TestDimorph (version 0.4.0)

univariate: Univariate Analysis Of Sexual Dimorphism

Description

Calculation and visualization of the differences in degree sexual dimorphism between multiple populations using a modified one way ANOVA and summary statistics as input

Usage

univariate(
  x,
  Pop = 1,
  type_anova = "II",
  interact_anova = TRUE,
  es_anova = "none",
  pairwise = FALSE,
  padjust = "none",
  ...,
  lower.tail = FALSE,
  CI = 0.95,
  N = NULL,
  digits = 4
)

Arguments

x

A data frame containing summary statistics.

Pop

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

type_anova

type of ANOVA test "I","II" or "III", Default:"II".

interact_anova

Logical; if TRUE calculates interaction effect, Default: TRUE.

es_anova

Type of effect size either "f" for f squared,"eta" for eta squared or "none", Default:"none".

pairwise

Logical; if TRUE runs multiple pairwise comparisons on different populations using t_greene Default: FALSE

padjust

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

...

Additional arguments that could be passed to the t_greene function

lower.tail

Logical; if TRUE probabilities are `P[X <= x]`, otherwise, `P[X > x]`., Default: FALSE

CI

confidence interval coverage takes value from 0 to 1, Default: 0.95.

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

digits

Number of significant digits, Default: 4

Value

ANOVA tale.

Details

Data is entered as a 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

Examples

# NOT RUN {
# Comparisons of femur head diameter in four populations
library(TestDimorph)
df <-
  data.frame(
    Pop = c("Turkish", "Bulgarian", "Greek", "Portuguese "),
    m = c(150.00, 82.00, 36.00, 34.00),
    M.mu = c(49.39, 48.33, 46.99, 45.20),
    M.sdev = c(3.01, 2.53, 2.47, 2.00),
    f = c(150.00, 58.00, 34.00, 24.00),
    F.mu = c(42.91, 42.89, 42.44, 40.90),
    F.sdev = c(2.90, 2.84, 2.26, 2.90)
  )
univariate(df, pairwise = TRUE, padjust = "bonferroni")
# }