Gabriel Leventhal gaberoo
gaberoo
/ # augustus - 2019-05-22_15-40-41.txt
Created
May 22, 2019 20:02
augustus on macOS 10.14.4 - Homebrew build logs
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| Homebrew build logs for augustus on macOS 10.14.4 | |
| Build date: 2019-05-22 15:40:41 |
gaberoo
/ intersecting.axis.R
Created
February 5, 2013 15:05
A replacement for R's "axis()" function that uses intersecting axes rather than the ugly stand-alone axes.
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| intersecting.axis <- function(side, at = NULL, labels = TRUE, | |
| line = NA, pos = NA, outer = FALSE, lty = "solid", | |
| lwd = 1, lwd.ticks = lwd, col = NULL, col.ticks = NULL, | |
| hadj = NA, padj = NA, ...) { | |
| if (! outer) { | |
| x.range <- extendrange(par("usr")[1:2],f=-0.01) | |
| y.range <- extendrange(par("usr")[3:4],f=-0.01) | |
| pos <- NA | |
| xline <- c(NA,NA) | |
| yline <- c(NA,NA) |
gaberoo
/ 1-metropolis.R
Last active
June 25, 2024 18:05
R code to run an **MCMC** chain using a **Metropolis-Hastings** algorithm with a Gaussian proposal distribution. Although there are hundreds of these in various packages, none that I could find returned the likelihood values along with the samples from the posterior distribution. However, if you have these likelihood values, it's very easy to ca…
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| ############################################################################## | |
| # Metropolis-Hastings MCMC | |
| # | |
| # Runs a Metropolis-Hasting MCMC chain for a given likelihood function. | |
| # Proposal steps are sampled from a Gaussian distribution, either in a single | |
| # step or sequentially over the parameter space. | |
| # | |
| # Input: | |
| # theta : starting value of the chain | |
| # lik.fun : likelihood function |
gaberoo
/ jelly.beans.R
Last active
July 12, 2016 17:02
A boxplot replacement that I call the "Jelly Bean Plot".
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| jelly.beans <- function(data, brewerset = "Set1", cols = brewer.pal(length(data),brewerset), | |
| diffs = rep(NA,length(data)), notches = FALSE, | |
| xlab = NA, ylab = NA, las = rep(0,2), bean.width = c(3,8,3,1,2), ...) { | |
| require(RColorBrewer) | |
| # data = list(grp1=vector(),grp2=vector(),grp3=vector()) | |
| # bean.width = c(0.95-lines,0.5-lines,median-line,grid-line) | |
| # diffs = c("A", "A", "B") eg to present TukeyHSD values | |
| do.call(rbind,lapply(1:length(data),function(i) c(i,data[[i]]))) | |
| num.grps <- length(data) | |
| grp.names <- names(data) |