simulation: Miscellaneous simulation functions

In SelectBoost: A General Algorithm to Enhance the Performance of Variable Selection Methods in Correlated Datasets

Miscellaneous simulation functions

Description

Define several simulation functions to be used in the demos of the package.

Usage

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, ...)

Arguments

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.

Details

simulation_cor returns a numeric symetric matrix c whose order is the number of variables. An entry c_{i,j} is equal to

• i=j, entries on the diagonal are equal to the v value

• i<>j, 0 if the variable i and j do not belong to the same group

• i<>j, 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).

Value

simulation_cor returns a numeric matrix.

simulation_X returns a numeric matrix.

simulation_DATA returns a list.

compsim.simuls returns a numerical vector.

Author(s)

Frederic Bertrand, [e-mail] with contributions from Nicolas Jung.

References

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

See Also

glmnet, cv.glmnet, AICc_BIC_glmnetB, lars, cv.lars, msgps

Examples

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)

SelectBoost documentation built on Dec. 1, 2022, 1:27 a.m.