similar to: glm

Dear list fellows, I want to study time series and use R to analyse time series of fishing data from several species (landings and cpue) investigating the correlation between them and with environmental factors (water temperature, wind, etc.). Searching at Amazon I found three books with examples in R: Time Series Analysis: With Applications in R by Jonathan D. Cryer and Jonathan D. Cryer

Hello, I have a doubt in using the function step (step wise) to select glm models. Usually I apply the gamma distribution to analyze fishery data. To select the terms I use a routine where I first compare single term models to the null model (eg. U~1 vs. U~depth; U~1 vs. U~latitude; etc. ? where U= abundance) and, by means of the result given by a likelihook function applied for each comparison,

Hi, I'm using nmds command (library vegan) to analyze some fishing data. I'd like to plot not only points, but also the names of species and stations in a specified position. I used the command text(nmds$points[,1], nmds $points[,2],labels=row.names(nmds $points),pos=3,cex=0.5) But the labels are sometimes overlapped. Is there any way to use identify, or a similar command, to plot the

Thanks for your reply Nassar. What I really want is to overlay (like add=TRUE) the plots 1 and 2. With plots "at the same screen" I meant overlaid and not one beside the other, i.e., only one plot with all the points (1 to 10) in blue (or any other color), the points 5 to 9 in red and a regression line from points 5 to 9. Best regards, Antonio Olinto ----- Original Message -----

Dear R-list members, Cynthia M. Jones wrote a paper (Fitting growth curves to retrospective size-at-age data, Fisheries Research 46(2000):123-129; abstract at http://www.elsevier.nl/gej-ng/10/19/44/70/24/37/abstract.html)where the SAS procedure MIXED, Macro NLINMIX (Littell et. al., 1996)was used to estimate the von Bertalanffy growth function parameters assuming that data from the same fish are

I am trying to reproduce an analysis done in SAS from which Type III Sums of squares are used. How can you get Type III sums of squares in an aov similar to what SAS provides? With best wishes and kind regards I am Sincerely, Corey A. Moffet Instructor Department of Range, Wildlife, and Fisheries Management Mail Stop 2125 Texas Tech University Lubbock, Texas 79409-2125 (806) 742-2843 Voice

Dear R-list members, Some months ago I wrote a message on the usage of gnls (nlme library) and here I come again. Let me give an example: I have a 10 year length-at-age data set of 10 fishes (see growth.dat at the end of this message) and I want to fit a von Bertalanffy growth model, Li= Linf*(1-exp(-k*(ti-t0))) where Li = length at age i, Linf= asymptotic length, k= curvature parameter, ti=

This works fine: fit42<-nls(Vfs~SSlogis(Months,Asym.Int+Asym.Group*Groupdum,xmid,scal), data=df, start=c(Asym.Int=22,Asym.Group=5,xmid=2,scal=6), na.action=na.omit) But this, identical except using gnls, doesn't converge: fit43<-gnls(Vfs~SSlogis(Months,Asym.Int+Asym.Group*Groupdum,xmid,scal), data=df, start=c(Asym.Int=22,Asym.Group=5,xmid=2,scal=6), na.action=na.omit) Error in gnls(Vfs

Hi, When doing a two-ways anova in R and comparing my same results with an SPSS output, I noticed that R calculated type I Sum of Squares. Is it possible to use Type III Sum of Squares? Thanks, S?bastien Plante Institut des Sciences de la Mer de Rimouski (ISMER) -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

Dear R-list members Here goes a question on data frames: I want to create a new data frame excluding some records (rows) from an existing one. Considering the data frame "fish.dat" at the end of this message, I can make a boxplot(Ring~Radius) and identify the points at rows 11 and 25 as outliers. With the command fish.dat[c(11, 25),] I can identify which fishes are related to

I've been poking around with GLMs (on which I am *not* an expert) on behalf of a student, particularly binomial (standard logit link) nested models with overdispersion. I have one possible bug to report (but I'm not confident enough to be *sure* it's a bug); one comment on the general inconsistency that seems to afflict the various functions for dealing with overdispersion in GLMs

Hello all, Here goes one of my first functions. I want to make a nonparametric multiple sample comparison with unequal sample sizes (see Zar?s Biostatistical Analysis, 3rd. Ed., pg. 201 Example 10.11, pg. 288 Example 11.10). In the real world, I want to compare samples of fish length captured with different fishing gears. After using the Kruskal-Wallis test I want to check the differences

Hi, I'm still trying to figure out that GLM procedure in SAS. Let's start with the simple example: PROC GLM; MODEL col1 col3 col5 col7 col9 col11 col13 col15 col17 col19 col21 col23 =/nouni; repeated roi 6, ord 2/nom mean; TITLE 'ABDERUS lat ACC 300-500'; That's the same setup that I had in my last email. I have three factors: facSubj,facCond and facRoi. I had this pretty

I've read something about this problem, but I don't know how can i avoid this problem. Why the order of the factors give different results? I suppose it's because the order of the factors, i've just changed "lcc" from the first position to the last in the model, and the significance change completely >

Dear experts, I have a few quick questions related to GLMs: 1) Suppose my response is of the type Yes/No, How can I control which response I'm modelling? 2) How can I perform Type III tests? Is it with -> drop1(mymodel, test="Chisq") ? 3) I have a numerical variable which I converted to an ordered factor as shown below, but in the summary results of the logistic regression I

Dear all, Last autumn there was some discussion on the list of the warning Warning message: fitted probabilities numerically 0 or 1 occurred in: (if (is.empty.model(mt)) glm.fit.null else glm.fit)(x = X, y = Y, when fitting binomial GLMs with many 0 and few 1. Parts of replies: "You should be able to tell which coefficients are infinite -- the coefficients and their standard errors will

Dear all, I have been trying to investigate the behaviour of different weights in weighted regression for a dataset with lots of missing data. As a start I simulated some data using the following: library(MASS) N <- 200 sigma <- matrix(c(1, .5, .5, 1), nrow = 2) sim.set <- as.data.frame(mvrnorm(N, c(0, 0), sigma)) colnames(sim.set) <- c('x1', 'x2') # x1 & x2 are

Dear all, I have a question on glm, family binomial. I do not see significant differences between the levels of a factor (treatment) if all data for a level is 0; and replacing a 0 for a 1 (in fact reducing the difference), then I detect the significant difference that I expected. Is there a way to overcome this problem? or this is an expected behaviour ? Here is an example: s <-

Hello everybody, I'm testing the randomForest package in order to do some simulations and I get some trouble with the prediction of new values. The random forest computation is fine but each time I try to predict values with the newly created object, I get an error message. I thought I was because NA values in the dataframe, but I cleaned them and still got the same error. What am I

Dear all, I wonder if it is possible to obtain marginal tests for effects in generalized linear models. Indeed, the anova function produces sequential tests and it doesn't have any "type" argument to specify that we would like marginal tests instead, as in the similar anova function for lme objects. Thanks a lot for your help! Eve CORDA Office national de la chasse et de la faune