shrinkage estimation
coefficients.unpen
initial unregularized estimator
cov.unpen
variance-covariance matrix of unpenalized model
cov.pen
variance-covariance matrix of penalized model
BIC
sequence of BIC evaluation at each lambda
n.pen
number use to penalize the degrees of freedom in BIC.
n
number of used rows of the data
idx.opt
index for the optimal BIC
BIC.opt
minimal BIC
family
family object of the model
lamba.opt
optimal lambda to minimize BIC
df
degrees of freedom at each lambda
p
number of covariates
iter
number of one-step iterations
Terms
term object of the model
Author(s)
Yan Wang yaw719@mail.harvard.edu, Tianxi Cai tcai@hsph.harvard.edu, Chuan Hong <Chuan_Hong@hms.harvard.edu>
References
Wang, Yan, Chuan Hong, Nathan Palmer, Qian Di, Joel Schwartz, Isaac Kohane, and Tianxi Cai. "A Fast Divide-and-Conquer Sparse Cox Regression." arXiv preprint arXiv:1804.00735 (2018).
Examples
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##### Time-independent #####
set.seed(1)
N = 1e5; p.x = 50; K = 100; n = N/K; cor = 0.2;
bb = c(rep(0.4,4),rep(0.2,4),rep(0.1,4),rep(0.05,4))
beta0 = c(1, bb, rep(0, p.x - length(bb)))
dat.mat0 = as.data.frame(SIM.FUN(N, p.x = p.x, cor = cor, family='Cox',beta0 = beta0))
dat.mat0[,'strat'] = rep(1:20, each = N/20)
## Without strata
# unicore
mod = dcalasso(as.formula(paste0('Surv(u,delta)~',paste(paste0('V',3:52),collapse='+'))),
family = 'cox.ph',data = dat.mat0,
K = 10, iter.os = 2)
sum.mod = summary(mod)
print(sum.mod, unpen = T)
plot(mod)
pred.link = predict(mod, newdata = dat.mat0)
pred.term = predict(mod, newdata = dat.mat0, type = 'terms')
pred.response = predict(mod, newdata = dat.mat0, type = 'response')
# parallel
modp = dcalasso(as.formula(paste0('Surv(u,delta)~',paste(paste0('V',3:52),collapse='+'))),
family = 'cox.ph',data = dat.mat0,
K = 10, iter.os = 4, ncores = 2)
sum.modp = summary(modp)
print(sum.modp, unpen = T)
plot(modp)
# Standard
std = coxph(as.formula(paste0('Surv(u,delta)~',paste(paste0('V',3:52),collapse='+'))),
data = dat.mat0)
plot(mod$coefficients.unpen, std$coefficients)
plot(modp$coefficients.unpen, std$coefficients)
## With strata
# unicore
mod = dcalasso(as.formula(paste0('Surv(u,delta)~strata(strat)+',paste(paste0('V',3:52),collapse='+'))),
family = 'cox.ph',data = dat.mat0,
K = 10, iter.os = 2)
sum.mod = summary(mod)
print(sum.mod, unpen = T)
plot(mod)
# parallel
modp = dcalasso(as.formula(paste0('Surv(u,delta)~strata(strat)+',paste(paste0('V',3:52),collapse='+'))),
family = 'cox.ph',data = dat.mat0,
K = 10, iter.os = 2, ncores = 2)
sum.modp = summary(modp)
print(sum.modp, unpen = T)
plot(modp)
# Standard
std = coxph(as.formula(paste0('Surv(u,delta)~strata(strat)+',paste(paste0('V',3:52),collapse='+'))),
data = dat.mat0)
plot(mod$coefficients.unpen, std$coefficients)
plot(modp$coefficients.unpen, std$coefficients)
##### Time-independent with separate file saving #####
set.seed(1)
N = 1e5; p.x = 50; K = 100; n = N/K; cor = 0.2;
bb = c(rep(0.4,4),rep(0.2,4),rep(0.1,4),rep(0.05,4))
beta0 = c(1, bb, rep(0, p.x - length(bb)))
dat.mat0 = as.data.frame(SIM.FUN(N, p.x = p.x, cor = cor, family='Cox',beta0 = beta0))
dat.mat0[,'strat'] = rep(1:20, each = N/20)
dir = "C:/"
ll = split(1:N, factor(1:10))
for (kk in 1: 10){
df = dat.mat0[ll[[kk]],]
saveRDS(df, file = paste0(dir,'dataTI',kk,'.rds'))
}
## Without strata
# unicore
mod = dcalasso(as.formula(paste0('Surv(u,delta)~',paste(paste0('V',3:52),collapse='+'))),
family = 'cox.ph',
data.rds = paste0(dir,'dataTI',1:10,'.rds'), iter.os = 2)
sum.mod = summary(mod)
print(sum.mod, unpen = T)
plot(mod)
# parallel
modp = dcalasso(as.formula(paste0('Surv(u,delta)~',paste(paste0('V',3:52),collapse='+'))),
family = 'cox.ph',
data.rds = paste0(dir,'dataTI',1:10,'.rds'), iter.os = 2, ncores = 2)
sum.modp = summary(modp)
print(sum.modp, unpen = T)
plot(modp)
# Standard
std = coxph(as.formula(paste0('Surv(u,delta)~',paste(paste0('V',3:52),collapse='+'))),
data = dat.mat0)
plot(mod$coefficients.unpen, std$coefficients)
plot(modp$coefficients.unpen, std$coefficients)
## With strata
# unicore
mod = dcalasso(as.formula(paste0('Surv(u,delta)~strata(strat)+',paste(paste0('V',3:52),collapse='+'))),
family = 'cox.ph',
data.rds = paste0(dir,'dataTI',1:10,'.rds'), K = 10, iter.os = 2)
sum.mod = summary(mod)
print(sum.mod, unpen = T)
plot(mod)
# parallel
modp = dcalasso(as.formula(paste0('Surv(u,delta)~strata(strat)+',paste(paste0('V',3:52),collapse='+'))),
family = 'cox.ph',
data.rds = paste0(dir,'dataTI',1:10,'.rds'), K = 10, iter.os = 2, ncores = 2)
sum.modp = summary(modp)
print(sum.modp, unpen = T)
plot(modp)
# Standard
std = coxph(as.formula(paste0('Surv(u,delta)~strata(strat)+',paste(paste0('V',3:52),collapse='+'))),
data = dat.mat0)
plot(mod$coefficients.unpen, std$coefficients)
plot(modp$coefficients.unpen, std$coefficients)
########### Time-dependent loading as a whole ####################
set.seed(1)
n.subject = 1e5; p.ti = 50; p.tv = 50; K = 20; n = n.subject/K; cor = 0.2; lambda.grid = 10^seq(-10,3,0.01);
beta0.ti = NULL
beta0.tv = NULL
dat.mat0 = as.data.frame(SIM.FUN.TVC(p.ti, p.tv, n.subject, cor, beta0.ti, beta0.tv))
dat.mat0[,'strat'] = dat.mat0[,dim(dat.mat0)[2]]%%(n.subject/20)
dat.mat0 = dat.mat0[,-(dim(dat.mat0)[2]-1)]
## Without strata
# unicore
mod = dcalasso(as.formula(paste0('Surv(t0,t1,status)~',paste(paste0('V',4:103),collapse='+'))),
family = 'cox.ph',data = dat.mat0,
K = 10, iter.os = 2)
sum.mod = summary(mod)
print(sum.mod, unpen = T)
plot(mod)
# parallel
modp = dcalasso(as.formula(paste0('Surv(t0,t1,status)~',paste(paste0('V',4:103),collapse='+'))),
family = 'cox.ph',data = dat.mat0,
K = 10, iter.os = 2, ncores = 2)
sum.modp = summary(modp)
print(sum.modp, unpen = T)
plot(modp)
# Standard
std = coxph(as.formula(paste0('Surv(t0,t1,status)~',paste(paste0('V',4:103),
collapse='+'))),
data = dat.mat0)
plot(mod$coefficients.unpen, std$coefficients)
plot(modp$coefficients.unpen, mod$coefficients.unpen)
# With strata
# unicore
mod = dcalasso(as.formula(paste0('Surv(t0,t1,status)~strata(strat)+',paste(paste0('V',4:103),collapse='+'))),
family = 'cox.ph',data = dat.mat0,
K = 10, iter.os = 4)
sum.mod = summary(mod)
print(sum.mod, unpen = T)
plot(mod)
# parallel
modp = dcalasso(as.formula(paste0('Surv(t0,t1,status)~strata(strat)+',paste(paste0('V',4:103),collapse='+'))),
family = 'cox.ph',data = dat.mat0,
K = 10, iter.os = 4, ncores = 2)
sum.modp = summary(modp)
print(sum.modp, unpen = T)
plot(modp)
# Standard
std = coxph(as.formula(paste0('Surv(t0,t1,status)~strata(strat)+',paste(paste0('V',4:103),
collapse='+'))),
data = dat.mat0)
plot(mod$coefficients.unpen, std$coefficients)
plot(modp$coefficients.unpen, mod$coefficients.unpen)
########### Time-dependent separate file saving ####################
set.seed(1)
n.subject = 1e5; p.ti = 50; p.tv = 50; K = 20; n = n.subject/K; cor = 0.2; lambda.grid = 10^seq(-10,3,0.01);
beta0.ti = NULL
beta0.tv = NULL
dat.mat0 = as.data.frame(SIM.FUN.TVC(p.ti, p.tv, n.subject, cor, beta0.ti, beta0.tv))
dat.mat0[,'strat'] = dat.mat0[,dim(dat.mat0)[2]]%%(n.subject/20)
dat.mat0 = dat.mat0[,-(dim(dat.mat0)[2]-1)]
ll = split(1:dim(dat.mat0)[1], factor(1:10))
for (kk in 1: 10){
df = dat.mat0[ll[[kk]],]
saveRDS(df, file = paste0(dir,'dataTV',kk,'.rds'))
}
## Without strata
# unicore
mod = dcalasso(as.formula(paste0('Surv(t0,t1,status)~',paste(paste0('V',4:103),collapse='+'))),
family = 'cox.ph',
data.rds = paste0(dir,'dataTV',1:10,'.rds'), K = 10, iter.os = 2)
sum.mod = summary(mod)
print(sum.mod, unpen = T)
plot(mod)
# parallel
modp = dcalasso(as.formula(paste0('Surv(t0,t1,status)~',paste(paste0('V',4:103),collapse='+'))),
family = 'cox.ph',
data.rds = paste0(dir,'dataTV',1:10,'.rds'), K = 10, iter.os = 2, ncores = 2)
sum.modp = summary(modp)
print(sum.modp, unpen = T)
plot(modp)
# Standard
std = coxph(as.formula(paste0('Surv(t0,t1,status)~',paste(paste0('V',4:103),
collapse='+'))),
data = dat.mat0)
plot(mod$coefficients.unpen, std$coefficients)
plot(modp$coefficients.unpen, mod$coefficients.unpen)
# With strata
# unicore
mod = dcalasso(as.formula(paste0('Surv(t0,t1,status)~strata(strat)+',paste(paste0('V',4:103),collapse='+'))),
family = 'cox.ph',
data.rds = paste0(dir,'dataTV',1:10,'.rds'), K = 10, iter.os = 4)
sum.mod = summary(mod)
print(sum.mod, unpen = T)
plot(mod)
# parallel
modp = dcalasso(as.formula(paste0('Surv(t0,t1,status)~strata(strat)+',paste(paste0('V',4:103),collapse='+'))),
family = 'cox.ph',
data.rds = paste0(dir,'dataTV',1:10,'.rds'), K = 10, iter.os = 4, ncores = 2)
sum.modp = summary(modp)
print(sum.modp, unpen = T)
plot(modp)
# Standard
std = coxph(as.formula(paste0('Surv(t0,t1,status)~strata(strat)+',paste(paste0('V',4:103),
collapse='+'))),
data = dat.mat0)
plot(mod$coefficients.unpen, std$coefficients)
plot(modp$coefficients.unpen, mod$coefficients.unpen)
michaelyanwang/dcalasso documentation built on Nov. 22, 2019, 3:51 a.m.
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Description Usage Arguments Value Author(s) References Examples
Description
dcalasso fits adaptive lasso for big datasets using multiple linearization methods,
including one-step estimation and least square approximation. This function is able to
fit the adaptive lasso model either when the dataset is being loaded as a whole into data or when
the datasets are splitted a priori and saved into multiple rds files.
The algorithm uses a divide-and-conquer one-step estimator as the initial estimator
and uses a least square approximation to the partial likelihood, which
reduces the computation cost. The algorithm currently supports adaptive lasso with
Cox proportional hazards model with or without
time-dependent covariates. Ties in survival data analysis are handled by Efron's method.
The first half of the routine computes an initial estimator (n^1/2 consistent estimator). It first obtains a warm-start by
fitting coxph to the first subset (first random split of data or first data file indicated by data.rds) and then uses one-step
estimation with iter.os rounds to update the warm-start. The one-step estimation loops through each subset and gathering scores
and information matrices. The second half of the routine then shrinks the initial estimator using a least square approximation-based adaptive lasso step.
Usage
1 2 3 |
Arguments
formula |
a formula specifying the model. For Cox model, the outcome should be specified as the Surv(start, stop, status) or Surv(start, status) object in the survival package. |
family |
For Cox model, family should be cox.ph(), or "cox.ph". |
data |
data frame containing all variables. |
data.rds |
when the dataset is too big to load as a whole into the RAM, one can specify |
weights |
a prior weights on each observation |
na.action |
how to handle NA |
offset |
an offset term with a fixed coefficient of one |
lambda |
tuning parameter for the adaptive lasso penalty. penalty = lambda * sum_j |beta_j|/|beta_j initial|^gamma |
gamma |
exponent of the adaptive penalty. penalty = lambda * sum_j |beta_j|/|beta_j initial|^gamma |
K |
number of division of the full dataset. It will be overwritten to |
iter.os |
number of iterations for one-step updates |
ncores |
number of cores to use. The iterations will be paralleled using |
subset |
an expression indicating subset of rows of data used in model fitting |
Value
coefficients.pen |
adaptive lasso shrinkage estimation |
coefficients.unpen |
initial unregularized estimator |
cov.unpen |
variance-covariance matrix of unpenalized model |
cov.pen |
variance-covariance matrix of penalized model |
BIC |
sequence of BIC evaluation at each lambda |
n.pen |
number use to penalize the degrees of freedom in BIC. |
n |
number of used rows of the data |
idx.opt |
index for the optimal BIC |
BIC.opt |
minimal BIC |
family |
family object of the model |
lamba.opt |
optimal lambda to minimize BIC |
df |
degrees of freedom at each lambda |
p |
number of covariates |
iter |
number of one-step iterations |
Terms |
term object of the model |
Author(s)
Yan Wang yaw719@mail.harvard.edu, Tianxi Cai tcai@hsph.harvard.edu, Chuan Hong <Chuan_Hong@hms.harvard.edu>
References
Wang, Yan, Chuan Hong, Nathan Palmer, Qian Di, Joel Schwartz, Isaac Kohane, and Tianxi Cai. "A Fast Divide-and-Conquer Sparse Cox Regression." arXiv preprint arXiv:1804.00735 (2018).
Examples
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set.seed(1)
N = 1e5; p.x = 50; K = 100; n = N/K; cor = 0.2;
bb = c(rep(0.4,4),rep(0.2,4),rep(0.1,4),rep(0.05,4))
beta0 = c(1, bb, rep(0, p.x - length(bb)))
dat.mat0 = as.data.frame(SIM.FUN(N, p.x = p.x, cor = cor, family='Cox',beta0 = beta0))
dat.mat0[,'strat'] = rep(1:20, each = N/20)
## Without strata
# unicore
mod = dcalasso(as.formula(paste0('Surv(u,delta)~',paste(paste0('V',3:52),collapse='+'))),
family = 'cox.ph',data = dat.mat0,
K = 10, iter.os = 2)
sum.mod = summary(mod)
print(sum.mod, unpen = T)
plot(mod)
pred.link = predict(mod, newdata = dat.mat0)
pred.term = predict(mod, newdata = dat.mat0, type = 'terms')
pred.response = predict(mod, newdata = dat.mat0, type = 'response')
# parallel
modp = dcalasso(as.formula(paste0('Surv(u,delta)~',paste(paste0('V',3:52),collapse='+'))),
family = 'cox.ph',data = dat.mat0,
K = 10, iter.os = 4, ncores = 2)
sum.modp = summary(modp)
print(sum.modp, unpen = T)
plot(modp)
# Standard
std = coxph(as.formula(paste0('Surv(u,delta)~',paste(paste0('V',3:52),collapse='+'))),
data = dat.mat0)
plot(mod$coefficients.unpen, std$coefficients)
plot(modp$coefficients.unpen, std$coefficients)
## With strata
# unicore
mod = dcalasso(as.formula(paste0('Surv(u,delta)~strata(strat)+',paste(paste0('V',3:52),collapse='+'))),
family = 'cox.ph',data = dat.mat0,
K = 10, iter.os = 2)
sum.mod = summary(mod)
print(sum.mod, unpen = T)
plot(mod)
# parallel
modp = dcalasso(as.formula(paste0('Surv(u,delta)~strata(strat)+',paste(paste0('V',3:52),collapse='+'))),
family = 'cox.ph',data = dat.mat0,
K = 10, iter.os = 2, ncores = 2)
sum.modp = summary(modp)
print(sum.modp, unpen = T)
plot(modp)
# Standard
std = coxph(as.formula(paste0('Surv(u,delta)~strata(strat)+',paste(paste0('V',3:52),collapse='+'))),
data = dat.mat0)
plot(mod$coefficients.unpen, std$coefficients)
plot(modp$coefficients.unpen, std$coefficients)
##### Time-independent with separate file saving #####
set.seed(1)
N = 1e5; p.x = 50; K = 100; n = N/K; cor = 0.2;
bb = c(rep(0.4,4),rep(0.2,4),rep(0.1,4),rep(0.05,4))
beta0 = c(1, bb, rep(0, p.x - length(bb)))
dat.mat0 = as.data.frame(SIM.FUN(N, p.x = p.x, cor = cor, family='Cox',beta0 = beta0))
dat.mat0[,'strat'] = rep(1:20, each = N/20)
dir = "C:/"
ll = split(1:N, factor(1:10))
for (kk in 1: 10){
df = dat.mat0[ll[[kk]],]
saveRDS(df, file = paste0(dir,'dataTI',kk,'.rds'))
}
## Without strata
# unicore
mod = dcalasso(as.formula(paste0('Surv(u,delta)~',paste(paste0('V',3:52),collapse='+'))),
family = 'cox.ph',
data.rds = paste0(dir,'dataTI',1:10,'.rds'), iter.os = 2)
sum.mod = summary(mod)
print(sum.mod, unpen = T)
plot(mod)
# parallel
modp = dcalasso(as.formula(paste0('Surv(u,delta)~',paste(paste0('V',3:52),collapse='+'))),
family = 'cox.ph',
data.rds = paste0(dir,'dataTI',1:10,'.rds'), iter.os = 2, ncores = 2)
sum.modp = summary(modp)
print(sum.modp, unpen = T)
plot(modp)
# Standard
std = coxph(as.formula(paste0('Surv(u,delta)~',paste(paste0('V',3:52),collapse='+'))),
data = dat.mat0)
plot(mod$coefficients.unpen, std$coefficients)
plot(modp$coefficients.unpen, std$coefficients)
## With strata
# unicore
mod = dcalasso(as.formula(paste0('Surv(u,delta)~strata(strat)+',paste(paste0('V',3:52),collapse='+'))),
family = 'cox.ph',
data.rds = paste0(dir,'dataTI',1:10,'.rds'), K = 10, iter.os = 2)
sum.mod = summary(mod)
print(sum.mod, unpen = T)
plot(mod)
# parallel
modp = dcalasso(as.formula(paste0('Surv(u,delta)~strata(strat)+',paste(paste0('V',3:52),collapse='+'))),
family = 'cox.ph',
data.rds = paste0(dir,'dataTI',1:10,'.rds'), K = 10, iter.os = 2, ncores = 2)
sum.modp = summary(modp)
print(sum.modp, unpen = T)
plot(modp)
# Standard
std = coxph(as.formula(paste0('Surv(u,delta)~strata(strat)+',paste(paste0('V',3:52),collapse='+'))),
data = dat.mat0)
plot(mod$coefficients.unpen, std$coefficients)
plot(modp$coefficients.unpen, std$coefficients)
########### Time-dependent loading as a whole ####################
set.seed(1)
n.subject = 1e5; p.ti = 50; p.tv = 50; K = 20; n = n.subject/K; cor = 0.2; lambda.grid = 10^seq(-10,3,0.01);
beta0.ti = NULL
beta0.tv = NULL
dat.mat0 = as.data.frame(SIM.FUN.TVC(p.ti, p.tv, n.subject, cor, beta0.ti, beta0.tv))
dat.mat0[,'strat'] = dat.mat0[,dim(dat.mat0)[2]]%%(n.subject/20)
dat.mat0 = dat.mat0[,-(dim(dat.mat0)[2]-1)]
## Without strata
# unicore
mod = dcalasso(as.formula(paste0('Surv(t0,t1,status)~',paste(paste0('V',4:103),collapse='+'))),
family = 'cox.ph',data = dat.mat0,
K = 10, iter.os = 2)
sum.mod = summary(mod)
print(sum.mod, unpen = T)
plot(mod)
# parallel
modp = dcalasso(as.formula(paste0('Surv(t0,t1,status)~',paste(paste0('V',4:103),collapse='+'))),
family = 'cox.ph',data = dat.mat0,
K = 10, iter.os = 2, ncores = 2)
sum.modp = summary(modp)
print(sum.modp, unpen = T)
plot(modp)
# Standard
std = coxph(as.formula(paste0('Surv(t0,t1,status)~',paste(paste0('V',4:103),
collapse='+'))),
data = dat.mat0)
plot(mod$coefficients.unpen, std$coefficients)
plot(modp$coefficients.unpen, mod$coefficients.unpen)
# With strata
# unicore
mod = dcalasso(as.formula(paste0('Surv(t0,t1,status)~strata(strat)+',paste(paste0('V',4:103),collapse='+'))),
family = 'cox.ph',data = dat.mat0,
K = 10, iter.os = 4)
sum.mod = summary(mod)
print(sum.mod, unpen = T)
plot(mod)
# parallel
modp = dcalasso(as.formula(paste0('Surv(t0,t1,status)~strata(strat)+',paste(paste0('V',4:103),collapse='+'))),
family = 'cox.ph',data = dat.mat0,
K = 10, iter.os = 4, ncores = 2)
sum.modp = summary(modp)
print(sum.modp, unpen = T)
plot(modp)
# Standard
std = coxph(as.formula(paste0('Surv(t0,t1,status)~strata(strat)+',paste(paste0('V',4:103),
collapse='+'))),
data = dat.mat0)
plot(mod$coefficients.unpen, std$coefficients)
plot(modp$coefficients.unpen, mod$coefficients.unpen)
########### Time-dependent separate file saving ####################
set.seed(1)
n.subject = 1e5; p.ti = 50; p.tv = 50; K = 20; n = n.subject/K; cor = 0.2; lambda.grid = 10^seq(-10,3,0.01);
beta0.ti = NULL
beta0.tv = NULL
dat.mat0 = as.data.frame(SIM.FUN.TVC(p.ti, p.tv, n.subject, cor, beta0.ti, beta0.tv))
dat.mat0[,'strat'] = dat.mat0[,dim(dat.mat0)[2]]%%(n.subject/20)
dat.mat0 = dat.mat0[,-(dim(dat.mat0)[2]-1)]
ll = split(1:dim(dat.mat0)[1], factor(1:10))
for (kk in 1: 10){
df = dat.mat0[ll[[kk]],]
saveRDS(df, file = paste0(dir,'dataTV',kk,'.rds'))
}
## Without strata
# unicore
mod = dcalasso(as.formula(paste0('Surv(t0,t1,status)~',paste(paste0('V',4:103),collapse='+'))),
family = 'cox.ph',
data.rds = paste0(dir,'dataTV',1:10,'.rds'), K = 10, iter.os = 2)
sum.mod = summary(mod)
print(sum.mod, unpen = T)
plot(mod)
# parallel
modp = dcalasso(as.formula(paste0('Surv(t0,t1,status)~',paste(paste0('V',4:103),collapse='+'))),
family = 'cox.ph',
data.rds = paste0(dir,'dataTV',1:10,'.rds'), K = 10, iter.os = 2, ncores = 2)
sum.modp = summary(modp)
print(sum.modp, unpen = T)
plot(modp)
# Standard
std = coxph(as.formula(paste0('Surv(t0,t1,status)~',paste(paste0('V',4:103),
collapse='+'))),
data = dat.mat0)
plot(mod$coefficients.unpen, std$coefficients)
plot(modp$coefficients.unpen, mod$coefficients.unpen)
# With strata
# unicore
mod = dcalasso(as.formula(paste0('Surv(t0,t1,status)~strata(strat)+',paste(paste0('V',4:103),collapse='+'))),
family = 'cox.ph',
data.rds = paste0(dir,'dataTV',1:10,'.rds'), K = 10, iter.os = 4)
sum.mod = summary(mod)
print(sum.mod, unpen = T)
plot(mod)
# parallel
modp = dcalasso(as.formula(paste0('Surv(t0,t1,status)~strata(strat)+',paste(paste0('V',4:103),collapse='+'))),
family = 'cox.ph',
data.rds = paste0(dir,'dataTV',1:10,'.rds'), K = 10, iter.os = 4, ncores = 2)
sum.modp = summary(modp)
print(sum.modp, unpen = T)
plot(modp)
# Standard
std = coxph(as.formula(paste0('Surv(t0,t1,status)~strata(strat)+',paste(paste0('V',4:103),
collapse='+'))),
data = dat.mat0)
plot(mod$coefficients.unpen, std$coefficients)
plot(modp$coefficients.unpen, mod$coefficients.unpen)
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