estSimpsonf {MCPAN} | R Documentation |
Calculate estimates of the Simpson index after pooling over several samples, grouped by a factor variable.
estSimpsonf(X, f)
X |
a data.frame of dimension n times p with integer entries, where n is the number of samples and p is the number of species |
f |
a factor variable of length n, grouping the observations in X |
The function splits X
according to the levels of the grouping variable f
, builds the sum over each column and calculates the Shannon index ove the resulting counts.
A list containing the items:
estimate |
the groupwise point estimates of the Simpson index |
varest |
the groupwise variance estimates of the Simpson index |
table |
a matrix of counts,containing the summed observations for each level of f in its rows |
Rogers, JA and Hsu, JC (2001): Multiple Comparisons of Biodiversity. Biometrical Journal 43, 617-625.
# Here, the estimates for the Hell Creek Dinosaur # example are compared to the estimates in # Tables 2 and 3 of Rogers and Hsu (2001). data(HCD) HCD # Groupwise point estimates: est<-estSimpsonf(X=HCD[,-1], f=HCD[,1]) est # Table 2: cmat<-rbind( "lower-middle"=c(1,-1,0), "lower-upper"=c(1, 0,-1), "middle-upper"=c(0,1,-1)) # the point estimates: # cmat crossprod(t(cmat), est$estimate) # the standard errors: # sqrt(diag(cmat sqrt(diag(crossprod(t(cmat), crossprod(diag(est$varest), t(cmat)) ) )) # Table 3: cmat<-rbind( "middle-lower"=c(-1,1,0), "upper-lower"=c(-1,0,1)) # cmat crossprod(t(cmat), est$estimate) # sqrt(diag(cmat sqrt(diag(crossprod(t(cmat), crossprod(diag(est$varest), t(cmat)) ) )) # Note, that the point estimates are exactly # the same as in Rogers and Hsu (2001), # but the variance estimates are not, whenever # the Upper group is involved.