m1=cov.wt(Craneos1)$center
    m1=data.matrix(m1)
    m2=cov.wt(Craneos2)$center
m2=data.matrix(m2)
D=((nrow(Craneos1)-1)*var(Craneos1)+(nrow(Craneos2)-1)*var(Craneos2))/30
d=solve(D)%*%(m1-m2)
d # Estos son los coeficientes de la función
0.5*t(m1+m2)%*%d # Este es el punto crítico

> d # Coefficients of the discriminant analysis
           [,1]
x1 -0.089306662
x2  0.155774683
x3  0.005231617
x4 -0.177194601
x5 -0.177408670

> 0.5*t(m1+m2)%*%d # Este es el punto crítico
          [,1]
[1,] -30.46349

lda(group~.,data=Datos)

> lda(group~.,data=Datos)
Call:
lda(group ~ ., data = Datos)

Prior probabilities of groups:
CRANEOS1 CRANEOS2
 0.53125  0.46875

Group means:
               X1       X2       X3       X4       X5
CRANEOS1 174.8235 139.3529 132.0000 69.82353 130.3529
CRANEOS2 185.7333 138.7333 134.7667 76.46667 137.5000

Coefficients of linear discriminants:
            LD1
X1  0.047726591
X2 -0.083247929
X3 -0.002795841
X4  0.094695000
X5  0.094809401