Generate Multiple Random Uniform Walks in Multiple Dimensions
Source:R/gen-uniform-walk.R
random_uniform_walk.RdThe random_uniform_walk function generates multiple random walks in 1, 2, or 3 dimensions.
Each walk is a sequence of steps where each step is a random draw from a uniform distribution.
The user can specify the number of walks, the number of steps in each walk, and the
parameters of the uniform distribution (min and max). The function
also allows for sampling a proportion of the steps and optionally sampling with replacement.
Usage
random_uniform_walk(
.num_walks = 25,
.n = 100,
.min = 0,
.max = 1,
.initial_value = 0,
.samp = TRUE,
.replace = TRUE,
.sample_size = 0.8,
.dimensions = 1
)Arguments
- .num_walks
An integer specifying the number of random walks to generate. Default is 25.
- .n
An integer specifying the number of steps in each walk. Default is 100.
- .min
A numeric value indicating the minimum of the uniform distribution. Default is 0.
- .max
A numeric value indicating the maximum of the uniform distribution. Default is 1.
- .initial_value
A numeric value indicating the initial value of the walks. Default is 0.
- .samp
A logical value indicating whether to sample the uniform distribution values. Default is TRUE.
- .replace
A logical value indicating whether sampling is with replacement. Default is TRUE.
- .sample_size
A numeric value between 0 and 1 specifying the proportion of
.nto sample. Default is 0.8.- .dimensions
An integer specifying the number of dimensions (1, 2, or 3). Default is 1.
Value
A tibble containing the generated random walks with columns depending on the number of dimensions:
walk_number: Factor representing the walk number.step_number: Step index.y: If.dimensions = 1, the value of the walk at each step.x,y: If.dimensions = 2, the values of the walk in two dimensions.x,y,z: If.dimensions = 3, the values of the walk in three dimensions.
The following are also returned based upon how many dimensions there are and could be any of x, y and or z:
walk_number: Factor representing the walk number.x: Step index.y: Uniform distribution values.cum_sum: Cumulative sum ofy.cum_prod: Cumulative product ofy.cum_min: Cumulative minimum ofy.cum_max: Cumulative maximum ofy.
The tibble includes attributes for the function parameters.
Details
This function is a flexible generator for random walks where each step is drawn from a uniform distribution. The user can control the number of walks, steps per walk, and the minimum and maximum values for the uniform distribution. The function supports 1, 2, or 3 dimensions, and augments the output with cumulative statistics for each walk. Sampling can be performed with or without replacement, and a proportion of steps can be sampled if desired.
See also
Other Generator Functions:
brownian_motion(),
discrete_walk(),
geometric_brownian_motion(),
random_normal_drift_walk(),
random_normal_walk(),
random_smirnov_walk(),
random_t_walk(),
random_weibull_walk(),
random_wilcox_walk()
Other Continuous Distribution:
brownian_motion(),
geometric_brownian_motion(),
random_normal_drift_walk(),
random_normal_walk(),
random_t_walk(),
random_weibull_walk()
Examples
set.seed(123)
random_uniform_walk()
#> # A tibble: 2,000 × 8
#> walk_number step_number y cum_sum_y cum_prod_y cum_min_y cum_max_y
#> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 1 0.656 0.656 0 0.656 0.656
#> 2 1 2 0.442 1.10 0 0.442 0.656
#> 3 1 3 0.693 1.79 0 0.442 0.693
#> 4 1 4 0.886 2.68 0 0.442 0.886
#> 5 1 5 0.902 3.58 0 0.442 0.902
#> 6 1 6 0.656 4.24 0 0.442 0.902
#> 7 1 7 0.985 5.22 0 0.442 0.985
#> 8 1 8 0.0246 5.24 0 0.0246 0.985
#> 9 1 9 0.232 5.48 0 0.0246 0.985
#> 10 1 10 0.147 5.62 0 0.0246 0.985
#> # ℹ 1,990 more rows
#> # ℹ 1 more variable: cum_mean_y <dbl>
set.seed(123)
random_uniform_walk(.dimensions = 3) |>
head() |>
t()
#> [,1] [,2] [,3] [,4]
#> walk_number "1" "1" "1" "1"
#> step_number "1" "2" "3" "4"
#> x "0.6557058" "0.4422001" "0.6928034" "0.8864691"
#> y "0.05795856" "0.54208037" "0.91568354" "0.61835123"
#> z "0.3580569634" "0.5763018376" "0.0004653491" "0.2151720827"
#> cum_sum_x "0.6557058" "1.0979059" "1.7907093" "2.6771783"
#> cum_sum_y "0.05795856" "0.60003893" "1.51572247" "2.13407369"
#> cum_sum_z "0.3580570" "0.9343588" "0.9348242" "1.1499962"
#> cum_prod_x "0" "0" "0" "0"
#> cum_prod_y "0" "0" "0" "0"
#> cum_prod_z "0" "0" "0" "0"
#> cum_min_x "0.6557058" "0.4422001" "0.4422001" "0.4422001"
#> cum_min_y "0.05795856" "0.05795856" "0.05795856" "0.05795856"
#> cum_min_z "0.3580569634" "0.3580569634" "0.0004653491" "0.0004653491"
#> cum_max_x "0.6557058" "0.6557058" "0.6928034" "0.8864691"
#> cum_max_y "0.05795856" "0.54208037" "0.91568354" "0.91568354"
#> cum_max_z "0.3580570" "0.5763018" "0.5763018" "0.5763018"
#> cum_mean_x "0.6557058" "0.5489529" "0.5969031" "0.6692946"
#> cum_mean_y "0.05795856" "0.30001946" "0.50524082" "0.53351842"
#> cum_mean_z "0.3580570" "0.4671794" "0.3116081" "0.2874991"
#> [,5] [,6]
#> walk_number "1" "1"
#> step_number "5" "6"
#> x "0.9022990" "0.6557058"
#> y "0.05462911" "0.48204261"
#> z "0.9506212641" "0.8118274161"
#> cum_sum_x "3.5794774" "4.2351832"
#> cum_sum_y "2.18870280" "2.67074541"
#> cum_sum_z "2.1006175" "2.9124449"
#> cum_prod_x "0" "0"
#> cum_prod_y "0" "0"
#> cum_prod_z "0" "0"
#> cum_min_x "0.4422001" "0.4422001"
#> cum_min_y "0.05462911" "0.05462911"
#> cum_min_z "0.0004653491" "0.0004653491"
#> cum_max_x "0.9022990" "0.9022990"
#> cum_max_y "0.91568354" "0.91568354"
#> cum_max_z "0.9506213" "0.9506213"
#> cum_mean_x "0.7158955" "0.7058639"
#> cum_mean_y "0.43774056" "0.44512423"
#> cum_mean_z "0.4201235" "0.4854075"