benwhalley

https://transfer.sh/adEXM/6-sem.pdf
https://transfer.sh/FObPj/6-sem-extra.pdf

benwhalley

https://www.dropbox.com/s/76vg2ortmid2x6t/Session5.pptx?dl=0

https://psy379-placebo.s3.amazonaws.com/index.html

benwhalley

https://www.dropbox.com/s/76vg2ortmid2x6t/Session5.pptx?dl=0

benwhalley

Keybase proof

I hereby claim:

• I am benwhalley on github.
• I am benwhalley (https://keybase.io/benwhalley) on keybase.
• I have a public key ASC19V1R0DG97deKeZwym3XYq0JBGYGSL4qscFyPL_bqXgo

To claim this, I am signing this object:

benwhalley

library(tidyverse)
library(lmerTest)
library(broom)


mu_type1 <- 20
mu_type2 <- 70
sd_both <- 20

pwr <- function(N, mu_type1, mu_type2, sd_both){

benwhalley

library(tidyverse)
library(lmerTest)
library(broom)


mu_type1 <- 20
mu_type2 <- 70
sd_both <- 20

pwr <- function(N, mu_type1, mu_type2, sd_both){

benwhalley

library(tidyverse)
library(lmerTest)
library(broom)

pwr <- function(N){
  simdata <- data_frame(person =1:N, c1=rnorm(N, 20, 25), c2 = rnorm(N, 77, 27)) %>% 
    reshape2::melt(id.var="person")
  lmer(value~variable+(1|person), data=simdata) %>% summary tidy %>% 
    mutate(N=N) 
}

benwhalley

library(lmerTest)
library(merTools)
library(tidyverse)



# linear model
m <- lmer(Reaction~Days*I(Days^2)+(1|Subject), data=sleepstudy)
newdata = expand.grid(Reaction=NA, Days=1:5, Subject=Inf) %>% as.data.frame
sims <- predictInterval(m, newdata=newdata, returnSims = T)

benwhalley

p1 <- 
    ggplot(group.table, aes(month, fit, group=grp, color=grp, shape=grp)) +
      geom_point() + geom_line() + 
      scale_color_discrete("") + scale_shape_discrete("") +
      geom_text(show_guide = FALSE, aes(label=fit, y=fit), size=3) +
      xlab(paste("Month")) +
      ylab(paste("Model adjusted", toupper(dv))) + 
    theme(legend.position="top") + 
    theme(axis.text.x=element_blank(),axis.title.x=element_blank())  
  

benwhalley

rf2.longs.with <- bind_sims(extract_sims(rf2.preds), newdata) 

rf2predswide <- rf2.longs %>%
  dcast(., month+sim+pd.b~grp, value.var="fitted", fun.aggregate=mean) 
  
intervals <- rf2predswide %>% 
  mutate(diff=tau-dbt) %>% 
  group_by(month) %>% 
  do(., as.data.frame(t(quantile(.$diff, probs=c(.025, .5, .975)))))