JWiley

library(brms)
library(bayestestR)

#### Simulate some data ####
nID <- 100 ## number of people
k <- 4 ## number of observations per person

## make it reproducible
set.seed(1234)

JWiley

  USEVARIABLES =
       FAM_SUPT ISEL_ALL
       ECEA6 PIEA17 SEEA8
       sash_langnew SASH_SOC
       screen
       ! covariates
       HCE2 MHEA26
       AGE US_BORN
       CENTER2
       CENTER3

JWiley

set.seed(1234)
dmat <- cbind(1, matrix(rnorm(1000 * 50), ncol = 50))
beta <- matrix(c(2, runif(50)))
yvec <- as.vector(dmat %*% beta + rnorm(1000, sd = 3))
dall <- cbind(y = yvec, as.data.frame(dmat[,-1]))
myindex <- matrix(sample(0:(1000 - 1), 1000 * 500, TRUE), ncol = 500)

library(Rcpp)
library(RcppEigen)
library(RcppParallel)

JWiley

// [[Rcpp::depends(RcppParallel)]]
// [[Rcpp::depends(RcppEigen)]]
#include <Rcpp.h>
#include <RcppEigen.h>
#include <RcppParallel.h>

using namespace std;
using namespace Rcpp;
using namespace RcppParallel;

JWiley

Interactive Graph Example
=========================

Function to do the work.  Aside from the packages, to run this, just load the knitr package, and then knit2html("graph.rmd").

```{r}
longplot <- function(dat, k, xlab, ylab, seed=4324, miter = 20) {
require(EMCluster)
require(mice)
require(reshape2)

JWiley

# import generics
# (cheap version of importFrom() in a package)
fixef <- nlme:::fixef
ranef <- nlme:::ranef

# define generic prediction function
# but using my functions/methods
predict2 <- function (object, ...) {
  UseMethod("predict2")
}

JWiley

## In the end, what is the twelth column of 'dat', a, and b?
## why does the local() not matter in thirteen?
a <- 10
b <- 10
dat <- local(
  data.frame(
    first = b,
    second = a <- 0 -> b,
    third = a <- rnorm(10, mean = a),
    fourth = a / 2,

JWiley

bsim <- function(reps = 10^2, groups = 200, L1k = 6, L2k = 3, seed = 10) {

  stopifnot(all.equal(L1k %% L2k, 0L))

  f <- function(x) {
    mdiff <- min(x, na.rm = TRUE) - 0
    (x - mdiff)/(max(x, na.rm = TRUE) - mdiff)
  }

  N <- reps * groups

JWiley

dfm <- data.frame(y = c(30, 43, 36, 33, 50, 45, 40) +
  scale(rnorm(7, 2, 5), scale = FALSE), x = 1:7)
dalt <- data.frame(x = seq(1, 7, length.out = 100))

m <- lapply(1:6, function(i) {lm(y ~ poly(x = x, degree = i), data = dfm)})

pdf(file = "tmp.pdf", height = 11, width = 8.5, onefile = TRUE)
par(mfrow = c(3, 2))
for(i in as.numeric(1:6)) {
  plot(y ~ x, data = dfm, main =

JWiley

navel.gazer <- function(month = NULL, year = NULL, entire = FALSE,
                         list = "r-help", n = 50, plot = TRUE) {
  # Ben Bolker came up with most of the code
  # Henrique Dallazuanna provided an edit to the z <- line of code
  # Brian Diggs provided capwords() to properly count Peter Dalgaard
  # Joshua Wiley adapted all of it to one function


  if(is.null(month)) {
    month <- format(Sys.Date(), format = "%B")