oliviergimenez

library(spatstat)

#------- HOMOGENEOUS

# spatial point pattern from a homogeneous Poisson process with intensity lambda = 100 
# in the window A = [0,1]x[0k2] which has area |A| = 2

phom <- rpoispp(lambda = 100,
                win = owin(xrange = c(0, 1), yrange = c(0, 2)))
phom$n

oliviergimenez

# Kod modifierad från Rafael Irizarry
# https://simplystatistics.org/2017/08/08/code-for-my-educational-gifs/
library(dplyr)
library(magick)
library(ggplot2)
theme_set(theme_minimal())

# Simulera data.
N <- 100
Sigma <- matrix(c(1, 0.75, 0.75, 1), 2, 2) * 1.5

oliviergimenez

# AUTHOR: Shade Wilson
# EMAIL: [email protected]
# DESCRIPTION: The following function is a simulation of the random walk phenomena.
# It uses the runif() funtion to return a uniform random number from 0 to 1, which is then
# scaled up to between 0 and 2pi. The number is then used as an angle, and the coordinate
# changes are calculated using the sin and cos values. random_walk() has three optional
# arguments: time, gradient, and step_size. 

# The time argument specifies how many points you want
# to create, or more generally, how long you want the simulation to run. There's a 1:1 ratio of

oliviergimenez

library(tidyverse)

url <- "https://www.metoffice.gov.uk/hadobs/hadcet/data/legacy/cetdl1772on.dat"

df_raw <- read_tsv(url, col_names = FALSE)

div_10 <- function(...){
  .../10
}

oliviergimenez

# libraries needed

library(tidyverse)
library(viridis)
library(gganimate)
library(wbstats)

# rosling chart in one command

# pull the country data down from the World Bank - three indicators

oliviergimenez

library(glmmTMB)
library(mgcv)
library(magrittr)

## Continuous covariate
x <- seq(1,10, length=100)

## Set up penalized thin-plate regression spline for x
sm <- mgcv::smoothCon(s(x), data=as.data.frame(x))[[1]]
## null space columns

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# The purpose of thise script is to generate a list of formula combinations,
# which can later be used for model fitting using unmarked::pcountOpen

library(tidyverse)
lambda_opts  <- c("1","NorthCove")
gamma_opts   <- c("1","NorthCove","Year")
omega_opts   <- c("1","NorthCove","Year")
detec_opts   <- c("1","NorthCove","Year")

param_list   <- list(lambda_opts, gamma_opts, omega_opts, detec_opts)

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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))

oliviergimenez

# Bioeconomic model from Milner-Gulland & Rowcliffe, p. 163-...

# define constants
K = 1000 # carrying capacity
r = 0.2 # intrinsic rate of increase
P = 105 # price
a = 200 # constant for cost calculation
b = 0.2 # constant for cost calculation
s = 10 # SD of the distribution for the cost
N1 = 500 # initial pop size

oliviergimenez

### Title: Back to basics: High quality plots using base R graphics
###	An interactive tutorial for the Davis R Users Group meeting on April 24, 2015
###
### Date created: 20150418
### Last updated: 20150423
###
### Author: Michael Koontz
### Email: [email protected]
###	Twitter: @michaeljkoontz
###