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Drew Tyre atyre2

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atyre2 / phase_planes.R
Created Oct 17, 2018
Animation demonstrating the mapping between time series and phase plane representations of the Lotka-Volterra predator prey equations
View phase_planes.R
# from https://github.com/ha0ye/personal-website/blob/master/figure%20code/causality-figures.R
library(tidyverse)
library(gganimate) # devtools::install_github("thomasp85/gganimate")
## generate model data ----
# params
t_max <- 100
h <- 0.01 # step size
View file4ca1dcc46fe.R
#' # Lake Ontario Cormorant matrix
cormorant_post_pop <- function(S = c(0.5, 0.87, 0.88, 0.89),
B = c(0, 0.5, 0.99),
F_ = c(0, 1.6, 2.4),
R = 0.5){
A <- matrix(0, nrow=3, ncol=3)
A[2,1] <- S[1]
A[3,2:3] <- S[2:3] # ends up slightly different than pre-breeding matrix
A[1,] <- B*F_*S[2:4]*R
A
View SO_example_data_1.R
dieet <- structure(list(Jaar = c(2013L, 2013L, 2013L, 2013L, 2013L, 2013L,
2014L, 2014L, 2014L, 2014L, 2014L, 2015L, 2015L, 2015L, 2013L,
2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 2014L,
2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L), Kuikenweek = c("Week 1", "Week -1", "Week 2",
"Week -2", "Week 3", "Week -3", "Week 1", "Week 2", "Week -2",
"Week 3", "Week -3", "Week -1", "Week -2", "Week -3", "Week 1",
"Week -1", "Week 2", "Week -2", "Week 3", "Week -3", "Week 4",
"Week 5", "Week 6", "Week 1", "Week -1", "Week 2", "Week 3",
"Week -3", "Week 4", "Week 5", "Week 1", "Week -1", "Week 2",
View file2949719a7b36.R
library(rvest)
library(tidyverse)
webpage <- read_html("http://snr.unl.edu/aboutus/who/people/wellness/flightascendingsummary.asp")
tbls <- html_nodes(webpage, "table") %>%
html_table() %>%
.[[1]] %>%
separate(Participant, into = c("Last Name", "First Name"), sep = ",") %>%
mutate(`Flights per day` = `Total Number of Flights` / `Total Number of Days`,
View file294966af0306.R
library(rvest)
library(tidyverse)
webpage <- read_html("http://snr.unl.edu/aboutus/who/people/wellness/flightascendingsummary.asp")
tbls <- html_nodes(webpage, "table") %>%
html_table() %>%
.[[1]] %>%
separate(Participant, into = c("Last Name", "First Name"), sep = ",") %>%
mutate(`Flights per day` = `Total Number of Flights` / `Total Number of Days`,
View gist:1f09ef79d7ca8d23021b259114255f8f
library(tidyverse)
xx <- read_table2(" 1 family1 0.8690661709 0.751969991 0.7188319163 0.8329348587 0.887535770
2 family1 0.2191712498 0.378020942 0.8583653572 0.4347398167 0.463576331
3 family1 0.6999901198 0.641613472 0.3160536611 0.6978992328 0.648399558
4 family3 0.7372355657 0.538900863 0.7362216445 0.7604946053 0.822828410
5 family1 0.7096292505 0.598671265 0.2544588205 0.6133470555 0.607725992
6 family1 0.6360280758 0.566566355 0.5877906233 0.6997789522 0.546953210
7 family1 0.5991758243 0.616002671 0.8369985343 0.8206614441 0.818448365
8 family1 0.8904527922 0.716208368 0.9011345475 0.5670958449 0.727816837
9 family2 0.1452406058 0.207274998 0.4986928476 0.1686444875 0.189754451
@atyre2
atyre2 / Check_assumptions.R
Last active Feb 6, 2020
ggplot version of plot(lmObject)
View Check_assumptions.R
## function to do graphical checks in one hit, given a model
## assumes that broom and gridExtra are available
## and that the model object has tidiers available
check_assumptions <- function(x, ...){
if (inherits(x,"gam")){
stop("check_assumptions doesn't work with gam() objects")
}
my_theme <- ggplot2::theme_classic() +
ggplot2::theme(text=ggplot2::element_text(size=rel(4)))
@atyre2
atyre2 / poisson_gamm_ar1.R
Created May 12, 2016
Trying out gamm() with auto-correlated errors and Poisson error distribution
View poisson_gamm_ar1.R
n <- 200;sig <- 2
x <- 0:(n-1)/(n-1)
f <- 0.2*x^11*(10*(1-x))^6+10*(10*x)^3*(1-x)^10
e <- rnorm(n,0,sig)
for (i in 2:n) e[i] <- 0.6*e[i-1] + e[i]
y <- f + e
op <- par(mfrow=c(2,2))
## Fit model with AR1 residuals
b <- gamm(y~s(x,k=20),correlation=corAR1())
plot(b$gam);lines(x,f-mean(f),col=2)
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