similar to: nested ANCOVA: still confused

I just had a manuscript returned with the biggest problem being the analysis. Instead of using principal components in a regression I've been asked to analyze a few variables separately. So that's what I'm doing. I pulled a feather from young birds and we quantified certain aspects of the color of those feathers. Since I often have more than one sample from a nest, I thought I

Dear R-users, I set up an experiment where I put up bluebird boxes across an urbanization gradient. I monitored these boxes and at some point I pulled a feather from a chick and a friend used spectral properties (rtot, a continuous var) to index chick health. There is an effect of sex that I would like to include but how would I set up a regression and look at the effect of urbanization

Hi. I'm trying to plot the ratio of used versus unused bird houses (coded 1 or 0) versus a continuous environmental gradient (proportion of urban cover [purban2]) that I would like to convert into bins (0 - 0.25, 0.26 - 0.5, 0.51 - 0.75, 0.76 - 1.0) and I'm not having much luck figuring this out. I ran a logistic regression and purban2 ends up driving the probability of a box being

Hi. I'm attempting to fit a logistic/binomial model so I can determine the influence of landscape on the probability that a box gets used by a bird. I've looked at a few sources (MASS text, Dalgaard, Fox and google) and the examples are almost always based on tabular predictor variables. My data, however are not. I'm not sure if that is the source of the problems or not because the

Hi folks, I am running a very simple regression using mylm <- lm(mass ~ tarsus, na.action=na.exclude) I would like the use the residuals from this analysis for more regression but I'm running into a snag when I try cbind(mylm$residuals, mydata) # where my data is the original data set The error tells me that it cannot use cbind because the length of mylm$residuals is

Hi, I have no experience with R and I'm finding the manuals a bit obtuse and written as if I already understood R. I'm trying to import a csv file from a floppy and it's not working. The code I'm using is read.table("F:\GEORGIA\species_richness\SR_use.csv", sep=",", header = TRUE, row.names = 1) I'm assuming that this command is case sensitive so

Hi everyone, I determined the presence of three types parasites in a passerine bird over two years. I would like to create a bar chart that shows the proportion infected on the y and year/parasite on the x such that each type of parasite is grouped together (single label) and a bar for each year . This would show if there have been changes in the prevalence of a the parasite over two years.

Hi, In the examples from the ReShape package there is a simple example of using melt followed by cast that produces a smallish amount of output about the chicks database. Here's the code: library(reshape) names(ChickWeight) <- tolower(names(ChickWeight)) chick_m <- melt(ChickWeight, id=2:4, na.rm=TRUE) DietResults <- cast(chick_m, diet + chick ~ time) DietResults My challenge

I've noticed that R has a number of very useful functions for obtaining descriptive statistics on groups of variables, including summary {stats}, describe {Hmisc}, and describe {psych}, but none that I have found is able to provided weighted descriptives of subsets of a data set (ex. descriptives for both males and females for age, where accurate results require use of sampling

according to the documentation of the cast function in the reshape function, I would expect this bit of code from the examples to calculate marginal means over only the 'diet' variable. #Chick weight example names(ChickWeight) <- tolower(names(ChickWeight)) chick_m <- melt(ChickWeight, id=2:4, na.rm=TRUE) cast(chick_m, diet + chick ~ time, mean, margins="diet") But,

Hi R experts, I am interested on the effects of two dietry compunds on the growth of chicks. Rather than extracting linear growth functions for each chick and using these in an analysis I thought using ReML might provide a neater and better way of doing this. (I have read the pdf vignette("MlmSoftRev") and "Fitting linear mixed models in R" by Douglas Bates but I am not

Dear R-experts, Quite new to R on this end, but learning fast (I hope). I am running version 2.7.1 on Windows Vista. I have small dataset which consists of: # NestID: nest indicator for each chicken. Siblings sharing the same nest have the same nest indicator. # Chick: chick indicator consisting of a unique ID for each single chick. # Year: 1, 2. # ClutchSize: 1-, 2- , 3-eggs. # HO:

R-users, Attached is the file (SR_use2.txt) I'd like to include and includes column headers. nat_est is the response variable and is the number of species at a particular point. The other variables are the explanatory vars (vark, var2, var1, UK, U2, U1, GK, G2, G1, PK, P2, P1). Here is Walsh's sample code for hier.part: data(urbanwq) env <- urbanwq[,2,8] hier.part(urbanwq$lec,

Estimado Oliver Nuñez Envío un ejemplo reproducible. Javier Marcuzzi # de donde tomo datos, y tiene el modelo (en el pdf) library(MCMCglmm) # librería con las funciónes que voy a usar library(nlme) datos0<-ChickWeight # creo algunos datos que agrego a los origonales Factor<-as.numeric(datos0$Chick) Factor[Factor > 0 & Factor <= 10] <- 'A' Factor[Factor > 10

HI Folks, I'm trying to create a list with named elements. Only, I don't know the names of the elements a priori (they come from the data being calculated). Currently, my approach is to create an environment, then assign things to the environement, then as.list the environment to get a list. Running the code gives, for example: > e2 <- new.env(FALSE, NULL) >

Dear R-experts, I am running R version 2.7.1 on Windows Vista. I have a small dataset which consists of ?chick ID?, ?year (0, 1)?, ?hatching order [HO, defined as first, second and third-hatched chick]?, and the binary outcome of interest ?death (0, 1)?. So a subset of my dataset looks like this on a txt file: y ID Yr HO 1 1 1 First 0 2 1 First 0 3 1 Second 0 4 1 First 1 5 1 First 0 6 1 Third

R-users, I'm having some trouble getting .glm and glm.nb to run a polynomial. I've used x*x and x^2 and neither works. I've checked out the archives and they refer to an archive that's no longer working. I've seen that they use poly() but I'm following up my analysis with cv.glm so I'd prefer to keep using glm. It's easier to just add a column to my data but

This patch implements persistent grants for the xen-blk{front,back} mechanism. The effect of this change is to reduce the number of unmap operations performed, since they cause a (costly) TLB shootdown. This allows the I/O performance to scale better when a large number of VMs are performing I/O. Previously, the blkfront driver was supplied a bvec[] from the request queue. This was granted to

Hello, Below are the first two rows from my dataset and the header. This dataset has 5749 rows and I want to select only certain rows to be used based on existing grouping values. I am trying to group the data based on the values under 'ex_bin'. (e.g a group for 250, 251, 252, 500, 501, 502) I would then like to perform a lm for each grouping. My data: > all[1:2,] year extent scape

All, I'm having a problem selecting directly from a vector. I've written ways to do this indirectly, but I'd rather do it directly and didn't see this in the manual. Essentially, I have: > group.label.new [1] 7 9 6 1 10 4 8 3 2 5 > junk [1] 1 2 > group.label.new[junk && 8:10] [1] 7 9 6 1 10 4 8 3 2 5 I'd like to select the elements