similar to: glm pairwise interaction coefficients

Hi, The program below work very well. (snps = c('rs621782_G', 'rs8087639_G', 'rs8094221_T', 'rs7227515_A', 'rs537202_C')) Selec = todos[ , colnames(todos) %in% snps] head(Selec) But, I have a data set with 1.000 columns and I need extract 70 to use (like snps in command above). This 70 snps are in a file. So I create a file to extract them with

Hello R users, I'm working with a time-series of several years and to analyze it, I?m using GAM smoothers from the package mgcv. I?m constructing models where zooplankton biomass (bm) is the dependent variable and the continuous explanatory variables are: -time in Julian days (t), to creat a long-term linear trend -Julian days of the year (t_year) to create an annual cycle - Mean temperature

Hello Everyone - I want to print a number of results from lme function objects out to a txt file. How could I do this more efficiently than what you see here: out2 <- capture.output(summary(mod2a)) out3 <- capture.output(summary(mod3)) out4 <- capture.output(summary(mod5)) out5 <- capture.output(summary(mod6)) out6 <- capture.output(summary(mod7))

Hi, I am trying to use piecewise linear regression to approximate a nonlinear function. Actually, I don't know how many linear functions I need, therefore, I want build an array of regression models to automate the approximation job. Could you please give me any clue? Attached is ongoing code: rawData = scan("c:/zyang/mass/data/A01/1.PRN", what=list(numeric(),numeric())); len =

> Hi Gareth Blades & Doug, > > Thanks so much for for the feedback. I have searched on lot of documents > but couldn't able to find clear answer regarding it. > > I hope you guys replies are very much help all in aterisk community. > > > Thanks & Regards, > > Vidura Senadeera, > > Network Engineer, > > Debug Solutions > > Sri Lanka .

This should be an easy one, but I haven't been able to figure it out, x<-c(2, 4, 6, 9, 10) #how do I remove a single value x<-x[-2] # will remove the second element of the vector not the number 2 This is part of a loop that looks a little like this... x<-c(2, 4, 6, 9, 10) remove.value<-4 #then I need to remove the value returned by the loop. Thanks, Josh -- View this

Dear R users, I need help with translating these SAS codes into R with lme()? I have a longitudinal data with repeated measures (measurements are equally spaced in time, subjects are measured several times a year). I need to allow slope and intercept vary. SAS codes are: proc mixed data = survey method=reml; class subject var1 var3 var2 time; model score = var2 score_base var4 var5 var3

What is the difference when including or not including the intercept when using lm()? x.noint <- lm(weight ~ group - 1))# omitting intercept x <- lm(weight ~ group)) This has nothing to do with forcing the intercept to 0, correct? Thank you kindly, Michael [[alternative HTML version deleted]]

Dear R friends A while a go I sent an email to the epi-list and later to the help-list and no answer could fully illuminate my question. So Im trying again with a more specific matter. Im trying to work on a script (function) to analyse data from a diagnostic test meta-analysis with random effects. This was first described by an author using SAS witn PROC NLMIXED. Im not an expert in R and much

Hi everyone, I want to fit the following mixed effect model Y_ij = b_0i + b_1i * (t_ij*grp_ij == 1) + b_2i * (t_ij*grp_ij == 2) + v_0i + v_1i*t_ij + e_ij with a different covariance matrix of random effects for each group. (Y is the response t is time grp is the group indicator b 's are fixed effects v 's are random effects) I know that this is possible in SAS

Hi All I am trying to translate a proc mixed into a lme() syntax. It seems that I was able to do it for part of the model, but a few things are still different. It is a 2-level bivariate model (some call it a pseudo-3-level model). PROC MIXED DATA=psdata.bivar COVTEST METHOD = ml; CLASS cluster_ID individual_id variable_id ; MODEL y = Dp Dq / SOLUTION NOINT; RANDOM Dp Dq / SUBJECT = cluster_ID

Sorry for the long letter! I have recently started using R. For the start I have tried to repeat examples from Milliken & Johnson "Analysis of Messy Data - Analysis of Covariance", but I can not replicate it in R. The example is chocolate chip experiment. Response variable vas time to dissolve chocolate chip in seconds (time), covariate was time to dissolve butterscotch chip

I am familiar with pairwise t-tests, corrections for multiple testing, etc. however I have a problem whose answer I have not found after extensive R-help archive and Google searching. What I have done in the past: I have N items which are measured, exposed to a condition, and then measured again. I wish to know if the condition changes the items so I can perform a t-test. Better yet I can

Dear all, I am looking for an equivalent of the pairwise.t.test() function using a complex survey design. Any suggestions on this or how to account for weights using the pairwise.t.test itself? Thanks, V [[alternative HTML version deleted]]

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Dear list I am comparing the results of several different experimental setups. With kruskal.test() I can test if there is any difference at all in any of them, if I understand it correctly. But now, when there is a difference, how do I generate a (half-) table of pairwise comparisons, using e.g. wilcox.test(), to find the ones where the difference actually occurs. I guess I don't have to

Does anybody know if there is some sort of "pairwise" option for handling missing cases in lm and computing the relevant statistics? I would be much obliged if anyone could help... Regards Alan Simpson Roberts Research Group

Hi, I have a number of genes (columns) for which I want to examine pairwise associations of genotypes (each row is an individual)...For example (see data below), I would like to compare M1 to M2, M2 to M3, and M1 to M3 (i.e. does ac from M1 tend to be found with bc from M2 more often than expected.) Down stream I will be performing chi square tests for each pair. But I am looking for a way to

I want to create a MDS plot with pairwise Fst values derived from a population genetics project. My Fst values are in a tab-delimited file (lower triangle only) that I view with Excel. When I use the cmdscale command I get the message: Error in cmdscale(x) : Distances must be result of dist or a square matrix In addition: Warning messages: 1: "^" not meaningful for factors in:

Hello R listing, I did two-way anova on lm. Further question the investigator interested in is what two treatments are different? I am looking for a command which could do pairwise comparison for every treatment. Could anyone help me out? Thanks a bunch Kevin