R/spectR.R

Defines functions spectR

Documented in spectR

#library(RPANDA)
#library(igraph)

spectR<-function (phylo, method = c("standard")) 
			{
#define skewness function				
skewness <- function(x, na.rm = FALSE) {
        if (is.matrix(x)) 
            apply(x, 2, skewness, na.rm = na.rm)
        else if (is.vector(x)) {
            if (na.rm) 
                x <- x[!is.na(x)]
            n <- length(x)
            (sum((x - mean(x))^3)/n)/(sum((x - mean(x))^2)/n)^(3/2)
        }
        else if (is.data.frame(x)) 
            sapply(x, skewness, na.rm = na.rm)
        else skewness(as.vector(x), na.rm = na.rm)
    }

#define density function
##gaussian kernel
sigma = 0.1
gKernel <- function(x) 1/(sigma*sqrt(2*pi)) * exp(-(x^2)/2*sigma^2)
kernelG <- function(x, mean=0, sd=1) dnorm(x, mean = mean, sd = sd)

##kernel density estimate
dens <- function(x, bw = bw.nrd0, kernel = kernelG, n = 4096,
                from = min(x) - 3*sd, to = max(x) + 3*sd, adjust = 1,
                ...) {
  if(has.na <- any(is.na(x))) {
    x <- na.omit(x)
    if(length(x) == 0)
        stop("no finite or non-missing data!")
  }
  sd <- (if(is.numeric(bw)) bw[1] else bw(x)) * adjust
  X <- seq(from, to, len = n)
  M <- outer(X, x, kernel, sd = sd, ...)
  structure(list(x = X, y = rowMeans(M), bw = sd,
                 call = match.call(), n = length(x),
                 data.name = deparse(substitute(x)),
                 has.na = has.na), class =  "density")
}

#define integral function
integr <- function(x, f)
{
       
       # var is numeric
       if (!is.numeric(x))
       {
              stop('The variable of integration "x" is not numeric.')
       }

       # integrand is numeric
       if (!is.numeric(f))
       {
              stop('The integrand "f" is not numeric.')
       }

       # length(var)=length(int)
       if (length(x) != length(f))
       {
              stop('The lengths of the variable of integration and the integrand do not match.')
       }

      # get lengths of var and integrand
       n = length(x)

       # trapezoidal integration
       integral = 0.5*sum((x[2:n] - x[1:(n-1)]) * (f[2:n] + f[1:(n-1)]))

       # print definite integral
       return(integral)
}
 if (method == "standard") {
e = eigen(graph.laplacian(graph.adjacency(data.matrix(dist.nodes(phylo)), 
            weighted = T), normalized = F), symmetric = T, only.values = T)
	x = subset(e$values, e$values >= 1)
	d = dens(log(x))
	dsc = d$y/(integr(d$x,d$y))
	principal_eigenvalue <- max(x)
	skewness <- skewness(dsc)
    peak_height <- max(dsc)
	gaps<-abs(diff(x))
        gapMat <- as.matrix(gaps)
        modalities <- c(1:length(gapMat))
        gapMatCol <- cbind(modalities, gapMat)
        eigenGap <- subset(gapMatCol, gapMatCol[, 2] == max(gapMatCol[,2]))
	res<-list(eigenvalues=x,principal_eigenvalue=principal_eigenvalue, 
            asymmetry=skewness, peakedness=peak_height,eigengap= eigenGap[,1])   
	}
 if (method == "normal") {
e = eigen(graph.laplacian(graph.adjacency(data.matrix(dist.nodes(phylo)), 
            weighted = T), normalized = T), symmetric = T, only.values = T)
	x = subset(e$values, e$values >= 0)
	d = dens(log(x))
	dsc = d$y/(integr(d$x,d$y))
	principal_eigenvalue <- max(x)
	skewness <- skewness(dsc)
    peak_height <- max(dsc)
	gaps <- abs(diff(x))
        gapMat <- as.matrix(gaps)
        modalities <- c(1:length(gapMat))
        gapMatCol <- cbind(modalities, gapMat)
        eigenGap <- subset(gapMatCol, gapMatCol[, 2] == max(gapMatCol[,2]))
	res<-list(eigenvalues=x,principal_eigenvalue=principal_eigenvalue, 
            asymmetry=skewness, peakedness=peak_height,eigengap= eigenGap[,1])    
		}
	class(res) <- "spectR"
    return(res)
}    	
hmorlon/PANDA documentation built on Aug. 13, 2018, 4:48 a.m.