| 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 |