library(tidyverse)
#> Warning: package 'tibble' was built under R version 4.0.2
#> Warning: package 'tidyr' was built under R version 4.0.2
#> Warning: package 'dplyr' was built under R version 4.0.2
tibble(x = 0:42,
y1 = pbinom(x, 42, 0.3),
y2 = 1-pbeta(0.3, x+1, 42-x)) %>%
ggplot(aes(x = x, y = y1)) +
geom_point(col = "green", size = 3) +
View prior_on_logit_scale.R
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library(shiny) | |
library(bslib) | |
library(thematic) | |
library(ggplot2) | |
thematic::thematic_shiny(font = "auto") | |
# take a vector, make a histogram |
View binomial_vs_beta.md
View example_data_manipulation.Rmd
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--- | |
title: "Exploring observations" | |
author: "Andrew, based on Tim's lecture!" | |
date: "18/05/2020" | |
output: html_document | |
--- | |
```{r setup, include=FALSE} | |
knitr::opts_chunk$set(echo = TRUE) | |
``` |
View curve_species.R
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m <- 0.086 | |
o <- (1-m)/m | |
curve((sqrt(o) - sqrt(o^-1)) / sqrt(x^2 - x + 1), xlim = c(0,50)) | |
abline(h = 1/3) |
View function_cv_plot.md
library(tidyverse)
cv <- 2.5
sigma = seq(0.2,5,by = 0.2)
list(mean = sigma / cv, sd = sigma) %>% transpose %>%
set_names(., nm = map_chr(., lift_dl(paste, sep="_"))) %>%
map(lift_dl(partial, .f = dnorm)) %>%
map_df(~ tibble(x = seq(-2,3.5,0.01),
y = .x(x)), .id = "mean_sd") %>%
ggplot(aes(x = x, ymax = y, ymin = 0, fill = mean_sd)) + geom_ribbon() +
View beta_binomial_power.R
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pwr <- function(S, a, mu = 0.086, phi=240){ | |
D <- S * (S - 1) | |
2 * (D + 1) * (D/(phi + 1) + 1) * mu * ( 1 - mu) + ( a * mu + mu) * (1 - a * mu - mu) * (2.8)^2 / ((D + 1)*a*mu)^2 | |
} | |
pwr(6, 0.10) | |
curve(pwr(x, 0.4), xlim = c(3,30)) | |
curve(pwr(x, 0.2), xlim = c(3,30), log = "y") |
View bifurcation.jl
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using Plots | |
# a vector of r values | |
Rs=collect(0.1:0.001:3) | |
T = 5000 | |
N=zeros(length(Rs), T) | |
#Set t0 values to 1 | |
N[:,1] .= 1 |
View info.R
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info <- function(x, a, b) { | |
a * (exp(a * (x - b))) / (exp(a * b) + exp(a * x)) ^ 2 | |
} |
View simulate_logistic.R
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library(tidyverse) | |
set.seed(4812) | |
islands <- data_frame(area = | |
# runif(35, min = 0, max = 29) | |
rlnorm(35, meanlog = log(5), sdlog = log(3)) | |
) | |
dragon_response <- function(b0, b1){ | |
force(b0) | |
force(b1) |
View simulate_one_cardoso.R
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simulate_one_cardoso <- function(.cardoso_island = cardoso_island){ | |
spp_names <- unique(.cardoso_island$morphospecies) | |
nspp <- length(spp_names) | |
bromeliad_names <- unique(.cardoso_island$Bromeliad) | |
nbrom <- length(bromeliad_names) | |
# simulate from the parameters: | |
b_intercept <- rnorm(1, 1, 0.2) |
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