mja

library(MCMCglmm)
library(mvtnorm)

# separate covariances for each sex
Rf <- matrix(c(1, .4, .4, 1), nrow=2)
Rm <- matrix(c(1, 0, 0, 1), nrow=2)

# Simulate correlated phenotypes
females <- data.frame(rmvnorm(100, mean=c(0, 0), sigma=Rf))
males <- data.frame(rmvnorm(100, mean=c(0, 0), sigma=Rm))

mja

> n <- nrow(CC)
> Q <- INLA:::inla.pc.bym.Q(border, scale.model=TRUE)
> u <- .2/.31
> alpha <- .01
> phi.u <- .5
> phi.alpha <- 2/3
> formula2 <- agr ~ age + sex + age:sex +
+                   f(geo, model='bym2', graph=border,
+                   constr=TRUE, scale.model=TRUE,
+                   hyper=list(phi=list(prior='pc',

mja

# GCTA-power analysis
# see http://spark.rstudio.com/ctgg/gctaPower/
#     http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1004269

# Bivariate analysis (traits measured on different sets of individuals)
bivariate_2sample_power <- function (N1=4000, N2=4000, rG=0.5, h2_1=0.2, h2_2=0.2, varA=0.00002, alpha=0.05) {
  
  # variance of genetic correlation (Equation 10)
  var_rG <- 

mja

#!/bin/sh
java -jar /path/to/GenomeAnalysisTK.jar $@

mja

dat <- data.frame(obs=c(rep(0, times=100), rnorm(100, 10, 1)))

# observed data looks something like
hist(dat$obs)

# break into the zero-part and the normal part of the distribution
# observed zeros get zero for the first part, otherwise 1
# observed zeros get NA for the second part, otherwise observed value
dat <- transform(dat, zero_part=ifelse(obs == 0, yes=0, no=1),
                      norm_part=ifelse(obs != 0, yes=obs, no=NA))

mja

# http://henrik.nyh.se/2008/12/git-dirty-prompt
# http://www.simplisticcomplexity.com/2008/03/13/show-your-git-branch-name-in-your-prompt/
# 
# boosty likes it like that:
#   ~/dev/dir[master]$   # clean working directory
#   ~/dev/dir[master⚡]$  # dirty working directory

function parse_git_dirty {
  [[ $(git status 2> /dev/null | tail -n1) != "nothing to commit (working directory clean)" ]] && echo "⚡"
}

mja

 *** caught bus error ***
address 0x8, cause 'non-existent physical address'

Traceback:
 1: .C("MCMCglmm", as.double(data$MCMC_y), as.double(data$MCMC_y.additional),     as.double(data$MCMC_liab), as.integer(mvtype), as.integer(length(data$MCMC_y)),     as.integer(X@i), as.integer(X@p), as.double(X@x), as.integer(X@Dim),     as.integer(length(X@x)), as.integer(Z@i), as.integer(Z@p),     as.double(Z@x), as.integer(Z@Dim), as.integer(length(Z@x)),     as.integer(A@i), as.integer(A@p), as.double(A@x), as.integer(A@Dim),     as.integer(length(A@x)), as.double(MSsd), as.integer(id -         1), as.integer(dam - 1), as.integer(sire - 1), as.integer(PedDim),     as.integer(Aterm), as.integer(nfl), as.integer(nrl), as.integer(update),     as.integer(split - 1), as.integer(c(nG, nR)), as.double(GRinv),     as.double(GRvpP), as.double(GRnpP), as.integer(nitt), as.integer(thin),     as.integer(burnin), as.integer(c(pr, pl)), as.double(Loc),     as.double(Var), as.double(PLiab), as.integer(data$MCMC_family.names),  

mja

survey_id
1

mja

require 'uri'

ARGV.first.split.each do |f|
	article = URI.parse(f)
	article.host = article.host + ".ezproxy.YOUR-INSTITUTION"
	puts article.to_s
end

mja

# Options for ggplot + opts(...)
# These don't seem to be documented on the web. Listed here for reference.

# list current setting
theme_get()

# Make a plot
g <- ggplot(...)

# Print the plot with options