similar to: Memory problem using predict function

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

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

Hi R-listers, Q1) What is the difference between the scatterplot and plot function? Q2) I am able to make a graph with the scatterplot function: scatterplot(DevelopIndex ~ Veg, + data = Turtle, + xlab = "Vegetation border (m)", + ylab = "Embryonic development index") And have been successful. But I do not know if the lines are for:

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))

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

Dear all , I would like to use the function randomForest to predict the probability of relocation failure of a GPS collar as a function of several environmental variables x (both factor and numeric: slope, vegetation, etc.) on a given area. The response variable y is thus success (0)/failure(1) of the relocation, and the sampling unit is the pixel of a raster map. My aim is to build a map

I am trying to make some plots for a poster. Each one needs to be 6 inches by inches. Right now the fonts are too small and the plots don't read well on my poster. I am pretty much an R newbie and I can't figure out how to change the fonts. It would also be nice to know how to specify the size of the plot and maybe how to save the plots as jpeg files. Any help would be much appreciated! I

Hi everybody, I know this might be very off topic and it took me quite a while to up my courage to post this?. But I remember a thread some time ago about how we can find the packages we need to do specific tasks in R if we don?t know before hand which ones actually do it. Now all the packages are listed alphabetically on the web site. Since I am not very advanced in writing my own functions I

I'm working with the randomForest package and have successfully build a model. I'd like to go one step further however, and use the output from the model to construct a map using the output. My input data are spatial, and I have an independent set of rasterize maps for each of the predictor variables, to map the classification solution. Using the getTree function: >

Hi R-listers, I have developed 47 GLM models with different combinations of interactions from 1 variable to 5 variables. I have manually made each model separately and put them into individual tables (organized by the number of variables) showing the AIC score. I want to compare all of these models. 1) What is the best way to compare various models with unique combinations and different number

Hi, everyone, I haven't found anything similar in the forum, so here's my problem (I'm no expert in R nor statistics): I have a data set of 59.000 cases with 9 variables each (fractional coverage of 9 different plant types, such as deciduous broad-leaved temperate trees or evergreen tropical trees etc.), which was generated by a vegetation model. In order to evaluate the quality of

Hi, I have three years of species presence/absence data for a set of ~100 ponds, with a list of associated habitat characteristics (park, vegetation, hydroperiod, etc.). The datasets differ slightly by year because some ponds were dry in some years and not in others. My goal is to look at habitat associations for a couple focal species. For each year, I have a data table that looks like

I'm generating several scatter3d visualizations of vegetation community distribution against a number parameters using the scatter3d function car package, The visualizations are fantastic and they provide great support for the analysis. As far as I can determine, scatter3d does not save the output of the routine as an object so I can not x <- scatter3d(MADep2010 ~ Day7Max2010 +

Hi R-users: [R 2.2 on OSX 10.4.3] I have a (sparse) vegetation data frame with 500 rows (sampling units) and 177 columns (plant species) where the data represent % cover. I need to summarize the cover data by returning the names of the most dominant and the second most dominant species per plot. I reduced the data frame to omit cover below 5%; this is what it looks like stacked. I have

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, 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 tried to post this earlier, but it seems that it did not appear on the list. If you've rec'd 2 m 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

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

Would anybody be able to advise on which package would offer the best approach for producing a model able to predict the probability of species occupation based upon a range of variables, some of them catagorical (eg. ten soil types where the numbers assigned are not related to any qualitative/quantitative continuum or vegetation type) and others continuous such as field size or vegetation height.