noamross

#' Create a random effect basis with integers rather than factors
#' @import mgcv
#' @export
smooth.construct.re2.smooth.spec <- function (object, data, knots) {
  if (!is.null(object$id))
    stop("random effects don't work with ids.")
  if(any(sapply(data, is.numeric))) data <- lapply(data, as.factor)   ## <-- All I did was this (and below)
  form <- as.formula(paste("~", paste(object$term, collapse = ":"),
                           "-1"))
  object$X <- model.matrix(form, data)

noamross

library(tidyverse)
library(gganimate)
library(mgcv)
library(mvtnorm)

# Fit a GAM to the data
mod <- gam(hp ~ s(mpg), data=mtcars, method="REML")

# Get the linear prediction matrix
newdat = data.frame(

smdabdoub

# NOTE: This was done on RHEL 7.3 with JAGS v4.3.0 and rjags v4-6. It should be fairly portable, but YMMV.

# Step 1: create an MKL environment variable for use in the --with-blas option for the JAGS configure script
# the below was taken from Martyn Plummer's "Building a portable R binary with Intel tools"
# https://martynplummer.files.wordpress.com/2014/10/r-intel-fedora.pdf
# I set MKL_LIB_PATH by looking at MKL_LIBS

export MKL="-Wl,--start-group ${MKL_LIB_PATH}/libmkl_gf_lp64.a \
> ${MKL_LIB_PATH}/libmkl_gnu_thread.a \
> ${MKL_LIB_PATH}/libmkl_core.a \

dill

library(gridBase)
library(grid)
library(mgcv)
library(statmod)
## nicer version of gam.check/rqgam.chack
# - deviance residuals for the Q-Q and histogram
# - RQR for resids vs linear pred
# - response vs fitted
# hist.p gives the quantiles of the residuals to show
better_check <- function(b, k.sample = 5000, k.rep = 200, rep = 0, level = 0.9,

dill

# makes plots (somewhat) like those in ver Hoef and Boveng (2007)

# Jay M. Ver Hoef and Peter L. Boveng 2007. QUASI-POISSON VS. NEGATIVE BINOMIAL REGRESSION: HOW SHOULD WE MODEL OVERDISPERSED COUNT DATA? Ecology 88:2766–2772. http://dx.doi.org/10.1890/07-0043.1
# http://www.utstat.utoronto.ca/reid/sta2201s/QUASI-POISSON.pdf


# calling with something like: 

# par(mfrow=c(1,2))
# # define the breaks

Thell

```{r setup, echo=FALSE, results='hide'}
chunkref <- local({
  function(chunklabel) {
    sprintf('[%s](#%s)', chunklabel, chunklabel )
  }  
})

secref <- local({
  function(seclabel) {
    sprintf('[%s](#%s)', seclabel, seclabel )

aaronberdanier

SankeyR <- function(inputs, losses, unit, labels, format="plot"){
########################
# SankeyR version 1.01 (updated August 10, 2010)
# is a function for creating Sankey Diagrams in R.
# See http://www.sankey-diagrams.com for excellent examples of Sankey Diagrams.
# 
# OPTIONS:
# 'inputs' is a vector of input values
# 'losses' is a vector of loss values
# 'unit' is a string of the unit