Morpho (version 2.12)

proc.weight: calculate weights inverse to the distances from the specified observation.

Description

for calculation of a shape model by averaging the observations neighbouring the configuration in question, it is necessary to calculate weights by similarity.

Usage

proc.weight(
  data,
  number,
  ref,
  report = TRUE,
  reg = 0,
  log = FALSE,
  mahalanobis = FALSE,
  weightfun = NULL
)

Value

data

dataframe containing id, procrustes/mahalanobis distance and weight according to the reference

reference

returns observations' names if available

rho.all

dataframe containing distances to references of all observations

Arguments

data

array containing landmark configurations

number

integer: how many of the neighbours are to be involved.

ref

integer: position in the array that is used as reference.

report

logical: require report about name of the reference.

reg

numeric: regularise mahalanobis distance by adding reg to the diagonal of eigenvalues of the covariance matrix.

log

logical: use the logarithm of the distances.

mahalanobis

logical: use mahalanobis distance.

weightfun

custom function that operates on a vector of distances (see examples) and generates weights accordingly.

Details

distances of zero will get a weight of 1e12 (this is scaled to all weights summing to one), thus weights for observations further away are converging to zero.

Examples

if (require(shapes)) {
proc <- procSym(gorf.dat)
##get weights for the the four specimen closest to the first observation.
weights <- proc.weight(proc$rotated,4,1)

##estimate the first specimen by weighted neighbour shapes.
estim <- proc$mshape*0;
for (i in 1:4)
{estim <-estim+proc$rotated[,,weights$data$nr[i]]*weights$data$weight[i]}

### visualise
plot(estim,asp=1)## show estimation
points(proc$rotated[,,1],col=3)##show original

## use a gaussian smoother to compute weights using a bandwidth of 0.05
gaussWeight <- function(r,sigma=0.05) {
   sigma <- 2*sigma^2
   return(exp(-r^2/ sigma))
}
weights <- proc.weight(proc$rotated,4,1,weightfun=gaussWeight)
}