daob

library(tidyverse)
library(metamicrobiomeR)

set.seed(3421)

# Sample size (fixed!)
n_CFS <- 50
n_control <- 50

# How many metabolites to be sent for biological screening

daob

library(tidyverse)
library(polycor)  # for hetcor

set.seed(7567)

N <- 1e4 # Sample size
J <- 100 # Number of groups
ρ  <- 0.7 # Within-group correlation
τ <- c(-Inf, -2, 0, 1, Inf) # Thresholds for categories
K <- length(τ) - 1 # Number of categories

daob

# A small simulation to investigate bagging
# 2020-10-30 DLO

set.seed(4232)

# Root mean squared error metric
rmse <- function(y_pred, y) {
  r <- y_pred - y
  sqrt(mean(r^2))
}

daob

# Function that takes desrired mean, distance, and probability, and outputs
#   another function to be optimized.
get_objective <- function(desired_mean, desired_dist, desired_mass) {
  # Return a function that can be passed to optim()
  function(shape1) {
    # Enforce desired mean:
    shape2 <- shape1 * ((1 / desired_mean) - 1)
    
    # Use R internals to get the definite integral:

daob

# This code is a lavaan implementation of the standard error correction found in 
#
# Oberski, D. L., & Satorra, A. (2013).
#   Measurement error models with uncertainty about the error variance. 
#   Structural equation modeling, 20, 409-428. 
#   DOI:10.1080/10705511.2013.797820 
#
# Author: Daniel Oberski
# Date 27 november 2018
# License: MIT (https://opensource.org/licenses/MIT)

daob

# MIT license
# Author: Daniel Oberski
# Input: result of a poLCA model fit
# Output: entropy R^2 statistic (Vermunt & Magidson, 2013, p. 71)
# See: daob.nl/wp-content/uploads/2015/07/ESRA-course-slides.pdf
# And: https://www.statisticalinnovations.com/wp-content/uploads/LGtecnical.pdf


machine_tolerance <- sqrt(.Machine$double.eps)
entropy.R2 <- function(fit) {

daob

library(lubridate)
library(rjson)
library(tidyverse)
library(ISOcodes)
library(ggplot2)
library(ggthemes)


setwd("~/Dropbox/ERC/templates/")

daob

# Author: Daniel Oberski
# Date: 2017-08-01

# Bivariate residual statistic for latent class analysis
# Calculate the BVR for poLCA objects

# Argument: a poLCA object
# Value: a dist object with BVRs
# Example: bvr(fit)

daob