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library(tidyverse) | |
# define levels | |
levs <- c("agree", "neither", "disagree") | |
# generate data | |
set.seed(1839) | |
(dat <- replicate(20, sample(levs, 100, TRUE)) %>% | |
as_tibble() %>% | |
# add other variables so that you have to be specific in across() |
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library(tidyverse) | |
library(gamlss) | |
set.seed(1839) | |
# simulate location and scale for measure 1 | |
mu1_i <- rnorm(100) | |
sigma1_i <- runif(100) | |
# simulate location and scale for measure 2, correlated with measure 1 | |
mu2_i <- mu1_i + rnorm(100) |
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library(MASS) | |
library(tidyverse) | |
n <- 1000 | |
nvars <- 10 | |
iter <- 500 | |
sim_var <- function(n, r) { | |
c(mvrnorm(n, mu = c(0, 0), Sigma = matrix(c(1, r, r, 1), ncol = 2))) | |
} |
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#' Make a Summary Table of Multiple t-tests | |
#' | |
#' TGenerates a table that includes variable names, means and standard | |
#' deviations for each condition, t-value and p-value, degrees of | |
#' freedom, and Cohen's d with confidence intervals. | |
#' | |
#' @param data A data.frame. | |
#' @param dvs String. Variable name(s) of the dependent variables. | |
#' @param iv String. Variable name for the independent variable. | |
#' @param var_equal Logical. Assumed equal variance across conditions? |
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library(tidyverse) | |
tibble(x = rep(letters[1:4], 2), y = runif(8)) %>% | |
mutate(y = 2 * y) %>% | |
group_by(x) %>% | |
summarise(mean_y = mean(y)) %>% | |
filter(z == "a") %>% | |
spread(x, mean_y) %>% | |
mutate_all(`*`, 10) |
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set.seed(1839) | |
iter <- 1000000 | |
results <- sapply(1:iter, function(zzz) { | |
m <- matrix(sample(c("X", "X", "X", "X", "X", "O", "O", "O", "O"), 9), 3) | |
xwin <- c( | |
all(m[1, ] == "X"), | |
all(m[2, ] == "X"), | |
all(m[3, ] == "X"), | |
all(m[, 1] == "X"), | |
all(m[, 2] == "X"), |
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structure(list(x = c(26, 49, 25, 34, 24, 32, 24, 37, 38, 47, | |
41, 33, 20, 37, 34, 32, 28, 48, 40, 42, 42, 39, 38, 39, 32, 45, | |
25, 43, 50, 33, 19, 44, 44, 37, 55, 47, 36, 36, 14, 23, 42, 35, | |
39, 46, 42, 40, 27, 26, 27, 47, 47, 46, 24, 50, 45, 52, 26, 50, | |
44, 48, 46, 49, 21, 36, 41, 31, 45, 44, 34, 43, 46, 37, 46, 46, | |
48, 28, 50, 25, 42, 51, 47, 36, 39, 26, 45, 27, 35, 27, 47, 32, | |
32, 39, 48, 29, 26, 38, 43, 37, 45, 18, 39, 52, 29, 53, 39, 42, | |
26, 44, 46, 26, 17, 50, 47, 50, 45, 47, 51, 43, 44, 24, 38, 42, | |
31, 40, 44, 50, 45, 40, 36, 48, 42, 33, 50, 32, 34, 36, 41, 47, | |
38, 36, 37, 51, 41, 47, 33, 41, 41, 48, 44, 41, 28, 51, 51, 54, |
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# define functions ------------------------------------------------------------- | |
inv_logit <- function(x) exp(x) / (exp(x) + 1) | |
sim_pop <- function(N = 5000, b_min = 0, b_max = 1.5) { | |
X <- cbind(1, rbinom(N, 1, .5), rbinom(N, 1, .5)) | |
B <- c(0, runif(1, b_min, b_max), runif(1, b_min, b_max)) | |
y <- rbinom(N, 1, inv_logit(X %*% B)) | |
return(as.data.frame(cbind(X[, -1], y))) | |
} |
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library(emmeans) | |
inv_logit <- function(x) exp(x) / (1 + exp(x)) | |
set.seed(1839) | |
n <- 100 | |
x <- rbinom(n, 1, .5) | |
y <- rbinom(n, 1, inv_logit(x)) | |
model <- glm(y ~ factor(x), binomial) | |
ci <- confint(model) | |
# getting y_hat | x = 1 --> my suggestion was too wide |
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library(tidyverse) | |
library(simstudy) | |
library(geepack) | |
set.seed(1839) | |
results <- lapply(1:2000, function(zzz) { | |
dat <- genCorGen(n = 1000, nvars = 2, params1 = c(.50, .53), | |
dist = "binary", rho = .80, corstr = "cs", wide = FALSE) | |
gee_mod <- geeglm(X ~ period, binomial, dat, id = id, corstr = "exchangeable") | |
glm_mod <- glm(X ~ period, binomial, dat) | |
c( |
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