| library(mgcv) | |
| library(spdep) | |
| library(sf) | |
| library(ggplot2) | |
| # Columbus crime data from mgcv/spdep | |
| example(columbus) | |
| # Make a neighborhood object compatible with mgcv's MRF | |
| nb <- poly2nb(columbus) |
| library(mgcv) | |
| set.seed(3);n <- 400 | |
| ############################################ | |
| ## First example: simulated Tweedie model... | |
| ############################################ | |
| dat <- gamSim(1,n=n,dist="poisson",scale=.2) | |
| dat$y <- rTweedie(exp(dat$f),p=1.3,phi=.5) ## Tweedie response | |
| b <- gam(y~s(x0)+s(x1)+s(x2)+s(x3),family=tw(), |
| # Distance sampling model with data augmentation. | |
| # Based on section 8.3.1 of the Applied Hierarchical Modeling book by Royle and Kery | |
| library(rstan) | |
| B <- 50 | |
| # note that I'm dividing by 10 and adding 1e-6 to put this on a manageable | |
| # scale and prevent values == 0 | |
| d_obs <- c(71.93, 26.05, 58.47, 92.35, 163.83, 84.52, 163.83, 157.33, |
| #' Function to partition unequal sized groups into shards of similar size. | |
| #' | |
| #' Based on the greedy algorithm described in [The Wikipedia article on the | |
| #' the partitioning problem](https://en.wikipedia.org/wiki/Partition_problem#The_greedy_algorithm) | |
| #' | |
| #' @param groups_vector An integer vector of group ids, such as a group ID | |
| #' column in a data frame | |
| #' @param n_shards The number of shards to split groups up into | |
| #' @examples | |
| #' n_groups <- 200 |
| 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( |
| ### species discovery curve from eBird | |
| library(lubridate) | |
| # life list | |
| life <- read.csv("ebird_world_life_list.csv") | |
| life$date <- lubridate::dmy(as.character(life$Date)) | |
| #sort | |
| life <- life[order(life$date),] |
| 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, |
| # little animation to illustrate uncertainty in a density map | |
| library(dsm) | |
| library(mvtnorm) | |
| library(ggplot2) | |
| library(animation) | |
| library(viridis) | |
| # load the models and prediction data | |
| # using data from the Duke spatial distance sampling course |
| #https://github.com/ateucher/useful_code/blob/master/R/numbers2words.r | |
| numbers2words <- function(x){ | |
| ## Function by John Fox found here: | |
| ## http://tolstoy.newcastle.edu.au/R/help/05/04/2715.html | |
| ## Tweaks by AJH to add commas and "and" | |
| helper <- function(x){ | |
| digits <- rev(strsplit(as.character(x), "")[[1]]) | |
| nDigits <- length(digits) | |
| if (nDigits == 1) as.vector(ones[digits]) |
This document described guidelines for contributions for projects led by members of the [Poisot lab of computational ecology][pl], Université de Montréal. It should be followed by lab members (at all times), as well as people with suggested added features, or willing to contribute enhancement or bug fixes.
Any contribution to the code of a software grants authorship on the next paper
