#### removed jackstraw file Andrew Bass authored on 10/09/2022 22:44:35
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 1 1 deleted file mode 100644 ... ... @@ -1,107 +0,0 @@ 1 -% Generated by roxygen2: do not edit by hand 2 -% Please edit documentation in R/AllGenerics.R, R/deSet-methods.R 3 -\docType{methods} 4 -\name{apply_jackstraw} 5 -\alias{apply_jackstraw} 6 -\alias{apply_jackstraw,deSet-method} 7 -\title{Non-Parametric Jackstraw for Principal Component Analysis (PCA)} 8 -\usage{ 9 -apply_jackstraw(object, r1 = NULL, r = NULL, s = NULL, B = NULL, 10 - covariate = NULL, verbose = TRUE, seed = NULL) 11 - 12 -\S4method{apply_jackstraw}{deSet}(object, r1 = NULL, r = NULL, s = NULL, 13 - B = NULL, covariate = NULL, verbose = TRUE, seed = NULL) 14 -} 15 -\arguments{ 16 -\item{object}{\code{S4 object}: \code{\linkS4class{deSet}}} 17 - 18 -\item{r1}{a numeric vector of principal components of interest. Choose a subset of r significant PCs to be used.} 19 - 20 -\item{r}{a number (a positive integer) of significant principal components.} 21 - 22 -\item{s}{a number (a positive integer) of synthetic null variables. Out of m variables, s variables are independently permuted.} 23 - 24 -\item{B}{a number (a positive integer) of resampling iterations. There will be a total of s*B null statistics.} 25 - 26 -\item{covariate}{a data matrix of covariates with corresponding n observations.} 27 - 28 -\item{verbose}{a logical indicator as to whether to print the progress.} 29 - 30 -\item{seed}{a seed for the random number generator.} 31 -} 32 -\value{ 33 -\code{apply_jackstraw} returns a \code{list} containing the following 34 -slots: 35 -\itemize{ 36 -\item{\code{p.value} the m p-values of association tests between variables 37 -and their principal components} 38 -\item{\code{obs.stat} the observed F-test statistics} 39 -\item{\code{null.stat} the s*B null F-test statistics} 40 -} 41 -} 42 -\description{ 43 -Estimates statistical significance of association between variables and 44 -their principal components (PCs). 45 -} 46 -\details{ 47 -This function computes m p-values of linear association between m variables 48 -and their PCs. Its resampling strategy accounts for the over-fitting 49 -characteristics due to direct computation of PCs from the observed data 50 -and protects against an anti-conservative bias. 51 - 52 -Provide the \code{\linkS4class{deSet}}, 53 -with m variables as rows and n observations as columns. Given that there are 54 -r significant PCs, this function tests for linear association between m 55 -varibles and their r PCs. 56 - 57 -You could specify a subset of significant PCs 58 -that you are interested in r1. If PC is given, then this function computes 59 -statistical significance of association between m variables and PC, while 60 -adjusting for other PCs (i.e., significant PCs that are not your interest). 61 -For example, if you want to identify variables associated with 1st and 2nd 62 -PCs, when your data contains three significant PCs, set r=3 and r1=c(1,2).  63 - 64 -Please take a careful look at your data and use appropriate graphical and 65 -statistical criteria to determine a number of significant PCs, r. The number 66 -of significant PCs depends on the data structure and the context. In a case 67 -when you fail to specify r, it will be estimated from a permutation test 68 -(Buja and Eyuboglu, 1992) using a function \code{\link{permutationPA}}. 69 - 70 -If s is not supplied, s is set to about 10% of m variables. If B is not 71 -supplied, B is set to m*10/s. 72 -} 73 -\examples{ 74 -library(splines) 75 -data(kidney) 76 -age <- kidney$age 77 -sex <- kidney$sex 78 -kidexpr <- kidney\$kidexpr 79 -cov <- data.frame(sex = sex, age = age) 80 -# create models 81 -null_model <- ~sex 82 -full_model <- ~sex + ns(age, df = 4) 83 -# create deSet object from data 84 -de_obj <- build_models(data = kidexpr, cov = cov, null.model = null_model, 85 - full.model = full_model) 86 -## apply the jackstraw 87 -out = apply_jackstraw(de_obj, r1=1, r=1) 88 -## Use optional arguments 89 -## For example, set s and B for a balance between speed of the algorithm and accuracy of p-values 90 -## out = apply_jackstraw(dat, r1=1, r=1, s=10, B=1000, seed=5678) 91 - 92 -} 93 -\author{ 94 -Neo Christopher Chung \email{nc@princeton.edu} 95 -} 96 -\references{ 97 -Chung and Storey (2013) Statistical Significance of 98 -Variables Driving Systematic Variation in 99 -High-Dimensional Data. arXiv:1308.6013 [stat.ME] 100 -\url{http://arxiv.org/abs/1308.6013} 101 - 102 -More information available at \url{http://ncc.name/} 103 -} 104 -\seealso{ 105 -\code{\link{permutationPA}} 106 -} 107 -