raw_gen: Raw Data Generation By Normal Or Truncated Normal...

In TestDimorph: Analysis of the Interpopulation Difference in Degree of Sexual Dimorphism Using Summary Statistics

Raw Data Generation By Normal Or Truncated Normal Distribution

Description

Generates raw data from summary statistics using uni/multivariate truncated normal distribution

Usage

raw_gen(
  x,
  Trait = 1,
  Pop = 2,
  R.res = NULL,
  lower = -Inf,
  upper = Inf,
  verbose = FALSE
)

Arguments

x	Data frame or list containing summary statistics for multiple parameters measured in both sexes in two or more populations.
Trait	Number of the column containing names of measured parameters, Default: 1
Pop	Number of the column containing populations' names, Default: 2
R.res	Pooled within correlation matrix, Default: NULL
lower	scalar of lower bounds, Default: -Inf
upper	scalar of upper bounds, Default: Inf
verbose	Logical; if TRUE displays a message with the method used for generation , Default: FALSE

Details

If data generation is desired using multivariate distribution data is entered in the form of a list of summary statistics and pooled within correlation matrix as in baboon.parms_list, or the summary statistics are entered separately in the form of a data frame as in baboon.parms_df with a separate correlation matrix as in baboon.parms_R. If data frame is entered without a correlation matrix, data generation is carried out using univariate distribution.

Value

a data frame of raw data

Examples

# Data generation using univariate distributions
raw_gen(baboon.parms_df, lower = 0)

# another univariate example
library(dplyr)
data <- Cremains_measurements[1, ] %>% mutate(Pop=c("A")) %>%
relocate(Pop,.after=1)
raw_gen(data)[, -2]

# Data generation using multivariate distribution
raw_gen(baboon.parms_list, lower = 0)

TestDimorph documentation built on Nov. 18, 2023, 1:11 a.m.