, Myriam
Maumy-Bertrand [aut] ,
Ismail Aouadi [ctb], Nicolas Jung [ctb]| Title: | A General Algorithm to Enhance the Performance of Variable Selection Methods in Correlated Datasets |
|---|---|
| Description: | An implementation of the selectboost algorithm (Bertrand et al. 2020, 'Bioinformatics', <doi:10.1093/bioinformatics/btaa855>), which is a general algorithm that improves the precision of any existing variable selection method. This algorithm is based on highly intensive simulations and takes into account the correlation structure of the data. It can either produce a confidence index for variable selection or it can be used in an experimental design planning perspective. |
| Authors: | Frederic Bertrand [cre, aut]
, Myriam
Maumy-Bertrand [aut] ,
Ismail Aouadi [ctb], Nicolas Jung [ctb] |
| Maintainer: | Frederic Bertrand <[email protected]> |
| License: | GPL-3 |
| Version: | 2.2.2 |
| Built: | 2025-01-27 04:42:14 UTC |
| Source: | https://github.com/fbertran/selectboost |
Compute AICc and BIC for glmnet logistic models.
rerr(v1, v2) ridge_logistic(X, Y, lambda, beta0, beta, maxiter = 1000, tol = 1e-10) BIC_glmnetB(Z, Y, glmnet.model, alpha, modelSet, reducer = "median") AICc_glmnetB(Z, Y, glmnet.model, alpha, modelSet, reducer = "median")rerr(v1, v2) ridge_logistic(X, Y, lambda, beta0, beta, maxiter = 1000, tol = 1e-10) BIC_glmnetB(Z, Y, glmnet.model, alpha, modelSet, reducer = "median") AICc_glmnetB(Z, Y, glmnet.model, alpha, modelSet, reducer = "median")
v1 |
A numeric vector. |
v2 |
A numeric vector. |
X |
A numeric matrix |
Y |
A numeric 0/1 vector. |
lambda |
A numeric value. |
beta0 |
A numeric value Initial intercept value. |
beta |
A numeric vector. Initial coefficient values. |
maxiter |
A numeric value. Maximum number of iterations. |
tol |
A numeric value. Tolerance value. |
Z |
A numeric matrix |
glmnet.model |
A fitted glmnet model. |
alpha |
A numeric value. |
modelSet |
Modelset to consider. |
reducer |
A character value. Reducer function. Either 'median' or 'mean'. |
Calculate AICc and BIC for glmnet logistic models from the glmnetB function of the package rLogistic https://github.com/echi/rLogistic and adapted to deal with non finite exponential values in AICc and BIC computations
A list relevant to model selection.
Frederic Bertrand, [email protected]
Robust Parametric Classification and Variable Selection by a Minimum Distance Criterion, Chi and Scott, Journal of Computational and Graphical Statistics, 23(1), 2014, p111–128, doi:10.1080/10618600.2012.737296.
set.seed(314) xran=matrix(rnorm(150),30,5) ybin=sample(0:1,30,replace=TRUE) glmnet.fit <- glmnet.fit <- glmnet::glmnet(xran,ybin,family="binomial",standardize=FALSE) set.seed(314) rerr(1:10,10:1) set.seed(314) ridge_logistic(xran,ybin,lambda=.5,beta0=rnorm(5),beta=rnorm(5,1)) set.seed(314) if(is.factor(ybin)){ynum=unclass(ybin)-1} else {ynum=ybin} subSample <- 1:min(ncol(xran),100) BIC_glmnetB(xran,ynum,glmnet.fit,alpha=1,subSample, reducer='median') set.seed(314) if(is.factor(ybin)){ynum=unclass(ybin)-1} else {ynum=ybin} subSample <- 1:min(ncol(xran),100) AICc_glmnetB(xran,ynum,glmnet.fit,alpha=1,subSample, reducer='median')set.seed(314) xran=matrix(rnorm(150),30,5) ybin=sample(0:1,30,replace=TRUE) glmnet.fit <- glmnet.fit <- glmnet::glmnet(xran,ybin,family="binomial",standardize=FALSE) set.seed(314) rerr(1:10,10:1) set.seed(314) ridge_logistic(xran,ybin,lambda=.5,beta0=rnorm(5),beta=rnorm(5,1)) set.seed(314) if(is.factor(ybin)){ynum=unclass(ybin)-1} else {ynum=ybin} subSample <- 1:min(ncol(xran),100) BIC_glmnetB(xran,ynum,glmnet.fit,alpha=1,subSample, reducer='median') set.seed(314) if(is.factor(ybin)){ynum=unclass(ybin)-1} else {ynum=ybin} subSample <- 1:min(ncol(xran),100) AICc_glmnetB(xran,ynum,glmnet.fit,alpha=1,subSample, reducer='median')
Find limits for selectboost analysis.
auto.analyze(x, ...) ## S3 method for class 'selectboost' auto.analyze(x, ...)auto.analyze(x, ...) ## S3 method for class 'selectboost' auto.analyze(x, ...)
x |
Numerical matrix. Selectboost object. |
... |
. Passed to the summary.selectboost function. |
plot.summary.selectboost returns an invisible list and creates four graphics.
Two plots the proportion of selection with respect to c0 (by step or according to real scale).
On the third graph, no bar means a proportion of selection less than prop.level.
Confidence intervals are computed at the conf.int.level level.
Barplot of the confidence index (1-min(c0, such that proportion|c0>conf.threshold)).
list of results.
Frederic Bertrand, [email protected]
selectBoost: a general algorithm to enhance the performance of variable selection methods in correlated datasets, Frédéric Bertrand, Ismaïl Aouadi, Nicolas Jung, Raphael Carapito, Laurent Vallat, Seiamak Bahram, Myriam Maumy-Bertrand, Bioinformatics, 2020. doi:10.1093/bioinformatics/btaa855
Other Selectboost analyze functions:
plot.summary.selectboost(),
trajC0()
data(autoboost.res.x) auto.analyze(autoboost.res.x) data(autoboost.res.x2) auto.analyze(autoboost.res.x2)data(autoboost.res.x) auto.analyze(autoboost.res.x) data(autoboost.res.x2) auto.analyze(autoboost.res.x2)
All in one use of selectboost that avoids redondant fitting of distributions and saves some memory.
autoboost( X, Y, ncores = 4, group = group_func_1, func = lasso_msgps_AICc, corrfunc = "cor", use.parallel = FALSE, B = 100, step.num = 0.1, step.limit = "none", risk = 0.05, verbose = FALSE, step.scale = "quantile", normalize = TRUE, steps.seq = NULL, debug = FALSE, version = "lars", ... )autoboost( X, Y, ncores = 4, group = group_func_1, func = lasso_msgps_AICc, corrfunc = "cor", use.parallel = FALSE, B = 100, step.num = 0.1, step.limit = "none", risk = 0.05, verbose = FALSE, step.scale = "quantile", normalize = TRUE, steps.seq = NULL, debug = FALSE, version = "lars", ... )
X |
Numerical matrix. Matrix of the variables. |
Y |
Numerical vector or factor. Response vector. |
ncores |
Numerical value. Number of cores for parallel computing.
Defaults to |
group |
Function. The grouping function.
Defaults to |
func |
Function. The variable selection function.
Defaults to |
corrfunc |
Character value or function. Used to compute associations between
the variables. Defaults to |
use.parallel |
Boolean. To use parallel computing (doMC) download the extended package from Github.
Set to |
B |
Numerical value. Number of resampled fits of the model.
Defaults to |
step.num |
Numerical value. Step value for the c0 sequence.
Defaults to |
step.limit |
Character value. If "Pearson", truncates the c0 sequence using a
Pearson based p-value.
Defaults to |
risk |
Numerical value. Risk level when finding limits based on c0=0 values.
Defaults to |
verbose |
Boolean.
Defaults to |
step.scale |
Character value. How to compute the c0 sequence if not user-provided:
either "quantile" or "linear".
Defaults to |
normalize |
Boolean. Shall the X matrix be centered and scaled?
Defaults to |
steps.seq |
Numeric vector. User provided sequence of c0 values to use.
Defaults to |
debug |
Boolean value. If more results are required. Defaults to |
version |
Character value. Passed to the |
... |
. Arguments passed to the variable selection function used in |
autoboost returns a numeric matrix. For each of the variable (column)
and each of the c0 (row), the entry is proportion of times that the variable was
selected among the B resampled fits of the model. Fitting to the same group of variables is
only perfomed once (even if it occured for another value of c0), which greatly speeds up
the algorithm.
A numeric matrix with attributes.
Frederic Bertrand, [email protected]
selectBoost: a general algorithm to enhance the performance of variable selection methods in correlated datasets, Frédéric Bertrand, Ismaïl Aouadi, Nicolas Jung, Raphael Carapito, Laurent Vallat, Seiamak Bahram, Myriam Maumy-Bertrand, Bioinformatics, 2020. doi:10.1093/bioinformatics/btaa855
boost, fastboost, plot.selectboost
Other Selectboost functions:
boost,
fastboost(),
plot_selectboost_cascade,
selectboost_cascade
set.seed(314) xran=matrix(rnorm(75),15,5) ybin=sample(0:1,15,replace=TRUE) yran=rnorm(15) set.seed(314) #For quick test purpose, not meaningful, should be run with greater value of B #and disabling parallel computing as well res.autoboost <- autoboost(xran,yran,B=3,use.parallel=FALSE) autoboost(xran,yran) #Customize resampling levels autoboost(xran,yran,steps.seq=c(.99,.95,.9)) #Binary logistic regression autoboost(xran,ybin,func=lasso_cv_glmnet_bin_min)set.seed(314) xran=matrix(rnorm(75),15,5) ybin=sample(0:1,15,replace=TRUE) yran=rnorm(15) set.seed(314) #For quick test purpose, not meaningful, should be run with greater value of B #and disabling parallel computing as well res.autoboost <- autoboost(xran,yran,B=3,use.parallel=FALSE) autoboost(xran,yran) #Customize resampling levels autoboost(xran,yran,steps.seq=c(.99,.95,.9)) #Binary logistic regression autoboost(xran,ybin,func=lasso_cv_glmnet_bin_min)
Result of autoboost analysis of diabetes data from lars package with lasso and first order model
autoboost.res.xautoboost.res.x
A numerical matrix frame with 13 rows and 10 variables with attributes.
Result of autoboost analysis of diabetes data from lars package with adaptative lasso and first order model
autoboost.res.x.adaptautoboost.res.x.adapt
A numerical matrix frame with 13 rows and 10 variables with attributes.
Result of autoboost analysis of diabetes data from lars package with lasso and second order model
autoboost.res.x2autoboost.res.x2
A numerical matrix frame with 13 rows and 64 variables with attributes.
Result of autoboost analysis of diabetes data from lars package with adaptative lasso and second order model
autoboost.res.x2.adaptautoboost.res.x2.adapt
A numerical matrix frame with 13 rows and 64 variables with attributes.
Step by step functions to apply the selectboost algorithm.
boost.normalize(X, eps = 1e-08) boost.compcorrs( Xnorm, corrfunc = "cor", verbose = FALSE, testvarindic = rep(TRUE, ncol(Xnorm)) ) boost.correlation_sign(Correlation_matrice, verbose = FALSE) boost.findgroups(Correlation_matrice, group, corr = 1, verbose = FALSE) boost.Xpass(nrowX, ncolX) boost.adjust( X, groups, Correlation_sign, Xpass = boost.Xpass(nrowX, ncolX), verbose = FALSE, use.parallel = FALSE, ncores = 4 ) boost.random( X, Xpass, vmf.params, verbose = FALSE, B = 100, use.parallel = FALSE, ncores = 4 ) boost.apply( X, cols.simul, Y, func, verbose = FALSE, use.parallel = FALSE, ncores = 4, ... ) boost.select(Boost.coeffs, eps = 10^(-4), version = "lars", verbose = FALSE)boost.normalize(X, eps = 1e-08) boost.compcorrs( Xnorm, corrfunc = "cor", verbose = FALSE, testvarindic = rep(TRUE, ncol(Xnorm)) ) boost.correlation_sign(Correlation_matrice, verbose = FALSE) boost.findgroups(Correlation_matrice, group, corr = 1, verbose = FALSE) boost.Xpass(nrowX, ncolX) boost.adjust( X, groups, Correlation_sign, Xpass = boost.Xpass(nrowX, ncolX), verbose = FALSE, use.parallel = FALSE, ncores = 4 ) boost.random( X, Xpass, vmf.params, verbose = FALSE, B = 100, use.parallel = FALSE, ncores = 4 ) boost.apply( X, cols.simul, Y, func, verbose = FALSE, use.parallel = FALSE, ncores = 4, ... ) boost.select(Boost.coeffs, eps = 10^(-4), version = "lars", verbose = FALSE)
X |
Numerical matrix. Matrix of the variables. |
eps |
Numerical value. Response vector. |
Xnorm |
Numerical matrix. Needs to be centered and l2 normalized. |
corrfunc |
Character value or function. The function to compute associations between the variables. |
verbose |
Boolean.
Defaults to |
testvarindic |
Boolean vector. Compute associations for a subset of variables.
By default, the scope of the computation is the whole dataset, i.e. |
Correlation_matrice |
Numerical matrix. |
group |
Character value or function. The grouping function. |
corr |
Numerical value. Thresholding value. Defaults to |
nrowX |
Numerical value |
ncolX |
Numerical value. |
groups |
List. List of groups or communities (compact form). |
Correlation_sign |
Numerical -1/1 matrix. |
Xpass |
Numerical value. Transformation matrix.
Defaults to |
use.parallel |
Boolean.
Defaults to |
ncores |
Numerical value. Number of cores to use.
Defaults to |
vmf.params |
List. List of the parameters ot the fitted von-Mises distributions. |
B |
Integer value. Number of resampling. |
cols.simul |
Numerical value. Transformation matrix. |
Y |
Numerical vector or factor. Response. |
func |
Function. Variable selection function. |
... |
. Additionnal parameters passed to the |
Boost.coeffs |
Numerical matrix. l2 normed matrix of predictors. |
version |
Character value. "lars" (no intercept value) or "glmnet" (first coefficient is the intercept value). |
boost.normalize returns a numeric matrix whose colun are centered and l2 normalized.
boost.compcorrs returns a correlation like matrix computed using the corrfunc function.
boost.Xpass returns the transformation matrix.
boost.findgroups returns a list of groups or communities found using the group function.
boost.Xpass returns the transformation matrix.
boost.adjust returns the list of the parameters ot the fitted von-Mises distributions.
boost.random returns an array with the resampled datasets.
boost.apply returns a matrix with the coefficients estimated using the resampled datasets.
boost.select returns a vector with the proportion of times each variable was selected.
Various types depending on the function.
Frederic Bertrand, [email protected]
selectBoost: a general algorithm to enhance the performance of variable selection methods in correlated datasets, Frédéric Bertrand, Ismaïl Aouadi, Nicolas Jung, Raphael Carapito, Laurent Vallat, Seiamak Bahram, Myriam Maumy-Bertrand, Bioinformatics, 2020. doi:10.1093/bioinformatics/btaa855
Other Selectboost functions:
autoboost(),
fastboost(),
plot_selectboost_cascade,
selectboost_cascade
set.seed(314) xran=matrix(rnorm(200),20,10) yran=rnorm(20) xran_norm <- boost.normalize(xran) xran_corr<- boost.compcorrs(xran_norm) xran_corr_sign <- boost.correlation_sign(xran_corr) xran_groups <- boost.findgroups(xran_corr, group=group_func_1, .3) xran_groups_2 <- boost.findgroups(xran_corr, group=group_func_2, .3) xran_Xpass <- boost.Xpass(nrow(xran_norm),ncol(xran_norm)) xran_adjust <- boost.adjust(xran_norm, xran_groups$groups, xran_corr_sign) #Not meaningful, should be run with B>=100 xran_random <- boost.random(xran_norm, xran_Xpass, xran_adjust$vmf.params, B=5) xran_random <- boost.random(xran_norm, xran_Xpass, xran_adjust$vmf.params, B=100) xran_apply <- boost.apply(xran_norm, xran_random, yran, lasso_msgps_AICc) xran_select <- boost.select(xran_apply)set.seed(314) xran=matrix(rnorm(200),20,10) yran=rnorm(20) xran_norm <- boost.normalize(xran) xran_corr<- boost.compcorrs(xran_norm) xran_corr_sign <- boost.correlation_sign(xran_corr) xran_groups <- boost.findgroups(xran_corr, group=group_func_1, .3) xran_groups_2 <- boost.findgroups(xran_corr, group=group_func_2, .3) xran_Xpass <- boost.Xpass(nrow(xran_norm),ncol(xran_norm)) xran_adjust <- boost.adjust(xran_norm, xran_groups$groups, xran_corr_sign) #Not meaningful, should be run with B>=100 xran_random <- boost.random(xran_norm, xran_Xpass, xran_adjust$vmf.params, B=5) xran_random <- boost.random(xran_norm, xran_Xpass, xran_adjust$vmf.params, B=100) xran_apply <- boost.apply(xran_norm, xran_random, yran, lasso_msgps_AICc) xran_select <- boost.select(xran_apply)
Result for confidence indices derivation using the Cascade package
net_confidence net_confidence_.5 net_confidence_thrnet_confidence net_confidence_.5 net_confidence_thr
A network.confidence object with four slots :
The confidence matrix
Names of the variables (genes)
F array, see Cascade for more details
Repeated measurements
Logical. Was crossvalidation carried out subjectwise?
An object of class network.confidence of length 1.
An object of class network.confidence of length 1.
Result for the reverse engineering of a simulated Cascade network
M Net Net_inf_CM Net Net_inf_C
Three objects :
Simulated microarray
Simulated network
Inferred network
An object of class network of length 1.
An object of class network of length 1.
All in one use of selectboost that avoids redondant fitting of distributions and saves some memory.
fastboost( X, Y, ncores = 4, group = group_func_1, func = lasso_msgps_AICc, corrfunc = "cor", use.parallel = FALSE, B = 100, step.num = 0.1, step.limit = "none", verbose = FALSE, step.scale = "quantile", normalize = TRUE, steps.seq = NULL, debug = FALSE, version = "lars", c0lim = TRUE, ... )fastboost( X, Y, ncores = 4, group = group_func_1, func = lasso_msgps_AICc, corrfunc = "cor", use.parallel = FALSE, B = 100, step.num = 0.1, step.limit = "none", verbose = FALSE, step.scale = "quantile", normalize = TRUE, steps.seq = NULL, debug = FALSE, version = "lars", c0lim = TRUE, ... )
X |
Numerical matrix. Matrix of the variables. |
Y |
Numerical vector or factor. Response vector. |
ncores |
Numerical value. Number of cores for parallel computing.
Defaults to |
group |
Function. The grouping function.
Defaults to |
func |
Function. The variable selection function.
Defaults to |
corrfunc |
Character value or function. Used to compute associations between
the variables. Defaults to |
use.parallel |
Boolean. To use parallel computing (doMC) download the extended package from Github.
Set to |
B |
Numerical value. Number of resampled fits of the model.
Defaults to |
step.num |
Numerical value. Step value for the c0 sequence.
Defaults to |
step.limit |
Defaults to |
verbose |
Boolean.
Defaults to |
step.scale |
Character value. How to compute the c0 sequence if not user-provided:
either "quantile" or "linear", "zoom_l", "zoom_q" and "mixed".
Defaults to |
normalize |
Boolean. Shall the X matrix be centered and scaled?
Defaults to |
steps.seq |
Numeric vector. User provided sequence of c0 values to use.
Defaults to |
debug |
Boolean value. If more results are required. Defaults to |
version |
Character value. Passed to the |
c0lim |
Boolean. Shall the c0=0 and c0=1 values be used?
Defaults to |
... |
. Arguments passed to the variable selection function used in |
fastboost returns a numeric matrix. For each of the variable (column)
and each of the c0 (row), the entry is proportion of times that the variable was
selected among the B resampled fits of the model. Fitting to the same group of variables is
only perfomed once (even if it occured for another value of c0), which greatly speeds up
the algorithm. In order to limit memory usage, fastboost uses a compact way to
save the group memberships, which is especially useful with community grouping function
and fairly big datasets.
A numeric matrix with attributes.
Frederic Bertrand, [email protected]
selectBoost: a general algorithm to enhance the performance of variable selection methods in correlated datasets, Frédéric Bertrand, Ismaïl Aouadi, Nicolas Jung, Raphael Carapito, Laurent Vallat, Seiamak Bahram, Myriam Maumy-Bertrand, Bioinformatics, 2020. doi:10.1093/bioinformatics/btaa855
boost, autoboost, plot.selectboost
Other Selectboost functions:
autoboost(),
boost,
plot_selectboost_cascade,
selectboost_cascade
set.seed(314) xran=matrix(rnorm(75),15,5) ybin=sample(0:1,15,replace=TRUE) yran=rnorm(15) set.seed(314) #For quick test purpose, not meaningful, should be run with greater value of B #and disabling parallel computing as well res.fastboost <- fastboost(xran,yran,B=3,use.parallel=FALSE) fastboost(xran,yran) #Customize resampling levels fastboost(xran,yran,steps.seq=c(.99,.95,.9),c0lim=FALSE) fastboost(xran,yran,step.scale="mixed",c0lim=TRUE) fastboost(xran,yran,step.scale="zoom_l",c0lim=FALSE) fastboost(xran,yran,step.scale="zoom_l",step.num = c(1,.9,.01),c0lim=FALSE) fastboost(xran,yran,step.scale="zoom_q",c0lim=FALSE) fastboost(xran,yran,step.scale="linear",c0lim=TRUE) fastboost(xran,yran,step.scale="quantile",c0lim=TRUE) #Binary logistic regression fastboost(xran,ybin,func=lasso_cv_glmnet_bin_min)set.seed(314) xran=matrix(rnorm(75),15,5) ybin=sample(0:1,15,replace=TRUE) yran=rnorm(15) set.seed(314) #For quick test purpose, not meaningful, should be run with greater value of B #and disabling parallel computing as well res.fastboost <- fastboost(xran,yran,B=3,use.parallel=FALSE) fastboost(xran,yran) #Customize resampling levels fastboost(xran,yran,steps.seq=c(.99,.95,.9),c0lim=FALSE) fastboost(xran,yran,step.scale="mixed",c0lim=TRUE) fastboost(xran,yran,step.scale="zoom_l",c0lim=FALSE) fastboost(xran,yran,step.scale="zoom_l",step.num = c(1,.9,.01),c0lim=FALSE) fastboost(xran,yran,step.scale="zoom_q",c0lim=FALSE) fastboost(xran,yran,step.scale="linear",c0lim=TRUE) fastboost(xran,yran,step.scale="quantile",c0lim=TRUE) #Binary logistic regression fastboost(xran,ybin,func=lasso_cv_glmnet_bin_min)
Result of fastboost analysis of diabetes data from lars package with lasso and first order model
fastboost.res.xfastboost.res.x
A numerical matrix frame with 13 rows and 10 variables with attributes.
Result of fastboost analysis of diabetes data from lars package with adaptative lasso and first order model
fastboost.res.x.adaptfastboost.res.x.adapt
A numerical matrix frame with 13 rows and 10 variables with attributes.
Result of fastboost analysis of diabetes data from lars package with lasso and second order model
fastboost.res.x2fastboost.res.x2
A numerical matrix frame with 13 rows and 64 variables with attributes.
Result of fastboost analysis of diabetes data from lars package with adaptative lasso and second order model
fastboost.res.x2.adaptfastboost.res.x2.adapt
A numerical matrix frame with 13 rows and 64 variables with attributes.
Post processes a selectboost analysis.
force.non.inc(object)force.non.inc(object)
object |
Numerical matrix. Result of selectboost (autoboost, fastboost, ...). |
force.non.inc returns a vector after ensuring that the proportion of times each variable was
selected is non increasing with respect to the 1-c0 value.
A matrix with the results.
Frederic Bertrand, [email protected]
selectBoost: a general algorithm to enhance the performance of variable selection methods in correlated datasets, Frédéric Bertrand, Ismaïl Aouadi, Nicolas Jung, Raphael Carapito, Laurent Vallat, Seiamak Bahram, Myriam Maumy-Bertrand, Bioinformatics, 2020. doi:10.1093/bioinformatics/btaa855
Other Selectboost analyse functions:
plot.selectboost(),
summary.selectboost()
data(autoboost.res.x) res.fastboost.force.non.inc <- force.non.inc(autoboost.res.x)data(autoboost.res.x) res.fastboost.force.non.inc <- force.non.inc(autoboost.res.x)
group_func_1 creates groups of variables based on thresholding the input matrix.
group_func_1(absXcor, c0)group_func_1(absXcor, c0)
absXcor |
A numeric matrix. The absolute value of a correlation or distance matrix. |
c0 |
A numeric scalar. The thresholding |
This is a function used to create a list of groups using an input matrix and a thresholding value c0. A group is made, for every column in the input matrix.
A list with one entry: the list of groups. Attributes:
"type": "normal"
"length.groups" the length of each groups.
Frederic Bertrand, [email protected]
selectBoost: a general algorithm to enhance the performance of variable selection methods in correlated datasets, Frédéric Bertrand, Ismaïl Aouadi, Nicolas Jung, Raphael Carapito, Laurent Vallat, Seiamak Bahram, Myriam Maumy-Bertrand, Bioinformatics, 2020. doi:10.1093/bioinformatics/btaa855
group_func_2 and boost.findgroups
set.seed(314) group_func_1(cor(matrix(rnorm(50),10,5)),.4)set.seed(314) group_func_1(cor(matrix(rnorm(50),10,5)),.4)
group_func_2 creates groups of variables based on community analysis.
group_func_2(absXcor, c0)group_func_2(absXcor, c0)
absXcor |
A numeric matrix. The absolute value of a correlation or distance matrix. |
c0 |
A numeric scalar. The thresholding |
This is a function used to create a list of groups using an input matrix and a
thresholding value c0. A group is made, for every column in the input matrix.
It uses the infomap.community function of the igraph package.
A list with one entry: the list of groups. Attributes:
"type": "normal"
"length.groups" the length of each groups.
Frederic Bertrand, [email protected]
selectBoost: a general algorithm to enhance the performance of variable selection methods in correlated datasets, Frédéric Bertrand, Ismaïl Aouadi, Nicolas Jung, Raphael Carapito, Laurent Vallat, Seiamak Bahram, Myriam Maumy-Bertrand, Bioinformatics, 2020. doi:10.1093/bioinformatics/btaa855
group_func_2 boost.findgroups, infomap.community and igraph.
set.seed(314) group_func_2(cor(matrix(rnorm(100),10,10)),.5)set.seed(314) group_func_2(cor(matrix(rnorm(100),10,10)),.5)
Define some additional plot functions to be used in the demos of the package.
## S3 method for class 'matrix' plot(x, ...)## S3 method for class 'matrix' plot(x, ...)
x |
A numeric matrix. A matrix to be plotted. |
... |
. Additionnal arguments passed to the plot function. |
matrixplot plots a numeric matrix x.
matrixplot returns 1.
Frederic Bertrand, [email protected] with contributions from Nicolas Jung.
selectBoost: a general algorithm to enhance the performance of variable selection methods in correlated datasets, Frédéric Bertrand, Ismaïl Aouadi, Nicolas Jung, Raphael Carapito, Laurent Vallat, Seiamak Bahram, Myriam Maumy-Bertrand, Bioinformatics, 2020. doi:10.1093/bioinformatics/btaa855
set.seed(3141) randmat=matrix(rnorm(360),60,60) plot(randmat)set.seed(3141) randmat=matrix(rnorm(360),60,60) plot(randmat)
Some details about this class and my plans for it in the body.
Matrix of confidence indices.
Vector.
F array
Vector
Logical. Was crossvalidation carried out subjectwise?
Plot result of Selectboost for Cascade inference.
## S4 method for signature 'network.confidence,ANY' plot(x, col = gray((1:99)/100, alpha = NULL), ...)## S4 method for signature 'network.confidence,ANY' plot(x, col = gray((1:99)/100, alpha = NULL), ...)
x |
A |
col |
Colors for the plot. |
... |
Additionnal arguments passed to the heatmap function. |
Extending results from the Cascade package: providing confidence indices for the reverse engineered links.
Reference for the Cascade modelling Vallat, L., Kemper, C. a., Jung, N., Maumy-Bertrand, M., Bertrand, F., Meyer, N., Pocheville, A., Fisher, J. W., Gribben, J. G. et Bahram, S. (2013). Reverse-engineering the genetic circuitry of a cancer cell with predicted intervention in chronic lymphocytic leukemia. Proceedings of the National Academy of Sciences of the United States of America, 110(2), 459-64.
Reference for the Cascade package Jung, N., Bertrand, F., Bahram, S., Vallat, L. et Maumy-Bertrand, M. (2014). Cascade : A R package to study, predict and simulate the diffusion of a signal through a temporal gene network. Bioinformatics. ISSN 13674803..
Nothing.
Frederic Bertrand, [email protected]
selectBoost: a general algorithm to enhance the performance of variable selection methods in correlated datasets, Frédéric Bertrand, Ismaïl Aouadi, Nicolas Jung, Raphael Carapito, Laurent Vallat, Seiamak Bahram, Myriam Maumy-Bertrand, Bioinformatics, 2020. doi:10.1093/bioinformatics/btaa855
boost, fastboost, selectboost, inference
Other Selectboost functions:
autoboost(),
boost,
fastboost(),
selectboost_cascade
data(net_confidences) plot(net_confidence) plot(net_confidence_.5) plot(net_confidence_thr)data(net_confidences) plot(net_confidence) plot(net_confidence_.5) plot(net_confidence_thr)
Plot a selectboostboost object.
## S3 method for class 'selectboost' plot( x, verbose = FALSE, prop.level = 0.95, conf.int.level = 0.95, conf.threshold = 0.95, ... )## S3 method for class 'selectboost' plot( x, verbose = FALSE, prop.level = 0.95, conf.int.level = 0.95, conf.threshold = 0.95, ... )
x |
Numerical matrix. Result of selectboost (autoboost, fastboost, ...). |
verbose |
Boolean.
Defaults to |
prop.level |
Numeric value. Used to compute the proportion of selection is
greater than prop.level. Defaults to |
conf.int.level |
Numeric value. Confidence level for confidence intervals on estimated
proportions of selection. Defaults to |
conf.threshold |
Numeric value. Used to compute the number of steps (c0) for which
the proportion of selection remains greater than conf.threshold. Defaults to |
... |
. Passed to the plotting functions. |
plot.selectboost returns an invisible list and creates four graphics.
Two plots the proportion of selection with respect to c0 (by step or according to real scale).
On the third graph, no bar means a proportion of selection less than prop.level.
Confidence intervals are computed at the conf.int.level level.
Barplot of the confidence index (1-min(c0, such that proportion|c0>conf.threshold)).
An invisible list.
Frederic Bertrand, [email protected]
selectBoost: a general algorithm to enhance the performance of variable selection methods in correlated datasets, Frédéric Bertrand, Ismaïl Aouadi, Nicolas Jung, Raphael Carapito, Laurent Vallat, Seiamak Bahram, Myriam Maumy-Bertrand, Bioinformatics, 2020. doi:10.1093/bioinformatics/btaa855
Other Selectboost analyse functions:
force.non.inc(),
summary.selectboost()
set.seed(314) xran=matrix(rnorm(75),15,5) ybin=sample(0:1,15,replace=TRUE) yran=rnorm(15) layout(matrix(1:4,2,2)) data(autoboost.res.x) plot(autoboost.res.x) data(autoboost.res.x2) plot(autoboost.res.x2)set.seed(314) xran=matrix(rnorm(75),15,5) ybin=sample(0:1,15,replace=TRUE) yran=rnorm(15) layout(matrix(1:4,2,2)) data(autoboost.res.x) plot(autoboost.res.x) data(autoboost.res.x2) plot(autoboost.res.x2)
Plot a summary of selectboost results.
## S3 method for class 'summary.selectboost' plot(x, ...)## S3 method for class 'summary.selectboost' plot(x, ...)
x |
Numerical matrix. Summary of selectboost object. |
... |
. Passed to the plotting functions. |
plot.summary.selectboost returns an invisible list and creates four graphics.
Two plots the proportion of selection with respect to c0 (by step or according to real scale).
On the third graph, no bar means a proportion of selection less than prop.level.
Confidence intervals are computed at the conf.int.level level.
Barplot of the confidence index (1-min(c0, such that proportion|c0>conf.threshold)).
An invisible list.
Frederic Bertrand, [email protected]
selectBoost: a general algorithm to enhance the performance of variable selection methods in correlated datasets, Frédéric Bertrand, Ismaïl Aouadi, Nicolas Jung, Raphael Carapito, Laurent Vallat, Seiamak Bahram, Myriam Maumy-Bertrand, Bioinformatics, 2020. doi:10.1093/bioinformatics/btaa855
fastboost, autoboost and summary.selectboost
Other Selectboost analyze functions:
auto.analyze(),
trajC0()
data(autoboost.res.x) plot(summary(autoboost.res.x)) data(autoboost.res.x2) plot(summary(autoboost.res.x2))data(autoboost.res.x) plot(summary(autoboost.res.x)) data(autoboost.res.x2) plot(summary(autoboost.res.x2))
Result of fastboost analysis applied to biological network reverse engineering
test.seq_C test.seq_PL test.seq_PL2 test.seq_PL2_W test.seq_PL2_tW test.seq_PSel test.seq_PSel.5 test.seq_PSel.e2 test.seq_PSel.5.e2 test.seq_PSel_W test.seq_robust test.seq_PB test.seq_PB_095_075 test.seq_PB_075_075 test.seq_PB_W sensitivity_C sensitivity_PL sensitivity_PL2 sensitivity_PL2_W sensitivity_PL2_tW sensitivity_PSel sensitivity_PSel.5 sensitivity_PSel.e2 sensitivity_PSel.5.e2 sensitivity_PSel_W sensitivity_robust sensitivity_PB sensitivity_PB_095_075 sensitivity_PB_075_075 sensitivity_PB_W predictive_positive_value_C predictive_positive_value_PL predictive_positive_value_PL2 predictive_positive_value_PL2_W predictive_positive_value_PL2_tW predictive_positive_value_PSel predictive_positive_value_PSel.5 predictive_positive_value_PSel.e2 predictive_positive_value_PSel.5.e2 predictive_positive_value_PSel_W predictive_positive_value_robust predictive_positive_value_PB predictive_positive_value_PB_095_075 predictive_positive_value_PB_075_075 predictive_positive_value_PB_W F_score_C F_score_PL F_score_PL2 F_score_PL2_W F_score_PL2_tW F_score_PSel F_score_PSel.5 F_score_PSel.e2 F_score_PSel.5.e2 F_score_PSel_W F_score_robust F_score_PB F_score_PB_095_075 F_score_PB_075_075 F_score_PB_W nv_C nv_PL nv_PL2 nv_PL2_W nv_PL2_tW nv_PSel nv_PSel.5 nv_PSel.e2 nv_PSel.5.e2 nv_PSel_W nv_robust nv_PB nv_PB_095_075 nv_PB_075_075 nv_PB_Wtest.seq_C test.seq_PL test.seq_PL2 test.seq_PL2_W test.seq_PL2_tW test.seq_PSel test.seq_PSel.5 test.seq_PSel.e2 test.seq_PSel.5.e2 test.seq_PSel_W test.seq_robust test.seq_PB test.seq_PB_095_075 test.seq_PB_075_075 test.seq_PB_W sensitivity_C sensitivity_PL sensitivity_PL2 sensitivity_PL2_W sensitivity_PL2_tW sensitivity_PSel sensitivity_PSel.5 sensitivity_PSel.e2 sensitivity_PSel.5.e2 sensitivity_PSel_W sensitivity_robust sensitivity_PB sensitivity_PB_095_075 sensitivity_PB_075_075 sensitivity_PB_W predictive_positive_value_C predictive_positive_value_PL predictive_positive_value_PL2 predictive_positive_value_PL2_W predictive_positive_value_PL2_tW predictive_positive_value_PSel predictive_positive_value_PSel.5 predictive_positive_value_PSel.e2 predictive_positive_value_PSel.5.e2 predictive_positive_value_PSel_W predictive_positive_value_robust predictive_positive_value_PB predictive_positive_value_PB_095_075 predictive_positive_value_PB_075_075 predictive_positive_value_PB_W F_score_C F_score_PL F_score_PL2 F_score_PL2_W F_score_PL2_tW F_score_PSel F_score_PSel.5 F_score_PSel.e2 F_score_PSel.5.e2 F_score_PSel_W F_score_robust F_score_PB F_score_PB_095_075 F_score_PB_075_075 F_score_PB_W nv_C nv_PL nv_PL2 nv_PL2_W nv_PL2_tW nv_PSel nv_PSel.5 nv_PSel.e2 nv_PSel.5.e2 nv_PSel_W nv_robust nv_PB nv_PB_095_075 nv_PB_075_075 nv_PB_W
A numerical matrix frame with 100 rows and 200 variables or a numerical vector of length 100.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class matrix (inherits from array) with 100 rows and 200 columns.
An object of class numeric of length 100.
An object of class numeric of length 100.
An object of class numeric of length 100.
An object of class numeric of length 100.
An object of class numeric of length 100.
An object of class numeric of length 100.
An object of class numeric of length 100.
An object of class numeric of length 100.
An object of class numeric of length 100.
An object of class numeric of length 100.
An object of class numeric of length 100.
An object of class numeric of length 100.
An object of class numeric of length 100.
An object of class numeric of length 100.
An object of class numeric of length 100.
Motivation: With the growth of big data, variable selection has become one of the major challenges in statistics. Although many methods have been proposed in the literature their performance in terms of recall and precision are limited in a context where the number of variables by far exceeds the number of observations or in a high correlated setting. Results: This package implements a new general algorithm which improves the precision of any existing variable selection method. This algorithm is based on highly intensive simulations and takes into account the correlation structure of the data. Our algorithm can either produce a confidence index for variable selection or it can be used in an experimental design planning perspective.
F. Bertrand, I. Aouadi, N. Jung, R. Carapito, L. Vallat, S. Bahram, M. Maumy-Bertrand (2020). SelectBoost: a general algorithm to enhance the performance of variable selection methods in correlated datasets, Bioinformatics. doi:10.1093/bioinformatics/btaa855
SelectBoost was used to decypher networks in C. Schleiss, [...], M. Maumy-Bertrand, S. Bahram, F. Bertrand, and L. Vallat. (2021). Temporal multiomic modelling reveals a B-cell receptor proliferative program in chronic lymphocytic leukemia. Leukemia.
set.seed(314) xran=matrix(rnorm(75),15,5) ybin=sample(0:1,15,replace=TRUE) yran=rnorm(15) #For quick test purpose, not meaningful, should be run with greater value of B #(disabling parallel computing as well) res.fastboost <- fastboost(xran,yran,B=3,use.parallel=FALSE) fastboost(xran,yran) #Customize resampling levels fastboost(xran,yran,steps.seq=c(.99,.95,.9),c0lim=FALSE) #Binary logistic regression fastboost(xran,ybin,func=lasso_cv_glmnet_bin_min)set.seed(314) xran=matrix(rnorm(75),15,5) ybin=sample(0:1,15,replace=TRUE) yran=rnorm(15) #For quick test purpose, not meaningful, should be run with greater value of B #(disabling parallel computing as well) res.fastboost <- fastboost(xran,yran,B=3,use.parallel=FALSE) fastboost(xran,yran) #Customize resampling levels fastboost(xran,yran,steps.seq=c(.99,.95,.9),c0lim=FALSE) #Binary logistic regression fastboost(xran,ybin,func=lasso_cv_glmnet_bin_min)
Selectboost for Cascade inference.
selectboost(M, ...) ## S4 method for signature 'micro_array' selectboost( M, Fabhat, K = 5, eps = 10^-5, cv.subjects = TRUE, ncores = 4, use.parallel = FALSE, verbose = FALSE, group = group_func_2, c0value = 0.95 )selectboost(M, ...) ## S4 method for signature 'micro_array' selectboost( M, Fabhat, K = 5, eps = 10^-5, cv.subjects = TRUE, ncores = 4, use.parallel = FALSE, verbose = FALSE, group = group_func_2, c0value = 0.95 )
M |
Microarray class from the Cascade package. |
... |
Additionnal arguments. Not used. |
Fabhat |
F matrix inferred using the inference function from the Cascade package. |
K |
Number of crossvalidation folds. |
eps |
Threshold for assinging a zero value to an inferred parameter. Defaults to 10^-5. |
cv.subjects |
Crossvalidation is made subjectwise using leave one out. Discards the K option. |
ncores |
Numerical value. Number of cores for parallel computing.
Defaults to |
use.parallel |
Boolean. To use parallel computing (doMC) download the extended package from Github.
Set to |
verbose |
Boolean.
Defaults to |
group |
Function. The grouping function.
Defaults to |
c0value |
Numeric. c0 value to use for confidence computation.
Defaults to |
Extending results from the Cascade package: providing confidence indices for the reverse engineered links.
Reference for the Cascade modelling Vallat, L., Kemper, C. a., Jung, N., Maumy-Bertrand, M., Bertrand, F., Meyer, N., Pocheville, A., Fisher, J. W., Gribben, J. G. et Bahram, S. (2013). Reverse-engineering the genetic circuitry of a cancer cell with predicted intervention in chronic lymphocytic leukemia. Proceedings of the National Academy of Sciences of the United States of America, 110(2), 459-64.
Reference for the Cascade package Jung, N., Bertrand, F., Bahram, S., Vallat, L. et Maumy-Bertrand, M. (2014). Cascade : A R package to study, predict and simulate the diffusion of a signal through a temporal gene network. Bioinformatics. ISSN 13674803..
A network.confidence object.
Frederic Bertrand, [email protected]
selectBoost: a general algorithm to enhance the performance of variable selection methods in correlated datasets, Frédéric Bertrand, Ismaïl Aouadi, Nicolas Jung, Raphael Carapito, Laurent Vallat, Seiamak Bahram, Myriam Maumy-Bertrand, Bioinformatics, 2020. doi:10.1093/bioinformatics/btaa855
boost, fastboost, plot.selectboost, inference
Other Selectboost functions:
autoboost(),
boost,
fastboost(),
plot_selectboost_cascade
set.seed(314) set.seed(314) data(Cascade_example) Fab_inf_C <- Net_inf_C@F #By default community grouping of variables set.seed(1) net_confidence <- selectboost(M, Fab_inf_C) net_confidence_.5 <- selectboost(M, Fab_inf_C, c0value = .5) #With group_func_1, variables are grouped by thresholding the correlation matrix net_confidence_thr <- selectboost(M, Fab_inf_C, group = group_func_1)set.seed(314) set.seed(314) data(Cascade_example) Fab_inf_C <- Net_inf_C@F #By default community grouping of variables set.seed(1) net_confidence <- selectboost(M, Fab_inf_C) net_confidence_.5 <- selectboost(M, Fab_inf_C, c0value = .5) #With group_func_1, variables are grouped by thresholding the correlation matrix net_confidence_thr <- selectboost(M, Fab_inf_C, group = group_func_1)
Define several simulation functions to be used in the demos of the package.
simulation_cor(group, cor_group, v = 1) simulation_X(N, Cor) simulation_DATA(X, supp, minB, maxB, stn) compsim(x, ...) ## S3 method for class 'simuls' compsim(x, result.boost, level = 1, ...)simulation_cor(group, cor_group, v = 1) simulation_X(N, Cor) simulation_DATA(X, supp, minB, maxB, stn) compsim(x, ...) ## S3 method for class 'simuls' compsim(x, result.boost, level = 1, ...)
group |
A numeric vector. Group membership of each of the variables. |
cor_group |
A numeric vector. Intra-group Pearson correlation. |
v |
A numeric value. The diagonal value of the generated matrix. |
N |
A numeric value. The number of observations. |
Cor |
A numeric matrix. A correlation matrix to be used for random sampling. |
X |
A numeric matrix. Observations*variables. |
supp |
A numeric vector. The true predictors. |
minB |
A numeric value. Minimum absolute value for a beta coefficient. |
maxB |
A numeric value. Maximum absolute value for a beta coefficient. |
stn |
A numeric value. A scaling factor for the noise in the response. The higher, the smaller the noise. |
x |
List. Simulated dataset. |
... |
For compatibility issues. |
result.boost |
Row matrix of numerical value. Result of selecboost for a given c0. |
level |
List. Threshold for proportions of selected variables. |
simulation_cor returns a numeric symetric matrix c whose order
is the number of variables. An entry is equal to
, entries on the diagonal are equal to the v value
, 0 if the variable i and j do not belong to the same group
, cor_group[k] if the variable i and j belong to the group k
simulation_X returns a numeric matrix of replicates (by row) of
random samples generated according to the Cor matrix.
simulation_DATA returns a list with the X matrix, the response vector Y,
the true predictors, the beta coefficients, the scaling factor and the standard deviation.
compsim.simuls computes recall (sensitivity), precision (positive predictive value), and several Fscores (non-weighted Fscore, F1/2 and F2 weighted Fscores).
simulation_cor returns a numeric matrix.
simulation_X returns a numeric matrix.
simulation_DATA returns a list.
compsim.simuls returns a numerical vector.
Frederic Bertrand, [email protected] with contributions from Nicolas Jung.
selectBoost: a general algorithm to enhance the performance of variable selection methods in correlated datasets, Frédéric Bertrand, Ismaïl Aouadi, Nicolas Jung, Raphael Carapito, Laurent Vallat, Seiamak Bahram, Myriam Maumy-Bertrand, Bioinformatics, 2020. doi:10.1093/bioinformatics/btaa855
glmnet, cv.glmnet, AICc_BIC_glmnetB, lars, cv.lars, msgps
N<-10 group<-c(rep(1:2,5)) cor_group<-c(.8,.4) supp<-c(1,1,1,0,0,0,0,0,0,0) minB<-1 maxB<-2 stn<-5 C<-simulation_cor(group,cor_group) set.seed(314) X<-simulation_X(10,C) G<-abs(cor(X)) hist(G[lower.tri(G)]) set.seed(314) DATA_exemple<-simulation_DATA(X,supp,1,2,stn) set.seed(314) result.boost = fastboost(DATA_exemple$X, DATA_exemple$Y, steps.seq = .7, c0lim = FALSE, use.parallel = FALSE, B=10) compsim(DATA_exemple, result.boost, level=.7)N<-10 group<-c(rep(1:2,5)) cor_group<-c(.8,.4) supp<-c(1,1,1,0,0,0,0,0,0,0) minB<-1 maxB<-2 stn<-5 C<-simulation_cor(group,cor_group) set.seed(314) X<-simulation_X(10,C) G<-abs(cor(X)) hist(G[lower.tri(G)]) set.seed(314) DATA_exemple<-simulation_DATA(X,supp,1,2,stn) set.seed(314) result.boost = fastboost(DATA_exemple$X, DATA_exemple$Y, steps.seq = .7, c0lim = FALSE, use.parallel = FALSE, B=10) compsim(DATA_exemple, result.boost, level=.7)
Summarize a selectboost analysis.
## S3 method for class 'selectboost' summary( object, crit.func = mean, crit.int = "mean", custom.values.lim = NULL, index.lim = NULL, alpha.conf.level = 0.99, force.dec = TRUE, ... )## S3 method for class 'selectboost' summary( object, crit.func = mean, crit.int = "mean", custom.values.lim = NULL, index.lim = NULL, alpha.conf.level = 0.99, force.dec = TRUE, ... )
object |
Numerical matrix. Result of selectboost (autoboost, fastboost, ...). |
crit.func |
Function . Defaults to the |
crit.int |
Character value. Mean or median based confidence intervals. Defaults to |
custom.values.lim |
Vector of numeric values. Defults to |
index.lim |
Vector of numeric values. Defults to |
alpha.conf.level |
Numeric value. Defults to |
force.dec |
Boolean. Force trajectories to be non-increasing. |
... |
Additionnal arguments. Passed to the |
summary.selectboost returns a list with the results.
A list with the results.
Frederic Bertrand, [email protected]
selectBoost: a general algorithm to enhance the performance of variable selection methods in correlated datasets, Frédéric Bertrand, Ismaïl Aouadi, Nicolas Jung, Raphael Carapito, Laurent Vallat, Seiamak Bahram, Myriam Maumy-Bertrand, Bioinformatics, 2020. doi:10.1093/bioinformatics/btaa855
Other Selectboost analyse functions:
force.non.inc(),
plot.selectboost()
data(autoboost.res.x) summary(autoboost.res.x) summary(autoboost.res.x, force.dec=FALSE) data(autoboost.res.x.adapt) summary(autoboost.res.x.adapt) data(autoboost.res.x2) summary(autoboost.res.x2) summary(autoboost.res.x2, force.dec=FALSE) data(autoboost.res.x2.adapt) summary(autoboost.res.x2.adapt) data(fastboost.res.x) summary(fastboost.res.x) summary(fastboost.res.x, force.dec=FALSE) data(fastboost.res.x.adapt) summary(fastboost.res.x.adapt) data(fastboost.res.x2) summary(fastboost.res.x2) summary(fastboost.res.x2, force.dec=FALSE) data(fastboost.res.x2.adapt) summary(fastboost.res.x2.adapt)data(autoboost.res.x) summary(autoboost.res.x) summary(autoboost.res.x, force.dec=FALSE) data(autoboost.res.x.adapt) summary(autoboost.res.x.adapt) data(autoboost.res.x2) summary(autoboost.res.x2) summary(autoboost.res.x2, force.dec=FALSE) data(autoboost.res.x2.adapt) summary(autoboost.res.x2.adapt) data(fastboost.res.x) summary(fastboost.res.x) summary(fastboost.res.x, force.dec=FALSE) data(fastboost.res.x.adapt) summary(fastboost.res.x.adapt) data(fastboost.res.x2) summary(fastboost.res.x2) summary(fastboost.res.x2, force.dec=FALSE) data(fastboost.res.x2.adapt) summary(fastboost.res.x2.adapt)
Plot trajectories.
trajC0(x, ...) ## S3 method for class 'selectboost' trajC0( x, summary.selectboost.res, lasso.coef.path, type.x.axis = "noscale", type.graph = "boost", threshold.level = NULL, ... )trajC0(x, ...) ## S3 method for class 'selectboost' trajC0( x, summary.selectboost.res, lasso.coef.path, type.x.axis = "noscale", type.graph = "boost", threshold.level = NULL, ... )
x |
Numerical matrix. Selectboost object. |
... |
. Passed to the plotting functions. |
summary.selectboost.res |
List. Summary of selectboost object. |
lasso.coef.path |
List. Result of |
type.x.axis |
Character value. "scale" or "noscale" for the X axis. |
type.graph |
Character value. Type of graphs: "bars", "lasso" and "boost". |
threshold.level |
Numeric value. Threshold for the graphs. |
trajC0 returns an invisible list and creates four graphics.
An invisible list.
invisible list.
Frederic Bertrand, [email protected]
selectBoost: a general algorithm to enhance the performance of variable selection methods in correlated datasets, Frédéric Bertrand, Ismaïl Aouadi, Nicolas Jung, Raphael Carapito, Laurent Vallat, Seiamak Bahram, Myriam Maumy-Bertrand, Bioinformatics, 2020. doi:10.1093/bioinformatics/btaa855
fastboost, autoboost and summary.selectboost
Other Selectboost analyze functions:
auto.analyze(),
plot.summary.selectboost()
data(autoboost.res.x) data(diabetes, package="lars") ### With lasso trajectories m.x<-lars::lars(diabetes$x,diabetes$y) plot(m.x) mm.x<-predict(m.x,type="coef",mode="lambda") autoboost.res.x.mean = summary(autoboost.res.x) par(mfrow=c(2,2),mar=c(4,4,1,1)) trajC0(autoboost.res.x,autoboost.res.x.mean,lasso.coef.path=mm.x,type.graph="lasso") trajC0(autoboost.res.x,autoboost.res.x.mean) trajC0(autoboost.res.x,autoboost.res.x.mean,type.graph="bars") trajC0(autoboost.res.x,autoboost.res.x.mean,type.x.axis ="scale")data(autoboost.res.x) data(diabetes, package="lars") ### With lasso trajectories m.x<-lars::lars(diabetes$x,diabetes$y) plot(m.x) mm.x<-predict(m.x,type="coef",mode="lambda") autoboost.res.x.mean = summary(autoboost.res.x) par(mfrow=c(2,2),mar=c(4,4,1,1)) trajC0(autoboost.res.x,autoboost.res.x.mean,lasso.coef.path=mm.x,type.graph="lasso") trajC0(autoboost.res.x,autoboost.res.x.mean) trajC0(autoboost.res.x,autoboost.res.x.mean,type.graph="bars") trajC0(autoboost.res.x,autoboost.res.x.mean,type.x.axis ="scale")
Compute coefficient vector after variable selection.
lasso_cv_glmnet_bin_min(X, Y) lasso_cv_glmnet_bin_1se(X, Y) lasso_glmnet_bin_AICc(X, Y) lasso_glmnet_bin_BIC(X, Y) lasso_cv_lars_min(X, Y) lasso_cv_lars_1se(X, Y) lasso_cv_glmnet_min(X, Y) lasso_cv_glmnet_min_weighted(X, Y, priors) lasso_cv_glmnet_1se(X, Y) lasso_cv_glmnet_1se_weighted(X, Y, priors) lasso_msgps_Cp(X, Y, penalty = "enet") lasso_msgps_AICc(X, Y, penalty = "enet") lasso_msgps_GCV(X, Y, penalty = "enet") lasso_msgps_BIC(X, Y, penalty = "enet") enetf_msgps_Cp(X, Y, penalty = "enet", alpha = 0.5) enetf_msgps_AICc(X, Y, penalty = "enet", alpha = 0.5) enetf_msgps_GCV(X, Y, penalty = "enet", alpha = 0.5) enetf_msgps_BIC(X, Y, penalty = "enet", alpha = 0.5) lasso_cascade(M, Y, K, eps = 10^-5, cv.fun)lasso_cv_glmnet_bin_min(X, Y) lasso_cv_glmnet_bin_1se(X, Y) lasso_glmnet_bin_AICc(X, Y) lasso_glmnet_bin_BIC(X, Y) lasso_cv_lars_min(X, Y) lasso_cv_lars_1se(X, Y) lasso_cv_glmnet_min(X, Y) lasso_cv_glmnet_min_weighted(X, Y, priors) lasso_cv_glmnet_1se(X, Y) lasso_cv_glmnet_1se_weighted(X, Y, priors) lasso_msgps_Cp(X, Y, penalty = "enet") lasso_msgps_AICc(X, Y, penalty = "enet") lasso_msgps_GCV(X, Y, penalty = "enet") lasso_msgps_BIC(X, Y, penalty = "enet") enetf_msgps_Cp(X, Y, penalty = "enet", alpha = 0.5) enetf_msgps_AICc(X, Y, penalty = "enet", alpha = 0.5) enetf_msgps_GCV(X, Y, penalty = "enet", alpha = 0.5) enetf_msgps_BIC(X, Y, penalty = "enet", alpha = 0.5) lasso_cascade(M, Y, K, eps = 10^-5, cv.fun)
X |
A numeric matrix. The predictors matrix. |
Y |
A binary factor. The 0/1 classification response. |
priors |
A numeric vector. Weighting vector for the variable selection. When used with the
|
penalty |
A character value to select the penalty term in msgps (Model Selection Criteria via Generalized Path Seeking). Defaults to "enet". "genet" is the generalized elastic net and "alasso" is the adaptive lasso, which is a weighted version of the lasso. |
alpha |
A numeric value to set the value of |
M |
A numeric matrix. The transposed predictors matrix. |
K |
A numeric value. Number of folds to use. |
eps |
A numeric value. Threshold to set to 0 the inferred value of a parameter. |
cv.fun |
A function. Fonction used to create folds. Used to perform corss-validation subkectwise. |
lasso_cv_glmnet_bin_min returns the vector of coefficients
for a binary logistic model estimated by the lasso using the lambda.min value
computed by 10 fold cross validation. It uses the glmnet function of
the glmnetpackage.
lasso_cv_glmnet_bin_1se returns the vector of coefficients
for a binary logistic model estimated by the lasso using the lambda.1se
(lambda.min+1se) value computed by 10 fold cross validation. It uses the glmnet
function of the glmnetpackage.
lasso_glmnet_bin_AICc returns the vector of coefficients
for a binary logistic model estimated by the lasso and selected according to the
bias-corrected AIC (AICC) criterion. It uses the glmnet
lasso_glmnet_bin_BIC returns the vector of coefficients
for a binary logistic model estimated by the lasso and selected according to the BIC
criterion. It uses the glmnet
lasso_cv_lars_min returns the vector of coefficients
for a linear model estimated by the lasso using the lambda.min value
computed by 5 fold cross validation. It uses the lars function of the
lars package.
lasso_cv_lars_1se returns the vector of coefficients
for a linear model estimated by the lasso using the lambda.1se
(lambda.min+1se) value computed by 5 fold cross validation.
It uses the lars function of the lars package.
lasso_cv_glmnet_min returns the vector of coefficients
for a linear model estimated by the lasso using the lambda.min value
computed by 10 fold cross validation. It uses the glmnet function of the
glmnet package.
lasso_cv_glmnet_min_weighted returns the vector of coefficients
for a linear model estimated by the weighted lasso using the lambda.min value
computed by 10 fold cross validation. It uses the glmnet function of the
glmnet package.
lasso_cv_glmnet_1se returns the vector of coefficients
for a linear model estimated by the lasso using the lambda.1se
(lambda.min+1se) value computed by 10 fold cross validation. It uses the glmnet
function of the
glmnet package.
lasso_cv_glmnet_1se_weighted returns the vector of coefficients
for a linear model estimated by the weighted lasso using the lambda.1se
(lambda.min+1se) value computed by 10 fold cross validation. It uses the glmnet
function of the glmnet package.
lasso_msgps_Cp returns the vector of coefficients
for a linear model estimated by the lasso selectd using Mallows' Cp.
It uses the msgps function of the msgps package.
lasso_msgps_AICc returns the vector of coefficients
for a linear model estimated by the lasso selected according to the bias-corrected AIC
(AICC) criterion. It uses the msgps function of the msgps package.
lasso_msgps_GCV returns the vector of coefficients
for a linear model estimated by the lasso selected according to the generalized
cross validation criterion. It uses the msgps function of the msgps package.
lasso_msgps_BIC returns the vector of coefficients
for a linear model estimated by the lasso selected according to the BIC criterion.
It uses the msgps function of the msgps package.
enetf_msgps_Cp returns the vector of coefficients
for a linear model estimated by the elastic net selectd using Mallows' Cp.
It uses the msgps function of the msgps package.
enetf_msgps_AICc returns the vector of coefficients
for a linear model estimated by the elastic net selected according to the bias-corrected AIC
(AICC) criterion. It uses the msgps function of the msgps package.
enetf_msgps_GCV returns the vector of coefficients
for a linear model estimated by the elastic net selected according to the generalized
cross validation criterion. It uses the msgps function of the msgps package.
enetf_msgps_BIC returns the vector of coefficients
for a linear model estimated by the elastic net selected according to the BIC criterion.
It uses the msgps function of the msgps package.
lasso_cascade returns the vector of coefficients
for a linear model estimated by the lasso.
It uses the lars function of the lars package.
A vector of coefficients.
Frederic Bertrand, [email protected]
selectBoost: a general algorithm to enhance the performance of variable selection methods in correlated datasets, Frédéric Bertrand, Ismaïl Aouadi, Nicolas Jung, Raphael Carapito, Laurent Vallat, Seiamak Bahram, Myriam Maumy-Bertrand, Bioinformatics, 2020. doi:10.1093/bioinformatics/btaa855
glmnet, cv.glmnet, AICc_BIC_glmnetB, lars, cv.lars, msgps
Other Variable selection functions:
var_select_all
set.seed(314) xran=matrix(rnorm(150),30,5) ybin=sample(0:1,30,replace=TRUE) yran=rnorm(30) set.seed(314) lasso_cv_glmnet_bin_min(xran,ybin) set.seed(314) lasso_cv_glmnet_bin_1se(xran,ybin) set.seed(314) lasso_glmnet_bin_AICc(xran,ybin) set.seed(314) lasso_glmnet_bin_BIC(xran,ybin) set.seed(314) lasso_cv_lars_min(xran,yran) set.seed(314) lasso_cv_lars_1se(xran,yran) set.seed(314) lasso_cv_glmnet_min(xran,yran) set.seed(314) lasso_cv_glmnet_min_weighted(xran,yran,c(1000,0,0,1,1)) set.seed(314) lasso_cv_glmnet_1se(xran,yran) set.seed(314) lasso_cv_glmnet_1se_weighted(xran,yran,c(1000,0,0,1,1)) set.seed(314) lasso_msgps_Cp(xran,yran) set.seed(314) lasso_msgps_AICc(xran,yran) set.seed(314) lasso_msgps_GCV(xran,yran) set.seed(314) lasso_msgps_BIC(xran,yran) set.seed(314) enetf_msgps_Cp(xran,yran) set.seed(314) enetf_msgps_AICc(xran,yran) set.seed(314) enetf_msgps_GCV(xran,yran) set.seed(314) enetf_msgps_BIC(xran,yran) set.seed(314) lasso_cascade(t(xran),yran,5,cv.fun=lars::cv.folds)set.seed(314) xran=matrix(rnorm(150),30,5) ybin=sample(0:1,30,replace=TRUE) yran=rnorm(30) set.seed(314) lasso_cv_glmnet_bin_min(xran,ybin) set.seed(314) lasso_cv_glmnet_bin_1se(xran,ybin) set.seed(314) lasso_glmnet_bin_AICc(xran,ybin) set.seed(314) lasso_glmnet_bin_BIC(xran,ybin) set.seed(314) lasso_cv_lars_min(xran,yran) set.seed(314) lasso_cv_lars_1se(xran,yran) set.seed(314) lasso_cv_glmnet_min(xran,yran) set.seed(314) lasso_cv_glmnet_min_weighted(xran,yran,c(1000,0,0,1,1)) set.seed(314) lasso_cv_glmnet_1se(xran,yran) set.seed(314) lasso_cv_glmnet_1se_weighted(xran,yran,c(1000,0,0,1,1)) set.seed(314) lasso_msgps_Cp(xran,yran) set.seed(314) lasso_msgps_AICc(xran,yran) set.seed(314) lasso_msgps_GCV(xran,yran) set.seed(314) lasso_msgps_BIC(xran,yran) set.seed(314) enetf_msgps_Cp(xran,yran) set.seed(314) enetf_msgps_AICc(xran,yran) set.seed(314) enetf_msgps_GCV(xran,yran) set.seed(314) enetf_msgps_BIC(xran,yran) set.seed(314) lasso_cascade(t(xran),yran,5,cv.fun=lars::cv.folds)
Compute coefficient vector after variable selection for the fitting criteria of a given model. May be used for a step by step use of Selectboost.
lasso_msgps_all(X, Y, penalty = "enet") enet_msgps_all(X, Y, penalty = "enet", alpha = 0.5) alasso_msgps_all(X, Y, penalty = "alasso") alasso_enet_msgps_all(X, Y, penalty = "alasso", alpha = 0.5) lasso_cv_glmnet_all_5f(X, Y) spls_spls_all(X, Y, K.seq = c(1:5), eta.seq = (1:9)/10, fold.val = 5) varbvs_linear_all(X, Y, include.threshold.list = (1:19)/20) lasso_cv_glmnet_bin_all(X, Y) lasso_glmnet_bin_all(X, Y) splsda_spls_all(X, Y, K.seq = c(1:10), eta.seq = (1:9)/10) sgpls_spls_all(X, Y, K.seq = c(1:10), eta.seq = (1:9)/10) varbvs_binomial_all(X, Y, include.threshold.list = (1:19)/20)lasso_msgps_all(X, Y, penalty = "enet") enet_msgps_all(X, Y, penalty = "enet", alpha = 0.5) alasso_msgps_all(X, Y, penalty = "alasso") alasso_enet_msgps_all(X, Y, penalty = "alasso", alpha = 0.5) lasso_cv_glmnet_all_5f(X, Y) spls_spls_all(X, Y, K.seq = c(1:5), eta.seq = (1:9)/10, fold.val = 5) varbvs_linear_all(X, Y, include.threshold.list = (1:19)/20) lasso_cv_glmnet_bin_all(X, Y) lasso_glmnet_bin_all(X, Y) splsda_spls_all(X, Y, K.seq = c(1:10), eta.seq = (1:9)/10) sgpls_spls_all(X, Y, K.seq = c(1:10), eta.seq = (1:9)/10) varbvs_binomial_all(X, Y, include.threshold.list = (1:19)/20)
X |
A numeric matrix. The predictors matrix. |
Y |
A binary factor. The 0/1 classification response. |
penalty |
A character value to select the penalty term in msgps (Model Selection Criteria via Generalized Path Seeking). Defaults to "enet". "genet" is the generalized elastic net and "alasso" is the adaptive lasso, which is a weighted version of the lasso. |
alpha |
A numeric value to set the value of |
K.seq |
A numeric vector. Number of components to test. |
eta.seq |
A numeric vector. Eta sequence to test. |
fold.val |
A numeric value. Number of folds to use. |
include.threshold.list |
A numeric vector. Vector of threshold to use. |
K |
A numeric value. Number of folds to use. |
lasso_msgps_all returns the matrix of coefficients
for an optimal linear model estimated by the LASSO estimator and selected
by model selection criteria including Mallows' Cp, bias-corrected AIC (AICc),
generalized cross validation (GCV) and BIC.
The the msgps function of the msgps package implements
Model Selection Criteria via Generalized Path Seeking to compute the degrees
of freedom of the LASSO.
enet_msgps_all returns the matrix of coefficients
for an optimal linear model estimated by the ELASTIC NET estimator and selected
by model selection criteria including Mallows' Cp, bias-corrected AIC (AICc),
generalized cross validation (GCV) and BIC.
The the msgps function of the msgps package implements
Model Selection Criteria via Generalized Path Seeking to compute the degrees
of freedom of the ELASTIC NET.
alasso_msgps_all returns the matrix of coefficients
for an optimal linear model estimated by the adaptive LASSO estimator and selected
by model selection criteria including Mallows' Cp, bias-corrected AIC (AICc),
generalized cross validation (GCV) and BIC.
The the msgps function of the msgps package implements
Model Selection Criteria via Generalized Path Seeking to compute the degrees
of freedom of the adaptive LASSO.
alasso_enet_msgps_all returns the matrix of coefficients
for an optimal linear model estimated by the adaptive ELASTIC NET estimator and selected
by model selection criteria including Mallows' Cp, bias-corrected AIC (AICc),
generalized cross validation (GCV) and BIC.
The the msgps function of the msgps package implements
Model Selection Criteria via Generalized Path Seeking to compute the degrees
of freedom of the adaptive ELASTIC NET.
lasso_cv_glmnet_all_5f returns the matrix of coefficients
for a linear model estimated by the LASSO using the lambda.min and lambda.1se
(lambda.min+1se) values computed by 5 fold cross validation. It uses the glmnet
and cv.glmnet functions of the glmnet package.
spls_spls_all returns the matrix of the raw (coef.spls)
and correct.spls and bootstrap corrected coefficients
for a linear model estimated by the SPLS (sparse partial least squares) and 5 fold cross validation.
It uses the spls, cv.spls, ci.spls, coef.spls and
correct.spls functions of the spls package.
varbvs_linear_all returns the matrix of the coefficients
for a linear model estimated by the varbvs (variational approximation for Bayesian
variable selection in linear regression, family = gaussian) and the requested threshold values.
It uses the varbvs, coef and variable.names functions of the varbvs package.
lasso_cv_glmnet_bin_all returns the matrix of coefficients
for a logistic model estimated by the LASSO using the lambda.min and lambda.1se
(lambda.min+1se) values computed by 5 fold cross validation. It uses the glmnet and cv.glmnet
functions of the glmnet package.
lasso_glmnet_bin_all returns the matrix of coefficients
for a logistic model estimated by the LASSO using the AICc_glmnetB and BIC_glmnetB
information criteria. It uses the glmnet function of the glmnet package and the
AICc_glmnetB and BIC_glmnetB functions of the SelectBoost package that were
adapted from the AICc_glmnetB and BIC_glmnetB functions of the rLogistic
(https://github.com/echi/rLogistic) package.
splsda_spls_all returns the matrix of the raw (coef.splsda) coefficients
for logistic regression model estimated by the SGPLS (sparse généralized partial least squares) and
5 fold cross validation. It uses the splsda, cv.splsda and coef.splsda functions
of the sgpls package.
sgpls_spls_all returns the matrix of the raw (coef.sgpls) coefficients
for logistic regression model estimated by the SGPLS (sparse généralized partial least squares) and
5 fold cross validation. It uses the sgpls, cv.sgpls and coef.sgpls functions
of the sgpls package.
varbvs_binomial_all returns the matrix of the coefficients
for a linear model estimated by the varbvs (variational approximation for Bayesian
variable selection in logistic regression, family = binomial) and the requested threshold values.
It uses the varbvs, coef and variable.names functions of the varbvs package.
A vector or matrix of coefficients.
Frederic Bertrand, [email protected]
selectBoost: a general algorithm to enhance the performance of variable selection methods in correlated datasets, Frédéric Bertrand, Ismaïl Aouadi, Nicolas Jung, Raphael Carapito, Laurent Vallat, Seiamak Bahram, Myriam Maumy-Bertrand, Bioinformatics, 2020. doi:10.1093/bioinformatics/btaa855
glmnet, cv.glmnet, msgps, AICc_BIC_glmnetB, spls, cv.spls, correct.spls, splsda, cv.splsda, sgpls, cv.sgpls, varbvs
Other Variable selection functions:
var_select
set.seed(314) xran <- matrix(rnorm(100*6),100,6) beta0 <- c(3,1.5,0,0,2,0) epsilon <- rnorm(100,sd=3) yran <- c(xran %*% beta0 + epsilon) ybin <- ifelse(yran>=0,1,0) set.seed(314) lasso_msgps_all(xran,yran) set.seed(314) enet_msgps_all(xran,yran) set.seed(314) alasso_msgps_all(xran,yran) set.seed(314) alasso_enet_msgps_all(xran,yran) set.seed(314) lasso_cv_glmnet_all_5f(xran,yran) set.seed(314) spls_spls_all(xran,yran) set.seed(314) varbvs_linear_all(xran,yran) set.seed(314) lasso_cv_glmnet_bin_all(xran,ybin) set.seed(314) lasso_glmnet_bin_all(xran,ybin) set.seed(314) splsda_spls_all(xran,ybin, K.seq=1:3) set.seed(314) sgpls_spls_all(xran,ybin, K.seq=1:3) set.seed(314) varbvs_binomial_all(xran,ybin)set.seed(314) xran <- matrix(rnorm(100*6),100,6) beta0 <- c(3,1.5,0,0,2,0) epsilon <- rnorm(100,sd=3) yran <- c(xran %*% beta0 + epsilon) ybin <- ifelse(yran>=0,1,0) set.seed(314) lasso_msgps_all(xran,yran) set.seed(314) enet_msgps_all(xran,yran) set.seed(314) alasso_msgps_all(xran,yran) set.seed(314) alasso_enet_msgps_all(xran,yran) set.seed(314) lasso_cv_glmnet_all_5f(xran,yran) set.seed(314) spls_spls_all(xran,yran) set.seed(314) varbvs_linear_all(xran,yran) set.seed(314) lasso_cv_glmnet_bin_all(xran,ybin) set.seed(314) lasso_glmnet_bin_all(xran,ybin) set.seed(314) splsda_spls_all(xran,ybin, K.seq=1:3) set.seed(314) sgpls_spls_all(xran,ybin, K.seq=1:3) set.seed(314) varbvs_binomial_all(xran,ybin)