adegenet (version 1.2-1)

spca: Spatial principal component analysis

Description

These functions are designed to perform a spatial principal component analysis and to display the results. They call upon multispati from the ade4 package. spca performs the spatial component analysis. Other functions are: - print.spca: prints the spca content - summary.spca: gives variance and autocorrelation statistics - plot.spca: usefull graphics (connection network, 3 different representations of map of scores, eigenvalues barplot and decomposition) - screeplot.spca: decomposes spca eigenvalues into variance and autocorrelation

Usage

spca(obj, xy=NULL, cn=NULL, scale=FALSE, scannf=TRUE, nfposi=1, nfnega=1, type=NULL, ask=TRUE,
plot.nb=TRUE, edit.nb=FALSE ,truenames=TRUE, d1=NULL, d2=NULL, k=NULL,
  a=NULL, dmin=NULL)
## S3 method for class 'spca':
print(x, \dots)
## S3 method for class 'spca':
summary(object, \dots, printres=TRUE)
## S3 method for class 'spca':
plot(x, axis = 1, \dots)
## S3 method for class 'spca':
screeplot(x, \dots, main=NULL)

Arguments

obj

a genind or genpop object.

xy

a matrix or data.frame with two columns for x and y coordinates. Seeked from obj$other$xy if it exists when xy is not provided. Can be NULL if a nb object is provided in cn. Longitude/latitude coordinates should be co

cn

a connection network of the class 'nb' (package spdep). Can be NULL if xy is provided. Can be easily obtained using the function chooseCN.

scale

a logical indicating whether alleles should be scaled to unit variance (TRUE) or not (FALSE, default).

scannf

a logical stating whether eigenvalues should be chosen interactively (TRUE, default) or not (FALSE).

nfposi

an integer giving the number of positive eigenvalues retained ('global structures').

nfnega

an integer giving the number of negative eigenvalues retained ('local structures').

type

an integer giving the type of graph (see details in chooseCN help page). If provided, ask is set to FALSE.

ask

a logical stating whether graph should be chosen interactively (TRUE,default) or not (FALSE).

plot.nb

a logical stating whether the resulting graph should be plotted (TRUE, default) or not (FALSE).

edit.nb

a logical stating whether the resulting graph should be edited manually for corrections (TRUE) or not (FALSE, default).

truenames

a logical stating whether true names should be used for 'obj' (TRUE, default) instead of generic labels (FALSE)

d1

the minimum distance between any two neighbours. Used if type=5.

d2

the maximum distance between any two neighbours. Used if type=5.

k

the number of neighbours per point. Used if type=6.

a

the exponent of the inverse distance matrix. Used if type=7.

dmin

the minimum distance between any two distinct points. Used to avoid infinite spatial proximities (defined as the inversed spatial distances). Used if type=7.

x

a spca object.

object

a spca object.

printres

a logical stating whether results should be printed on the screen (TRUE, default) or not (FALSE).

axis

an integer between 1 and (nfposi+nfnega) indicating which axis should be plotted.

main

a title for the screeplot; if NULL, a default one is used.

...

further arguments passed to other methods.

Value

• The class spca are given to lists with the following components:
• eig
  a numeric vector of eigenvalues.
• nfposi
  an integer giving the number of global structures retained.
• nfnega
  an integer giving the number of local structures retained.
• c1
  a data.frame of alleles loadings for each axis.
• li
  a data.frame of row (individuals or populations) coordinates onto the sPCA axes.
• ls
  a data.frame of lag vectors of the row coordinates; useful to clarify maps of global scores .
• as
  a data.frame giving the coordinates of the PCA axes onto the sPCA axes.
• call
  the matched call.
• xy
  a matrix of spatial coordinates.
• lw
  a list of spatial weights of class listw.
• Other functions have different outputs: - summary.spca returns a list with 3 components: Istat giving the null, minimum and maximum Moran's I values; pca gives variance and I statistics for the principal component analysis; spca gives variance and I statistics for the sPCA. - plot.spca returns the matched call. - screeplot.spca returns the matched call.

encoding

UTF-8

Details

The spatial principal component analysis (sPCA) is designed to investigate spatial patterns in the genetic variability. Given multilocus genotypes (individual level) or allelic frequency (population level) and spatial coordinates, it finds individuals (or population) scores maximizing the product of variance and spatial autocorrelation (Moran's I). Large positive and negative eigenvalues correspond to global and local structures.

References

Jombart, T., Devillard, S., Dufour, A.-B. and Pontier, D. Revealing cryptic spatial patterns in genetic variability by a new multivariate method. Submitted to Heredity.

Wartenberg, D. E. (1985) Multivariate spatial correlation: a method for exploratory geographical analysis. Geographical Analysis, 17, 263--283.

Moran, P.A.P. (1948) The interpretation of statistical maps. Journal of the Royal Statistical Society, B 10, 243--251.

Moran, P.A.P. (1950) Notes on continuous stochastic phenomena. Biometrika, 37, 17--23.

de Jong, P. and Sprenger, C. and van Veen, F. (1984) On extreme values of Moran's I and Geary's c. Geographical Analysis, 16, 17--24.

See Also

spcaIllus, a set of simulated data illustrating the sPCA global.rtest and local.rtest chooseCN, multispati, multispati.randtest conUL to convert longitude/latitude to UTM coordinates.

Examples

## data(spcaIllus) illustrates the sPCA
## see ?spcaIllus
##

example(spcaIllus)