drsimonj

library(tidyverse)

# Simulation and test function --------------------------------------------

#' n = number of user IDs to simulate
#' p = Percentage of Experiment 1 traffic in treatment to exclude from experiment 2
simulate_and_test <- function(n = 1000, p = 0) {
  #message("n = ", n, " and p = ", p)
  
  # Simulate a random data set of users in or out of two experiments

drsimonj

# Packages
library(tidyverse)
library(broom)

# Set seed for reproducible results
set.seed(20200513)

# Data settings to simulate
n_units   <- 100
n_metrics <- 10

drsimonj

library(tidyverse)
library(corrr)

d <- mtcars

stretch_triangle <- function(cordf, upper) {
  cordf %>% shave(upper = upper) %>% stretch(na.rm = TRUE)
}

spearman <- correlate(d, method = "spearman") %>% stretch_triangle(upper = FALSE)

drsimonj

## BASE FUNCTION
base <- function(obj) {
  UseMethod("base")
}

base.default <- function(obj) {
  as.character(obj)
}

base.regex <- function(obj) {

drsimonj

library(tidyverse)
library(lavaan)
library(semTools)

# Function to fit unrotated EFA with specific number of factors
fit_unrotated_efa <- function(n_factors) {
  data %>% efaUnrotate(nf = n_factors, estimator = "mlr")
}

# Fit EFAs (unrotated) with a range of factors

drsimonj

library(dplyr)

d <- data_frame(x = seq_len(100))
d
#> # A tibble: 100 x 1
#>        x
#>    <int>
#>  1     1
#>  2     2
#>  3     3

drsimonj

## predict for Analysic of Variance (aov) searches for object in global environment
d <- datasets::mtcars
fit <- aov(hp ~ am * cyl, d)
predict(fit)
d <- NULL
predict(d)

## predict for Principal Components (prcomp) can't recreate new variables defined in formula
fit <- prcomp(~.*., mtcars[1:25, ])
predict(fit, mtcars[26:32,])