fawda123 fawda123

plot.nnet<-function(mod.in,nid=T,all.out=T,all.in=T,bias=T,wts.only=F,rel.rsc=5,circle.cex=5,
                    node.labs=T,var.labs=T,x.lab=NULL,y.lab=NULL,line.stag=NULL,struct=NULL,cex.val=1,
                    alpha.val=1,circle.col='lightblue',pos.col='black',neg.col='grey',...){
  
  require(scales)
  
  #sanity checks
  if('mlp' %in% class(mod.in)) warning('Bias layer not applicable for rsnns object')
  if('numeric' %in% class(mod.in)){
    if(is.null(struct)) stop('Three-element vector required for struct')

lek.fun<-function(mod.in,var.sens=NULL,resp.name=NULL,exp.in=NULL,steps=100,split.vals=seq(0,1,by=0.2),val.out=F){
  
  require(ggplot2)
  require(reshape)
  
  ##
  #sort out exp and resp names based on object type of call to mod.in
  #get matrix for exp vars
  
  #for nnet

#credit http://stackoverflow.com/questions/13649473/add-a-common-legend-for-combined-ggplots
 
require(ggplot2)
require(reshape)
require(gridExtra)
 
p.dat<-data.frame(step=row.names(grp.dat),grp.dat,stringsAsFactors=F)
p.dat<-melt(p.dat,id='step')
p.dat$step<-as.numeric(p.dat$step)

plot.area<-function(x,col=NULL,horiz=F,prop=T,stp.ln=T,grp.ln=T,axs.cex=1,axs.lab=T,lab.cex=1,
                    names=c('Group','Step','Value'),...){
  
  #sort out color fector  
  if(!is.null(col)){
    if(sum(col %in% colors()) != length(col)) stop('col vector must be in "colors()"')
    col<-colorRampPalette(col)(ncol(x))
  }
  else col<-colorRampPalette(c('lightblue','green'))(ncol(x))
  

gar.fun<-function(out.var,mod.in,bar.plot=T,x.names=NULL,...){
 
  #gets weights for neural network, output is list
  #if rescaled argument is true, weights are returned but rescaled based on abs value
  nnet.vals<-function(mod.in,nid,rel.rsc,struct.out=struct){
    
    require(scales)
    require(reshape)
    
    if('numeric' %in% class(mod.in)){

shinyServer(function(input, output) {
 
  myData <- reactive({
   read.table(
    input$file$datapath,
    sep='\t',
 	  header=T,
 	  stringsAsFactors=F
    ) 
   })

\documentclass[a4paper,12pt]{article} %document class declaration (article, report, book, etc) has to go first
\usepackage{times} %package imports times new roman font
\usepackage[margin=1in]{geometry} %change margins to 1in
 
\begin{document}
\renewcommand{\rmdefault}{ptm}
 
\setlength{\parindent}{0mm} %set indentation at start of new paragraphs
\setlength{\parskip}{0mm} %set white space between paragraphs

SiteName  Year	Restoration	Reference	ObserverNames	Precipitation	Temperature
IGH	2005	3	3	Tyler_Amanda	0	48
Kelly	2005	4	2	Patrick_Chelsea	0	48
Carlton	2005	2	3	David_Megan	0	48
IGH	2006	9	6	Tyler_Amanda	0	52
Kelly	2006	9	1	David_Megan	0	52
Carlton	2006	7	3	Patrick_Chelsea	0	52
IGH	2007	12	7	David_Megan	12	41
Kelly	2007	2	18	Jeremy_Lucy	12	41
Carlton	2007	11	2	Patrick_Chelsea	12	41

column.int<-function(int.fun='dnorm(x)',num.poly=20,from.x=-1.96,to.x=1.96,plot.cum=F){
	
	if (!require(ggplot2) | !require(scales) | !require(gridExtra)) {
	  stop("This app requires the ggplot2, scales, and gridExtra packages. To install, run 'install.packages(\"name\")'.\n")
	}
 
	if(from.x>to.x) stop('Starting x must be less than ending x')
	
	if(is.character(int.fun)) int.fun<-eval(parse(text=paste('function(x)',int.fun)))
	

if (!require(ggplot2) | !require(scales) | !require(gridExtra) | !require(shiny)) {
  stop("This app requires the shiny, ggplot2, scales, and gridExtra packages. To install, run 'install.packages(\"name\")'.\n")
}
 
# Define server logic required to generate and plot a random distribution
shinyServer(function(input, output) {
 
  output$intPlot <- renderPlot({
    require(ggplot2)
    require(scales)