dplot: dplot

In Prize: Prize: an R package for prioritization estimation based on analytic hierarchy process

Description

Computing and plotting the distance between individuals and group judgement. Distances are computed using classical multidimensional scaling (MDS) approach.

Usage

dplot(srcfile, fontsize = 15, xcex = 10, ycex = 10, lcex = 5,
  hjust = 0.5, vjust = 1, xlab = "Coordinate 1", ylab = "Coordinate 2",
  main = NULL)

Arguments

srcfile	a numeric matrix of individual and group priorities.
fontsize	the font size of the plot title, and x and y axis labels. The default value is 15.
xcex,ycex	the font size of the x and y axis, respectively. The default values is 10.
lcex	the font size of point labels in dplot
hjust,vjust	the horizontal and vertical justification of point labels, respectively.
xlab,ylab	the label of the x and y axis, respectively.
main	the plot title

Value

An object created by 'ggplot'.

Author(s)

Daryanaz Dargahi

References

J.C. Gower. Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika, 53(3/4):pp. 325-338, 1966.

Examples

mat <- matrix(nrow = 5, ncol = 4, data = NA)
rownames(mat) <- c('Ind1','Ind2','Ind3', 'Ind4' ,'Group judgement')
colnames(mat) <- c('Tumor_expression','Normal_expression','Frequency','Epitopes')
mat[1,] <- c(0.4915181, 0.3058879, 0.12487821, 0.07771583)
mat[2,] <- c(0.3060687, 0.4949012, 0.12868606, 0.07034399)
mat[3,] <- c(0.4627138, 0.3271881, 0.13574662, 0.07435149)
mat[4,] <- c(0.6208484, 0.2414021, 0.07368481, 0.06406465)
mat[5,] <- c(0.4697298, 0.3406738, 0.11600194, 0.07359445)

dplot(mat, xlab = 'Coordinate 1', ylab = 'Coordinate 2', main = 'Distance plot')

Example output

Prize documentation built on April 28, 2020, 7:48 p.m.