fixLMtps | R Documentation |

Missing landmarks are estimated by deforming a sample average or a weighted estimate of the configurations most similar onto the deficient configuration. The deformation is performed by a Thin-plate-spline interpolation calculated by the available landmarks.

fixLMtps(data, comp = 3, weight = TRUE, weightfun = NULL)

`data` |
array containing landmark data |

`comp` |
integer: select how many of the closest observations are to be taken to calculate an initial estimate. |

`weight` |
logical: requests the calculation of an estimate based on the procrustes distance. Otherwise the sample's consensus is used as reference. |

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

This function tries to estimate missing landmark data by mapping weighted
averages from complete datasets onto the missing specimen. The weights are
the inverted Procrustes (see `proc.weight`

) distances between
the 'comp' closest specimen (using the available landmark configuration).

`out ` |
array containing all data, including fixed configurations - same order as input |

`mshape ` |
meanshape - calculated from complete datasets |

`checklist ` |
list containing information about missing landmarks |

`check ` |
vector containing position of observations in data where at least one missing coordinate was found |

Be aware that these estimates might be grossly wrong when the missing landmark is quite far off the rest of the landmarks (due to the radial basis function used in the Thin-plate spline interpolation.

Stefan Schlager

Bookstein FL. 1989. Principal Warps: Thin-plate splines and the decomposition of deformations IEEE Transactions on pattern analysis and machine intelligence 11.

`proc.weight`

, `tps3d`

if (require(shapes)) { data <- gorf.dat ### set first landmark of first specimen to NA data[1,,1] <- NA repair <- fixLMtps(data,comp=5) ### view difference between estimated and actual landmark plot(repair$out[,,1],asp=1,pch=21,cex=0.7,col=2)#estimated landmark points(gorf.dat[,,1],col=3,pch=20)#actual landmark } ## 3D-example: data(boneData) data <- boneLM ### set first and 5th landmark of first specimen to NA data[c(1,5),,1] <- NA repair <- fixLMtps(data,comp=10) ## view difference between estimated and actual landmark ## Not run: deformGrid3d(repair$out[,,1], boneLM[,,1],ngrid=0) ## End(Not run) ## Now use a gaussian kernel to compute the weights and use all other configs gaussWeight <- function(r,sigma=0.05) { sigma <- 2*sigma^2 return(exp(-r^2/ sigma)) } repair <- fixLMtps(data,comp=79,weightfun=gaussWeight)

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