another experiment
Evangeline Reynolds
2026-06-05
Intro Thoughts
Status Quo
library(tidyverse)
library(distributional)Experiment
distributional::dist_normal(c(1,2,3), sigma = 1:3) |>
tibble(x = _) |>
ggplot() +
aes(x = )
library(ggdist)
library(ggplot2)
df <- data.frame(
name = c("Gamma(2,1)", "Normal(5,1)", "Mixture"),
dist = c(dist_gamma(2,1), dist_normal(5,1),
dist_mixture(dist_gamma(2,1), dist_normal(5, 1), weights = c(0.5, 0.5)))
)
df |>
ggplot() +
aes(dist = dist,
y = 0,
fill = name) +
# aes(y = factor(name,
# levels = rev(name)),
# fill = "hotpink" |> I()) +
stat_dist_halfeye() +
labs(title = "Density function for a mixture of distributions",
y = NULL,
x = NULL) +
annotate("text",
label = "👶",
x = 2.5, y = .5,
size = 5)
df |>
ggplot() +
aes(dist = dist,
y = 0,
fill = name) +
stat_dist_halfeye() +
labs(title = "Density function for a mixture of distributions",
y = NULL,
x = NULL) 
stamp_normal <- function(mean = 0, sd = 1){
stat_slab(aes(dist = dist_normal(mu = 0, sigma = 1)), inherit.aes = T, ...)
}df = expand.grid(
mean = 1:3,
input = seq(-2, 6, length.out = 100)
) %>%
mutate(
group = letters[4 - mean],
density = dnorm(input, mean, 1)
)
df## mean input group density
## 1 1 -2.00000000 c 4.431848e-03
## 2 2 -2.00000000 b 1.338302e-04
## 3 3 -2.00000000 a 1.486720e-06
## 4 1 -1.91919192 c 5.629249e-03
## 5 2 -1.91919192 b 1.842953e-04
## 6 3 -1.91919192 a 2.219645e-06
## 7 1 -1.83838384 c 7.103626e-03
## 8 2 -1.83838384 b 2.521381e-04
## 9 3 -1.83838384 a 3.292322e-06
## 10 1 -1.75757576 c 8.905818e-03
## 11 2 -1.75757576 b 3.427099e-04
## 12 3 -1.75757576 a 4.851600e-06
## 13 1 -1.67676768 c 1.109255e-02
## 14 2 -1.67676768 b 4.627846e-04
## 15 3 -1.67676768 a 7.102836e-06
## 16 1 -1.59595960 c 1.372630e-02
## 17 2 -1.59595960 b 6.208623e-04
## 18 3 -1.59595960 a 1.033101e-05
## 19 1 -1.51515152 c 1.687483e-02
## 20 2 -1.51515152 b 8.275148e-04
## 21 3 -1.51515152 a 1.492855e-05
## 22 1 -1.43434343 c 2.061054e-02
## 23 2 -1.43434343 b 1.095772e-03
## 24 3 -1.43434343 a 2.143170e-05
## 25 1 -1.35353535 c 2.500942e-02
## 26 2 -1.35353535 b 1.441547e-03
## 27 3 -1.35353535 a 3.056749e-05
## 28 1 -1.27272727 c 3.014961e-02
## 29 2 -1.27272727 b 1.884090e-03
## 30 3 -1.27272727 a 4.331387e-05
## 31 1 -1.19191919 c 3.610971e-02
## 32 2 -1.19191919 b 2.446461e-03
## 33 3 -1.19191919 a 6.097590e-05
## 34 1 -1.11111111 c 4.296654e-02
## 35 2 -1.11111111 b 3.156016e-03
## 36 3 -1.11111111 a 8.528126e-05
## 37 1 -1.03030303 c 5.079264e-02
## 38 2 -1.03030303 b 4.044866e-03
## 39 3 -1.03030303 a 1.184986e-04
## 40 1 -0.94949495 c 5.965342e-02
## 41 2 -0.94949495 b 5.150308e-03
## 42 3 -0.94949495 a 1.635824e-04
## 43 1 -0.86868687 c 6.960396e-02
## 44 2 -0.86868687 b 6.515178e-03
## 45 3 -0.86868687 a 2.243490e-04
## 46 1 -0.78787879 c 8.068571e-02
## 47 2 -0.78787879 b 8.188107e-03
## 48 3 -0.78787879 a 3.056862e-04
## 49 1 -0.70707071 c 9.292303e-02
## 50 2 -0.70707071 b 1.022362e-02
## 51 3 -0.70707071 a 4.138011e-04
## 52 1 -0.62626263 c 1.063198e-01
## 53 2 -0.62626263 b 1.268207e-02
## 54 3 -0.62626263 a 5.565081e-04
## 55 1 -0.54545455 c 1.208563e-01
## 56 2 -0.54545455 b 1.562930e-02
## 57 3 -0.54545455 a 7.435590e-04
## 58 1 -0.46464646 c 1.364860e-01
## 59 2 -0.46464646 b 1.913608e-02
## 60 3 -0.46464646 a 9.870142e-04
## 61 1 -0.38383838 c 1.531339e-01
## 62 2 -0.38383838 b 2.327719e-02
## 63 3 -0.38383838 a 1.301654e-03
## 64 1 -0.30303030 c 1.706940e-01
## 65 2 -0.30303030 b 2.813016e-02
## 66 3 -0.30303030 a 1.705421e-03
## 67 1 -0.22222222 c 1.890295e-01
## 68 2 -0.22222222 b 3.377365e-02
## 69 3 -0.22222222 a 2.219892e-03
## 70 1 -0.14141414 c 2.079720e-01
## 71 2 -0.14141414 b 4.028541e-02
## 72 3 -0.14141414 a 2.870756e-03
## 73 1 -0.06060606 c 2.273235e-01
## 74 2 -0.06060606 b 4.773993e-02
## 75 3 -0.06060606 a 3.688287e-03
## 76 1 0.02020202 c 2.468584e-01
## 77 2 0.02020202 b 5.620562e-02
## 78 3 0.02020202 a 4.707791e-03
## 79 1 0.10101010 c 2.663271e-01
## 80 2 0.10101010 b 6.574183e-02
## 81 3 0.10101010 a 5.969992e-03
## 82 1 0.18181818 c 2.854612e-01
## 83 2 0.18181818 b 7.639553e-02
## 84 3 0.18181818 a 7.521325e-03
## 85 1 0.26262626 c 3.039784e-01
## 86 2 0.26262626 b 8.819789e-02
## 87 3 0.26262626 a 9.414107e-03
## 88 1 0.34343434 c 3.215900e-01
## 89 2 0.34343434 b 1.011609e-01
## 90 3 0.34343434 a 1.170652e-02
## 91 1 0.42424242 c 3.380076e-01
## 92 2 0.42424242 b 1.152739e-01
## 93 3 0.42424242 a 1.446241e-02
## 94 1 0.50505051 c 3.529510e-01
## 95 2 0.50505051 b 1.305009e-01
## 96 3 0.50505051 a 1.775079e-02
## 97 1 0.58585859 c 3.661563e-01
## 98 2 0.58585859 b 1.467776e-01
## 99 3 0.58585859 a 2.164506e-02
## 100 1 0.66666667 c 3.773832e-01
## 101 2 0.66666667 b 1.640101e-01
## 102 3 0.66666667 a 2.622189e-02
## 103 1 0.74747475 c 3.864229e-01
## 104 2 0.74747475 b 1.820729e-01
## 105 3 0.74747475 a 3.155972e-02
## 106 1 0.82828283 c 3.931037e-01
## 107 2 0.82828283 b 2.008094e-01
## 108 3 0.82828283 a 3.773692e-02
## 109 1 0.90909091 c 3.972972e-01
## 110 2 0.90909091 b 2.200325e-01
## 111 3 0.90909091 a 4.482950e-02
## 112 1 0.98989899 c 3.989219e-01
## 113 2 0.98989899 b 2.395267e-01
## 114 3 0.98989899 a 5.290848e-02
## 115 1 1.07070707 c 3.979463e-01
## 116 2 1.07070707 b 2.590508e-01
## 117 3 1.07070707 a 6.203701e-02
## 118 1 1.15151515 c 3.943892e-01
## 119 2 1.15151515 b 2.783428e-01
## 120 3 1.15151515 a 7.226707e-02
## 121 1 1.23232323 c 3.883200e-01
## 122 2 1.23232323 b 2.971250e-01
## 123 3 1.23232323 a 8.363618e-02
## 124 1 1.31313131 c 3.798556e-01
## 125 2 1.31313131 b 3.151102e-01
## 126 3 1.31313131 a 9.616387e-02
## 127 1 1.39393939 c 3.691572e-01
## 128 2 1.39393939 b 3.320090e-01
## 129 3 1.39393939 a 1.098484e-01
## 130 1 1.47474747 c 3.564251e-01
## 131 2 1.47474747 b 3.475372e-01
## 132 3 1.47474747 a 1.246636e-01
## 133 1 1.55555556 c 3.418923e-01
## 134 2 1.55555556 b 3.614238e-01
## 135 3 1.55555556 a 1.405561e-01
## 136 1 1.63636364 c 3.258175e-01
## 137 2 1.63636364 b 3.734190e-01
## 138 3 1.63636364 a 1.574432e-01
## 139 1 1.71717172 c 3.084776e-01
## 140 2 1.71717172 b 3.833011e-01
## 141 3 1.71717172 a 1.752113e-01
## 142 1 1.79797980 c 2.901595e-01
## 143 2 1.79797980 b 3.908839e-01
## 144 3 1.79797980 a 1.937155e-01
## 145 1 1.87878788 c 2.711528e-01
## 146 2 1.87878788 b 3.960223e-01
## 147 3 1.87878788 a 2.127799e-01
## 148 1 1.95959596 c 2.517419e-01
## 149 2 1.95959596 b 3.986168e-01
## 150 3 1.95959596 a 2.321994e-01
## 151 1 2.04040404 c 2.321994e-01
## 152 2 2.04040404 b 3.986168e-01
## 153 3 2.04040404 a 2.517419e-01
## 154 1 2.12121212 c 2.127799e-01
## 155 2 2.12121212 b 3.960223e-01
## 156 3 2.12121212 a 2.711528e-01
## 157 1 2.20202020 c 1.937155e-01
## 158 2 2.20202020 b 3.908839e-01
## 159 3 2.20202020 a 2.901595e-01
## 160 1 2.28282828 c 1.752113e-01
## 161 2 2.28282828 b 3.833011e-01
## 162 3 2.28282828 a 3.084776e-01
## 163 1 2.36363636 c 1.574432e-01
## 164 2 2.36363636 b 3.734190e-01
## 165 3 2.36363636 a 3.258175e-01
## 166 1 2.44444444 c 1.405561e-01
## 167 2 2.44444444 b 3.614238e-01
## 168 3 2.44444444 a 3.418923e-01
## 169 1 2.52525253 c 1.246636e-01
## 170 2 2.52525253 b 3.475372e-01
## 171 3 2.52525253 a 3.564251e-01
## 172 1 2.60606061 c 1.098484e-01
## 173 2 2.60606061 b 3.320090e-01
## 174 3 2.60606061 a 3.691572e-01
## 175 1 2.68686869 c 9.616387e-02
## 176 2 2.68686869 b 3.151102e-01
## 177 3 2.68686869 a 3.798556e-01
## 178 1 2.76767677 c 8.363618e-02
## 179 2 2.76767677 b 2.971250e-01
## 180 3 2.76767677 a 3.883200e-01
## 181 1 2.84848485 c 7.226707e-02
## 182 2 2.84848485 b 2.783428e-01
## 183 3 2.84848485 a 3.943892e-01
## 184 1 2.92929293 c 6.203701e-02
## 185 2 2.92929293 b 2.590508e-01
## 186 3 2.92929293 a 3.979463e-01
## 187 1 3.01010101 c 5.290848e-02
## 188 2 3.01010101 b 2.395267e-01
## 189 3 3.01010101 a 3.989219e-01
## 190 1 3.09090909 c 4.482950e-02
## 191 2 3.09090909 b 2.200325e-01
## 192 3 3.09090909 a 3.972972e-01
## 193 1 3.17171717 c 3.773692e-02
## 194 2 3.17171717 b 2.008094e-01
## 195 3 3.17171717 a 3.931037e-01
## 196 1 3.25252525 c 3.155972e-02
## 197 2 3.25252525 b 1.820729e-01
## 198 3 3.25252525 a 3.864229e-01
## 199 1 3.33333333 c 2.622189e-02
## 200 2 3.33333333 b 1.640101e-01
## 201 3 3.33333333 a 3.773832e-01
## 202 1 3.41414141 c 2.164506e-02
## 203 2 3.41414141 b 1.467776e-01
## 204 3 3.41414141 a 3.661563e-01
## 205 1 3.49494949 c 1.775079e-02
## 206 2 3.49494949 b 1.305009e-01
## 207 3 3.49494949 a 3.529510e-01
## 208 1 3.57575758 c 1.446241e-02
## 209 2 3.57575758 b 1.152739e-01
## 210 3 3.57575758 a 3.380076e-01
## 211 1 3.65656566 c 1.170652e-02
## 212 2 3.65656566 b 1.011609e-01
## 213 3 3.65656566 a 3.215900e-01
## 214 1 3.73737374 c 9.414107e-03
## 215 2 3.73737374 b 8.819789e-02
## 216 3 3.73737374 a 3.039784e-01
## 217 1 3.81818182 c 7.521325e-03
## 218 2 3.81818182 b 7.639553e-02
## 219 3 3.81818182 a 2.854612e-01
## 220 1 3.89898990 c 5.969992e-03
## 221 2 3.89898990 b 6.574183e-02
## 222 3 3.89898990 a 2.663271e-01
## 223 1 3.97979798 c 4.707791e-03
## 224 2 3.97979798 b 5.620562e-02
## 225 3 3.97979798 a 2.468584e-01
## 226 1 4.06060606 c 3.688287e-03
## 227 2 4.06060606 b 4.773993e-02
## 228 3 4.06060606 a 2.273235e-01
## 229 1 4.14141414 c 2.870756e-03
## 230 2 4.14141414 b 4.028541e-02
## 231 3 4.14141414 a 2.079720e-01
## 232 1 4.22222222 c 2.219892e-03
## 233 2 4.22222222 b 3.377365e-02
## 234 3 4.22222222 a 1.890295e-01
## 235 1 4.30303030 c 1.705421e-03
## 236 2 4.30303030 b 2.813016e-02
## 237 3 4.30303030 a 1.706940e-01
## 238 1 4.38383838 c 1.301654e-03
## 239 2 4.38383838 b 2.327719e-02
## 240 3 4.38383838 a 1.531339e-01
## 241 1 4.46464646 c 9.870142e-04
## 242 2 4.46464646 b 1.913608e-02
## 243 3 4.46464646 a 1.364860e-01
## 244 1 4.54545455 c 7.435590e-04
## 245 2 4.54545455 b 1.562930e-02
## 246 3 4.54545455 a 1.208563e-01
## 247 1 4.62626263 c 5.565081e-04
## 248 2 4.62626263 b 1.268207e-02
## 249 3 4.62626263 a 1.063198e-01
## 250 1 4.70707071 c 4.138011e-04
## 251 2 4.70707071 b 1.022362e-02
## 252 3 4.70707071 a 9.292303e-02
## 253 1 4.78787879 c 3.056862e-04
## 254 2 4.78787879 b 8.188107e-03
## 255 3 4.78787879 a 8.068571e-02
## 256 1 4.86868687 c 2.243490e-04
## 257 2 4.86868687 b 6.515178e-03
## 258 3 4.86868687 a 6.960396e-02
## 259 1 4.94949495 c 1.635824e-04
## 260 2 4.94949495 b 5.150308e-03
## 261 3 4.94949495 a 5.965342e-02
## 262 1 5.03030303 c 1.184986e-04
## 263 2 5.03030303 b 4.044866e-03
## 264 3 5.03030303 a 5.079264e-02
## 265 1 5.11111111 c 8.528126e-05
## 266 2 5.11111111 b 3.156016e-03
## 267 3 5.11111111 a 4.296654e-02
## 268 1 5.19191919 c 6.097590e-05
## 269 2 5.19191919 b 2.446461e-03
## 270 3 5.19191919 a 3.610971e-02
## 271 1 5.27272727 c 4.331387e-05
## 272 2 5.27272727 b 1.884090e-03
## 273 3 5.27272727 a 3.014961e-02
## 274 1 5.35353535 c 3.056749e-05
## 275 2 5.35353535 b 1.441547e-03
## 276 3 5.35353535 a 2.500942e-02
## 277 1 5.43434343 c 2.143170e-05
## 278 2 5.43434343 b 1.095772e-03
## 279 3 5.43434343 a 2.061054e-02
## 280 1 5.51515152 c 1.492855e-05
## 281 2 5.51515152 b 8.275148e-04
## 282 3 5.51515152 a 1.687483e-02
## 283 1 5.59595960 c 1.033101e-05
## 284 2 5.59595960 b 6.208623e-04
## 285 3 5.59595960 a 1.372630e-02
## 286 1 5.67676768 c 7.102836e-06
## 287 2 5.67676768 b 4.627846e-04
## 288 3 5.67676768 a 1.109255e-02
## 289 1 5.75757576 c 4.851600e-06
## 290 2 5.75757576 b 3.427099e-04
## 291 3 5.75757576 a 8.905818e-03
## 292 1 5.83838384 c 3.292322e-06
## 293 2 5.83838384 b 2.521381e-04
## 294 3 5.83838384 a 7.103626e-03
## 295 1 5.91919192 c 2.219645e-06
## 296 2 5.91919192 b 1.842953e-04
## 297 3 5.91919192 a 5.629249e-03
## 298 1 6.00000000 c 1.486720e-06
## 299 2 6.00000000 b 1.338302e-04
## 300 3 6.00000000 a 4.431848e-03# orientation is detected automatically based on
# use of x or y
df %>%
ggplot(aes(y = group, x = input, thickness = density)) +
geom_slab()
library(ggt.test)
library(tidyverse)
library(ggdist)
library(distributional)
dist_binomial(prob = .5, size = 10) |>
data.frame(dist = _) |>
ggplot() +
aes(dist = dist, y = 1) +
stat_dots()
Closing remarks, Other Relevant Work, Caveats
tribble(~n, ~mean, ~sd, ~who, ~who2,
1545, 3.65, .73, "Adults, Group 1", "Adults",
690, 3.41, .67, "Adults, Group 2", "Adults",
138, 3.46, .61, "Ivy League Undergraduates", "Ivy League",
1218, 3.78, .53, "West Point Cadets '08", "Cadets",
1308, 3.75, .54, "West Point Cadets '10", "Cadets",
175, 3.5, .67, "Spelling Bee Finalists", "Spelling Bee") |>
mutate(dist = dist_normal(mean, sd)) |>
ggplot() +
aes(dist = dist) +
stat_slab() +
aes(y = who) +
aes(fill = who) +
aes(alpha = after_stat(x < 3.2)) +
scale_alpha(range = c(.5,.9))
library(ggprop.test)##
## Attaching package: 'ggprop.test'## The following objects are masked from 'package:ggt.test':
##
## data_add_synth, geom_supportggplot(donor_data) +
aes(x = decision) +
geom_stack()
library(tidyverse)
library(ggdist)
library(distributional)
dist_binomial(prob = .5, size = 10) |>
data.frame(dist = _) |>
ggplot() +
aes(dist = dist, y = 1) +
stat_spike()
stat_spike## function (mapping = NULL, data = NULL, geom = "spike", position = "identity",
## ..., at = "median", p_limits = c(NA, NA), density = "bounded",
## adjust = waiver(), trim = waiver(), breaks = waiver(), align = waiver(),
## outline_bars = waiver(), expand = FALSE, limits = NULL, n = waiver(),
## orientation = NA, na.rm = FALSE, show.legend = NA, inherit.aes = TRUE,
## check.aes = TRUE, check.param = TRUE)
## {
## .Deprecated_arguments(c(".prob", "limits_function", "limits_args",
## "slab_function", "slab_args", "interval_function", "fun.data",
## "interval_args", "fun.args"), ...)
## layer_slabinterval(data = data, mapping = mapping, stat = StatSpike,
## geom = geom, position = position, show.legend = show.legend,
## inherit.aes = inherit.aes, check.aes = check.aes, check.param = check.param,
## params = list(at = at, p_limits = p_limits, density = density,
## adjust = adjust, trim = trim, breaks = breaks, align = align,
## outline_bars = outline_bars, expand = expand, limits = limits,
## n = n, orientation = orientation, na.rm = na.rm,
## ...))
## }
## <bytecode: 0x137851af8>
## <environment: namespace:ggdist>
https://gwern.net/doc/psychology/personality/conscientiousness/2007-duckworth.pdf