similar to: Fw: nested linear model; with common intercept

Dear R-help, I am sorry if this is more of a stats question than an R-question, but I have found it difficult to get a clear answer by other means. Q. Would it be "wrong" to specify a nested model and retain a common intercept, e.g. lm(NH4 ~ Site/TideCode + 1) I am aware (?) that my Site-coefficients are now calculated relative to my reference Site (treatment.contrasts), *but* that

A general question that I have been pursuing for some time but have set aside. When finishing some analysis, I can have multiple matrices that have specific column names. Ideally, I would like to combine these separate matrices for a final output as a csv file. A generic example: Matrix 1 var1A var1B var1C x x x x x

I have would like to use logistic regression to analyze the percentage of correct responses in a 2 alternative forced choice task. The question is whether one needs to take into account the fact expected probabilities for the percentage of correct responses ranges between 0.5 and 1 in this case and how to adjust the link function accordingly in R (see details below). Gabriel Subjects were asked

I am not sure if this is an R-users question, but since most of you here are statisticians, I decided to give it a shot. I am using the lm() function in R to fit a dependent variable to a set of 3 to 5 independent variables. For this, I used the following commands: >model1<-lm(function=PBW~SO4+NO3+NH4) Coefficients: (Intercept) SO4 NO3 NH4 0.01323 0.01968

Good day, I'm trying to get more time series in one plot. As there are bigger differences in values of variables I need logaritmic y axis. The code I use is the following: nvz_3_data <- read.csv('/home/tomas/R_outputs/nvz_3.csv') date <- (nvz_3_data$date) NO3 <- (nvz_3_data$NO3) NH4 <- (nvz_3_data$NH4) date_p <- as.POSIXct(date, "CET") par(mfrow=c(2,1), ylog

Good day, I read some data from a PostgreSQL database by a following script: library(Rdbi) library(RdbiPgSQL) # conn becomes an object which contains the DB connection: conn <- dbConnect(PgSQL(), host="localhost", dbname="BVS", user="postgres", password = "*******") query_duj_kal <- dbSendQuery(conn, "select zdroj as well, cas as date, fe2,

On Sun, 2009-07-26 at 13:17 +0200, Poersching wrote: > stvienna wiener schrieb: > > Hi all, > > > > I am plotting a financial time series, but I need a more detailed X-Axis. > > > > Example: > > x <- zoo(rnorm(1:6000), as.Date("1992-11-11")+c(1:6000)) > > plot(x) > > > > The X-Axis is labeled "1995", "2000"

Dear List, I would appreciate help on the following matter: I am aware that higher dimensional contingency tables can be analysed using either log-linear models or as a poisson regression using a generalized linear model: log-linear: loglm(~Age+Site, data=xtabs(~Age+Site, data=SSites.Rev, drop.unused.levels=T)) GLM: glm.table <- as.data.frame(xtabs(~Age+Site, data=SSites.Rev,

Dear R-help, Has anyone implemented sparklines in the strips of a lattice plot? What I have in mind is, say, highlighting that part of a time series that one is examining in more detail in a set of lattice plots. Regads,. Mark Difford. PS: (Andreas Loffler has implemented a simple but functional version for TeX/LaTeX: http://www.tug.org/tex-archive/help/Catalogue/entries/sparklines.html)

Dear List, I would greatly appreciate help on the following matter: The RuleFit program of Professor Friedman uses partial dependence plots to explore the effect of an explanatory variable on the response variable, after accounting for the average effects of the other variables. The plot method [plot(summary(rq(y ~ x1 + x2, t=seq(.1,.9,.05))))] of Professor Koenker's quantreg program

Dear List-Members, Is the application of multiple comparison procedures (using the multcomp package) to the output from a rank-based ANOVA straightforward, or do I need to take heed ? That is, is it as simple as: glht( aov(rank(NH4) ~ Site, data=mydat), linfct=mcp(Site="Tukey") ) Thanks in advance for your help. Regards, Mark Difford. -- View this message in context:

How can I read a space-delimited file, where the data values for each case are folded before column 80, and so appear on two lines for each case? The first few cases look like this loc type bio H2S sal Eh7 pH buf P K Ca Mg Na Mn Zn Cu NH4 OI DVEG 676 -610 33 -290 5.00 2.34 20.238 1441.67 2150.00 5169.05 35184.5 14.2857 16.4524 5.02381 59.524 OI DVEG 516 -570 35 -268 4.75 2.66 15.591 1299.19

Dear R-core/development-team, The problem noted in the subject-line has been a problem in the last three development versions of R for Windows that I have downloaded and tested, the most recent of them being a version I downloaded this morning. Update.packages() using the graphical option, i.e. called as update.packages(ask='graphics', checkBuilt=TRUE) does not work as it should, but

Hello. I am wondering why I am getting NA for all in cors=sapply(pred,cor,y=resp). I suppose that each column in pred has NAs in them. Is there some way to fix this? Thanks > str(pred) 'data.frame':   200 obs. of  13 variables:  $ mnO2: num  9.8 8 11.4 4.8 9 13.1 10.3 10.6 3.4 9.9 ...  $ Cl  : num  60.8 57.8 40 77.4 55.4 ...  $ NO3 : num  6.24 1.29 5.33 2.3 10.42 ...  $ NH4 : num  578

grDevices::convertColor has arguments 'from' and 'to' which can take on value 'XYZ'. Can someone confirm that 'XYZ' is the same as the CIE chromaticity coordinates that are also sometimes refered to as 'xyY' in the literature? Or are these the CIE tristimulus values? It looks to me like the first case is true, but I would appreciate hearing from one of

I have a data frame with 1 factor, one date, and 37 numeric values: str(waterchem) 'data.frame': 3525 obs. of 39 variables: site : Factor w/ 64 levels "D-1","D-2","D-3",..: 1 1 1 1 1 ... $ sampdate : Date, format: "2007-12-12" "2008-03-15" ... $ CO3 : num 1 1 6.7 1 1 1 1 1 1 1 ... $ HCO3 : num 231 228 118 246

All, I am using a sum of squared differences in the objective function of an optimization problem I am doing and I have managed to speed it up using the outer function versus the nested for loops, but my suspicion is that the calculation could be done even quicker. Please see the code below for a simple example. If anyone can point out a faster way I would appreciate it greatly. Thanks, Roger

On May 24, 2011; 11:06pm Belle wrote: > Does anyone know how to run Multinomial logistical Model in R in order to > get predicted probability? Yes. I could stop there but you shouldn't. The author of the package provides plenty of examples (and two good vignettes) showing you how to do this. Suggest you do some work in that area. Look especially at how model formulas are

On Nov 12, 2011 at 8:29pm Alex wrote: > Has anyone used SIAR package add on? I posted a reply to an earlier question from you on this subject. See http://r.789695.n4.nabble.com/Errors-in-SIAR-td4029804.html. In it I note that there are problems with the function from siar (not SIAR) you are using, but that this may not be "your" problem, that the function calls for matrices (you

Dear list, I am using R 2.1.1 on a Fedora 3 Linux, 32 bit PC. If I compute the aggregated mean and the standard deviation I get standard deviation values for factors where the mean was not computed. It seems to me that this is somehow related to the NA values. But I don't quite understand what is going wrong? Could it be related to the data import already? Some of the imported data got the