BootSE: Bootstrap error estimation and significance for the GLSE...

In GLSE: Graphical Least Square Estimation

Description

The function provides bootstrap error estimation and significance for the estimates obtained via the GLSE().

Usage

BootSE(Graph, x, y, n.boot, sig.level = 0.05, centering = TRUE)

Arguments

Graph	An object of class GLSE.
x	A matrix of n x v dimension consisting of the predictors values.
y	A vector of length n of the response variable.
n.boot	Number of bootstrap samples.
sig.level	Significance level. Must be pre-specified.
centering	Logical. If equal to TRUE then all the variables in x and the response y are centered. The default is TRUE.

Value

A data frame consisting of

Beta	The estimated regression coefficients based on the provided graph.
Sttandard.Error	Bootstrap estimates of the standard errors.
Significance	Significance of the estimates based on the specified quantile points. "**" mean significance at the specified level while "+" means no significance.

Author(s)

Saeed Aldahmani

References

Alan, G. Frank, B. (2009), Computation of Multivariate Normal and t Probabilities. Lecture Notes in Statistics, Vol. 195., Springer-Verlag, Heidelberg. ISBN 978-3-642-01688-2.

Alan, G. Frank, B. Tetsuhisa, M. Xuefe, M. Friedrich, L. Fabian, S. and Torsten, H. (2019). mvtnorm: Multivariate Normal and t Distributions. R package version 1.0-7. URL http://CRAN.R-project.org/package=mvtnorm.

Aldahmani, S. and Dai, H. (2015). Unbiased Estimation for Linear Regression When n< v. International Journal of Statistics and Probability, 4(3), p61.

Csardi, G., and Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695(5), 1-9.

Dethlefsen, C., and H?jsgaard, S. (2005). A common platform for graphical models in R: The gRbase package. Journal of Statistical Software, 14(17), 1-12.

See Also

GLSE

Examples

library(gRbase)
#load the data
data(qsbralks)
x <- as.matrix(qsbralks[,1:15])
y <- as.matrix(qsbralks[,22])

# Call GLSE(.) .


Graph <- GLSE(x,y,parallel=FALSE)$Graph


# Call BootSE(.) .

results <- BootSE(Graph,x,y,n.boot=150,sig.level =.3,centering = TRUE)

results

GLSE documentation built on May 2, 2019, 6:34 a.m.