similar to: [Fwd: Re: mixed models for analyzing survey data with unequal selection probability]

Han-Lin I don't think I have seen a reply so I will suggest that maybe you could try a different approach than what you are thinking about doing. I believe the current best practice is to use the weights as a covariate in a regression model - and bytheway - the weights are the inverse of the probabilities of selection - not the probabilities. Fundamentally, there is a difficulty in making

Dear R users, I am querying an Oracle database using sqlQuery() from the RODBC package. What I would like to do is embed my R Sql query in a for loop. For example suppose the data.frame "vessel" contains two columns: vessel[,1]= vessel id and vessel[,2]=permit year I am using "vessel" as an input in a SQL based function that require inputs of permit year and vessel id

Hello, I want to select same data of a data frame that has information of fish catches: catch, vessel, trip, day, month, year where trip is the fishing operation number done in each day by each vessel, i.e. for each day, any vesel can have several register of catch corresponding to each operation. >From this data frame I want to do: 1. Add the catches of all trip by vessel and day; then

Hi all. Has anyone ever done a Deming Regression in R? I'm wondering if there's a simple way to do it. Thanks for all your help! -Ed -- View this message in context: http://www.nabble.com/Deming-Regression-tp17949318p17949318.html Sent from the R help mailing list archive at Nabble.com.

Hi, I'm trying to create a plot showing the density distribution of some shipping data. I like the look of violin plots, but my data is not continuous but rather binned and I want to make sure its binned nature (not smooth) is apparent in the final plot. So for example, I have the number of individuals per vessel, but rather than having the actual number of individuals I have data in the

Hi, I wonder if anyone can help me with specifying a right model for my analysis. I am a beginner to lme methods and though have spent already many hours studying from various books an on-line helps, I was unfortunately not able to find a solution to my problem on my own. Data structure: I studied escape behavior of three species of a prey to a predator. The prey specimens (many) were in a

Hello- I have run an ANOVA on 4 treatments with unequal sample sizes (n=9,7,10 and 10). I want to determine where my sig. differences are between treatments using a Bonferroni test, and have run the code: pairwise.t.test(Wk16, Treatment, p.adf="bonf") I receive an error message stating that my arguments are of unequal length: Error in tapply(x, g, mean, na.rm = TRUE) :

Dear R users, I have a question about the patterned variance-covariance structure for the random effects in linear mixed effect model. I am reading section 4.2.2 of "Mixed-Effects Models in S and S-Plus" by Jose Pinheiro and Douglas Bates. There is an example of defining a compound symmetry variance-covariance structure for the random effects in a split-plot experiment on varieties of

Hello, When I run the following glm model: modelresult=glm(CID~WS+SS+DV+DS, data=kimu, family=binomial) I get the following warning messages: 1: glm.fit: algorithm did not converge 2: glm.fit: fitted probabilities numerically 0 or 1 occurred What I am trying to do is model my response variable (CID: correct bird identification) as a function of the predictor variables weather state (WS), sea

Dear Douglas and R-help, Does lme assume normal distribution AND equal variance among groups like anova() does? If it does, is there any method like unequal variance T-test (Welch T) in lme when each group has unequal variance in my data? Thanks, Shirley

Hi, I am doing multiple regression, and have several X variables that are categorical. I read that I can use dummy or contrast codes for that, but are there any special rules when there're unequal #observations in each groups (4 females vs 7 males in a "gender" variable)? Also, can R generate these codes for me? THanks.

Hello, I have some patient data for my masters thesis with three groups (n=16, 19 & 20) I have completed compiling the results of 7 tests, for which one of these tests the variances are unequal. I wish to perform an ANOVA between the three groups but for the one test with unequal variance (<0.001 by both bartlett and levene's test) I am not sure what to do. I thought i would run

I am using levelplot() from lattice with grids that have unequal cell sizes. This means that the boundary between two cells is not always half-way between nodes, as levelplot() assumes. The result is that some cell sizes are rendered incorrectly, which can be painfully obvious if using relatively large cells. Is there any work-around? I am using the conditioning capability of lattice and

Dear R users, I need to merge two data frames based on both equal and unequal comparisons. The "sqldf" package used to work well , but today, I cannot resolve the following error by reinstallation of the sqldf package. Can anyone suggest a different way to perform this kind of merge function? Thank you, Ding > DMRlog2pbde47DMS <- sqldf("select * from DMR_log2pbde47 as a

Hi, i'm looking for an R function to fit a one-way ANOVA with one factor containing 10 levels. The factor levels have different numbers of observations (varying between 20 to 40). For most of the dependent variables i'm testing there are unequal variances among the factor levels. I see the function oneway.test: oneway.test(variable ~ factor, data=dataset) which by default does not

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Hello All, Would anyone be able to help me understand how R computes a quantile-quantile plot for comparing two data samples with unequal sample sizes? Normally, the procedure should be to rearrange the larger data sample into n equally-spaced parts using interpolation, where n is the sample size of the smaller sample, and then plot the matching data pairs. I tried using different plotting

This is in followup to a thread on R-help with subject "Sampling". I claim that R does the wrong thing by default when sampling with unequal probabilities without replacement - the selection probabilities are not proportional to 'prob', for any draw after the first: I suggest that R do what S-PLUS now does (though you're free to choose a better implementation). What S-PLUS

Hi, I am having problems getting cohen's kappa to work. I have been using the function: ><-ckappa(x,y) from the psy package. I am trying to test for inter-observer reliability, I have 2 observers and 26 categories, however, the two observers might not necessarily have the same range of categories (I have unequal score ranges). However, I thought R could cope with this. Each time I

I am getting a repeated error when I try to run a logistic regression in R 2.8.1 >(glm(prop1~x1,data=glm1,family=binomial("logit"),weights=nt1)) Error in model.frame.default(formula = prop1 ~ x1, data = glm1, weights = nt1, : invalid type (list) for variable 'x1' x1 is multistate categorical (3 categories). 2 of the categories have 12 observation, one has 9. Is this what