similar to: Different sings for correlations in OLS and TSA

Hello, Any advice or pointers for implementing Sobel's test for mediation in 2-level model setting? For fitting the hierarchical models, I am using "lme4" but could also revert to "nlme" since it is a relatively simple varying intercept model and they yield identical estimates. I apologize for this is an R question with an embedded statistical question. I noticed that a

Dear All, I am trying to put some Confidence intervals on some regressions from a linear model with no luck. I can extract the fitted values using 'predict', but am having difficulty in getting at the confidence intervals, or the standard errors. Any suggestions would be welcome Cheers Guy Using Version 2.1.0 (2005-04-18) on a PC vol.mod3 <-

Dear R-list, I would like to use the lattice library to show several groups on the same graph. Here's my example : ## the data f1 <- factor(c("mod1","mod2","mod3"),levels=c("mod1","mod2","mod3")) f1 <- rep(f1,3) f2 <-

Hi Dears, When I introduce an interaciton in a piecewise model I obtain some quite unusual results. If that would't take u such a problem I'd really appreciate an advise from you. I've reproduced an example below... Many thanks x<-rnorm(1000) y<-exp(-x)+rnorm(1000) plot(x,y) abline(v=-1,col=2,lty=2) mod<-lm(y~x+x*(x>-1)) summary(mod) yy<-predict(mod)

Dear List-members, I found a strange behaviour in the lqs function. Suppose I have the following data: y <- c(7.6, 7.7, 4.3, 5.9, 5.0, 6.5, 8.3, 8.2, 13.2, 12.6, 10.4, 10.8, 13.1, 12.3, 10.4, 10.5, 7.7, 9.5, 12.0, 12.6, 13.6, 14.1, 13.5, 11.5, 12.0, 13.0, 14.1, 15.1) x1 <- c(8.2, 7.6,, 4.6, 4.3, 5.9, 5.0, 6.5, 8.3, 10.1, 13.2, 12.6, 10.4, 10.8, 13.1, 13.3, 10.4, 10.5, 7.7, 10.0, 12.0,

I can use stepAIC on an lme object in 1.6.2, but I get the following error if I try to do the same in 1.7.0: Error in lme(fixed = resp ~ cov1 + cov2, data = a, random = structure(list( : unused argument(s) (formula ...) Does anybody know why? Here's an example: library(nlme) library(MASS) a <- data.frame( resp=rnorm(250), cov1=rnorm(250), cov2=rnorm(250),

Hi All, I was wondering if someone could help me to solve this issue with lmer. In order to understand the best mixed effects model to fit my data, I compared the following options according to the procedures specified in many papers (i.e. Baayen <http://www.google.it/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0CDsQFjAA

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

Greetings, I am interested in creating a stepwise fixed order regression function. There's a function for this already called add1( ). The F statistics are calculated using type 2 anova (the SS and the F changes don't match SPSS's). You can see my use of this at the very end of the email. What I want: a function to make an anova table with f changes and delt R^2. I ran into

vps*CLI> iax2 show channels Channel Peer Username ID (Lo/Rem) Seq (Tx/Rx) Lag Jitter JitBuf Format 0 active IAX channels vps*CLI> core show channels Channel Location State Application(Data) 0 active channels 0 active calls vps*CLI> sip show channels Peer User/ANR Call ID Seq (Tx/Rx) Format

Hi I would like to shorten mod1 <- nls(ColName2 ~ ColName1, data = table, ...) mod2 <- nls(ColName3 ~ ColName1, data = table, ...) mod3 <- nls(ColName4 ~ ColName1, data = table, ...) ... is there something like cols = c(ColName2,ColName3,ColName4,...) for i in ... mod[i-1] <- nls(ColName[i] ~ ColName1, data = table, ...) I am looking forward to help Christof

Hi! I'm trying an asterisk-FWD connection. I'm using X-Lite OR SIPPS as the IP phone. I configured the X-Lite and SIPPS to use GSM codec. Whe I call FWD, I get this info on the channels when the call has not been stablished yet: sip show channels Peer User/ANR Call ID Seq (Tx/Rx) Lag Jitter Format 192.246.69.223 613 1770bf3430d 00102/00000

Hi all, i'm trouble with codec setup on an asterisk machine 1.4.18 and some Thomson ST2030 as extensions. In the users.conf file for internal extension i have: disallow=all allow=g729 allow=alaw allow=ulaw Without any codec installed (i mean with original g729 of asterisk) all go fine, calling from an extension to one other: Peer User/ANR Call ID Seq (Tx/Rx) Format

Has anyone seen this before? This channel is consistently present on both of my asterisk servers. Sometimes they disappear for a few seconds and then come back. It always has the same Call ID. voip1*CLI> sip show channels Peer User/ANR Call ID Seq (Tx/Rx) Lag Jitter Format 192.168.0.102 (None) df92fb1b-8a 00101/03059 00000ms 0000ms UNKN

In Ubuntu 10.04, from the R command line install.packages('TSA',dep=TRUE) downloads & installs about 100 packages from CRAN, including some big ones like Zelig. It's only supposed to depend on leaps, locfit, mgcv, & tseries & through them akima, lattice, quadprog, zoo, & stats. Not a big problem of course, but I wondered if anyone else had noticed this behaviour.

Dear all, I have been using the contrast library in my teaching for the last couple of years and am right in the middle of this year's round. In the last week R has been updated to version 2.14.0 on our computers. This has had the unfortunate effect of meaning the contrasts library no longer works, as the Design library is no longer available. I wonder if anyone has a fix for this...or

Hi, I'm wondering if this is normal asterisk behaviour: asterisk*CLI> sip show channels Peer User/ANR Call ID Format Hold Last Message Expiry Peer 10.12.0.2 (None) 3c2f7ff2975e-wp 0x0 (nothing) No Rx: PUBLISH <guest> 10.12.0.2 (None) 3c2f7f21b71b-9q 0x0 (nothing) No

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 R Users, After some research I haven't find what I want. I'm manipulating a dataframe with 70k rows and 30 variables, and I run out of memory when exporting this in a *.txt file after some computing I have used : > memory.size()/1048576.0 [1] 103.7730 and I make my export : > write.table(cox,"d:/tablefinal2.txt",row.names=F,sep=';') >

Hi R Users, I'm fairly new to R (about 3 months use thus far.) I wanting to use the arimax function from the TSA library to incorporate some exogenous inputs into the basic underllying arima model.Then with that newly model of type arimax, I would like to make a prediction. To avoid being bogged down with issues specific to my own work, I would like to refer to readers to the example