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| ################################################ | |
| ## Functions for derivatives of GAM(M) models ## | |
| ################################################ | |
| Deriv <- function(mod, n = 200, eps = 1e-7, newdata, term) { | |
| if(inherits(mod, "gamm")) | |
| mod <- mod$gam | |
| m.terms <- attr(terms(mod), "term.labels") | |
| if(missing(newdata)) { | |
| newD <- sapply(model.frame(mod)[, m.terms, drop = FALSE], | |
| function(x) seq(min(x), max(x), length = n)) |
| --- | |
| title: "Untitled" | |
| author: "Francisco Rodriguez-Sanchez" | |
| date: "Thursday, January 22, 2015" | |
| output: html_document | |
| --- | |
| <style type="text/css"> | |
| body, td { |
| library(imager) | |
| library(tidyverse) | |
| URL1<-"1280px-CLaude_Monet_037.jpg" | |
| IMAGE <- imager::load.image(URL1) | |
| df1 <- as.data.frame(IMAGE,wide="c") %>% mutate(rgb.val=rgb(c.1,c.2,c.3)) | |
| df1<-df1%>%mutate(xNtile=ntile(x,60), | |
| yNtile=ntile(y,60))%>% | |
| group_by(xNtile,y)%>% | |
| mutate(Xrang=rank(-x), |
| library(raster) | |
| # remotes::install_github("statnmap/cartomisc") | |
| library(cartomisc) | |
| library(dplyr) | |
| library(purrr) | |
| # Create original raster | |
| fn <- system.file("external/test.grd", package="raster") | |
| s <- stack(fn, fn) |
| ## plots and animations of how basis functions are used to make | |
| ## splines and how these are fitted to data | |
| library('ggplot2') | |
| library('tibble') | |
| library('tidyr') | |
| library('dplyr') | |
| library('mgcv') | |
| library('mvnfast') | |
| library('purrr') |
| library(tidyverse) | |
| library(sf) | |
| library(osmdata) # to get openstreetmap geo data | |
| # settings | |
| bb <- getbb('Lausanne, Switzerland') # define the bbox, will be used to fetch OSM data within that box | |
| dest_proj <- 2056 | |
| # OSM overpass query | |
| q <- opq(bbox = bb) |
| library(glmmTMB) | |
| library(mgcv) | |
| ######################################################################### | |
| ## Function to create prediction matrices from mgcv for glmmTMB | |
| ## from ?mgcv::smooth2random | |
| ######################################################################## | |
| s2rPred <- function(sm,re,data) { | |
| ## Function to aid prediction from smooths represented as type==2 | |
| ## random effects. re must be the result of smooth2random(sm,...,type=2). |
iTerm2
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