Nothing
## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ----setup--------------------------------------------------------------------
library(ViSe)
library(cowplot)
library(ggplot2)
library(plotly)
## -----------------------------------------------------------------------------
internal_unadjusted <- list(
mean_diff = 3.68,
lower_diff = 1.73,
upper_diff = 5.62,
sd = 8.29
)
internal_adjusted <- list(
mean_diff = 2.73,
lower_diff = 0.77,
upper_diff = 4.69,
sd = 8.28
)
external_unadjusted <- list(
mean_diff = 3.72,
lower_diff = 2.13,
upper_diff = 5.32,
sd = 6.81
)
external_adjusted <- list(
mean_diff = 3.10,
lower_diff = 1.49,
upper_diff = 4.71,
sd = 6.81
)
list_values <- list(internal_unadjusted, internal_adjusted,
external_unadjusted, external_adjusted)
list_names <- c("internal_unadjusted", "internal_adjusted",
"external_unadjusted", "external_adjusted")
names(list_values) <- list_names
for (i in list_names){
list_values[[i]][["d"]] <- list_values[[i]][["mean_diff"]] / list_values[[i]][["sd"]]
list_values[[i]][["lower_d"]] <- list_values[[i]][["lower_diff"]] / list_values[[i]][["sd"]]
list_values[[i]][["upper_d"]] <- list_values[[i]][["upper_diff"]] / list_values[[i]][["sd"]]
}
unlist(list_values)
## -----------------------------------------------------------------------------
internal_unadj_output <- calculate_d(
d = list_values$internal_adjusted$d, # d value
a = .05, # alpha for confidence interval
lower = TRUE, # you expect d to be positive
n1 = 71, # sample size group 1
n2 = 3653 # sample size group 2
)
internal_unadj_output$dlow_central
internal_unadj_output$done_low_central
# note, the program also provide noncentral t confidence intervals
# in this case, they are unusable because d has been calculated from
# mean difference / control rather than mean difference / spooled
# therefore the approximation of t and the noncentral
# limits is not appropriate
## -----------------------------------------------------------------------------
# from dataframe
calculate_d(
df = mtcars,
x_col = "am",
y_col = "hp",
a = .05,
lower = TRUE
)
# from two columns
x <- mtcars$am
y <- mtcars$hp
calculate_d(
x_col = x,
y_col = y,
a = .05,
lower = TRUE
)
# from summary statistics
calculate_d(m1 = 14.37, # neglect mean
sd1 = 10.716, # neglect sd
n1 = 71, # neglect n
m2 = 10.69, # none mean
sd2 = 8.219, # none sd
n2 = 3653, # none n
a = .05, # alpha/confidence interval
lower = TRUE) # lower or upper bound
# from t-test model
output <- t.test(mtcars$hp ~ mtcars$am, var.equal = TRUE)
n_values <- tapply(mtcars$hp, mtcars$am, length)
calculate_d(
model = output,
n1 = unname(n_values[1]),
n2 = unname(n_values[2]),
a = .05,
lower = TRUE
)
# from t-values
calculate_d(
t = 1.37,
n1 = unname(n_values[1]),
n2 = unname(n_values[2]),
a = .05,
lower = TRUE
)
## -----------------------------------------------------------------------------
other_to_d(nnt = 35)
## -----------------------------------------------------------------------------
visualize_effects(d = list_values$internal_adjusted$d,
circle_color = "lightblue",
circle_fill = "gray",
percent_color = "darkblue",
percent_size = 10,
text_color = "black",
font_family = "Times")
# note graphs look better scaled, try saving them
# ggsave(filename = "visualize_effects.png")
# you can make very ugly graphs if you want
visualize_effects(d = .2,
circle_color = "green",
circle_fill = "orange",
percent_color = "pink",
percent_size = 20,
text_color = "purple",
font_family = "Arial")
## -----------------------------------------------------------------------------
d_to_r(d = list_values$internal_adjusted$d)
d_to_f2(d = list_values$internal_adjusted$d)
d_to_nnt(d = list_values$internal_adjusted$d)
probability_superiority(d = list_values$internal_adjusted$d)
proportion_overlap(d = list_values$internal_adjusted$d)
## -----------------------------------------------------------------------------
estimate_d(d = .09,
fill_1 = "red",
fill_2 = "blue",
text_color = "black")$graph
## -----------------------------------------------------------------------------
estimate_r(r = .30)$graph
## -----------------------------------------------------------------------------
visual_c_mapped <-
# your lower confidence limit required
visualize_c_map(dlow = list_values$internal_adjusted$lower_d,
# correlation values required
r_values = c(.1, .4, .3),
# other effect sizes you want to plot
d_values = c(.2, .8),
nnt_values = c(60),
# if you think d will be positive
lower = TRUE,
# as many values as the max number effects
point_colors = c("red", "green", "blue"),
# a size for the shapes
size = 2,
# shape 1
shape_1 = 2,
# shape 2, make these the same number if you
# want the shapes overlapping
# we think two different ones helps readability
shape_2 = 3,
# color of the background highlighted area
ribbon_color = "lightblue"
)
visual_c_mapped$graph
ggsave(filename = "visualize_c_map.png", width = 8,
height = 6, dpi = 300)
# ggplotly(visual_c_mapped$graph)
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