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Guangchuang Yu GuangchuangYu
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GuangchuangYu
/ SICP.md
Created
October 23, 2015 06:06
require(grid)
require(ggplot2)
require(magrittr)
theme_transparent <- function(...) {
theme(panel.background = element_rect(
fill = "transparent",
colour = NA),
plot.background = element_rect(
GuangchuangYu
/ ppurl-alternative.md
Last active
August 29, 2015 14:19
— forked from zjhiphop/ppurl-alternative.md
https://github.com/milkypostman/melpa
for installing/updating emacs add-on packages
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| ## http://ygc.name/2012/05/29/t-test/ | |
| p1 <- rnorm(1000) | |
| p2 <- rnorm(1000, sd=3) | |
| hist(p2, col="green", xlab="", main="") | |
| hist(p1, col="red", add=T) | |
| legend(legend=c("sd=1", "sd=3"), fill=c("green", "red"), x="topright") |
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| x <- read.csv("/Volumes/YGC/Researches/ARCHIVE/Others/AL-1-by-GR.Yan/2D-data-and-analysis-result/2D.csv") | |
| eg <- as.character(x[,1]) | |
| require(clusterProfiler) | |
| xx <- enrichGO(eg, ont="BP") | |
| gg <- xx@geneInCategory | |
| names(gg) <- clusterProfiler:::TERM2NAME.BP(names(gg)) | |
| g <- gg[1:4] | |
| require(gplots) | |
| vm <- venn(g) |
GuangchuangYu
/ bootstrapEnrichment.R
Created
May 2, 2012 02:28
bootstrap method for enrichment analysis
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| # http://ygc.name/2012/04/29/bootstrap-for-enrichment-analysis/ | |
| goboot <- cmpfun(function(goid, | |
| sampleSize, | |
| nboot=1000) { | |
| ratio <- rep(0, nboot) | |
| allgene <- unique(mappedkeys(org.Hs.egGO)) | |
| allgeneInCategory <- unique(get(goid, org.Hs.egGO2ALLEGS)) |
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| set.seed(123) | |
| x <- rnorm(100) | |
| y <- rnorm(100) | |
| points.df <- data.frame(x=x,y=y) | |
| center <- c(0.2, 0.3) | |
| diameter <- 2 | |
| d <- apply(points.df, 1, function(x) x-center) |
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| ## http://ygc.name/2011/08/02/q-q-plots/ | |
| qqplot <- function(y, distribution=qnorm) { | |
| x <- distribution(ppoints(y)) | |
| plot(x, sort(y), | |
| xlab="Theoretical Quantiles", | |
| ylab="Sample Quantiles", | |
| main="Normal Q-Q Plot" | |
| ) | |
| lines(y,y) |
GuangchuangYu
/ Antithetic_variates.R
Created
February 29, 2012 06:40
The antithetic variates method is a variance reduction technique used in Monte Carlo methods.
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| ## Antithetic sampling reframes our estimate as a sum of negatively | |
| ## correlated random variables, using the fact that negative | |
| ## correlation reduces the variance of a sum. | |
| ## http://en.wikipedia.org/wiki/Antithetic_variates | |
| g <- function(x) 1/(1+x) | |
| N <- 1500 | |
| n <- 50 | |
| u1 <- matrix(runif(2 * n * N), ncol = n) |