similar to: xyplot of a specific ID number

Dear R list members, I'd like to make a graph of coefficients of the intercept, variable 1, and variable 2 (and possibly the interaction between variable 1 and variable 2). When I use the lmList function as attached below, it shows a nice coefficient graph. > PACRP.lis <- lmList(PAffect ~ CRPC + CRPT + CINT | ID, redinteract) > coef(PACRP.lis) > PACRPlis.coef <-

The formula attribute of the builtin CO2 dataset seems a bit strange: > formula(CO2) Plant ~ Type + Treatment + conc + uptake What is one supposed to do with that? Certainly its not suitable for input to lm and none of the examples in ?CO2 use the above.

Hi! Here is some output from an str command: > str(CO2) Classes ‘nfnGroupedData’, ‘nfGroupedData’, ‘groupedData’ and 'data.frame': 84 obs. of 5 variables: $ Plant : Ord.factor w/ 12 levels "Qn1"<"Qn2"<"Qn3"<..: 1 1 1 1 1 1 1 2 2 2 ... $ Type : Factor w/ 2 levels "Quebec","Mississippi": 1 1 1 1 1 1 1 1 1 1 ... $

Hello all, I have a data frame with year, month, species, fishing gear and catch (Y, M, S, F, C) and I want the sum of C by Y for species "A" and fishing gear "trawl". I tried things like aggregate(C[S=="A" & F=="trawl"], list (Year = Y[S=="A" & F=="trawl"]), fun=sum), but it didn't worked. To overcome this problem I did

Is there a conventional way to test for nested factors? I.e., if 'a' and 'b' are lists of same-length factors, does each level specified by 'a' correspond to exactly one level specified by 'b'? The function below seems to suffice, but I'd be happy to know of a more succinct solution, if it already exists. Thanks, Tim. --- "%nested.in%" <-

Hi, I'm using 'geepack' to run Generalized Estimating Equations. I'm aware that I can use anova to compare two models, but would it be possible to test QIC on R? It seems that there were similar questions a couple of years ago, but the question has not been answered yet. I'd appreciate if someone could show me the code! Thank you, Sachi [[alternative HTML version

Hi, I'm running GEE using geepack. I set corstr = "ar1" as below: > m.ar <- geeglm(L ~ O + A, + data = firstgrouptxt, id = id, + family = binomial, corstr = "ar1") > summary(m.ar) Call: geeglm(formula = L ~ O + A, family = binomial, data = firstgrouptxt, id = id, corstr = "ar1") Coefficients:

Dear forum members, What is the formula to calculate denominator degrees of freedom (den df) for nonlinear mixed-effect models with covariates? My model is similar to a CO2 uptake example from Pinheiro and Bates (2000, page 376). In this CO2 dataset, there are two treatments and two types (84 observations in total), but den df for each parameter of the model is 64. Isn’t it too high? Your

Dear R users, Using R lme function, I found that both fixed and random effects of variable A on variable B are significant. Now, I'd like to analyze what variables are predicting differences in the slope. In other words, I'd like to know what variables (e.g., variable C) are predicting individual differences in the effects of A on B. I have many data points for A and B for each

An unwanted side effect of the new restrictions on abrreviated names. The anova.coxph command, in a slavish copy of anova.lm etc, returns a data frame with column labels of loglik Chisq Df Pr(>|Chi|) If one tries to extract the final column of the table errors result since it is not a standard R variable name. > afit <- anova(lm(conc ~ uptake, CO2)) > afit$P [1]

Hi, I just started a new course this semester on R, I never used it in my life and i'm stuck on these questions from 3 days, it would be really nice if someone could explain me the answers with the relative commands. thanks a lot in advance The following 7 questions are based on the CO2 dataset of R. 1) How many of the plants in CO2 are Mc2 for Plant? 2) How many are either Mc2 or Mn2?

I need to compute the mean and the standard deviation of a data set and would like to have the results in one table/data frame. I call tapply() two times and do then merge the resulting tables to have them all in one table. Is there any way to tell tapply() to use the functions mean and sd within one function call? Something like tapply(data$response, list(data$targets, data$conditions), c(mean,

Dear R-users I have 5 dependent variables (y1 to y5) and one independent variable (x) and 3 conditioning variables (m, n, and 0). Each of the conditioning variables has 2 levels. I created 2*4 panel plots. xyplot(y1+y2+y3+y4+y5 ~ x | m*n*o,layout = c(4,2)) I would like to reorder the 8 panels. I tried to use index.cond (e.g., index.cond = list(c(1,3,2,4,5,7,6,8)) but it didn't work out.

Hi, Probably this is a very simple question for all the programmers, but how do you change from 32-bit builds (default) to 64-bit builds? I've been trying to run Anova to compare two models, but I get the following error message: Error: cannot allocate vector of size 1.2 Gb R(3122,0xa0ab44e0) malloc: *** mmap(size=1337688064) failed (error code=12) *** error: can't allocate region ***

Dear All, I am using R for my research and I have two questions about it: 1) is it possible to create a loop using a string, instead of a numeric vector? I have in mind a specific problem: Suppose you have 2 countries: UK, and USA, one dependent (y) and one independent variable (y) for each country (vale a dire: yUK, xUK, yUSA, xUSA) and you want to run automatically the following regressions:

Hi, I'm analyzing my data using GEE, which looks like below: > interact <- geeglm(L ~ O + A + O:A, + data = data1, id = id, + family = binomial, corstr = "ar1") > summary(interact) Call: geeglm(formula = lateral ~ ontask + attachment + ontask:attachment, family = binomial, data = firstgroupnowalking, id = id, corstr = "ar1") Coefficients:

Dear R-users, A while ago, Deepayan Sarkar suggested some code that uses the group argument in bwplot to create some 'side-by-side' boxplots (https://stat.ethz.ch/pipermail/r-help/2010-February/230065.html). The example he gave was relatively specific and I wanted to generalize his approach into a function. Unfortunately, I seem to have some issues passing the correct arguments to the

Hello, How could I generalize the following statement for an arbitrary number of columns instead of 7? result[[i]]<-lm( returns[,i] ~ factors[,1] + factors[,2] + factors[,3] + factors[,4] + factors[,5] + factors[,6] + factors[,7] ) Thank you, Mathgurl [[alternative HTML version deleted]]

Does anyone know if there is a way to specify regressors dynamically rather than explicitly? More specifically, I have a data set in "long format" that details a number of individuals and their responses to a question (which can be positive, negative, or no answer). Each individual answers as many questions as they want, so there are a different number of rows per individual. For each

The following works fine with the x11 device, though it may well be that an initial plot is overwritten. With a pdf or postscript device, I get two plots, the first of which still has the red border from having the focus, while the second is the plot that I want. library(lattice); library(grid) plt <- xyplot(uptake ~ conc, groups=Plant, data=CO2) print(plt)