TestDimorph (version 0.3.0)

Tg: Greene t-test of Sexual Dimorphism

Description

Calculation and visualization of the differences in degree sexual dimorphism between two populations using summary statistics as input.

Usage

Tg(
  x = NULL,
  Pop = 1,
  es = FALSE,
  plot = FALSE,
  ...,
  alternative = "two.sided",
  padjust = "none",
  letters = FALSE,
  digits = 4,
  sig.level = 0.05,
  N = NULL,
  m = NULL,
  m2 = NULL,
  f = NULL,
  f2 = NULL,
  M.mu = NULL,
  M.mu2 = NULL,
  F.mu = NULL,
  F.mu2 = NULL,
  M.sdev = NULL,
  M.sdev2 = NULL,
  F.sdev = NULL,
  F.sdev2 = NULL
)

Arguments

x

Tibble/data frame containing summary statistics, Default: NULL

Pop

Number of the column containing populations' names, Default: 1

es

Logical; if TRUE effect size is included in the output , Default: FALSE

plot

Logical; if TRUE graphical matrix of p-values, Default: TRUE

...

additional arguments that can be passed to corrplot function.

alternative

a character string specifying the alternative hypothesis, must be one of "two.sided", "greater" or "less", Default: 'two.sided'

padjust

Method of p.value adjustment for multiple comparisons following p.adjust.methods, Default: 'none'

letters

Logical; if TRUE returns letters for pairwise comparisons where significantly different populations are given different letters, Default: FALSE'

digits

Number of significant digits, Default: 4

sig.level

Critical p.value, Default: 0.05

N

Number of pairwise comparisons for p.adjust.methods, if left NULL it will follow the formula n(n-1)/2 where n is the number of populations , Default: NULL

m

Number of male sample size in the first population, Default: NULL

m2

Number of male sample size in the second population, Default: NULL

f

Number of female sample size in the first population, Default: NULL

f2

Number of female sample size in the second population, Default: NULL

M.mu

Means for males in the first population, Default: NULL

M.mu2

Means for males in the second population, Default: NULL

F.mu

Means for females in the first population, Default: NULL

F.mu2

Means for females in the second population, Default: NULL

M.sdev

Standard deviation for males in the first population, Default: NULL

M.sdev2

Standard deviation for males in the second population, Default: NULL

F.sdev

Standard deviation for females in the first population, Default: NULL

F.sdev2

Standard deviation for females in the second population, Default: NULL

Value

Tibble of t.test results

Details

Summary statistics can be entered directly as arguments in case of comparing two populations or as a tibble/data frame of summary statistics where the column containing population names is chosen by position (first by default), other columns of summary data should have specific names (case sensitive) similar to baboon.parms_df

References

greene1989comparisonTestDimorph timonov2014studyTestDimorph gulhan2015newTestDimorph

See Also

multcompLetters corrplot

Examples

# NOT RUN {
#Comparisons of femur head diameter in four populations
library(TestDimorph)
m <- c(150.00, 82.00, 36.00, 34.00)
f <- c(150.00, 58.00, 34.00, 24.00)
M.mu <- c(49.39, 48.33, 46.99, 45.20)
F.mu <- c(42.91, 42.89, 42.44, 40.90)
M.sdev <- c(3.01, 2.53, 2.47, 2.00)
F.sdev <- c(2.90, 2.84, 2.26, 2.90)
df <- cbind.data.frame(
  Pop = c('Turkish', 'Bulgarian', 'Greek', 'Portuguese '),
  m,
  f,
  M.mu,
  F.mu,
  M.sdev,
  F.sdev,
  stringsAsFactors = TRUE
)
Tg(
   df,
   plot = TRUE,
   method = 'ellipse',
  type = 'lower',
   col = c(
       '#AEB6E5',
       '#B1A0DB',
       '#B788CD',
       '#BC6EB9',
       '#BC569E',
       '#B6407D',
       '#A93154'
   ),
   tl.cex = 0.8,
   tl.col = 'black',
  insig =
       'label_sig',
   tl.srt = 0.1,
   pch.cex = 2.5,
   tl.pos = 'ld',
   win.asp = 1,
   number.cex = 0.5,
   na.label = 'NA'
)
# }