Type:	Package
Title:	Simulated Grouped Hyper Data Frame
Version:	0.1.0
Date:	2025-04-03
Description:	An intuitive interface to simulate (1) superimposed (marked) point patterns with vectorized parameterization of random point pattern and distribution of marks; and (2) grouped hyper data frame based on population parameters and subject-specific random effects.
RoxygenNote:	7.3.2
Encoding:	UTF-8
License:	GPL-2
Depends:	R (≥ 4.4), spatstat.random, groupedHyperframe
Language:	en-US
Imports:	cli, spatstat.geom, MASS
Suggests:	knitr, rmarkdown, spatstat.data
VignetteBuilder:	knitr
NeedsCompilation:	no
Packaged:	2025-04-03 20:25:10 UTC; tingtingzhan
Author:	Tingting Zhan [aut, cre], Inna Chervoneva [aut]
Maintainer:	Tingting Zhan <tingtingzhan@gmail.com>
Repository:	CRAN
Date/Publication:	2025-04-07 16:20:05 UTC

groupedHyperframe.random: Simulated Grouped Hyper Data Frame

Description

An intuitive interface to simulate (1) superimposed (marked) point patterns with vectorized parameterization of random point pattern and distribution of marks; and (2) grouped hyper data frame based on population parameters and subject-specific random effects.

Note

Help files of individual functions are intentionally suppressed in the pdf manual. Users are encouraged to get started with

vignette('intro', package = 'groupedHyperframe.random')

Author(s)

Maintainer: Tingting Zhan tingtingzhan@gmail.com (ORCID)

Authors:

• Inna Chervoneva Inna.Chervoneva@jefferson.edu (ORCID)

Simulate (Marked) Point Pattern

Description

To generate ppp.object(s), with none or one or multiple marks.

Usage

.rppp(
  ...,
  dots,
  win = square(),
  n = 1L,
  element1 = TRUE,
  envir = parent.frame()
)

Arguments

Value

Function .rppp() returns a ppp.object if (n==1L)&element1, otherwise returns a length-n solist (which also has class 'ppplist').

The returned ppp.object(s) contain only x- and y-coords, if only one call is present in the ... dyn-dots argument. Otherwise, they contain one or more marks according to the rest of the call(s) in the ... argument.

Note

The name rppp() is too aggressive, which might be claimed in future by package spatstat.random. Therefore we name this function .rppp() as if it is hidden (see parameter all.names of function ls).

Append to marks

Description

Append to marks

Usage

append_marks.ppp(x) <- value

Arguments

Value

This syntax sugar returns a ppp.object.

Examples

library(spatstat.geom)

# no marks
data(vesicles, package = 'spatstat.data')
plot(vesicles)
append_marks.ppp(vesicles) = rlnorm(n = npoints(vesicles))
plot(vesicles)

# vector marks, numeric
data(waka, package = 'spatstat.data')
plot(waka)
append_marks.ppp(waka) = rlnorm(n = npoints(waka))
plot(waka)

# vector marks, multitype
data(urkiola, package = 'spatstat.data')
plot(urkiola)
append_marks.ppp(urkiola) = rlnorm(n = npoints(urkiola))
plot(urkiola)

# dataframe marks
data(stonetools, package = 'spatstat.data')
plot(stonetools)
append_marks.ppp(stonetools) = rlnorm(n = npoints(stonetools))
plot(stonetools)

Simulate groupedHyperframe

Description

Simulate groupedHyperframe

Usage

grouped_rppp(..., n, win = square(), envir = parent.frame())

Arguments

Value

Function grouped_rppp() returns a groupedHyperframe.

Expand Types of Sigma in mvrnorm

Description

To accommodate more types of Sigma in function mvrnorm.

Usage

mvrnorm2(n, mu, Sigma, ...)

Arguments

Details

Argument of parameter Sigma could be

scalar

First, Sigma is recycled to the length of mu. Then a diagonal matrix with vector Sigma on the diagonal elements is used as the variance-covariance matrix \Sigma

vector

First, check that length of Sigma and mu must be the same. Then the diagonal matrix is used as \Sigma

matrix

Sigma is used as \Sigma

Value

Function mvrnorm2() returns a double matrix.

Note

Workhorse function mvrnorm from package MASS is faster than ?mvtnorm::rmvnorm.

Generate Random factor

Description

..

Usage

rfactor(n, prob, levels = as.character(seq_along(prob)))

Arguments

Value

Function rfactor() returns a factor

Examples

rfactor(n = 100L, prob = c(4,2,3))
rfactor(n = 100L, prob = c(4,2,3), levels = letters[1:3])

Create Random marks Generation Function for ppp.object

Description

Create random marks generation function for ppp.object.

Usage

rmarks_ppp(f)

Arguments

Value

Function rmarks_ppp() returns a function, which generates random marks of a ppp.object following the probability distribution specified by argument f. The returned function

• has first parameter x taking an argument of a ppp.object.

• returns a ppp.object.

Examples

rmarks_ppp(rlnorm)
rmarks_ppp('rnbinom')

plot(pp <- rpoispp(lambda = 100))

plot(pp |>
 rmarks_ppp(rlnorm)(sdlog = .5) |>
 rmarks_ppp(rnbinom)(size = 5L, prob = .3) |>
 rmarks_ppp(rfactor)(prob = c(2,1,3), levels = letters[1:3]))
 

Batch Process of Random Multivariate Normal Generation

Description

Batch process of random multivariate normal generation mvrnorm2.

Usage

rmvnorm_(n, mu, Sigma, ...)

Arguments

Value

Function rmvnorm_() returns a list of matrixes.

Examples

rmvnorm_(n = 5L, mu = list(a = c(0, 3), b = c(3, 7)), Sigma = list(a = .5, b = 1.2))
rmvnorm_(n = 5L, mu = list(a = 0, b = c(3, 7)), Sigma = list(a = .5, b = 1.2))