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Type: Package
Title: Gene-Based Association Tests using the Actual Impurity Reduction (AIR) Variable Importance
Version: 1.0.0
Date: 2018-07-25
Author: Stefano Nembrini <stefanonembrini@gmail.com>
Maintainer: Stefano Nembrini <stefanonembrini@gmail.com>
Description: Gene-based association tests using the actual impurity reduction (AIR) variable importance. The function aggregates AIR importance measures from a group of SNPs or probes and outputs a p-value for each gene. The procedures builds upon the method described in <doi:10.1093/Bioinformatics/Bty373> and will be published soon.
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
LazyData: TRUE
Imports: stats, ranger
Depends: R(≥ 3.3.1), EmpiricalBrownsMethod(≥ 1.6.0), Hmisc(≥ 4.1)
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2018-07-30 07:18:24 UTC; S
Repository: CRAN
Date/Publication: 2018-07-30 17:30:08 UTC

fisher

Description

fisher

Usage

fisher(p, adjust, R)

Arguments

p

vector of pvalues

adjust

if correlation has to be taken into account

R

correlation matrix


gaussianize null variable importances

Description

gaussianize null variable importances

Usage

gaussianize(x, a)

Arguments

x

distr

a

value to interpolate/extrapolate


meff.

Description

meff.

Usage

m_effective(R)

Arguments

R

R


after the Actual Impurity Reduction Importance is computed with a Random Forest, pvalues from different probes or SNPs belonging to the same gene can be aggregated in order to obtain a single pvalue for that gene. Correlation between probes can also be taken into account.

Description

after the Actual Impurity Reduction Importance is computed with a Random Forest, pvalues from different probes or SNPs belonging to the same gene can be aggregated in order to obtain a single pvalue for that gene. Correlation between probes can also be taken into account.

Usage

poolVIM(rf, genenames, x, method = "Tippett", adjust)

Arguments

rf

a ranger object with "importance="impurity_corrected"

genenames

a vector of the name of the gene to which each probe or SNP belongs, it has to be of size dim(x)[1]

x

design matrix used by the random forest

method

one of Tippett, Fisher, Kost, EBM

adjust

"no" / "yes" depending if correlation has to be taken into account

Examples

n <- 250
x=replicate(50, runif(n))
dat <- data.frame(y = factor(rbinom(n, 1, .5)), x)
library(ranger)
rf <- ranger(y ~ ., dat, importance = "impurity_corrected",num.trees=100)
genenames=colnames(x)=rep(c("G1","G2"),50/2)
poolVIM(rf,genenames,x,method="Fisher",adjust="no")


tippett.

Description

tippett.

Usage

tippett(p, adjust, R)

Arguments

p

vector of pvalues

adjust

if correlation has to be taken into account

R

correlation matrix

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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