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sim.mruns <- function(model, file, param, pdfout=F, lab.out="") {
# multiple runs
# required: model as a list
# path/name of input file
# param set for multiple runs
# optional: switch to produce pdf files, default F
# label to identify output files, default none
# read input, file format is csv
dat <- c("character","numeric","character","character")
input <- read.csv(file,header=T,colClasses=dat)
# input values and labels
v <- t(input$Val); vl <- t(input$Lab)
# t0,tf,dt,tw are in v[1:4]
t <- seq(v[1],v[2],v[4]); dt <- v[3]
digX <- v[7]
# units ind and dep vars
unit <- c(input$Uni[1], input$Uni[6])
# number of time stamps and param values
nt <- length(t); np <- length(param$pval)
# array to store results
X <- matrix(ncol=np,nrow=nt)
# loop for parameter p
for(i in 1:np) {
# pick a value of X0 or p from param
for(j in 5:6) if(param$plab==vl[j]) v[j]<- param$pval[i]
# p,X0, in v[5:6]
p <- v[5]; X0 <- v[6]
# integration
X[,i] <- RK4(X0, t, model$f, p, dt)
} # end of parameter p loop
# prep and organize output
X <- signif(X,digX); output <- data.frame(t,X)
tXlab <- paste(c("t","X"),"[",unit,"]", sep="")
var.run <- c(tXlab[1], paste(tXlab[2],paste(".Run",c(1:np),sep=""),sep=""))
names(output) <- var.run
# call output function to generate output files
mruns.out(prefix=file, lab.out=param$plab, input, param, output, tXlab, pdfout)
return(list(input=input, param=param, output=output))
} # end of function
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