strengejacke

library(easystats)
library(palmerpenguins)
library(lme4)
library(ggplot2)


# data generation -----------------------

data(penguins)
d <- penguins |>

strengejacke

easystats::install_latest(force= TRUE)
library(datawizard)  # data wrangling and preparation
library(parameters)  # model summaries
library(glmmTMB)     # multilevel modelling

# sample data set
data(efc, package = "ggeffects")

efc <- efc |>
  # numeric to factors, set labels as levels

strengejacke

// save to windows-user directory

linters: with_defaults(object_name_linter = NULL,
                       object_length_linter(50),
                       commented_code_linter = NULL,
                       object_usage_linter = NULL,
                       line_length_linter(120),
                       cyclocomp_linter = cyclocomp_linter(50))

strengejacke

[
    {
        "description": "R Build Docs",
        "key": "ctrl+shift+d",
        "command": "r.document",
        "when": "resourceLangId == 'r'"
    },
    {
        "description": "R Pipe Operator",
        "key": "ctrl+shift+m",

strengejacke

library(insight)
library(datawizard)
library(modelbased)
library(ggeffects)
library(emmeans)
library(marginaleffects)

data(efc, package = "ggeffects")
efc <- data_to_factor(efc, select = "c161sex")
model <- lm(neg_c_7 ~ barthtot + c160age * c161sex, data = efc)

strengejacke

library(easystats)
#> # Attaching packages: easystats 0.4.3
#> ✔ insight     0.17.0.6   ✔ datawizard  0.4.0.14
#> ✔ bayestestR  0.12.1     ✔ performance 0.9.0.3 
#> ✔ parameters  0.17.0.9   ✔ effectsize  0.6.0.7 
#> ✔ modelbased  0.8.0      ✔ correlation 0.8.0.1 
#> ✔ see         0.7.0.2    ✔ report      0.5.1.1
library(emmeans)
library(marginaleffects)

strengejacke

library(ggeffects)
library(parameters)
library(brms)

income_options <- c("below_20", "20_to_40", "40_to_100", "greater_100")
income <- factor(sample(income_options, 100, TRUE), 
                 levels = income_options, ordered = TRUE)
mean_ls <- c(30, 60, 70, 75)
ls <- mean_ls[income] + rnorm(100, sd = 7)
dat <- data.frame(income, ls)

strengejacke

library(glmmTMB)
library(ggeffects)

data("Salamanders")
m1 <- glmmTMB(
  count ~ mined + (1 | site),
  zi =  ~ mined,
  family = poisson,
  data = Salamanders
)

strengejacke

library(parameters)
library(performance)
library(ggplot2)

set.seed(123)
x <- 3 + rnorm(200)
y <- 4 + .5 * x + rnorm(100)
dat <- data.frame(x, y)

ggplot(dat, aes(x, y)) + 

strengejacke

# Second Generation P-value

p_delta <- function(lb, ub, delta_lb, delta_ub) {
  
  # special case: infinite CI and H0 bounds in the same direction
  if ((delta_lb == -Inf & lb == -Inf) | (delta_ub == Inf & ub == Inf)) {
    return(1)
  }
  
  # usual case: non-point CI & non-point Ho