## ----setup, include = FALSE------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ----vignette-setup--------------------------
knitr::opts_chunk$set(echo = TRUE)
# Set a random seed
set.seed(5989320)
# Libraries necessary for this vignette
library(rio)
library(flextable)
library(dplyr)
library(tidyr)
library(psych)
library(semanticprimeR)
# Function for simulation
item_power <- function(data, # name of data frame
dv_col, # name of DV column as a character
item_col, # number of items column as a character
nsim = 10, # small for cran
sample_start = 20,
sample_stop = 200,
sample_increase = 5,
decile = .5){
DF <- cbind.data.frame(
"dv" = data[ , dv_col],
"items" = data[ , item_col]
)
# just in case
colnames(DF) <- c("dv", "items")
# figure out the "sufficiently narrow" ci value
SE <- tapply(DF$dv, DF$items, function (x) { sd(x)/sqrt(length(x)) })
cutoff <- quantile(SE, probs = decile)
# sequence of sample sizes to try
samplesize_values <- seq(sample_start, sample_stop, sample_increase)
# create a blank table for us to save the values in
sim_table <- matrix(NA,
nrow = length(samplesize_values)*nsim,
ncol = length(unique(DF$items)))
# make it a data frame
sim_table <- as.data.frame(sim_table)
# add a place for sample size values
sim_table$sample_size <- NA
iterate <- 1
for (p in 1:nsim){
# loop over sample sizes
for (i in 1:length(samplesize_values)){
# temp that samples and summarizes
temp <- DF %>%
group_by(items) %>%
sample_n(samplesize_values[i], replace = T) %>%
summarize(se = sd(dv)/sqrt(length(dv)))
# dv on items
colnames(sim_table)[1:length(unique(DF$items))] <- temp$items
sim_table[iterate, 1:length(unique(DF$items))] <- temp$se
sim_table[iterate, "sample_size"] <- samplesize_values[i]
sim_table[iterate, "nsim"] <- p
iterate <- iterate + 1
}
}
# figure out cut off
final_sample <- sim_table %>%
pivot_longer(cols = -c(sample_size, nsim)) %>%
dplyr::rename(item = name, se = value) %>%
group_by(sample_size, nsim) %>%
summarize(percent_below = sum(se <= cutoff)/length(unique(DF$items))) %>%
ungroup() %>%
# then summarize all down averaging percents
dplyr::group_by(sample_size) %>%
summarize(percent_below = mean(percent_below)) %>%
dplyr::arrange(percent_below) %>%
ungroup()
return(list(
SE = SE,
cutoff = cutoff,
DF = DF,
sim_table = sim_table,
final_sample = final_sample
))
}
## --------------------------------------------
DF <- import("data/batres_data.sav")
str(DF)
## --------------------------------------------
metadata <- import("data/batres_metadata.xlsx")
flextable(metadata) %>% autofit()
## --------------------------------------------
# Reformat the data
DF_long <- pivot_longer(DF, cols = -c(Participant_Number)) %>%
dplyr::rename(item = name, score = value)
flextable(head(DF_long)) %>% autofit()
## --------------------------------------------
# Function for simulation
var1 <- item_power(data = DF_long, # name of data frame
dv_col = "score", # name of DV column as a character
item_col = "item", # number of items column as a character
nsim = 10,
sample_start = 20,
sample_stop = 300,
sample_increase = 5,
decile = .4)
## --------------------------------------------
# individual SEs
var1$SE
var1$cutoff
## --------------------------------------------
cutoff <- calculate_cutoff(population = DF_long,
grouping_items = "item",
score = "score",
minimum = 1,
maximum = 7)
# showing how this is the same as the person calculated version versus semanticprimeR's function
cutoff$cutoff
## --------------------------------------------
final_table <- calculate_correction(
proportion_summary = var1$final_sample,
pilot_sample_size = nrow(DF),
proportion_variability = cutoff$prop_var
)
flextable(final_table) %>%
autofit()
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