simulation: 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 simuls
compsim(x, result.boost, level = 1, ...)

Value

simulation_cor returns a numeric matrix.

simulation_X returns a numeric matrix.

simulation_DATA returns a list.

compsim.simuls returns a numerical vector.

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.

Author

Frederic Bertrand, frederic.bertrand@utt.fr with contributions from Nicolas Jung.

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).

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. tools:::Rd_expr_doi("10.1093/bioinformatics/btaa855")

Examples

Run this code

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)

Run the code above in your browser using DataLab

Last chance! 50% off unlimited learning