Management and exploratory analysis of spatial data in landscape genetics. Easy integration of information from multiple sources with "ecogen" objects.
| Package: | EcoGenetics |
| Type: | Package |
| Version: | 1.2.1-1 |
| Date: | 2017-11-14 |
| License: | GPL (>=2) |
----------------------- o-----------------------
I. STRUCTURE OF THE PACKAGE
EcoGenetics has four basic modules. The base module is composed by general functions (multiple lm, detrending spatial data utility, etc.). The general spatial module computes global (Moran's I, Mantel test, etc.) and local (Getis-Ord's G*, local Moran's I, etc.) spatial tests. These analyses use a spatial weights matrix, provided by the spatial weights module. The lag analysis module performs two basic analyses: the obtention of variograms and correlograms (see also this link). This module uses the other tool provided by the spatial weights module: a sequence of spatial weights matrices.
The package have also special plot methods, as rankplot, forestplot, rasterplot (implemented the last two for local spatial analysis). Several conversor of data from/to other programs are available, (as to genepop - an importer tool is also defined for genepop-, SPAGeDi, etc.). Basic manipulation of genetic matrices is allowed by eco.convert and eco.format. Tools for computation of NDVI in Landsat imaginery, post-process of rasters and temporal analysis can be found in eco.NDVI, eco.NDVI.post and eco.theilsen. Other useful functions are aue.sort (for ordering alleles), eco.alfreq (to comput histograms of allelic frequencies for detection of bottlenecks), eco.post.geneland and eco.pairtest.
The results obtained with the main functions defined in EcoGenetics are object of class S4. As a default characteristic of the package design, these objects have a "show"" method for a general overview of the results, and methods to extract the results stored in slots (generic accessors and double square brackets ("[[") definitions).
For storing and pre-processing the data to analyze, the package
defines a special class: the class ecogen.
II. STRUCTURE OF ECOGEN OBJECTS: HANDLING AND INTEGRATING INFORMATION
Landscape genetics research requires the integration of data originated in different sources. The class ecogen has been designed for handling multidimensional data. Its basic structure is the following:
- An XY slot, storing a data frame with geographic coordinates.
- A P slot, storing a phenotypic data frame.
- A G slot, storing a genotypic data frame.
- An A slot containing as allelic frequencies the information of G (only available for codominant markers.)
- An E slot, storing an environmental data frame.
- A S slot, storing a data frame with classes assigned to the individuals.
- A C slot, for a custom data frame.
- An OUT slot, containing a list for the storage of the results.
For dominant (presence-absence) markers, the slot A is empty.
III. A BRIEF OVERVIEW OF THE DATA HANDLING METHODS DEFINED FOR ECOGEN OBJECTS
The construction of a new "ecogen" object from a data frame is made with the homonymous function. library("EcoGenetics") data(eco.test) eco <- ecogen(XY = coordinates, P = phenotype, G = genotype, E = environment, S = structure, order.G = TRUE)
# The following methods can be used with ecogen objects:
# - - - - - - # 1. subsetting/ordering by row-method, using single square brackets ("[") eco.sub <- eco[1:50] eco.ord <- eco[c(50:1)] eco.ord[["XY"]] # object ordered and subset rows. This is a powerful method for handling the stack of ecogen data frames eco <- eco[order(eco[["S"]][, 1])] #ordering S[, 1] and then the object # 2. merging-method, for two objects eco1 <- eco merged <- eco.merge(eco, eco1) # 3. subsetting-method, in reference to a group in the S slot (in this case, "1") eco.subS <- eco.subset(eco,"pop", 1)
# 4. binding by row-method (duplicated row names not allowed) eco2 <- eco; rownames(eco2[["P"]]) <-226:450 eco.r <- eco.rbind(eco, eco2) # 5. binding by column-method eco.c <- eco.cbind(eco, eco1)
# 6. get-method using generic accessors and "[[" (equivalent methods): ecoslot.XY(eco) ; eco[["XY"]] ecoslot.P(eco); eco[["P"]] ecoslot.G(eco); eco[["G"]] ecoslot.A(eco); eco[["A"]] (only available for codominant data ecoslot.E(eco); eco[["E"]] ecoslot.S(eco); eco[["S"]] ecoslot.C(eco); eco[["C"]] ecoslot.OUT(eco); eco[["OUT"]]
# 7. set-method using accessors and "[[" (equivalent methods): eco.temp <- ecogen(XY = coordinates, P = phenotype) eco.temp ecoslot.G(eco.temp, order.G = TRUE) <- genotype
## this is equivalent, in square brackets notation, to: eco[["G", order.G=TRUE]] <- genotype ecoslot.E(eco.temp) <- environment
## identical to eco[["E"]] <- environment ecoslot.S(eco.temp) <- structure
## identical to eco[["S"]] <- structure eco.temp # 8. appending-method (storing information generated via accessors)
## fitting a multiple linear regression model: linear.analysis <- eco.lmtree(eco[["P"]], eco[["E"]], "mlm")
## storing the results: ecoslot.OUT(eco) <- linear.analysis eco
## Storing multiple result at once: a <- c(1:10) b <- c(2:30) ecoslot.OUT(eco) <- list(eco, a, b) eco
## the use of the accessor OUT has its equivalence in double square brackets notation: eco[["OUT"]] <- list(eco, a, b)
## summary table ecoslot.OUT(eco)
## the data frame shows the results stored in alphabetical order and their classes. The specification of a second name, return the corresponding stored object: ecoslot.OUT(eco, "a")
## note that the append method is a particular case of the ## set method with accessors / "[[" for the slot OUT. # 9. removing-method
## removing objects a and b from eco eco <- eco.remove(eco, a, b)
- - - - - -
The workspace can be cleared, only storing the desired object: ls() eco.clear(eco) ls()
The cleaner and compact option, is to save the object as RDS, which is a way to save R objects as a file: saveRDS(eco, "myObject.rds")
The object can be loaded in the future with the function readRDS: eco <- readRDS("myObject.rds")
----------------------- o -----------------------
Anselin L. 1995. Local indicators of spatial association-LISA. Geographical analysis. 27: 93-115.
Borcard D., F. Gillet, and P. Legendre. 2011. Numerical ecology with R. Springer Science & Business Media.
Chander G., B. Markham, and D. Helder. 2009. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote sensing of environment, 113: 893-903.
Chavez P. 1989. Radiometric calibration of Landsat Thematic Mapper multispectral images. Photogrammetric Engineering and Remote Sensing, 55: 1285-1294.
Chavez P. 1996. Image-based atmospheric corrections-revisited and improved. Photogrammetric engineering and remote sensing, 62: 1025-1035.
Double M., R. Peakall, N. Beck, and Y. Cockburn. 2005. Dispersal, philopatry, and infidelity: dissecting local genetic structure in superb fairy-wrens (Malurs cyaneus). Evolution 59: 625-635.
Dray S., and A., Dufour. 2007. The ade4 package: implementing the duality diagram for ecologists. Journal of statistical software, 22: 1-20.
Freedman D., and P. Diaconis. 1981. On the histogram as a density estimator: L 2 theory. Probability theory and related fields, 57: 453-476.
Geary R. 1954. The contiguity ratio and statistical mapping. The incorporated statistician, 115-146.
Getis A., and J. Ord. 1992. The analysis of spatial association by use of distance statistics. Geographical analysis, 24: 189-206.
Goslee S. 2011. Analyzing remote sensing data in R: the landsat package. Journal of Statistical Software, 43: 1-25.
Goudet J. 2005. Hierfstat, a package for R to compute and test hierarchical F-statistics. Molecular Ecology Notes, 5: 184-186.
Guillot G., F. Mortier and A. Estoup. 2005. GENELAND: a computer package for landscape genetics. Molecular Ecology Notes, 5: 712-715.
Jombart T. 2008. adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics, 24: 1403-1405.
Kalisz S., J. Nason, F.M. Handazawa, and S. Tonsor. 2001. Spatial population genetic structure in Trillium grandiflorum: the roles of dispersal, mating, history, and selection. Evolution 55: 1560-1568.
Legendre P., and L. Legendre. 2012. Numerical ecology. Third English edition .Elsevier Science, Amsterdam, Netherlands.
Lichstein J., T. Simons, S. Shriner, and K. Franzreb. 2002. Spatial autocorrelation and autoregressive models in ecology. Ecological monographs, 72: 445-463.
Loiselle B., V. Sork, J. Nason, and C. Graham. 1995. Spatial genetic structure of a tropical understory shrub, Psychotria officinalis (Rubiaceae). American Journal of Botany 1420-1425.
Moran P. 1950. Notes on continuous stochastic phenomena. Biometrika, 17-23.
Oden, N., 1984. Assessing the significance of a spatial correlogram. Geographical Analysis, 16: 1-16.
Oden N., and R. Sokal. 1986. Directional autocorrelation: an extension of spatial correlograms to two dimensions. Systematic Zoology, 35: 608-617.
Ord J., and A. Getis. 1995. Local spatial autocorrelation statistics: distributional issues and an application. Geographical analysis, 27: 286-306.
Reich R., R. Czaplewski and W. Bechtold. 1994. Spatial cross-correlation of undisturbed, natural shortleaf pine stands in northern Georgia. Environmental and Ecological Statistics, 1: 201-217.
Sokal R., and N. Oden 1978. Spatial autocorrelation in biology: 1. Methodology. Biological journal of the Linnean Society, 10: 199-228.
Sokal R., and N. Oden. 1978. Spatial autocorrelation in biology. 2. Some biological implications and four applications of evolutionary and ecological interest. Biological Journal of the Linnean Society, 10: 229-49.
Sokal R. 1979. Ecological parameters inferred from spatial correlograms. In: G. Patil and M. Rosenzweig, editors. Contemporary Quantitative Ecology and elated Ecometrics. International Co-operative Publishing House: Fairland, MD, pp. 167-96.
Sokal R. 1986. Spatial data analysis and historical processes. In: E. Diday, Y. Escoufier, L. Lebart, J. Pages, Y. Schektman, and R. Tomassone, editors. Data analysis and informatics, IV. North-Holland, Amsterdam, The Netherlands, pp. 29-43.
Sokal R., N. Oden and B. Thomson. 1998. Local spatial autocorrelation in a biological model. Geographical Analysis, 30: 331-354.
Sokal R., and B. Thomson. 2006. Population structure inferred by local spatial autocorrelation: an example from an Amerindian tribal population. American journal of physical anthropology, 129: 121-131.
Song C., C. Woodcock, K. Seto, M. Lenney and S. Macomber. 2001. Classification and change detection using Landsat TM data: when and how to correct atmospheric effects?. Remote sensing of Environment, 75: 230-244.
Sturges H. 1926. The choice of a class interval. Journal of the American Statistical Association, 21: 65-66.
Tucker C. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote sensing of Environment, 8: 127-150.
Vekemans, X., and O. Hardy. 2004. New insights from fine-scale spatial genetic structure analyses in plant populations. Molecular Ecology, 13: 921-935.
Wu. 1986. Jackknife, bootstrap and other resampling methods in regression analysis. the Annals of Statistics, 1261-1295.