# lda.r
> #
> # Author: Amsha Nahid, Jairus Bowne, Gerard Murray
> # Purpose: Perform Linear Discriminant Analysis (LDA)
> #
> # Input: Data matrix as specified in Data-matrix-format.pdf
> # Output: LDA plot
> #
> # Notes: Missing values (if any) are replaced by the half of the lowest
> # value in the entire data matrix.
>
>
> #
> # Load necessary libraries, and install them if they are missing
> #
> tryCatch(library(MASS), error=function(err)
+ # if this produces an error:
+ install.packages("MASS",repos="http://cran.ms.unimelb.edu.au/";))
>
> #
> # Prepare the data matrix
> #
> # Read in the .csv file
> data<-read.csv("C:/Users/nadya/Desktop/praktikal UTM/TASK2/new(40data)S2 100
>EMS EPI 300-399.csv", sep=",", row.names=1, header=TRUE)
> # Get groups information
> groups<-data[,1]
> # Remove groups for data processing
> lda_data<-data[,-1]
> # Replace any missing values (see Notes)
> lda_data[is.na(lda_data)]<-0.5*(min(lda_data,na.rm=TRUE))
>
> #
> # Perform the LDA
> #
> lda_result<-lda(lda_data,groups)
Error in lda.default(x, grouping, ...) :
variables 1 3 5 8 10 15 17 20 27 29 34 appear to be constant within groups
>
> #
> # Generate the figures (on screen)
> #
> # Image generation - function definition
> pic_onscr<-function(matrix, title, cex_val=1)
+ {x11()
+ par(mgp=c(5,2,0), # axis margins
+ # (title, labels, line)
+ mar=c(7,4,4,2), # plot margins (b,l,t,r)
+ las=1) # horizontal labels
+ plot(matrix, # data to plot
+ cex=cex_val, # font size
+ dimen=2 # dimensions to plot
+ )
+ title(main=title) # title of plot
+ }
> # Plot LDA scores with sample names
> pic_onscr(lda_result,"Linear Discriminant Analysis")
Error in plot(matrix, cex = cex_val, dimen = 2) :
error in evaluating the argument 'x' in selecting a method for function
'plot': Error: object 'lda_result' not found
> # For plotting with larger font size, use a different value of cex:
> # pic_onscr(lda_result, "LDA Plot", dimen=2, cex=3)
>
> #
> # Generate figures as image files
> #
> # (Uncomment blocks as necessary)
>
> ##### jpeg #####
> # pic_jpg<-function(filename, matrix, title, cex_val=1)
> # {# Start jpeg device with basic settings
> # jpeg(filename,
> # quality=100, # image quality (percent)
> # bg="white", # background colour
> # res=300, # image resolution (dpi)
> # units="in", width=8.3, height=5.8 # image dimensions (inches)
> # )
> # par(mgp=c(5,2,0), # axis margins
> # # (title, labels, line)
> # mar=c(7,4,4,2), # plot margins (b,l,t,r)
> # las=1 # horizontal labels
> # )
> # # Draw the plot
> # plot(matrix, # data to plot
> # cex=cex_val, # font size
> # dimen=2 # dimensions to plot
> # )
> # title(main=title) # title of plot
> #
> # dev.off()
> # }
> # pic_jpg("LDA.jpg", lda_result, "Linear Discriminant Analysis")
> ##### end jpeg #####
>
> ##### png #####
> # pic_png<-function(filename, matrix, title, cex_val=1)
> # {# Start png device with basic settings
> # png(filename,
> # bg="white", # background colour
> # res=300, # image resolution (dpi)
> # units="in", width=8.3, height=5.8 # image dimensions (inches)
> # )
> # par(mgp=c(5,2,0), # axis margins
> # # (title, labels, line)
> # mar=c(7,4,4,2), # plot margins (b,l,t,r)
> # las=1 # horizontal labels
> # )
> # # Draw the plot
> # plot(matrix, # data to plot
> # cex=cex_val, # font size
> # dimen=2 # dimensions to plot
> # )
> # title(main=title) # title of plot
> #
> # dev.off()
> # }
> # pic_png("LDA.png", lda_result, "Linear Discriminant Analysis")
> ##### end png #####
>
> ##### tiff #####
> # pic_tiff<-function(filename, matrix, title, cex_val=1)
> # {# Start tiff device with basic settings
> # tiff(filename,
> # bg="white", # background colour
> # res=300, # image resolution (dpi)
> # units="in", width=8.3, height=5.8, # image dimensions (inches)
> # compression="none" # image compression
> # # (one of none, lzw, zip)
> # )
> # par(mgp=c(5,2,0), # axis margins
> # # (title, labels, line)
> # mar=c(7,4,4,2), # plot margins (b,l,t,r)
> # las=1 # horizontal labels
> # )
> # # Draw the plot
> # plot(matrix, # data to plot
> # cex=cex_val, # font size
> # dimen=2 # dimensions to plot
> # )
> # title(main=title) # title of plot
> #
> # dev.off()
> # }
> # pic_tiff("LDA.tif", lda_result, "Linear Discriminant Analysis")
> ##### end tiff #####
>
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