similar to: fitting a hyperbole

mac osx 10.5.4 R 2.7.1 I have fit a model d<-lm(y~x) with an R^2 of 0.963 but when I issue the command abline(d) the line is below where it ought to be. Looks like the right slope, but not the right intercept. thanks -- Stephen Sefick Research Scientist Southeastern Natural Sciences Academy Let's not spend our time and resources thinking about things that are so little or so large

I would like to be able to reverse the xlim on qplot this is the code that I am using qplot(a[,"River.Mile"], a[,26] ,ylab=colnames(a)[26], xlab="RiverMile", xlim=rev(c(60, 216)))+geom_smooth()+scale_x_continuous(breaks=c(215,202,198,190,185,179,148,119,61),

Title says it all remember cast() with sum as the aggregation function -- Stephen Sefick Research Scientist Southeastern Natural Sciences Academy Let's not spend our time and resources thinking about things that are so little or so large that all they really do for us is puff us up and make us feel like gods. We are mammals, and have not exhausted the annoying little problems of being

R version 2.8.0 (2008-10-20) i386-pc-mingw32 locale: LC_COLLATE=English_United States.1252;LC_CTYPE=English_United States.1252;LC_MONETARY=English_United States.1252;LC_NUMERIC=C;LC_TIME=English_United States.1252 attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] StreamMetabolism_0.01 chron_2.3-24 zoo_1.5-4 loaded

##This is a function that I am trying to write to calculate sunrise and sunset and works "mostly", but returns nonsensical values. What am I #missing? Thanks in advance. ###remember to include maptools as dependence### library(maptools) sunrise.set <- function(lat, long, date, timezone="UTC", num.days=1){ #this needs to be long lat# lat.long <- matrix(c(long, lat),

a <- c(1:26) b <- rnorm(25) e <- rnorm(25) f <- rnorm(25) g <- data.frame(b,e, a,f) I would like to plot a agianst all possibilities and then shoot it out to a pdf one graph per page. I think it would be okay to have this as a lattice plot or a ggplot with many graphs per page. I can figure all of that out I think, but I need something like r <- as.matrix(g) plot(.~a, data=r)

I have insect data from twelve sites and like most environmental data it is non-normal mostly. I would like to preform an anova and a means seperation like tukey's HSD in a nonparametric sense (on some sort of central tendency measure - median?). I am searching around at this time on the internet. Any suggestions, books, etc. would be greatly appreciated. -- Stephen Sefick Research

I am wondering if there is a better alternative than Excel for data storage that does not require database knowledge (I will eventually have to learn this, but it is not on my immediate todo list). I need something that is not limited to 256 columns... I don't need any of the built in functions in excel just a spreadsheet like program with cells that hold data in a data.frame format for a

I would like to use the data below where the plots are close to what I want. Instead of color I would like to use different symbols, and have the symbols in the legend match the graphs. I am also going to add a regression line to these I know about the type="r" (which is fine for these particular graphs) argument, but it fits the subsets instead of the entire data set-- should I use a

I would like to reformat this data frame into something that I can produce some descriptive statistics. I have been playing around with the reshape package and maybe this is not the best way to proceed. I would like to use RiverMile and constituent as the grouping variables to get the summary statistics: 198a 198b mean mean sd sd ... ... etc. for all of these. I have tried

i have a list of 6 each containing a dataframe of 96 observations as a zoo object. Is there a way to plot these in one frame par(mfrow=c(3,2)) this is what I tried lapply(d, FUN=plot) I can provide data, list is large. thanks -- Stephen Sefick Research Scientist Southeastern Natural Sciences Academy Let's not spend our time and resources thinking about things that are so little or so

a <- c(1:10) b <- c(.5, .6, .9, 10, .4, 3, 4, 9, 0, 11) d <- c(21:30) z <- data.frame(a,b,d) library(fields) results <- c() for(i in 1:(length(rownames(z))-1)){ results[i] <- rdist(z[i,], z[(i+1),]) } results.1 <- data.frame(results) f <- rownames(z) r <- f[-1] rownames(results.1) <- r colnames(results.1) <- f[1] this does what I want it to do - is

library(zoo) d<-(structure(c(1.39981554315924, 0.89196314359498, 0.407816250252697, 0.823496839063978, 1.14429021220358, 1.23971035967413, 0.960868900583432, 0.927685306209829, 1.22072345292821, 0.249842897450642, 1.00879641624694, 0.925372139878243, 0.317259909172362, 0.382677149697482), index = structure(c(11808, 11869, 11961, 11992, 12084, 12173, 12265, 12418, 12600, 12631, 12753, 12996,

#Is there a way to vectorize the for loop #maybe a fancy indexing trick? #thanks d=0.5 L=20 x=seq(20, by=1, length.out=20) reflecting <- function(pre, d, L){ r=L-1 x=rep(0, L) for(j in 2:r){ x[j]=((1-(2*d))*pre[j])+(d*pre[(j+1)])+(d*pre[(j-1)]) } x[1]=((1-d)*pre[1])+(d*pre[2]) x[L]=((1-d)*pre[L])+(d*pre[(L-1)]) y=x } f = reflecting(x, d, L) -- Stephen Sefick Research Scientist

I would like to be able to do the xyplot in ggplot below. I read in the archive that Hadley was working on this for the next release, and I can not find the documentation (Aug. 23rd). River.Mile <- c(215 ,202, 198, 190, 185, 179, 148, 119, 61) Cu <- rnorm(9) Fe <- rnorm(9) Mg <- rnorm(9) Ti <- rnorm(9) Ir <- rnorm(9) r <- data.frame(River.Mile, Cu, Fe, Mg, Ti, Ir) z <-

I have one hundred and six independent variable that I would like to preform a correlation analysis on. Is there anyway to only get the values that are abolute value 0.6 or greater. thanks -- Stephen Sefick Research Scientist Southeastern Natural Sciences Academy Let's not spend our time and resources thinking about things that are so little or so large that all they really do for us is

I am was going to look at the as.yearmon function in the zoo package and write a as.day function to aggregate a time series of 96 observations per day into the mean for each day, but I don't know how to look at the code so that I can convert it into something I can use. On top of that I believe that it is probably an S3 method and I haven't quite gotten that far in my programming

Hadley et al., I was using the cast function to reshape some data (aggregate a melted data frame) and I did not put in the fill and for the most part the values that came out were fine, but there were value great than an order of magnitude from the actual value. When I put in the fill argument everything is okay. I don't provide a reproducible example because the data set is to large to post

h <- structure(list(V1 = c(-0.351714766, 0.188298251, 0.042951816, -0.072490327, -0.691885485, -0.816169763, -0.7066502, -0.856286332, -0.839723411, -0.427242353, -0.372911996, 0.326707494, 0.07847893, 0.687447841, 0.516105863, 0.267076547, 0.727867663, 0.432699191, 0.258610632), V2 = c(0.256636068, -0.824072121, -0.149185618, 0.153280492, 0.01649528, 0.409410528, 0.286015324, 0.366323539,

I would like to do the following in ggplot: what am I missing? River.Mile <-c(202, 198, 190, 185, 179, 148, 119, 61) TSS <- c(1:8) DOC <- seq(2, by= 0.6, length.out=8) z <- data.frame(River.Mile, TSS, DOC) xyplot(TSS+DOC~River.Mile, data=z, auto.key=TRUE) thanks -- Stephen Sefick Research Scientist Southeastern Natural Sciences Academy Let's not spend our time and resources