similar to: Follow-up to Reply: Overdispersion with binomial distribution

I understand how R treats NAs but in the situation below it would be nice to have a na.skip argument to paste so it does not convert the NAs to "NA"s and simply skips those elements of the vector for pasting. > x [1] "2.13" "2.3" NA NA "2.83" NA > paste(x, "0", sep="") [1] "2.130" "2.30"

I have data on the proportion of clutches experiencing different fates (e.g., 4 different sources of mortality) for 5 months . I need to test 1) if the overall proportion of these different fates is different over the entire study and 2) to see if there are monthly differences within (and among) fate types. Thus, I am pretty sure this is an RM analysis -( I measure the same quadrats each month).

I have a data set that I display using barplot. I don't know what you call it, but when I look at it, it looks like this: > lsu (0,0.1] (0.1,0.2] (0.2,0.3] (0.3,0.4] (0.4,0.5] (0.5,0.6] A 0.052631579 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 B 0.000000000 0.000000000 0.001007049 0.003021148 0.000000000 0.000000000 E 0.200000000 0.000000000

I have a stock price dataset a snippet of which is: > plcm60[1:15, c(1,3,4,5,6,7)] DATE BIDLO ASKHI PRC VOL RET 1 1/2/03 9.450 9.79 9.700 1531819 0.018907 2 1/3/03 9.670 9.94 9.940 1582192 0.024742 3 1/6/03 9.830 10.05 9.960 1843298 0.002012 4 1/7/03 9.835 10.38 10.350 1412441 0.039157 5 1/8/03 10.220 10.67 10.260 961400 -0.008696 6 1/9/03 10.280

I have a nested ANOVA, with a fixed factor "tmt" nested within "site" (random). There are missing values in the data set. aeucs, tmt and site have been defined as objects I have tried: model1=lme(aeucs~tmt,random=~1|tmt/site) I get the following error message Error in na.fail.default(list(aeucs = c(0.833333333, 1, 1, 1, 1, 1, 0.666666667, : missing values in object I

Dear All: Could you please tell me how I can draw figure formatted like figure 4.18 of MASS (4th) with the attached data set? Thanks Zhongming Yang --------------------------------- -------------- next part -------------- An embedded and charset-unspecified text was scrubbed... Name: sample.txt Url: https://stat.ethz.ch/pipermail/r-help/attachments/20050316/abfdb85e/sample.txt

hello, i need to calculate ci's for each of 4 groups within a dataset, to be able to infere about differences in the variable "similarity". the problem is that data within groups is dependent, as assigned by the blocking-factor "site". my guess was to use a block bootstrap but samples within in these blocks / sites are not of same length. i was not able to find a method to

Hello everyone I'm a beginner in Stats and R, I'm using R 2.10.1. I need to create a multivariate qq plot, there is 8 variable group with each has 55 number of input. An example of what I did so far, just to get my point out: > data=read.csv(file.choose(),header=T) > data country village group av_expen P2ary_ed no_fisher 1 Cook Islands Aitutaki D

hello, i'd appreciate help with my glmer. i have a dependent which is an index (MH.index) ranging from 0-1. this index can also be considered as a propability. as i have a fixed factor (stage) and a nested random factor (site) i tried to model with glmer. i read that it's possible to use a quasibinomial distribution, for this kind of data, which i than actually did - but firstly (1)

Hi all, I have a question about linear model with interaction: I created a data frame df like this: >df V1 V2 V3 V4 V5 1 6.414094 c t a g 2 6.117286 t a g t 3 5.756922 a g t g 4 6.090402 g t g t ... which holds the response in the first column and letters (a,c,g,t) in the other columns. I am interested to see if there are interactions between the neigbouring letters

Hi all, I have a question about linear model with interaction: I created a data frame df like this: >df V1 V2 V3 V4 V5 1 6.414094 c t a g 2 6.117286 t a g t 3 5.756922 a g t g 4 6.090402 g t g t ... which holds the response in the first column and letters (a,c,g,t) in the other columns. I am interested to see if there are interactions between the neigbouring letters

Dear All, I'm not sure of the interpretation of interactions with contrasts. Can anyone help? I do an ANCOVA, dryweight is covariate, block and treatment are factors, c4 the response variable. model<-aov(log(c4+1)~dryweight+treatment+block+treatment:block) summary(model); Df Sum Sq Mean Sq F value Pr(>F) dryweight 1 3.947 3.947 6.6268 0.01076 *

Dear List members, I would like to ask for advise on quantile regression in R. I am trying to perform an analysis of a relationship between species abundance and its habitat requirements - the habitat requirements are, however, codes - 0,1,2,3... where 0<1<2<3 and the scale is linear - so I would be happy to treat them as continuos The analysis of the data somehow does not work, I am

On Mon, Dec 16, 2013 at 04:16:29PM -0800, Michael Dalton wrote: > Commit 2613af0ed18a ("virtio_net: migrate mergeable rx buffers to page frag > allocators") changed the mergeable receive buffer size from PAGE_SIZE to > MTU-size, introducing a single-stream regression for benchmarks with large > average packet size. There is no single optimal buffer size for all >

On Mon, Dec 16, 2013 at 04:16:29PM -0800, Michael Dalton wrote: > Commit 2613af0ed18a ("virtio_net: migrate mergeable rx buffers to page frag > allocators") changed the mergeable receive buffer size from PAGE_SIZE to > MTU-size, introducing a single-stream regression for benchmarks with large > average packet size. There is no single optimal buffer size for all >