noamross

proftable <- function(file) {
  require(plyr)
  sample.interval <- as.numeric(strsplit(readLines(file, 1), "=")[[1L]][2L])/1e+06
  profdata <- as.matrix(read.table(file, header=FALSE, sep=" ", colClasses="character", skip=1, fill=TRUE, na.strings=""))
  total.time <- nrow(profdata)*sample.interval
  stacktable <- data.frame(table(aaply(profdata, 1, function(x) paste(rev(na.omit(x)), collapse=" > "))))
  names(stacktable) <- c("Stack","PctTime")
  stacktable$PctTime <- 100*stacktable$PctTime/nrow(profdata)
  stacktable <- stacktable[order(stacktable$PctTime, decreasing=TRUE), c("PctTime", "Stack")]
  rownames(stacktable) <- NULL

noamross

#' Use the data editor for a CSV file
#' 
#' This function loads a CSV file, lets the user edit it in the native data
#' editor, then re-saves it, prompting the user for a new name if desired.
#' 
fix.csv <- function(file, new.name=TRUE, sep=",", comment.char="") {
  tmpframe <- read.csv(file, sep=sep,quote="", colClasses="character",
                       stringsAsFactors=FALSE, comment.char="",
                       blank.lines.skip=FALSE, na.strings="")
  tmpframe <- edit(tmpframe)

noamross

#' Use the data editor for a CSV file
#' 
#' This function loads a CSV file, lets the user edit it in the native data
#' editor, then re-saves it, prompting the user for a new file name if desired.
#' 
fix.csv <- function(file, new.name=TRUE, sep=",", comment.char="") {
  tmpframe <- read.csv(file, sep=sep,quote="", colClasses="character",
                       stringsAsFactors=FALSE, comment.char="",
                       blank.lines.skip=FALSE, na.strings="")
  tmpframe <- edit(tmpframe)

noamross

A discrete time, age-structured model of a salmon population (semelparous) that can live to age 5, with fishing and environmental stochasticity (by Lauren Yamane, presented to UC Davis R Users' Group)
========================================================

Parameter values
```{r parameters, comment="#"}
a=60 #alpha for Beverton-Holt stock-recruitment curve
b=0.00017 #beta for Beverton-Holt
tf=2000 #number of time steps
N0=c(100,0,0,0,0) #initial population vector for 5 age classes
s=0.28 #survival rate with fishing

noamross

## Some examples using the package plyr

library(plyr)

## Example dataset from ggplot

library(ggplot2)
data(mpg)
str(mpg)

noamross

Noam Ross
---------

### 13-01-22 14:50:15

Abstract
========

Forest disease spreads through plant communities structured by species composition, age distribution, and spatial arrangement. I propose to examine the consequences of the interaction of these components of population structure on a model systems of *Phytophthora ramorum* invasion in California redwood forests. First, I will compare the dynamic behavior of a series of epidemiological models that include different configurations of population structures. Then I will fit these models to time-series data from a network of disease monitoring plots to determine what components of forest population structure are most important for prediction of disease spread. Using the most parsimoniuous models, I will determine optimal schedules of treatment to minimize the probability of disease outbreak.

noamross

noamross

noamross

noamross

"","X","Lake.ID","Name","treatment","shorline","sizehectars","elevation","UTM.zone","northing","easting","BUBO.breeding","BUBO.breeding2","PSRE.breeding.Y.N","RACA.y.n","veg.extent","X..of.transects.with.veg","bank.slope","silt.0.1.","silt.total","X10cm.area","vegetated.area","RACA.1K","lakes.1k","dist.ave","ls","raca.basin","fish.basin","lakes.basin","co.lakes.basin","ls2","woody","herbacious","unveg","total","woodyprop","herbprop","unvegprop","logveg","archerb","arcsilt"
"1",1,531,"Mill Creek",0,377.96,0.713,2008.9368,10,518882,4565085,"y",1,"n","y",0.691,0.682,0.308095216,0.6818,0.71212,111.5,261.17,3,4,0.5538,0.75,3,0,3,0,1,6082255.229,54367.17182,1399749.194,7536371.595,0.807053521,0.007213972,0.185732508,2.41692328905509,0.0850375574910141,1.00445938904933
"2",2,533,"East Boulder",1,1356.4,11.4,2034.8448,10,518069,4564441,"y",1,"y","y",2.28,0.292,0.092475736,0.376,0.3472,980.56,3092.59,5,10,0.8268,0.5,4,4,8,2,0.5,5699686.549,1302747.189,1470012.242,8472445.979,0.67273212,0.15376282,0.173505059,3.4903223