seananderson

library(TMB)
compile("lm1.cpp")
dyn.load(dynlib("lm1")) # can't call it 'lm'!
set.seed(123)
x <- seq(0, 10, length.out = 10)
data <- list(Y = rnorm(length(x)) + x, x = x)
parameters <- list(a = 0, b = 0, logSigma = 0)
obj <- MakeADFun(data, parameters, DLL = "lm1")
opt <- nlminb(obj$par, obj$fn, obj$gr)
sdr <- sdreport(obj)

seananderson

If you work on a Windows computer without admin privileges to modify system files (e.g., many government employees), you can likely still install R in your user folder and replace the default BLAS library there.^* Instructions:

1. Download the latest R installer.
2. Run the installer and select a user folder for installation when prompted, e.g. C:\Users\USERNAME\SUBFOLDER.
3. In RStudio go to 'Tools' > 'Global Options' > 'General' > 'R version' to change the R installation being used to the one you just installed in your user folder.
4. Download OpenBLAS following these instructions. A summarized version of this and subsequent steps follows.
5. Extract libopenblas.dll from the OpenBLAS zip file.

seananderson

library(sdmTMB)
library(dplyr)
library(ggplot2)

mesh <- make_mesh(pcod_2011, c("X", "Y"), cutoff = 10)
fit <- sdmTMB(
  density ~ poly(depth, 2),
  data = pcod_2011, mesh = mesh,
  family = tweedie(link = "log")
)

seananderson

---
title: "Individual vs. blocked spatial data modelling"
format: 
  html:
    embed-resources: true
editor: source
execute: 
  echo: true
  eval: true
  cache: true

seananderson

set.seed(138593)
N <- 100
x <- runif(N, -2, 2)
a <- 1
b <- 1.5
sigma <- 0.3
tau <- 0.4
re_levels <- gl(10, 10)
re <- rnorm(10, 0, tau)
y_true <- a + re[re_levels] + b * x

seananderson

Thanks for filling in the survey on your experience levels and interests in an introductory workshop on Bayesian statistics in R. I'll do my best to tailor the content based on your responses.

I'm going to assume you know the basics of ggplot and dplyr — the content will make a lot more sense if you understand the elements of a ggplot plot and the main dplyr verbs: mutate, filter, summarise, select, and group_by. If you need to brush up on these, I'd recommend these chapters from 'R for Data Science': http://r4ds.had.co.nz/data-visualisation.html http://r4ds.had.co.nz/transform.html

We can talk about textbooks at the workshop, but my favorite textbook on Bayesian modelling is 'Bayesian Models: A Statistical Primer for Ecologists' by Hobbs and Hooten. https://www.amazon.com/Bayesian-Models-Statistical-Primer-Ecologists/dp/0691159289

seananderson

Thanks for filling in the survey on your experience levels and interests in an introductory workshop on Bayesian statistics in R. I'll do my best to tailor the content based on your responses.

I'm going to assume you know the basics of ggplot and dplyr — the content will make a lot more sense if you understand the elements of a ggplot plot and the main dplyr verbs: mutate, filter, summarise, select, and group_by. If you need to brush up on these, I'd recommend these chapters from 'R for Data Science': http://r4ds.had.co.nz/data-visualisation.html http://r4ds.had.co.nz/transform.html

We can talk about textbooks at the workshop, but my favorite textbook on Bayesian modelling is 'Bayesian Models: A Statistical Primer for Ecologists' by Hobbs and Hooten. https://www.amazon.com/Bayesian-Models-Statistical-Primer-Ecologists/dp/0691159289

seananderson

library(tidyverse)
d <- tibble(group = letters[1:5], x = 1:5, y = rnorm(5))
ggplot(d, aes(x, y, colour = group)) + geom_point(size = 3)

# lose group
d <- d[-2,]
ggplot(d, aes(x, y, colour = group)) + geom_point(size = 3)

# fix by joining in all known groups to create NAs:
all_groups <- tibble(group = letters[1:5])

seananderson

set.seed(42)
N <- 400
x <- runif(N, -1, 1)

Omega <- rbind( # correlation matrix
  c(1, 0.9),
  c(0.9, 1)
)
sigma <- c(0.6, 0.4) # residual SDs
Sigma <- diag(sigma) %*% Omega %*% diag(sigma) # covariance matrix

seananderson

# compare the datalimited::prm function to the original fits
# start with setwd("analysis") from the ensembles root folder

library("datalimited")
library("dplyr")
library("ggplot2")

d <- dplyr::inner_join(datalimited::ram_ts, datalimited::spp_categories,
  by = "scientificname")
ram_prm_dat <- plyr::ddply(d, "stockid", function(x) {