?cca #(constrained correspondence analysis)
# Constrained Correspondence Analysis (CCA) is a canonical ordination method similar to RDA that preserve
# Chi-square distances among object (instead of Euclidean distances in RDA). This method is well suited for the
# analysis of large ecological gradients.
 
 
 
?CCorA # Canonical Correlation Analysis
 
# Canonical Correlation Analysis (CCorA) differs from RDA given that the two matrices are considered symmetric 
# while in RDA the Y matrix is dependent on the X matrix. The main use of this technique is to test the 
# significance of the correlation between two multidimensional data sets, then explore the structure of the data by 
# computing the correlations (which are the square roots of the CCorA eigenvalues) that can be found between 
# linear functions of two groups of descriptors.
 
 
help(coinertia, package=ade4) # Coinertia Analysis 
 
#Coinertia Analysis (CoIA) is a symmetric canonical ordination method that is appropriate to compare pairs 
# of data sets that play equivalent roles in the analysis. The method finds a common space onto which the objects 
# and variables of these data sets can be projected and compared. Compared to CCorA, co-inertia analysis
# imposes no constraint regarding the number of variables in the two sets, so that it can be used to compare
# ecological communities even when they are species-rich. Co-inertia analysis is not well-suited, however, to 
# analyse pairs of data sets that contain the same variables, because the analysis does not establish one-to-one 
# correspondences between variables in the two data sets; the method does not ‘know’ that the first variable is the 
# same in the first and the second data sets, and likewise for the other variables.
 
 
help(mfa, package=ade4) # Multiple Factorial Analysis
 
# Multiple factor analysis (MFA) can be used to compare several data sets describing the same objects. MFA 
# consists in projecting objects and variables of two or more data sets on a global PCA, computed from all data 
# sets, in which the sets receive equal weights.
 
 
# Spatial analysis can be performed using packages AEM and PCNM : http://r-forge.r-project.org/R/?group_id=195