Resultados: Rasgo con dominancia parcial, modelo ajustado = aditivo
x <- replicate(1e3, simulation(nind = 152, a = 1, d = 0.3,
p = 0.2, total.sd = 3.04,
p.masc = 0.0974,
sex.effect = 0.8,
modelo = "aditivo"))
hist(x, freq = F, border = "gray60", col = adjustcolor("gray98", alpha.f = 0.5),
xlab= "a estimado", ylab = "Densidad", main ="Distribución de a estimado", ylim = c(0,1))
lines(density(x), col = "gray70")
abline(v = mean(x), col = adjustcolor("dodgerblue4", alpha.f = 0.35), lty = 1, lwd = 3)
abline(v = 1, col = adjustcolor("darkgreen", alpha.f = 0.25), lty = 2, lwd = 2)
shapiro.test(x)
##
## Shapiro-Wilk normality test
##
## data: x
## W = 0.99905, p-value = 0.8968
t.test(x, mu = 1)
##
## One Sample t-test
##
## data: x
## t = 1.0464, df = 999, p-value = 0.2956
## alternative hypothesis: true mean is not equal to 1
## 95 percent confidence interval:
## 0.987726 1.040317
## sample estimates:
## mean of x
## 1.014021
Resultados: Rasgo con dominancia parcial, modelo ajustado = dominante
x <- replicate(1e3, simulation(nind = 152, a = 1, d = 0.3,
p = 0.2, total.sd = 3.04,
p.masc = 0.0974,
sex.effect = 0.8,
modelo = "dominante"))
hist(x, freq = F, border = "gray60", col = adjustcolor("gray98", alpha.f = 0.5),
xlab= "a estimado", ylab = "Densidad", main ="Distribución de a estimado", ylim = c(0,1))
lines(density(x), col = "gray70")
abline(v = mean(x), col = adjustcolor("dodgerblue4", alpha.f = 0.35), lty = 1, lwd = 3)
abline(v = 1, col = adjustcolor("darkgreen", alpha.f = 0.25), lty = 2, lwd = 2)
shapiro.test(x)
##
## Shapiro-Wilk normality test
##
## data: x
## W = 0.99387, p-value = 0.0004075
t.test(x, mu = 1)
##
## One Sample t-test
##
## data: x
## t = 0.35728, df = 999, p-value = 0.721
## alternative hypothesis: true mean is not equal to 1
## 95 percent confidence interval:
## 0.965379 1.050034
## sample estimates:
## mean of x
## 1.007706
Resultados: Rasgo con dominancia completa, modelo ajustado = aditivo
x <- replicate(1e3, simulation(nind = 152, a = 1, d = 1,
p = 0.2, total.sd = 3.04,
p.masc = 0.0974,
sex.effect = 0.8,
modelo = "aditivo"))
hist(x, freq = F, border = "gray60", col = adjustcolor("gray98", alpha.f = 0.5),
xlab= "a estimado", ylab = "Densidad", main ="Distribución de a estimado", ylim = c(0,1))
lines(density(x), col = "gray70")
abline(v = mean(x), col = adjustcolor("dodgerblue4", alpha.f = 0.35), lty = 1, lwd = 3)
abline(v = 1, col = adjustcolor("darkgreen", alpha.f = 0.25), lty = 2, lwd = 2)
shapiro.test(x)
##
## Shapiro-Wilk normality test
##
## data: x
## W = 0.99779, p-value = 0.2059
t.test(x, mu = 1)
##
## One Sample t-test
##
## data: x
## t = -0.0029477, df = 999, p-value = 0.9976
## alternative hypothesis: true mean is not equal to 1
## 95 percent confidence interval:
## 0.9736562 1.0262648
## sample estimates:
## mean of x
## 0.9999605
Resultados: Rasgo con dominancia completa, modelo ajustado = dominante
x <- replicate(1e3, simulation(nind = 152, a = 1, d = 1,
p = 0.3, total.sd = 3.04,
p.masc = 0.0974,
sex.effect = 0.8,
modelo = "dominante"))
hist(x, freq = F, border = "gray60", col = adjustcolor("gray98", alpha.f = 0.5),
xlab= "a estimado", ylab = "Densidad", main ="Distribución de a estimado", ylim = c(0,1))
lines(density(x), col = "gray70")
abline(v = mean(x), col = adjustcolor("dodgerblue4", alpha.f = 0.35), lty = 1, lwd = 3)
abline(v = 1, col = adjustcolor("darkgreen", alpha.f = 0.25), lty = 2, lwd = 2)
shapiro.test(x)
##
## Shapiro-Wilk normality test
##
## data: x
## W = 0.99794, p-value = 0.2566
t.test(x, mu = 1)
##
## One Sample t-test
##
## data: x
## t = -0.39657, df = 999, p-value = 0.6918
## alternative hypothesis: true mean is not equal to 1
## 95 percent confidence interval:
## 0.9669158 1.0219603
## sample estimates:
## mean of x
## 0.994438