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Function | Description | Notes |
---|---|---|
aov_ss | Calculates sex specific one-way ANOVA from summary statistics and performs pairwise comparisons | Uses the summary statistics |
D_index | Dissimilarity index (Chakraborty and Majumder 1982) for statistical computation and visualization of the area of non-overlap in the trait distribution between the sexes. | Provides a table and a graphical representation of the selected traits and their corresponding dissimilarity indices. Also provides confidence intervals via a bias-corrected parametric bootstrap. |
extract_sum | Extract summary statistics needed for the other functions from uploaded raw data directly without need to go to a third-party package. | Can also run the aov_ss, multivariate, t_greene, univariate, or van_vark functions after extracting the summary statistics. |
Hedges_g | Calculates Hedges’ (1981) for effect size between the sexes for a single trait. The confidence interval is found using a method described in Goulet-Pelletier and Cousineau (2018). | Can also find the confidence interval using a bias-corrected parametric bootstrap. |
MI_index | Mixture Index is the mixture intersection measure of sexual dimorphism (Ipiña and Durand 2010). Ipiña and Durand (2010) also define a normal intersection NI measure which is the overlap coefficient of two normal distributions, equivalent to Inman and Bradley’s (1989) overlap coefficient. | Can produce confidence intervals using a bias-corrected parametric bootstrap. |
multivariate | An extension of the univariate analysis of sexual dimorphism between different samples. MANOVA test is used to analyze the interaction effects and main effects. | Type of MANOVA test employed can be “I”, “II” or “III” sum of squares and cross products. The test statistics can be Wilks’ lambda, Pillai’s trace, Hotelling-Lawley’s trace or Roy’s largest root. If univariate argument is TRUE, the function conducts ANOVAs on each variable. |
raw_gen | Raw data generation from summary statistics using univariate or multivariate normal distributions (with truncation as an option). | |
t_greene | Relethford and Hodges’ (1985) and Greene’s (1989) t-test of sexual dimorphism. | A plot of p-values for differences in sexual dimorphism across all pairs of samples can be produced with plot=TRUE |
univariate | Univariate analysis of sexual dimorphism using two-way ANOVA. | Type of sums of squares can type type “I”, “II”, or “III.” |
van_vark | Provides testing for differences in sexual dimorphism between samples using van Vark et al.’s (1989) method. |
Table.02=function ()
{
library(TestDimorph)
options(width=100) # This option just for output from Rmarkdown
NHANES_univariate<<-extract_sum(NHANES_1999,test='uni',run=FALSE) # BMXWT (Body mass)
univariate(NHANES_univariate,es_anova = "eta2",pairwise = TRUE)
}
Table.02()
The parameter used is BMXWT
$univariate
term df sumsq meansq statistic p.value signif eta2 lower.eta2 upper.eta2
1 Sex 1 25378.2 25378.2 63.8746 0.0000 *** 0.0429 0.0247 0.0652
2 Pop 2 20970.0 10485.0 26.3898 0.0000 *** 0.0357 0.0186 0.0558
3 Sex*Pop 2 4141.8 2070.9 5.2123 0.0056 ** 0.0073 0.0007 0.0177
4 Residuals 1424 565773.3 397.3 NA NA <NA> NA NA NA
$pairwise
populations df mean.diff conf.low conf.high statistic p.value signif
1 Black-Mex.Am 764 -5.9980 -11.8657 -0.1304 -2.0067 0.0451 *
2 Black-White 965 -8.9769 -14.8397 -3.1142 -3.0048 0.0027 **
3 Mex.Am-White 1119 -2.9789 -7.4263 1.4685 -1.3142 0.1890 ns
Table.03=function()
{
library(TestDimorph)
NHANES_multivariate<<-extract_sum(NHANES_1999,test='multi',run=FALSE)
multivariate(NHANES_multivariate)
}
Table.03()
The parameters used are BMXWT,BMXHT,BMXARML
term df Wilks approx.f num.df den.df p.value signif
1 Sex(E) 1 0.5223 433.5580 3 1422 0.0000 ***
2 Pop(E) 2 0.7637 68.4009 6 2844 0.0000 ***
3 Sex*Pop(E) 2 0.9851 3.5834 6 2844 0.0015 **
Table.04=function()
{
library(TestDimorph)
print(univariate(NHANES_univariate, type_anova='III'))
t_greene(NHANES_univariate,plot = TRUE,padjust ="fdr")
}
Table.04()
term df sumsq meansq statistic p.value signif
1 Sex 1 17356.4 17356.4 43.6845 0.0000 ***
2 Pop 2 18902.3 9451.2 23.7877 0.0000 ***
3 Sex*Pop 2 4141.8 2070.9 5.2123 0.0056 **
4 Residuals 1424 565773.3 397.3 NA NA <NA>
populations df mean.diff conf.low conf.high statistic p.value signif
1 Black-Mex.Am 764 -5.9980 -11.8657 -0.1304 -2.0067 0.06765 ns
2 Black-White 965 -8.9769 -14.8397 -3.1142 -3.0048 0.00810 **
3 Mex.Am-White 1119 -2.9789 -7.4263 1.4685 -1.3142 0.18900 ns
Table.05=function()
{
library(TestDimorph)
to_van_Vark=extract_sum(Howells,test='van',run=F)
van_vark(to_van_Vark)
}
Table.05()
The parameters used are GOL,NOL,BNL,BBH,XCB,XFB,ZYB,AUB
The maximum possible value of q is (7).
populations statistic df p.value signif
1 NORSE-EGYPT 1.2809 2 0.5271 ns
2 NORSE-TOLAI 8.8981 2 0.0117 *
3 NORSE-PERU 0.4268 2 0.8078 ns
4 EGYPT-TOLAI 5.2097 2 0.0739 ns
5 EGYPT-PERU 0.7477 2 0.6881 ns
6 TOLAI-PERU 5.4584 2 0.0653 ns
Table.06=function ()
{
# 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),
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)
)
print(aov_ss(x = df, CI=0.95),digits=6)
}
Table.06()
$`Male model`
term df sumsq meansq statistic p.value signif
1 Populations 3 566.214 188.7379 25.4042 0 ***
2 Residuals 298 2213.959 7.4294 NA NA <NA>
$`Male posthoc`
populations mean.diff conf.low conf.high p.value signif
1 Greek-Bulgarian -1.34 -2.7479 0.0679 0.0686 ns
2 Portuguese-Bulgarian -3.13 -4.5664 -1.6936 0.0000 ***
3 Turkish-Bulgarian 1.06 0.0929 2.0271 0.0254 *
4 Portuguese-Greek -1.79 -3.4741 -0.1059 0.0323 *
5 Turkish-Greek 2.40 1.0930 3.7070 0.0000 ***
6 Turkish-Portuguese 4.19 2.8524 5.5276 0.0000 ***
$`Female model`
term df sumsq meansq statistic p.value signif
1 Populations 3 88.4265 29.4755 3.7221 0.012 *
2 Residuals 262 2074.8100 7.9191 NA NA <NA>
$`Female posthoc`
populations mean.diff conf.low conf.high p.value signif
1 Greek-Bulgarian -0.45 -2.0216 1.1216 0.8807 ns
2 Portuguese-Bulgarian -1.99 -3.7560 -0.2240 0.0202 *
3 Turkish-Bulgarian 0.02 -1.1050 1.1450 1.0000 ns
4 Portuguese-Greek -1.54 -3.4798 0.3998 0.1716 ns
5 Turkish-Greek 0.47 -0.9120 1.8520 0.8156 ns
6 Turkish-Portuguese 2.01 0.4104 3.6096 0.0071 **
Table.07=function (i.which=13)
{
library(TestDimorph)
print(MI_index(Cremains_measurements[i.which,],B=1000,rand=F,verbose=F,plot=T))
print(MI_index(Cremains_measurements[i.which,],index_type='NI',
B=1000,rand=F,plot=T,verbose=F))
print(D_index(Cremains_measurements[i.which,],B=1000,rand=F,verbose=F,plot=T))
print(Hedges_g(Cremains_measurements[i.which,],B=1000,rand=F,verbose=F))
}
Table.07()
Trait lower MI upper
1 PA-MXW 0.025 0.1108 0.231
Trait lower NI upper
1 PA-MXW 0.0544 0.2496 0.521
Trait lower D upper
1 PA-MXW 0.4788 0.7504 0.9454
Trait lower g upper
1 PA-MXW 1.2094 2.2429 3.7713
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.