similar to: nonparametric density and probability methods

Hello, I'm trying to calculate non-parametric probabilities using the np package and having some difficulties. OS is Windows, R version 2.11.1 Here is what I've done so far. library(np) veg <- data.frame(factor(Physiogomy), meanAnnualDepthAve, TP) attach(veg) : for clarification dim(veg) returns 1292 3 fy.x <- npcdens(veg$factor.Physiogomy ~ veg$meanAnnualDepthAve, nmulti=1) #

hello, I am trying to develop a triangle plot but am having difficultly assigning the row.names to the 3 columns in the data.frame Here is what I've done, attach(SoilVegHydro) dim(SoilVegHydro) 1292 39 # now take 3 variables from main data.frame for plotting dat <- cbind.data.frame(TP, meanAnnualDepthAve, BulkDensity) # These are variables held in the data frame SoilVegHydro

Hello, This is a re-submittal of question I submitted last week, but haven't rec'd any responses. I need to extract the probabilities used to construct the barplots displayed as part of the graph produced by plot("ctree"). For example, library(party) iris.ct <- ctree(Species ~ . , data = iris) plot(iris.ct) Instead of a simple example with only 4 terminal nodes, my

Hello, I would like to produce a series of graphs comparing the probability distributions for 8 factors against a continuous metric. The kind of graph I'm hoping to produce would look like the density comparison graphs (library sm) using the function sm.density.compare. However, instead of calculating the density distributions for comparisons, I'd like this comparison to be based on

Dear R users, Version 0.30-1 of the np package has been released and uploaded to CRAN. The np package provides nonparametric kernel smoothing methods for mixed data types. We encourage anyone using the package to upgrade to the latest version. Description: This package provides a variety of nonparametric (and semiparametric) kernel methods that seamlessly handle a mix of continuous, unordered,

Dear R users, Version 0.30-1 of the np package has been released and uploaded to CRAN. The np package provides nonparametric kernel smoothing methods for mixed data types. We encourage anyone using the package to upgrade to the latest version. Description: This package provides a variety of nonparametric (and semiparametric) kernel methods that seamlessly handle a mix of continuous, unordered,

Hello, vegMeans <- by(SoilVegHydro[3:37] , SoilVegHydro$Physiogomy, mean) vegSD <- by(SoilVegHydro[3:37] , SoilVegHydro$Physiogomy, sd) write.table(vegMeans, file="A:\\Work_Area\\Steve\\Hydrology_Data\\data\\vegMeans.txt") Error in as.data.frame.default(x[[i]], optional = TRUE, stringsAsFactors = stringsAsFactors) : cannot coerce class '"by"' into a

Hi, I would like to construct a transition matrix from a data frame with annual transitions of marked plants. plant<-c(1:6) class<-c("seed","seed", "seed", "veg", "rep", "rep") fate<-c("dead", "veg","veg","rep", "rep", "veg") trans<-data.frame(plant, class, fate)

Hello all, turns out i'm having a bad R week. I am at my wits end with a function that I am trying to write. When I run the lines of code outside of a function, I get the desired output. When I wrap the lines of code into a function it doesn't work as expected. Not sure what is going on here. I suspected that the syntax of the if statement with the for loop was the culprit, but when I

i want to tag categories to its menuname. i have a csv containing menu item name and in other csv i have a column containing some strings, i want to pick that strings from categories and look into menu items if any menu item containing that string i want to create a new column next to menu item name flagged as 1 otherwise 0 and the only condition is once a menu item flagged as 1 i don't need

Hello. First post, though I have read scores. Mostly i can solve my problems reading the archives, but I lack the vocabulary to ask this one right. Any social faux pais, I apologize. I am sure this is a very basic question and I am embarrassed to ask, but I spent several hours scouring the archives and trying various suggestions, but I am too new to get any of them to work. I am trying to

I am having difficulty understanding how I would constrain a data set by filtering out 'records' based on certain criteria. Using SQL I could query using 'select * from my.data where LithClass in ('sand', 'clay')' or some such. Using subset, there seem to be ghosts left behind (that is, all of the LithClass *.Labels* remain after subset) > dput(tcc)

Dear R community, Below you may find the details of my model (lm11). I receive the error message "Error: cannot allocate vector of size 220979 Kb" after applying the autocorrelation function update(lm11, corr=corAR1()). lm11<-lme(Soil.temp ~ Veg*M+Veg*year, data=a, random = list(Site=pdDiag(~Veg), Plot=pdDiag(~Veg))

Hello I've created a 3d scatterplot, and had no problems labeling the points. However, I've been really struggling to change the color of the points based on a factor (see 'group' below). Is such a thing possible? My data look like this: food group x y z apple fruit 0.216 -0.112 -0.893 orange fruit 0.814 0.097 0.460 broccoli veg -0.239 0.240 -0.425 banana

Hello I'm trying to create a barplot with a couple of stacked positive values and with one negative value for each group. example: trees<-c(20,30,10) shrubs<-c(12,23,9) veg<-c(2,3,4) soil<-c(-100,-123,-89) example1<-t(cbind(trees,shrubs,veg)) barplot(example1) #this works so far #but now: example2<-t(cbind(trees,shrubs,veg,soil)) barplot(example2) This shows no more

Hi All, I am wondering if anyone can tell me what the warning message below the model means? J add1(DTA.glm,~ Aeventexhumed + Veg + Berm + HTL + Estuary + Rayos) Single term additions Model: cbind(MaxHatch, TotalEggs - MaxHatch) ~ Aeventexhumed + Veg + Berm + HTL Df Deviance AIC <none> 488.86 4232.9 Estuary 1 454.96 4201.0 Rayos 3 258.80 4008.9 Warning

Hello all, I am going slightly mad trying to create a table for running co-correspondence analysis. What I have is seed bank and vegetation data, and my aim is to see if the vegetation found in a site (containing several seed bank samples) can predict the composition of a seed bank sample within that site. So for this I need two tables with matching rows. I have created an empty matrix,

Hi R listers, I am trying to group distances of nests to the vegetation into classes that are define by (0-5m, 6-10m, 11-15m, 16-20m, 21-25m, 26-30m, 31-35m, 36-40m, 41-45m, 46-50m, 51-55m, 56-60m). Each row is a nest and all the distances to the vegetation is in a column. In plyr, I have tried - below script but I think I am going about this the wrong way and am not successful. #Veg index

Hi all, How can I overlay these two histograms? ggplot(gg, aes(gg$Alz, fill = gg$veg)) + geom_histogram(alpha = 0.2) ggplot(tt, aes(tt$Cont, fill = tt$veg)) + geom_histogram(alpha = 0.2) thanks for any help! Elahe

Hi R-listers, I am trying to do an ANOVA for the following scatterplot and received the following error: library(car) scatterplot(HSuccess ~ Veg, data = data.to.analyze, xlab = "Vegetation border (m)", ylab = "Hatching success (%)") anova(HSuccess ~ Veg, data=data.to.analyze) Error in UseMethod("anova") : no applicable