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library(glmmTMB) | |
set.seed(101) | |
dd <- data.frame(x = rnorm(1000)) | |
dd$y0 <- simulate_new( ~ 1, | |
seed = 101, | |
ziformula = ~ 1, | |
family = ziGamma(link = "log"), | |
newdata = dd, | |
newparams = list( |
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download.file("https://www.census.gov/foreign-trade/balance/country.xlsx", dest = "balance.xlsx") | |
library(readxl) | |
library(dplyr) | |
library(ggplot2); theme_set(theme_bw()) | |
library(ggrepel) | |
bdat <- read_excel("balance.xlsx") |> | |
filter(year == 2024) |> | |
select(countrycode=CTY_CODE, country=CTYNAME,imports=IYR,exports=EYR) |
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library(gstat) | |
library(lattice) | |
# Create Data Points (Random) | |
n <- 50 | |
data3D <- data.frame(x = runif(n), y = runif(n), z = runif(n), v = rnorm(n)) | |
coordinates(data3D) = ~x+y+z | |
# Create empty grid to krige | |
range1D <- seq(from = 0, to = 1, length = 20) |
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## https://fediscience.org/@[email protected]/113439584737810431 | |
library(MASS) # for eqscplot | |
library(RTMB) | |
set.seed(101) | |
n <- 1000 | |
X <- cbind( | |
x1 = rnorm(n , 10, 2), | |
x2 = rnorm(n, 7, 1), |
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## simulate example (NOT satisfying constraints, but large and close enough that we | |
## probably won't hit singular fits etc. etc.) | |
library(glmmTMB) | |
set.seed(1001) | |
nx <- 3 | |
N <- 1000 | |
ng <- 20 | |
dd <- replicate(nx, rnorm(N), simplify = FALSE) |> as.data.frame() |> setNames(paste0("x", 1:nx)) | |
dd$g <- factor(sample(1:ng, replace = TRUE, size = N)) |
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## https://ivelasq.rbind.io/blog/politely-scraping/index.html | |
## To clean data | |
library(tidyverse) | |
library(lubridate) | |
library(janitor) | |
# To scrape data | |
library(rvest) | |
library(httr) | |
library(polite) |
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## https://stats.stackexchange.com/questions/649052/selecting-the-correct-r-lme4-syntax-for-nested-models | |
library(lme4) | |
df <- data.frame(Subject = rep(factor(1:120), each = 200), | |
Group = rep(LETTERS[1:3], each = 40*200)) | |
with(df, table(Group)) | |
with(df, table(table(Subject))) | |
df$Accuracy <- simulate(~ Group + (1|Subject), | |
newdata = df, |
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library(brms) | |
library(metafor) | |
library(broom) | |
library(broom.mixed) | |
set.seed(101) | |
y <- rnorm(10) | |
sei <- rgamma(10, shape = 1) | |
## metafor: fixed-effect meta-analysis |
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i1 <- installed.packages() | |
null <- matrix(nrow=0, ncol = 2, | |
dimnames=list(NULL, c("Item", "type"))) | |
get_data <- function(pkg) { | |
cat(pkg,"\n") | |
dd <- data(package=pkg)$results | |
## library(pkg, character.only = TRUE) | |
## on.exit(try(detach(paste0("package:", pkg)))) | |
if (nrow(dd) == 0) return(NULL) | |
FUN <- function(x) { |
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## original by Richard McElreath at https://twitter.com/rlmcelreath/status/1701165075493470644, | |
## https://gist.github.com/rmcelreath/39dd410fc6bb758e54d79249b11eeb2f | |
## originally based on https://doi.org/10.1006/jmva.1999.1820 | |
## with improvements from https://gist.github.com/murphyk/94205bcf335837108ff0e6d51331785a | |
post_prior <- function( | |
m_pr = 10, m_lik = 0, ## mean values for prior and likelihood | |
sd_pr = 1, sd_lik = 1, ## standard deviations | |
df_pr = 1000, df_lik = 1000, ## dfs (df = 1000 is effectively Gaussian) | |
xlim = c(-5, 15), n = 1001, ## range and delta for x-vector |
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