similar to: Adding picture to graph?

Hi all- I fit a zero-inflated Poisson model to model bycatch rates using an offset term for effort. I need to apply the fitted model to a datasets of varying levels of effort to predict the associated levels of bycatch. I am seeking assistance as to the correct way to code this. Thanks in advance! Laura [[alternative HTML version deleted]]

R users doing data analysis may be interested in the following paper: http://methodsblog.wordpress.com/2009/11/13/first-paper-now-online/?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+wordpress%2Fmethodsblog+(methods.blog) All data and R code is available. Alain ----- -------------------------------------------------------------------- Dr. Alain F. Zuur First author of:

Let me start with an example: par(mfrow=c(2,3)) for (i in 1:5){ x = rnorm(100) y = .5*x + rnorm(100, 0, sqrt(1-.5^2)) plot(x,y) } Note that there's five plots and six spaces for those plots via mfrow, leaving one row empty. Is there a way to have the bottom two plots centered? I think it looks weird to have them left-justified. Thanks in advance for the help! -- Dustin Fife PhD

Hello, I am using R to analyze a large multilevel data set, using lmer() to model my data, and using anova() to compare the fit of various models. When I run two models, the output of each model is generated correctly as far as I can tell (e.g. summary(f1) and summary(f2) for the multilevel model output look perfectly reasonable), and in this case (see below) predictor.1 explains vastly more

Dear All, the lm() function has the possibility to create a subset of the possible explaining variables that you have. However, in the help there is no example how to use this subset option. I tried the following: model<-lm(dependent.data$MPFD~.,data=explaining.data,subset=c(1,0,0,0,0,0,0,0,1,1,0,0)) MPFD is the dependent variable stored in the data frame dependent.data, and all 12

Dear all, I'm a beginner of R and I need to carry out some three-way mixed ANOVAs. Following examples at http://personality-project.org/r/r.anova.html, I managed to get the ANOVA part, but I don't know how can I check data normality and homogeneity of variance in R (since they're the required assumptions of ANOVA analysis). Are there any special command/packages? Could anyone give me

Hi, I am trying to add multiple variance structures such as the first example below: vf1 <- varComb(varIdent(form = ~1|Sex), varPower()) However my code below will not work can anybody please advise me? VFcomb<-varComb(varExp(form=~depcptwithextybf),varFixed(form=~FebNAO)) also if you have two variables with the same weights function would you write that as:

Hi I am comparing four different linear mixed effect models, derived from updating the original one. To compare these, I want to use anova(). I therefore do the following (not reproducible - just to illustration purpose!): dat <- loadSPECIES(SPECIES) subs <- expression(dead==FALSE & recTreat==FALSE) feff <- noBefore~pHarv*year # fixed effect in the model reff <-

Hi, All, I have a dataset with binary response ( 0 and 1) and some numerical covariates. I know I can use logistic regression to fit the data. But I want to consider more locally. So I am wondering how can I fit the data with 'loess' function in R? And what will be the response: 0/1 or the probability in either group like in logistic regression? Thank you, Cindy [[alternative HTML

Hi - I read through dse package manual a bit. I'm not quite certain how I can use it to estimate a time varying coefficient regression model? I might pick up an inappropriate package. Any suggestion would be greatly appreciated. Thank you. rh

Dear Group, I am an economics student starting with PhD work in London. As preparation I would like to get to know R a little bit better. For Stata there are tons of books, however, can you recommend a book for R? I have some substantiated econometrics knowledge, so it should be more a how-to book. Best regards Thiemo --- Thiemo Fetzer, Economist http://freigeist.devmag.net

Hello, I'm having an error when trying to fit the next GLM: >>model<-glm(response ~ CLONE_M + CLONE_F + HATCHING +(CLONE_M*CLONE_F) + (CLONE_M*HATCHING) + (CLONE_F*HATCHING) + (CLONE_M*CLONE_F*HATCHING), family=quasipoisson) >> anova(model, test="Chi") >Error in if (dispersion == 1) Inf else object$df.residual : missing value where TRUE/FALSE needed If I fit

Dear R-helpers, I would like to compare the fit of two models, one of which I fit using lm() and the other using glm(family=poisson). The latter doesn't provide r-squared, so I wonder how to go about comparing these models (they have the same formula). Thanks very much, Mark Na [[alternative HTML version deleted]]

Hi, I wrote a simple master function, run(), that has inside six qplot functions. The goal is to type run() and have all six graphs appear as separate windows so that I can copy them into PowerPoint for a client. When I type run(), only the last graph appears, the first five apparently being overwritten. How do I get all six in separate windows, ready for copying? By the way, is the a way

Hi I want to use order() to get the order of a vector. But I would need a different behavior when ties occur: similar to the parameter ties.method = "random" in the rank() function, I would need to randomise the ties. Is this possible? Example: x <- rep(1:10, 2) order(x) [1] 1 11 2 12 3 13 4 14 5 15 6 16 7 17 8 18 9 19 10 20 order(x) [1] 1 11 2 12 3 13 4 14 5 15

Hi if I try to import a raster layer which consists only of NULL values from grass by using the readRAST6, I get an error message: > readRAST6("HSericea_seedsDisperse_2007") ERROR: Invalid value for null (integers only) Error in readBinGrid(rtmpfl11, colname = vname[i], proj4string = p4, integer = to_int) : no such file:

Hi this might be a little bit off topic, but here it goes: lets assume I have the following: set.seed(13) dat1 <- rnorm(2000, mean=10, sd=10) dat2 <- rnorm(100, mean=10, sd=20) d.all <- density(dat, n=1024) d.co <- density(x[[v]], , from=min(d.all$x), to=max(d.all$x), n=1024) d.diff <- list( x = d.all$x,

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 Hi I have 10.000 simulations for a sensitivity analysis. I have done a few sensitivity analysis for different response variables already, but now, as most of the simulations (if not all) show some cyclic behaviour, see how the independent input parameter influence the frequency of the cyclic changes and "how cyclic" they actually are. So

Hi I want to do a series of linear models, and would like to define the input arguments for lm() as variables. I managed easily to define the formula arguments in a variable, but I also would like to have the "subset" in a variable. My reasoning is, that I have the subset in the results object. So I wiould like to add a line like: subs <- dead==FALSE & recTreat==FALSE which

Hi is there an alternative to par(new), for ading data to a plot for a different y-axis? My problem with par(new=TRUE) is, that it re-defines all axis and labels (as in example 1) and one has to use xlim=... to fix the x-axis. I am looking for something, which simply resets the y-axis, so that a new plot() (or points()/lines()) keeps the x-axis, but re-defines the y-axis. Is there something