typprob: calculate typicality probabilities

In Morpho: Calculations and Visualisations Related to Geometric Morphometrics

calculate typicality probabilities

Description

calculate typicality probabilities

Usage

typprob(
  x,
  data,
  small = FALSE,
  method = c("chisquare", "wilson"),
  center = NULL,
  cova = NULL,
  robust = c("classical", "mve", "mcd"),
  ...
)

typprobClass(
  x,
  data,
  groups,
  small = FALSE,
  method = c("chisquare", "wilson"),
  outlier = 0.01,
  sep = FALSE,
  cv = TRUE,
  robust = c("classical", "mve", "mcd"),
  ...
)

Arguments

x	vector or matrix of data the probability is to be calculated.
data	Reference data set. If missing x will be used.
small	adjustion of Mahalanobis D^2 for small sample sizes as suggested by Wilson (1981), only takes effect when method="wilson".
method	select method for probability estimation. Available options are "chisquare" (or any abbreviation) or "wilson". "chisquare" exploits simply the chisquare distribution of the mahalanobisdistance, while "wilson" uses the methods suggested by Wilson(1981). Results will be similar in general.
center	vector: specify custom vector to calculate distance to. If not defined, group mean will be used.
cova	covariance matrix to calculate mahalanobis-distance: specify custom covariance matrix to calculate distance.
robust	character: determines covariance estimation methods, allowing for robust estimations using MASS::cov.rob. Default is the standard product-moment covariance matrix.
...	additional parameters passed to MASS::cov.rob for robust covariance and mean estimations.
groups	vector containing grouping information.
outlier	probability threshold below which a specimen will not be assigned to any group-
sep	logical: if TRUE, probability will be calculated from the pooled within group covariance matrix.
cv	logical: if data is missing and cv=TRUE, the resulting classification will be validated by leaving-one-out crossvalidation.

Details

get the probability for an observation belonging to a given multivariate nromal distribution

Value

typprob: returns a vector of probabilities.

typprobClass:

probs	matrix of probabilities for each group
groupaffin	vector of groups each specimen has been assigned to. Outliers are classified "none"
probsCV	cross-validated matrix of probabilities for each group
groupaffinCV	cross-validated vector of groups each specimen has been assigned to. Outliers are classified "none"
self	logical: if TRUE, the data has been classified by self-inference.

Author(s)

Stefan Schlager

References

Albrecht G. 1992. Assessing the affinities of fossils using canonical variates and generalized distances Human Evolution 7:49-69.

Wilson S. 1981. On comparing fossil specimens with population samples Journal of Human Evolution 10:207 - 214.

Examples

if (require(shapes)) {
data <- procSym(gorf.dat)$PCscores[,1:3]
probas <- typprob(data,data,small=TRUE)### get probability for each specimen

### now we check how this behaves compared to the mahalanobis distance
maha <- mahalanobis(data,colMeans(data),cov(data))
plot(probas,maha,xlab="Probability",ylab="Mahalanobis D^2")

data2 <- procSym(abind(gorf.dat,gorm.dat))$PCscores[,1:3]
fac <- as.factor(c(rep("female",dim(gorf.dat)[3]),rep("male",dim(gorm.dat)[3])))
typClass <- typprobClass(data2,groups=fac,method="w",small=TRUE,cv=TRUE)
## only 59 specimen is rather small.
typClass2 <- typprobClass(data2,groups=fac,method="c",cv=TRUE)## use default settings

### check results for first method:
typClass


### check results for second method:
typClass2
}

Morpho documentation built on June 22, 2024, 7:19 p.m.