puterleat

----------------------------
 month   patient   hasahamd 
------- --------- ----------
   0      10008       1     

   0      10011       1     

   0      10015       1     

   0      10023       1     

puterleat

----------------------------
 month   patient   hasahamd 
------- --------- ----------
   0      10008       1     

   0      10011       1     

   0      10015       1     

   0      10023       1     

puterleat

# calculate ICC for lmer model object
icc <- function(m){
  vc <- as.data.frame((VarCorr(m)))
  l <- vc$vcov
  data_frame(grp=vc$grp, icc=sapply(l, function(x){x/sum(l)}))
}

puterleat

<a href="javascript:var%20auto%20={names:%20'Steve%20Buscemi%20Catherine%20Keener%20Dermot%20Mulroney%20Danielle%20Zerneck%20James%20LeGros%20Rica%20Martens%20Peter%20Dinklage%20Kevin%20Corrigan%20Hilary%20Gilford%20Robert%20Wightman%20Tom%20Jarmusch%20Michael%20Griffiths%20Matthew%20Grace%20Ryan%20Bowker%20Francesca%20DiMauro',blurb:%20'Excepteur%20pariatur%20cillum%20labore%20ea%20proident%20et%20deserunt.%20Ex%20commodo%20ea%20esse%20incididunt%20deserunt%20dolor%20ea%20minim%20do%20qui%20irure.%20Qui%20aute%20irure%20ut%20ad%20id%20duis%20esse%20tempor%20occaecat%20exercitation%20excepteur%20do%20nostrud.%20Aute%20ex%20fugiat%20tempor%20laboris%20ex%20consequat%20consequat%20tempor%20anim%20ipsum%20consectetur%20et%20proident%20cupidatat%20duis%20nulla%20reprehenderit.%20Velit%20adipisicing%20occaecat%20nulla%20officia%20irure%20anim%20dolor%20quis%20cupidatat%20cupidatat%20deserunt%20esse%20Lorem%20exercitation%20in%20commodo.%20Amet%20ea%20labore%20duis%20mollit%20sit%20labore%20laborum%20sit%20aliqua%2

puterleat

# The example below shows why WAIC could be useful when studies are underpowered. 
# Here we simulate data with a weak polynomial relationship between x an y, with 
# only a small sample. Although the effect of the polynomial term does not reach 
# conventional standards for statistical significance, comparing WAIC (an estimate 
# of how effective each model will be at predicting new, out-of-sample data) indicates 
# we should assign a higher probability that `m2` is 'correct', when compared with `m1` 
# (more than twice as likely).


library(rethinking)

puterleat

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The example below shows why WAIC could be useful when studies are underpowered. Here we simulate data with a weak polynomial relationship between x an y, with only a small sample. Although the effect of the polynomial term does not reach conventional standards for statistical significance, comparing WAIC (an estimate of how effective each model will be at predicting new, out-of-sample data) indicates we should assign a higher probability that `m2` is 'correct', when compared with `m1` (more than twice as likely)."
   ]
  },
  {

puterleat

If I have these data:

ID  T   Y
1   1   33
1   2   32  
2   1   21
2   2   26

How do I make this extra column:

puterleat

. do "/var/folders/47/f2b3zfds1y5gq4vp7m6___cr0000gn/T//SD44048.000000"

. clear all

. set seed 123

. 
. * recruit subjects (if only this easy...)
. set obs 2000

puterleat

clear all
set seed 123

* recruit subjects (if only this easy...)
set obs 2000

* assign to groups
gen mbct =  mod(_n, 2)

* depression baseline score

puterleat

. do "/var/folders/47/f2b3zfds1y5gq4vp7m6___cr0000gn/T//SD44048.000000"

. clear all

. set seed 123

. 
. * recruit subjects (if only this easy...)
. set obs 2000