similar to: Stratified data summaries

Rers: What is the preferred library/function for doing stratified random sampling from a dataset, given I want to control the number of samples (rather than the proportion of samples) per strata? Thanks! --j -- Jonathan A. Greenberg, PhD Postdoctoral Scholar Center for Spatial Technologies and Remote Sensing (CSTARS) University of California, Davis One Shields Avenue The Barn, Room

Greetings, I am a bit confused by the results returned by the functions: cohen.kappa {concord} classAgreement {e1071} when using a two-way table. for example, if I have an matrix A, and a similar matrix B (same dimensions), then: matrix A and B can be found: http://casoilresource.lawr.ucdavis.edu/drupal/files/a_40.txt http://casoilresource.lawr.ucdavis.edu/drupal/files/b_40.txt A <-

Hi, is there a function in R to convert data read with read.shape and which is originally in UTM coordinates into longitude / latitude coordinates? I found the convUL() function from the PBSmapping package but I have no idea how I could apply that to the read.shape object. Many thanks, Werner __________________________________________________ Do sragenden Schutz gegen Massenmails.

Hi, I have been trying to use the new .parallel argument with the most recent version of plyr [1] to speed up some tasks. I can run the example in the NEWS file [1], and it seems to be working correctly. However, R will only use a single core when I try to apply this same approach with ddply(). 1. http://cran.r-project.org/web/packages/plyr/NEWS Watching my CPUs I see that in both cases

I would like to use a map or aerial photo as a background to plotting solid lines and text, and semi-transparent color contours, in base and lattice graphics. Plot coordinates need to be consistent with the georeferenced background. For example, a color contour plot would have an gray-toned aerial photograph as a background for overprinted semi-transparent color contours of some spatially

Hi, Are there any functions in R that could be used to estimate the phase-shift between two semi-sinusoidal vectors? Here is what I have tried so far, using the spectrum() function -- possibly incorrectly: # generate some fake data, normalized to unit circle x <- jitter(seq(-2*pi, 2*pi, by=0.1), amount=pi/8) # functions defining two out-of-phase phenomena f1 <- function(x)

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Hello: I would like to superimpose vector polygons (state outlines) from a Shape file on top of a satellite image, imported into a SpatialGridDataFrame from GEOTIFF via gdal_translate and readGDAL. When I plot polygon and point shape files in R, into SpatialPointDataFrame and SpatialPolygonDataFrame, the two feature sets line up geographically, so it seems logical that a SpatialGridDataFrame

>Hello, I am hoping for assistance in regards to examining the contribution of stratified variables in a cox regression. A previous post by Terry Therneau noted that "That is the point of a strata; you are declaring a variable to NOT be proportional hazards, and thus there is no single "hazard ratio" that describes it". Given this purpose of stratification, in the

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Hello, I am using Dr. Harrell's design package to make a nomogram. I was able to make a beautiful one without stratifying, however, I will need to stratify to meet PH assumptions. This is where I go wrong, but I'm not sure where. Non-Stratified Nomogram:

Hi, Is there a simple way to suppress the plotting of polygon boundaries with spplot() ? # simple list of 12 colors cols <- brewer.pal(12, "Paired") # plot pile of polygons, with 12 classes: spplot(x, zcol='class2', col.regions=cols, scales=list(draw=T), xlab="Easting (m)", ylab="Northing (m)") ... seems to work well. However the polygon boundaries

Hi, Is anyone on the list familiar with an R implementation of Piper Diagrams? Example: http://faculty.uml.edu/nelson_eby/89.315/IMAGES/Figure%209-78.jpg I am thinking that two calls to triax.plot (plotrix) along with some kind of affine-transformed standard plot would do the trick. Not so sure about the final layout, or a nice generalized version for something like lattice. Cheers, Dylan

Hi, I am curious about how to interpret the results of a polynomial regression-- using poly(raw=TRUE) vs. poly(raw=FALSE). set.seed(123456) x <- rnorm(100) y <- jitter(1*x + 2*x^2 + 3*x^3 , 250) plot(y ~ x) l.poly <- lm(y ~ poly(x, 3)) l.poly.raw <- lm(y ~ poly(x, 3, raw=TRUE)) s <- seq(-3, 3, by=0.1) lines(s, predict(l.poly, data.frame(x=s)), col=1) lines(s,

Hi, How would one go about determining if the slope terms from an analysis of covariance model are different from eachother? Based on the example from MASS: library(MASS) # parallel slope model l.para <- lm(Temp ~ Gas + Insul, data=whiteside) # multiple slope model l.mult <- lm(Temp ~ Insul/Gas -1, data=whiteside) # compare nested models: anova(l.para, l.mult) Analysis of Variance

Hi, I am trying to use quantile regression to perform weighted-comparisons of the median across groups. This works most of the time, however I am seeing some odd output in summary(rq()): Call: rq(formula = sand ~ method, tau = 0.5, data = x, weights = area_fraction) Coefficients: Value Std. Error t value Pr(>|t|) (Intercept) 45.44262 3.64706 12.46007

Hi, Running the following example from ?Predict() throws an error I have never seen before: set.seed(1) x1 <- runif(300) x2 <- runif(300) ddist <- datadist(x1,x2); options(datadist='ddist') y <- exp(x1+ x2 - 1 + rnorm(300)) f <- ols(log(y) ~ pol(x1,2) + x2) p1 <- Predict(f, x1=., conf.type='mean') Error in paste(nmc[i], "=", if (is.numeric(x))

Hi, I have fit a series of ols() models, by group, in this manner: l <- ols(y ~ rcs(x, 4)) ... where the series of 'x' values in each group is the same, however knots are not always identical between groups. The result is a table of 'coefs' derived from the ols objects, by group: group Intercept top top' top'' 1 6.864 0.01 2.241 -2.65

Hi All, I want to take a simple random sample from a large dataset, gly, but I'm getting an error message. Any help? dim(gly) [1] 112371 37 > s1 <- sample(gly,100) Error in `[.data.frame`(x, .Internal(sample(length(x), size, replace, : cannot take a sample larger than the population when 'replace = FALSE' Thanks, Rachel [[alternative HTML version

Hi all, This is probably going to come off as unnecessary (and show my ignorance) but I am trying to understand the parameter estimates I am getting from R when doing an ANCOVA. Basically, I am accustomed to the estimate for the categorical variable being equivalent to the respective cell means minus the grand mean. I know is the case in JMP - all other estimates from these data match the