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# 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 |
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# 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 |
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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 | |
} |
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# 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 |
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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)) |
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# 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 |
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### 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 | |
### |
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# Implements Lasso for logistic regression, both classical/bayesian ways | |
## 1. SIMULATION | |
# for reproducibility | |
set.seed(666) | |
# sample size | |
n <- 100 |
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