| Type: | Package |
| Title: | Maximum Homogeneity Clustering for Univariate Data |
| Version: | 0.3.0 |
| Description: | Maximum homogeneity clustering algorithm for one-dimensional data described in W. D. Fisher (1958) <doi:10.1080/01621459.1958.10501479> via dynamic programming. |
| License: | GPL-3 |
| URL: | https://nanx.me/oneclust/, https://github.com/nanxstats/oneclust |
| Encoding: | UTF-8 |
| VignetteBuilder: | knitr |
| BugReports: | https://github.com/nanxstats/oneclust/issues |
| Depends: | R (≥ 3.5.0) |
| LinkingTo: | Rcpp |
| Imports: | Rcpp |
| Suggests: | knitr, rmarkdown |
| RoxygenNote: | 7.3.1 |
| NeedsCompilation: | yes |
| Packaged: | 2024-03-11 21:38:58 UTC; nanx |
| Author: | Nan Xiao  [aut,
cre] |
| Maintainer: | Nan Xiao <me@nanx.me> |
| Repository: | CRAN |
| Date/Publication: | 2024-03-11 23:00:02 UTC |
Masataka Okabe and Kei Ito's Color Universal Design palette
Description
Masataka Okabe and Kei Ito's Color Universal Design palette
Usage
cud(x, shift = TRUE, reverse = FALSE)
Arguments
x |
Vector, color index. |
shift |
Start from the second color in the CUD palette? |
reverse |
Reverse the color order? |
Value
A vector of color hex values.
Examples
barplot(rep(1, 7), col = cud(1:7))
barplot(rep(1, 8), col = cud(1:8, shift = FALSE))
barplot(rep(1, 8), col = cud(1:8, shift = FALSE, reverse = TRUE))
Maximum homogeneity clustering for one-dimensional data
Description
Maximum homogeneity clustering for one-dimensional data
Usage
oneclust(x, k, w = NULL, sort = TRUE)
Arguments
x |
Numeric vector, samples to be clustered. |
k |
Integer, number of clusters. |
w |
Numeric vector, sample weights (optional). Note that the weights here should be sampling weights (for example, a certain proportion of the population), not frequency weights (for example, number of occurrences). |
sort |
Should we sort |
Value
A list containing:
-
cluster- cluster id of each sample. -
cut- index of the optimal cut points.
References
Fisher, Walter D. 1958. On Grouping for Maximum Homogeneity. Journal of the American Statistical Association 53 (284): 789–98.
Examples
set.seed(42)
x <- sample(c(
rnorm(50, sd = 0.2),
rnorm(50, mean = 1, sd = 0.3),
rnorm(100, mean = -1, sd = 0.25)
))
oneclust(x, 3)
Simulate the levels and their sizes in a high-cardinality feature
Description
Simulate the levels and their sizes in a high-cardinality feature
Usage
sim_postcode_levels(nlevels = 100L, seed = 1001)
Arguments
nlevels |
Number of levels to generate. |
seed |
Random seed. |
Value
A data frame of postal codes and sizes.
Note
The code is derived from the example described in the "rare levels"
vignette in the vtreat package.
Examples
df_levels <- sim_postcode_levels(nlevels = 500, seed = 42)
head(df_levels)
Simulate a high-cardinality feature and a binary response
Description
Simulate a high-cardinality feature and a binary response
Usage
sim_postcode_samples(
df_levels,
n = 2000L,
threshold = 1000,
prob = c(0.3, 0.1),
seed = 1001
)
Arguments
df_levels |
Number of levels. |
n |
Number of samples. |
threshold |
The threshold for determining if a postal code is rare. |
prob |
Occurrence probability vector of the class 1 event in rare and non-rare postal codes. |
seed |
Random seed. |
Value
A data frame of samples with postal codes, response labels, and level rarity status.
Note
The code is derived from the example described in the "rare levels"
vignette in the vtreat package.
Examples
df_levels <- sim_postcode_levels(nlevels = 500, seed = 42)
df_postcode <- sim_postcode_samples(
df_levels,
n = 10000, threshold = 3000, prob = c(0.2, 0.1), seed = 43
)
head(df_postcode)