similar to: aggregate factor

I apologize if you have already seen my question. I am new to the mailing list and I did not get any responses the first time I posted my question. However, I am not sure my post went through. Here is my situation: I have a spreadsheet with columns of fish species (text) and length (numbers). In the fish species column I have 5 different fish. I want R to calculate the mean length and

I've been using lattice/wireframe succesfully to visualize some data. I have one question. I want to be able to change the viewpoint ( i.e. rotate the plotted figure a bit left or right or up and down ) Is there a way to do that? Or is there some other package around that could help? Thanks.

Hi all, apologies for seeking advice on a general stats question. I ve run normality tests using 8 different methods: - Lilliefors - Shapiro-Wilk - Robust Jarque Bera - Jarque Bera - Anderson-Darling - Pearson chi-square - Cramer-von Mises - Shapiro-Francia All show that the null hypothesis that the data come from a normal distro cannot be rejected. Great. However, I don't think it looks

Hello R experts, I have having a difficult time figuring out how to perform and interpret an ANCOVA of my nested experimental data and would love any suggestions that you might have. Here is the deal: 1) I have twelve tanks of fish (1-12), each with a bunch of fish in them 2) I have three treatments (1-3); 4 tanks per treatment. (each tank only has one treatment applied to it) 3) I sampled

Hi-- This is a question with a trivial and obvious answer, I'm sure, but I can't seem to find it in the help files and books that I have handy. I have a dataframe consisting of two columns, "Gene_Name," a list of gene symbols, and "Number," a numeric measure of how frequently a tag representing that gene showed up in a SAGE library. Several of the genes are

Hi, Recently we changed our samba server to a bigger and more powerful system ( centos 5.2 ) The config file and smbpasswd and other passwd and group files were copied to the new server and it then assumed the same identity as the old one in the dns and ip address. It seems to have gone very well except we now seem to have a problem. There are windows XP workstations that were domain joined to the

Hello! Below is my exmample. "myref" is my reference data frame with columns a and b. "temp" is my data with column c analogous to column a in "myref". I am trying to create a new variable b - in "temp" - that matches values from b in "myref" to values in c. If you look at the resulting data frame (temp - at the bottom), you'll notice that

All - I have data that looks like psd Species Lake Length Weight St.weight Wr Wr.1 vol 432 substock SMB Clear 150 41.00 0.01 95.12438 95.10118 0.0105 433 substock SMB Clear 152 39.00 0.01 86.72916 86.70692 0.0105 434 substock SMB Clear 152 40.00 3.11 88.95298 82.03689 3.2655 435 substock SMB Clear

Hello all, I recently bought a brand new Dell Precision 7540 laptop.? Before buying I checked the specifications from the RedHat hardware list and they said that it was "fully RedHat 8 Certified" and listed all the hardware that was "certified" to work with that laptop. https://access.redhat.com/ecosystem/hardware/3979771 I have had good results the last few years with

Dear All, I am trying to put some Confidence intervals on some regressions from a linear model with no luck. I can extract the fitted values using 'predict', but am having difficulty in getting at the confidence intervals, or the standard errors. Any suggestions would be welcome Cheers Guy Using Version 2.1.0 (2005-04-18) on a PC vol.mod3 <-

Hi, there I first sent this e-mail a couple months ago, to no avail.Since I am not a member on your mailing list, so could you please cc: a response to me? I'll be sure to check the list today for replies. I am currently attempting to perform an ANOVA with both nested and normal factors. My problem is that R is treating my nested factors the exact same way as it would interaction terms.

Hi all, I am fairly new to mixed effects models and lmer, so bear with me. Here is a subset of my data, which includes a binary variable (lake (TOM or JAN)), one other fixed factor (Age) and a random factor (Year). lake FishID Age Increment Year 1 TOM 1 1 0.304 2007 2 TOM 1 2 0.148 2008 3 TOM 1 3 0.119 2009 4 TOM 1 4 0.053 2010 5

All - Not sure if this is a real programming question, but here goes: I have data that looks like Lake Length Weight 1 158 45 1 179 70 1 200 125 1 202 150 1 206 145 1 209 165 1 210 140 1 215 175 1 216 152 1 220 150 1 221 165 ... where lake goes from 1 - 84 and the number of rows for each lake is variable (but > ~20). I'm trying to do two things: 1) build a simple linear model of the

Dear All, I have a model to predict time series data for example: data(LakeHuron) Lake.fit <- arima(LakeHuron,order=c(1,0,1)) then the function predict() can be used for predicting future data with the model: LakeH.pred <- predict(Lake.fit,n.ahead=5) I can see the result LakeH.pred$pred and LakeH.pred$se but I did not see residual in predict function. If I have a model: [\ Z_t =

Hi, I am trying to test the proportional-odds model using the "polr" function in the MASS library with the dataset of "housing" contained in the MASS book ("Sat" (factor: low, medium, high) is the dependent variable, "Infl" (low, medium, high), "Type" (tower, apartment, atrium, terrace) and "Cont" (low, high) are the predictor variables

I have a dataset I need to sort: test.df<-data.frame(Zone=c("Floodplain", "Lake", "Shoreline"),Cover=c(50,60,70)) However, I don't want it sorted ascending/descending but in an order that I define via a vector: sort.v<-data.frame(c("Lake","Shoreline","Floodplain")) I realize I could probably just create the vector of

Hi everyone, On Monday, November 14, 2016 the LLVM in HPC workshop will be held in Salt Lake City, Utah (in conjunction with the SC16 conference). For last year's workshop, which was in Austin, we held an LLVM social the evening of the workshop, and I think that turned out really well. If you'll be in Salt Lake City and are interested in attending an LLVM social on the evening of November

Large for loops are slow. Try to avoid them using apply, sapply, etc. I've made the paste statements a lot shorter by using collapse. See ?paste for more info. Append.SQL <- function(x, channel){ sql="INSERT INTO logger (time, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10) VALUES("d1=strptime(x[2],"%d/%m/%y %H:%M:%S %p '", d1, "' ,", paste(x[3:12],

Hi Everyone, I am a graduate student who will be using R to do my analysis. I need to do a spatial analysis, and the first step is to calculate the geographic distance between my study sites. I am hoping to use earth.dist because it allows for multiple pairwise distances to be calculated at one time. I have done a sample calculation, and I seem to have a problem between the steps of using

Dear r-help, Sleep-deprivation from having 2 youngsters under 2 around the house is fuzzing my brain, so please be gentle if the answer to this query is obvious! In the example below, I'm trying to use grid.rect to add grey rectangles to the panels of a lattice plot to indicate which months spawning occurred of a (very cute) native Tasmanian fish. The fish in the two lakes spawned at