-
Notifications
You must be signed in to change notification settings - Fork 3
/
pdqr-01-create.Rmd
288 lines (210 loc) · 10.1 KB
/
pdqr-01-create.Rmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
---
title: "Create pdqr-functions with `new_*()`"
output:
rmarkdown::html_vignette:
fig_width: 6.5
fig_height: 4
vignette: >
%\VignetteIndexEntry{Create pdqr-functions with `new_*()`}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
library(pdqr)
set.seed(101)
```
Package 'pdqr' supports two types of distributions:
- **Type "discrete"**: random variable has finite number of output values. It is explicitly defined by the collection of its values with their corresponding probability.
- **Type "continuous"**: there are infinite number of output values in the form of continuous random variable. It is explicitly defined by piecewise-linear density function.
**Note** that all distributions assume **finite support** (output values are bounded from below and above) and **finite values of density function** (density function in case of "continuous" type can't go to infinity).
All `new_*()` functions create a pdqr-function of certain type ("discrete" or "continuous") based on sample or data frame of appropriate structure:
- **Sample input** is processed based on type. For "discrete" type it gets tabulated with frequency of unique values serving as their probability. For "continuous" type distribution density is estimated using [`density()`](https://rdrr.io/r/stats/density.html) function if input has at least 2 elements. For 1 element special "dirac-like" pdqr-function is created: an *approximation single number* with triangular distribution of very narrow support (1e-8 of magnitude). Basically, sample input is converted into data frame of appropriate structure that defines distribution (see next list item).
- **Data frame input** should completely define distribution. For "discrete" type it should have "x" and "prob" columns for output values and their probabilities. For "continuous" type - "x" and "y" columns for points, which define piecewise-linear continuous density function. Columns "prob" and "y" will be automatically normalized to represent proper distribution: sum of "prob" will be 1 and total square under graph of piecewise-linear function will be 1.
We will use the following data frame inputs in examples:
```{r setup_data-frame-inputs}
# For type "discrete"
dis_df <- data.frame(x = 1:4, prob = 4:1 / 10)
# For type "continuous"
con_df <- data.frame(x = 1:4, y = c(0, 1, 1, 1))
```
This vignette is organized as follows:
- Four sections about how to create p-, d-, q-, and r-functions (both from sample and data frame).
- Section "Special cases", which describes two special cases of pdqr-functions: dirac-like and boolean.
- Section "Using `density()` arguments" describes how to use `density()` arguments to tweak smoothing during creation of "continuous" pdqr-functions.
- "Metadata of pdqr-functions" describes the concept of metadata of pdqr-functions.
## P-functions
P-function (analogue of `p*()` functions in base R) represents a cumulative distribution function of distribution.
### From sample
```{r p-fun_sample}
# Treating input as discrete
p_mpg_dis <- new_p(mtcars$mpg, type = "discrete")
p_mpg_dis
# Treating input as continuous
p_mpg_con <- new_p(mtcars$mpg, type = "continuous")
p_mpg_con
# Outputs are actually vectorized functions
p_mpg_dis(15:20)
p_mpg_con(15:20)
# You can plot them directly using base `plot()` and `lines()`
plot(p_mpg_con, main = "P-functions from sample")
lines(p_mpg_dis, col = "blue")
```
### From data frame
```{r p-fun_data-frame}
p_df_dis <- new_p(dis_df, type = "discrete")
p_df_dis
p_df_con <- new_p(con_df, type = "continuous")
p_df_con
plot(p_df_con, main = "P-functions from data frame")
lines(p_df_dis, col = "blue")
```
## D-functions
D-function (analogue of `d*()` functions in base R) represents a probability mass function for "discrete" type and density function for "continuous":
### From sample
```{r d-fun_sample}
# Treating input as discrete
d_mpg_dis <- new_d(mtcars$mpg, type = "discrete")
d_mpg_dis
# Treating input as continuous
d_mpg_con <- new_d(mtcars$mpg, type = "continuous")
d_mpg_con
# Outputs are actually vectorized functions
d_mpg_dis(15:20)
d_mpg_con(15:20)
# You can plot them directly using base `plot()` and `lines()`
op <- par(mfrow = c(1, 2))
plot(d_mpg_con, main = '"continuous" d-function\nfrom sample')
plot(d_mpg_dis, main = '"discrete" d-function\nfrom sample', col = "blue")
par(op)
```
### From data frame
```{r d-fun_data-frame}
d_df_dis <- new_d(dis_df, type = "discrete")
d_df_dis
d_df_con <- new_d(con_df, type = "continuous")
d_df_con
op <- par(mfrow = c(1, 2))
plot(d_df_con, main = '"continuous" d-function\nfrom data frame')
plot(d_df_dis, main = '"discrete" d-function\nfrom data frame', col = "blue")
par(op)
```
## Q-functions
Q-function (analogue of `q*()` functions in base R) represents a quantile function, an inverse of corresponding p-function:
### From sample
```{r q-fun_sample}
# Treating input as discrete
q_mpg_dis <- new_q(mtcars$mpg, type = "discrete")
q_mpg_dis
# Treating input as continuous
q_mpg_con <- new_q(mtcars$mpg, type = "continuous")
q_mpg_con
# Outputs are actually vectorized functions
q_mpg_dis(c(0.1, 0.3, 0.7, 1.5))
q_mpg_con(c(0.1, 0.3, 0.7, 1.5))
# You can plot them directly using base `plot()` and `lines()`
plot(q_mpg_con, main = "Q-functions from sample")
lines(q_mpg_dis, col = "blue")
```
### From data frame
```{r q-fun_data-frame}
q_df_dis <- new_q(dis_df, type = "discrete")
q_df_dis
q_df_con <- new_q(con_df, type = "continuous")
q_df_con
plot(q_df_con, main = "Q-functions from data frame")
lines(q_df_dis, col = "blue")
```
## R-functions
R-function (analogue of `r*()` functions in base R) represents a random generation function. For "discrete" type it will generate only values present in input. For "continuous" function it will generate values from distribution corresponding to one estimated with `density()`.
### From sample
```{r r-fun_sample}
# Treating input as discrete
r_mpg_dis <- new_r(mtcars$mpg, type = "discrete")
r_mpg_dis
# Treating input as continuous
r_mpg_con <- new_r(mtcars$mpg, type = "continuous")
r_mpg_con
# Outputs are actually functions
r_mpg_dis(5)
r_mpg_con(5)
# You can plot them directly using base `plot()` and `lines()`
op <- par(mfrow = c(1, 2))
plot(r_mpg_con, main = '"continuous" r-function\nfrom sample')
plot(r_mpg_dis, main = '"discrete" r-function\nfrom sample', col = "blue")
par(op)
```
### From data frame
```{r r-fun_data-frame}
r_df_dis <- new_r(dis_df, type = "discrete")
r_df_dis
r_df_con <- new_r(con_df, type = "continuous")
r_df_con
op <- par(mfrow = c(1, 2))
plot(r_df_con, main = '"continuous" r-function\nfrom data frame')
plot(r_df_dis, main = '"discrete" r-function\nfrom data frame', col = "blue")
par(op)
```
## Special cases
### Dirac-like
When creating "continuous" pdqr-function with `new_*()` from single number, a special "dirac-like" pdqr-function is created. It is an *approximation of single number* with triangular distribution of very narrow support (1e-8 of magnitude):
```{r dirac}
r_dirac <- new_r(3.14, type = "continuous")
r_dirac
r_dirac(4)
# Outputs aren't exactly but approximately equal
dput(r_dirac(4))
```
### Boolean
Boolean pdqr-function is a special case of "discrete" function, which values are exactly 0 and 1. Those functions are usually created after transformations involving logical operators (see vignette on transformation for more details). It is assumed that 0 represents that some expression is false, and 1 is for being true. Corresponding probabilities describe distribution of expression's logical values. The only difference from other "discrete" pdqr-functions is in more detailed printing.
```{r boolean}
new_d(data.frame(x = c(0, 1), prob = c(0.25, 0.75)), type = "discrete")
```
## Using `density()` arguments
When creating pdqr-function of "continuous" type, `density()` is used to estimate density. To tweak its performance, supply its extra arguments directly to `new_*()` functions. Here are some examples:
```{r density-args}
plot(
new_d(mtcars$mpg, "continuous"), lwd = 3,
main = "Examples of `density()` options"
)
# Argument `adjust` of `density()` helps to define smoothing bandwidth
lines(new_d(mtcars$mpg, "continuous", adj = 0.3), col = "blue")
# Argument `n` defines number of points to be used in piecewise-linear
# approximation
lines(new_d(mtcars$mpg, "continuous", n = 5), col = "green")
# Argument `cut` defines the "extending" property of density estimation.
# Using `cut = 0` assumes that density can't go outside of input's range
lines(new_d(mtcars$mpg, "continuous", cut = 0), col = "magenta")
```
## Metadata of pdqr-functions
Every pdqr-function has metadata, information which describes underline distribution and pdqr-function. Family of `meta_*()` functions are implemented to extract that information:
- **"x_tbl" metadata** (returned by `meta_x_tbl()`) completely defines distribution. It is a data frame with structure depending on type of pdqr-function:
- For "discrete" type it has columns "x" (output values), "prob" (their probability), and "cumprob" (their cumulative probability).
- For "continuous" type it has columns "x" (knots of piecewise-linear density), "y" (density values at those points), "cumprob" (their cumulative probability).
- **Pdqr class** (returned by `meta_class()`) - class of pdqr-function. This can be one of "p", "d", "q", "r". Represents how pdqr-function describes underlying distribution.
- **Pdqr type** (returned by `meta_type()`) - type of pdqr-function. This can be one of "discrete" or "continuous". Represents type of underlying distribution.
- **Pdqr support** (returned by `meta_support()`) - support of distribution. This is a range of "x" column from "x_tbl" metadata.
```{r meta_x_tbl}
# Type "discrete"
d_dis <- new_d(1:4, type = "discrete")
meta_x_tbl(d_dis)
meta_class(d_dis)
meta_type(d_dis)
meta_support(d_dis)
# Type "continuous"
p_con <- new_p(1:4, type = "continuous")
head(meta_x_tbl(p_con))
meta_class(p_con)
meta_type(p_con)
meta_support(p_con)
# Dirac-like "continuous" function
r_dirac <- new_r(1, type = "continuous")
dput(meta_x_tbl(r_dirac))
dput(meta_support(r_dirac))
# `meta_all()` returns all metadata in a single list
meta_all(d_dis)
```
For more details go to help page of `meta_all()`.