pipe-sine-boa function

Simulated sine, pipe, and gaussian mixture

Simulated sine and pipe data for calculating optimisation features. Each dataset has 1000 observations and the last two columns contain the intended structure with the rest being noise. data

Format

An object of class matrix (inherits from array) with 1000 rows and 6 columns.

An object of class matrix (inherits from array) with 1000 rows and 8 columns.

An object of class matrix (inherits from array) with 1000 rows and 6 columns.

An object of class matrix (inherits from array) with 1000 rows and 8 columns.

An object of class matrix (inherits from array) with 1000 rows and 10 columns.

An object of class matrix (inherits from array) with 1000 rows and 12 columns.

An object of class tbl_df (inherits from tbl, data.frame) with 1000 rows and 10 columns.

An object of class tbl_df (inherits from tbl, data.frame) with 1000 rows and 5 columns.

An object of class tbl_df (inherits from tbl, data.frame) with 1000 rows and 6 columns.

sine1000

sine1000_8d

pipe1000

pipe1000_8d

pipe1000_10d

pipe1000_12d

boa

boa5

boa6

Examples

library(ggplot2)
library(tidyr)
library(dplyr)
boa %>%
  pivot_longer(cols = x1:x10, names_to = "var", values_to = "value") %>%
  mutate(var = forcats::fct_relevel(as.factor(var), paste0("x", 1:10))) %>%
  ggplot(aes(x = value)) +
  geom_density() +
  facet_wrap(vars(var))

sine1000 |> ggplot(aes(x = V5, y = V6)) + geom_point() + theme(aspect.ratio = 1)
pipe1000_8d |> ggplot(aes(x = V5, y = V6)) + geom_point() + theme(aspect.ratio = 1)
pipe1000_8d |> ggplot(aes(x = V7, y = V8)) + geom_point() + theme(aspect.ratio = 1)