similar to: glm models with more than one response

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

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: >

Hello Trying to develop variograms and kriged surfaces from a point file. Here is what I've done so far. library(gstat) # also loads library(sp) library(lattice) soilpts$x <- soilpts$UTM_X soilpts$y <- soilpts$UTM_Y soil.dat <- subset(soilpts, select=c(x, y, Area, BulkDensity, LOI, TP, TN, TC, Total_Mg)) dim(soil.dat) [1] 1292 7 coordinates(soil.dat) <- ~ x+y

Hello, I'd like to automate this script a bit more and cycle several parameters(both the species and the metric). For example where AnnualDepth occurs, I need to process about 12 metrics so instead of writing this entire script 12 times once for each metric I'd like to be able to automatically get another metric. Any suggestion will be greatly appreciated. Currently running Windows

I've been working on a multinomial logit model, trying to predict vegetation types as a function of total phosphorus. Previous responses to my postings have pointed me to the mlogit package. I'm now trying to work examples and my data using this package. data("Fishing", package = "mlogit") Fish <- mlogit.data(Fishing, varying = c(4:11), shape = "wide",

Full_Name: Charles J. Geyer Version: 1.8.0 OS: i686-pc-linux-gnu (Suse 8.2) Submission from: (NULL) (134.84.86.22) Two bugs (perhaps related, perhaps independent) revealed by the same Poisson regression with offset mydata <- read.table(url("http://www.stat.umn.edu/geyer/5931/mle/seeds.txt")) out.fubar <- glm(seedlings ~ burn01 + vegtype * burn02 + offset(log(totalseeds)),

I am sure this question has come up, but searching hasn't given me any results. So I need to enter this line: mx1 <- randomForest(X1 ~ elevation + slope + vegtype, data = moths.train) But the problem is that X1 is currently hard coded. I would instead like to be able to put in the value of X1 through a list. For example: list <- list("X1", "X2", "X3")

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 all, I would like to use the values in vegaggr.BLMCMR02$colony str(vegaggr.BLMCMR02) `data.frame': 1678 obs. of 3 variables: $ vegtype : Factor w/ 27 levels "2010","2020",..: 3 4 5 19 4 5 19 5 $ colony : Factor w/ 406 levels "0","1","10","100",..: 1 1 1 1 2 2 2 $ Totvegproparea: num 0.00055 0.03956 0.95705

Dear friends, when i use glm() to fit my data, i use glm(formula = snail ~ vegtype + mhveg + humidity + elevation + soiltem, *family = Gamma(link = inverse),* data =a,)) It shows: error in eval(expr, envir, enclos) : *gamma distribution don't allow negative value*. But i use result<-glm(formula = snail ~ vegtype + mhveg + humidity + elevation + soiltem, family = poisson, data =a) #this

Hi Folks, I think I have searched exhaustively, including, of course R-help (D. Bates, S. Graves, and others) and but I remain uncertain about testing fixed effects with lmer(..., family=binomial). I gather that mcmcsamp does not work with Do we rely exclusively on z values of model parameters, or could we use anova() with likelihood ratios, AIC and BIC, with (or without)

Hi folks, Warning: I don't know if the result I am getting makes sense, so this may be a statistics question. The fitted values from my binomial lmer mixed model seem to consistently overestimate the cell means, and I don't know why. I assume I am doing something stupid. Below I include code, and a binary image of the data is available at this link:

Hello, If I have two variables that are factors or characters and I want to create a new variable that is the combination of both what function can I use to accomplish this? Ex. Var1 Var2 SA100055113 19851113 And I want NewVar SA10005511319851113 Thanks in advance. Cameron Guenther, Ph.D. Associate Research Scientist FWC/FWRI, Marine Fisheries Research 100 8th Avenue S.E. St.

Hello everyone, I have a large dataset (x) with some rows that have duplicate variables that I would like to remove. I find which rows are the duplicates with X1<-which(duplicated(x)). That gives me the rows with duplicated variables. Now, how can I remove just those rose from the original data frame. I think I can create a new data frame without the duplicates using subset. I have tried:

Hello everyone, If I have an incidence matrix of 0 and 1's P=[1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 0 0 0 1 1 1 0 0 0 1 1 0 0 0 0] I want to create a new uniform random matrix [a] that is filled with 0's and 1's but constrained so that the row and column sums are the same as in [P]. Does anyone know how to accomplish this? Thanks in advance Cameron Guenther, Ph.D.

I am trying to great a new column of effort data from an existing vector of gears used. It is a simple code where effort[Gear==300]=(DIST_TOW*7412) effort[Gear==301]=(DIST_TOW*7412) The code appears to work for some of the data but fails for others and inserts a NA value I also get this warning message Warning message: number of items to replace is not a multiple of replacement length Can

Dear All, This is my first post, and probably (and hence apologies that) my question is very silly! I'm having issues with a the mvabund package (http://cran.r-project.org/web/packages/mvabund/index.html), and would be great to get some help! Here is the code (and files are attached): library(mvabund) ##visualizing data florabund <- read.csv("CPL_floristics_abund_v1d.csv",

Hello, I have a much larger dataset that is similar in form to: year species length count 1998 1 150 1 1998 2 200 1 1998 3 250 2 1999 1 150 3 1999 2 200 4 1999 3 250 5 2000 1 150 1 2000 2 200 1 2000 3 250 1 2001 1 150 2 2001 2 200 3 2001

Hello, I have sample data set that looks like: YEAR MONTH DAY CONTINUE SPL TIMEFISH TIMEUNIT AREA COUNTY DEPTH DEPUNIT GEAR TRIPID CONVUNIT 1992 1 26 1 SP0073928 8 H 7 25 4 NA 1000000 02163399054 161 1992 1 26 1 SP0073928 8 H 7 25 4 NA 1000000 02163399054 8 1992 1 26 2 SP0004228 8 H 7 25 4 NA 1000000 02163399054 161 1992 1 26 2 SP0004228 8 H 7 25 4 NA 1000000 02163399054 8 1992

Hello. I'm running a generalized linear model and am interested in using the random effects that are output for further analysis. My random effect is interacting with two different fixed effects (which which are factors with two levels each). When I retrieve the random effects I get something like this: (Intercept) nutrient (Intercept) light (Intercept) Aa-0 0.59679192