similar to: Discrepancy between R and SPSS in 2-way, repeated measures ANOVA

Dear friends - this is a very simple question - I have a data frame 'data.frame': 87 obs. of 3 variables: $ ID : int 1 1 1 2 2 2 3 3 3 4 ... $ prep : num 1.18 1.38 1.34 1.93 2.38 2.24 1.17 1.13 1.21 1.89 ... $ postp: num 0.63 0.71 0.75 1.01 1.12 1.07 0.87 0.64 0.7 0.8 ... - 29 persons (ID) each measured three times before and after an intervention: prep and postp - I need

Dear R user: I am studying the allele data of two populations. the following is the data: a1 a2 a3 a4 a5 a6 a7 a8 a9 a10 a11 a12 a13 a14 a15 a16 a17 pop1 0.0217 0.0000 0.0109 0.0435 0.0435 0.0000 0.0109 0.0543 0.1739 0.0761 0.1413 0.1522 0.1087 0.0870 0.0435 0.0217 0.0109 pop2 0.0213 0.0213 0.0000 0.0000 0.0000 0.0426 0.1702 0.2128 0.1596 0.1809 0.0957 0.0745 0.0106

I want to ascertain the basis of the table ranking, i.e. the meaning of "extreme", in Fisher's Exact Test as implemented in 'fisher.test', when applied to RxC tables which are larger than 2x2. One can summarise a strategy for the test as 1) For each table compatible with the margins of the observed table, compute the probability of this table conditional on the

I am using windows XP with R 2.8.1 I am generating a lattice plot of annual rain patterns using the following function: > xyplot(rain.stats$min+ rain.stats$max + rain.stats$ave ~ rain.stats$month |rain.stats$year, lty = 1, data = rain.stats, type = c("l","l", "l"), col = c("red", "blue", "green"), distribute.type =

Dear list members, I have a 2-factor ANOVA where the summary.lm output looks like this (using treatment contrasts): Value Std. Error t value Pr(>|t|) (Intercept) 0.0389 0.0220 1.7695 0.0817 as.factor(Block)1 0.0156 0.0066 2.3597 0.0215 as.factor(Block)2 -0.0018 0.0037 -0.4857 0.6289 as.factor(Block)3 -0.0007 0.0026 -0.2812 0.7795

Dear all, I'm using R 2.8.1 under Windows Vista on a dual core 2,4 GhZ with 4 GB of RAM. I'm trying to reproduce a result out of "Analysis of Financial Time Series" by Ruey Tsay. In R I'm using the fGarch library. After fitting a ar(3)-garch(1,1)-model > model<-garchFit(~arma(3,0)+garch(1,1), analyse) I'm saving the results via > result<-model

Hi All, Is there an easy way to reduce a data.frame to 1 'id' per row while keeping information from the other rows of that same variable, if applicable? e.g.: # data multi[1:15,] id r n wi wi.tau z k alliance a.rater eml treatment outcome o.rater german 1 100 0.2800000 44 41 21.72514 0.2876821 210 <NA> <NA> <NA> <NA>

Hello, Setup: I have data with ~10K observations. Observations come from 16 different laboratories (labs). I am interested in how a continuous factor, X, affects my dependent variable, Y, but there are big differences in the variance and mean across labs. I run this model, which controls for mean but not variance differences between the labs: lm(Y ~ X + as.factor(labs)). The effect of X is

Peter You question is difficult to answer without more information about the distribution of your residuals. Different residual patterns call for different transformations to stabilize the variance. One very common form of heterocedasticity is increasing variance with increasing values of an independent predictor, i.e. the variance of the residuals of y=x increase as x increases. In this case a

I apologize if this question is not completely appropriate for this list. I have been using SAS for a while and am now in the process of learning some C and R as a part of my graduate studies. All of the statistical packages I have used generally yield p-values as a default output to standard procedures. This week I have been reading "Testing Precise Hypotheses" by J.O. Berger

---------------------------- Original Message ---------------------------- Subject: Different results from survreg with version 2.6.1 and 2.10.1 From: nathalcs at ulrik.uio.no Date: Fri, March 19, 2010 16:00 To: r-help at r-project.org -------------------------------------------------------------------------- Dear all I'm using survreg command in package survival.

Hi, Most networks are using either 10.x.x.x or 172.x.x.x or 192.168.x.x , but was curious If I can use the range 223.255.255.x for my backbone routing, this looks like a nice block to use as most ppl don''t use this, specially if you build quite a big intranet what about the whole 223.x.x.x block, will this be used on the internet? sorry if its a stupid questions Thanks Sew

Dear all I have a problem with splitting up a data frame called ReVerb: ?? str(ReVerb) `data.frame': 92713 obs. of 16 variables: $ CHILD : Factor w/ 7 levels "ABE","ADA","EVE",..: 1 1 1 1 1 1 1 1 1 1 ... $ AGE : Factor w/ 484 levels "1;06.00","1;06.16",..: 43 43 43 99 99 99 99 99 99 99 ... $ AGE_Q : num 2.0 2.0 2.0 2.4 2.4

Hello Everyone,   I'm learning how to perform various statistical analyses in R. I'm checking my understanding by replicating examples from my SAS books. Below is an attempt to replicate a Cox Proportional Hazards model with a time-varying covariate. I think I'm doing this correctly but am not completely sure. I would appreciate it if someone could double-check my results. In case

Hello, I would like to perform a Partial least square discriminate analysis (PLSDA) in R. To do this I use the package mixOmics. I could perform the PLSDA in R. however I would also like to perform a leave-one-out cross validation in order to assess the performance of my model. My supervisor told me that I should focus on the R2/Q2 ratios. However when I read the instruction for running the

Dear R users, I have been using the pdMat class "pdIdnot" (from the mgcv package)instead of "pdIdent" to avoid overflow in GLMM fits with the MASS package function glmmPQL, of the following form: fit1 <- glmmPQL(fixed=y0~-1+xx0, random=list(gp=pdIdent(~-1+zz0)), family=binomial) # vulnerable to overflow fit2 <- glmmPQL(fixed=y0~-1+xx0,

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

Suppose I am reading data from a file and the data contains some outliers. I want to know if it is possible in R to automatically detect outliers in a dataset and remove them -- View this message in context: http://n4.nabble.com/How-to-detect-and-exclude-outliers-in-R-tp1017285p1017285.html Sent from the R help mailing list archive at Nabble.com.

Hello Summary: I have ifplugd managing eth0 and wlan0 (both dhcp). When I plug in the cat5 (which brings up eth0) applications which have already bound to wlan0 stop working, obviously because wlan0 for some reason is unable to get non-local packets out that interface. I figured if poodoze is able to have both interfaces working, then linux must surely be able to do it as well. The

I've had a nagging problem for a long time where I cannot connect to my printers from Windows 2000 or XP. I'm using CUPS printing and Samba 3.0.14. I've seen people report a similar problem where they can print, but can't open the queue or manage the printers. The message you get is "Operation could not be complete. Access is denied." I found the problem to be