In npalmer11/psyc7709:

knitr::opts_chunk$set(echo = TRUE)

1. Consider a 2x2 design. Assume the DV is measured from a normal distribution with mean 0, and standard deviation 1. Assume that the main effect of A causes a total shift of .5 standard deviations of the mean between the levels. Assume that level 1 of B is a control, where you expect to measure the standard effect of A. Assume that level 2 of B is an experimental factor intended to reduce the effect of A by .25 standard deviations. A. create a ggplot2 figure that depicts the expected results from this design

B. How many subjects are needed to detect the main effect of A with power = .8?

#load libraries
#load libraries
library(tibble)
library(ggplot2)
library(patchwork)
library(tidyverse)
library(ggplot2)
df <- data.frame(A = c("A1", "A1", "A2","A2"),
                 B = c("B1", "B2", "B1", "B2"),
                 dv = c(0,0,0.5,0.25)
)
ggplot(df, aes(y=dv, x= B, fill = A)) +
  geom_bar(stat = "identity", position = "dodge")

ggplot(df, aes(y=dv, x=B, color = A)) +
  geom_point()+
  geom_line()

N <- 60

A_pvalue <- c()
B_pvalue <- c()
AB_pvalue <- c()
for(i in 1:1000){
  IVA <- rep(rep(c("1","2"), each=2),N)
  IVB <- rep(rep(c("1","2"), 2),N)
  DV <- c(replicate(N,c(rnorm(1,0,1), # means A1B1
                        rnorm(1,0,1), # means A1B2
                        rnorm(1,.5,1), # means A2B1
                        rnorm(1,.25,1)  # means A2B2
          )))
  sim_df <- data.frame(IVA,IVB,DV)

  aov_results <- summary(aov(DV~IVA*IVB, sim_df))
  A_pvalue[i]<-aov_results[[1]]$`Pr(>F)`[1]
  B_pvalue[i]<-aov_results[[1]]$`Pr(>F)`[2]
  AB_pvalue[i]<-aov_results[[1]]$`Pr(>F)`[3]
}

length(A_pvalue[A_pvalue<0.05])/1000
length(B_pvalue[B_pvalue<0.05])/1000
length(AB_pvalue[AB_pvalue<0.05])/1000

C. How many subjects are needed to detect the interaction effect with power = .8?

N <- 525

A_pvalue <- c()
B_pvalue <- c()
AB_pvalue <- c()
for(i in 1:1000){
  IVA <- rep(rep(c("1","2"), each=2),N)
  IVB <- rep(rep(c("1","2"), 2),N)
  DV <- c(replicate(N,c(rnorm(1,0,1), # means A1B1
                        rnorm(1,0,1), # means A1B2
                        rnorm(1,.5,1), # means A2B1
                        rnorm(1,.25,1)  # means A2B2
          )))
  sim_df <- data.frame(IVA,IVB,DV)

  aov_results <- summary(aov(DV~IVA*IVB, sim_df))
  A_pvalue[i]<-aov_results[[1]]$`Pr(>F)`[1]
  B_pvalue[i]<-aov_results[[1]]$`Pr(>F)`[2]
  AB_pvalue[i]<-aov_results[[1]]$`Pr(>F)`[3]
}

length(A_pvalue[A_pvalue<0.05])/1000
length(B_pvalue[B_pvalue<0.05])/1000
length(AB_pvalue[AB_pvalue<0.05])/1000

B1. Create a power curve showing how power for the interaction effect in this example is influenced by number of subjects. Choose a range of N from 25 to 800 (per cell) and run a simulation-based power analysis for increments of 25 subjects. Then plot the results using ggplot2 (2 points).

npalmer11/psyc7709 documentation built on June 3, 2022, 7:04 a.m.