bbolker

library(readxl)
library(dplyr)
library(ggplot2)
library(ragg)
## https://www.sthda.com/english/wiki/ggplot2-themes-and-background-colors-the-3-elements#google_vignette
theme_set(theme_classic(base_size = 16) +
            theme(panel.background = element_rect(fill = "lightgray")))


library(scales) # trans_new() is in the scales library

bbolker

## https://bsky.app/profile/benjiturnbull.bsky.social/post/3lpevi67z4c2w
library(lme4)
dd <- data.frame(f = factor(rep(1:3, each = 10)))
set.seed(105) ## seed-hack to get a singular fit ...
dd$y <- simulate(~1 + (1|f), newdata = dd,
                 newparams = list(beta = 0, sigma = 1, theta = 0.01))[[1]]
m <- lmer(y ~ 1 + (1|f), data = dd)
performance::icc(m) ## NA
multilevelTools::iccMixed("y", "f", data = dd) ## ICC(f) == 0
flexplot::icc(m)$icc ## 0

bbolker

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(

bbolker

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)

bbolker

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)

bbolker

## 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),

bbolker

## 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))

bbolker

## 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)

bbolker

## 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,

bbolker

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