univariate {TestDimorph}

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,
  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 "f2" for f squared,"eta2" for eta squared, "omega2" for omega 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 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.

digits

Number of significant digits, Default: 4

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

Value

ANOVA table.

References

Hector, Andy, Stefanie Von Felten, and Bernhard Schmid. "Analysis of variance with unbalanced data: an update for ecology & evolution." Journal of animal ecology 79.2 (2010): 308-316.

Examples

#'
# See Tables 6 and 8 and from Fidler and Thompson (2001).
# The “eta2” and “omega2” CIs match those in Table 8.
# See “FT” dataset for Fidler and Thompson (2001) reference

# acquiring summary data
FT_sum <- extract_sum(FT, test = "uni", run = FALSE)
# univariate analysis on summary data
univariate(FT_sum, CI = 0.90, es_anova = "eta2", digits = 5)
univariate(FT_sum, CI = 0.90, es_anova = "omega2", digits = 5)


# Reproduces Table 2 from Shaw and Mitchell-Olds (1993) using their Table 1.
# See “SMO” dataset for Shaw and Mitchell-Olds (1993) reference
# Note that Table 2 residual df is incorrectly given as 6,
# but is correctly given as 7 in Hector et al. (2010)

# acquiring summary data
univ_SMO <- extract_sum(SMO, test = "uni", run = FALSE)
# univariate analysis on summary data
print(univariate(univ_SMO, type_anova = "I")[[1]])
print(univariate(univ_SMO, type_anova = "II"))
univariate(univ_SMO, type_anova = "III")