search for: gestational

Dear R What does NaN mean? I recently did a correlation on a batch of data for some reason it didn't like one column cor(sleep,use="complete.obs") BodyWt BrainWt SlowSleep ParaSleep TotalSleep BodyWt 1.00000000 0.95584875 -0.3936373 -0.07488845 -0.3428373 BrainWt 0.95584875 1.00000000 -0.3867947 -0.07427740 -0.3370815 SlowSleep -0.39363729

Hi, As part of the analysis of a longitudinal study, I have a list, called wg, on R 1.3.1 for windows created as follows - wg <- by(xx, xx$mrn, function(xx) lm(xx$maternal.wt ~ xx$gestation, data=xx)) xx is a data.frame, and so wg contains the results of 293 linear regressions, as lm objects. Now what I want is to extract the 293 intercepts and regression coefficients as a vector, and

We are trying to read a sas export file into R. (Works fine on Mac by ftping the export file from solaris box and then importing to R with read.xport, this gives rational numbers etc.) Trying to do this on an IBM Power p655 running linux, reading the same sas export file as used on MAC, in this case the results are not rational. Many of the numbers are interpreted as INF etc. Is it possible

...Sent: Friday, January 18, 2002 5:33 AM To: r-help at stat.math.ethz.ch Subject: [R]: z-scores for different factor levels Hi there. I am trying to generate z-scores for a variable according to it's factor level, leaving this result in the original dataframe. ie. standardised birth weight for gestational age in weeks. BWT is birthweight GEST is gestational age in weeks (equivalent to the factor level) I can generate the factor level mean & SD using tapply tapply(BWT,GEST,mean) etc. but this creates a new array of means & SDs. - Can anyone suggest how I can slot these means & SDs by fa...

Well, as Andrew shouted at me again this weekend it's getting to be about that time.... :-) :-). Yep, we're thinking we should do an official 2.2 Samba release early next week, so if you have anything you can't live with, now would be a good time to tell us.... :-). It's been gestating longer than an elephant, so it's really time to let it out into the world and do its own

...of the graphics device. My graphics are correct horizontally, but are confined to about one-fifth of the vertical extent of the graphics device. As a result they look odd, and are almost usless for analysis. They do seem, however, to be the correct graphics (In this case maternal weight against gestational age for two groups of women). I have read the manual for the lattice commands, and the nlme command, and indeed for the plain plot command, but failed to get any ideas. Roughly, I need to tell lattice to use the full vertical extent of the display device. If it helps, I can e-mail the offending...

>From owner-r-help at stat.math.ethz.ch Wed Jan 9 10:08:44 2002 > >Does anyone know how to access/list the value labels stored in a SPSS .sav >file? >At the moment I can read in variables using read.spss() (foreign package), >but have to power up SPSS to check value labels. > >Stuart There may be a solution to this in R, and I would love to hear it, but I was just faced

Hello, I'm trying to create birth centile charts from about 90000 birth weights using the package lmsqreg. As this is not a CRAN package I have problems finding any examples, which explain how to estimate the LMS parameters needed for the equivalent degrees of freedom. (I only have some basic understanding of statistics, I roughly understand the formulas, but then I don't know how to

...Sent: Friday, January 18, 2002 5:33 AM To: r-help at stat.math.ethz.ch Subject: [R]: z-scores for different factor levels Hi there. I am trying to generate z-scores for a variable according to it's factor level, leaving this result in the original dataframe. ie. standardised birth weight for gestational age in weeks. BWT is birthweight GEST is gestational age in weeks (equivalent to the factor level) I can generate the factor level mean & SD using tapply tapply(BWT,GEST,mean) etc. but this creates a new array of means & SDs. - Can anyone suggest how I can slot these means & SDs by fa...

...ns: ? (fitmodel <- lmer(modelformula,data,family=binomial(link=logit),nAGQ=1)) where modelformula is set up in another part of the program.? Locations are treated as random and the model is random intercept only.? The program is set?to run 1000 simulations. ? I have temperature, five levels of gestational age (GA), birth wieght (BW) and?four? other categorical pedictors, all binary.? I scaled everything so that all my slopes are in the range of -5.2 to 1.6 and?variances from .01 to .08.? I have a couple of categories of GA that have small probabilities (<.10).? I'm using a structured sampli...

There is currently a bug in the arima function. Namely, for arima models with differencing or seasonal differencing, the innovation variance estimator uses the wrong denominator whenever xreg is non-null. This is the case, for example, when fitting an ARIMA(p,1,q) model with a drift term (common in financial applications). I reported the bug (and a fix) at

The bug repository is like an elephant: It doesn't forget, but the gestation period is long. In the present case, it is clear that something is not right, but someone needs to have sufficient recall and insight to check that your proposed fix is not unfixing a deliberate change. We should get to it eventually. (For some value of "we" not including "me"...) -pd On 20 Apr

After an extremely long gestation, a new version of the coxme package is now available on CRAN. This function fits very general mixed-effects Cox models using an lmer-like syntax. For instance here is a model that fits a random treatment effect, using data from a multi-study collaboration. fit <- coxme(Surv(pg.time, pg.stat) ~ stage + trt + (trt|site) + stata(site), data=

Dear All, We are happy to announce, after a long gestation, the release of the new version of FRAILTYPACK (version 2.2-9.5) which is now available from CRAN. The package fit general frailty models using penalized likelihood estimation, for clustered or recurrent events. For instance : -- ADDITIVE FRAILTY MODELS for proportional hazards models with two correlated random effects (intercept

After an extremely long gestation, a new version of the coxme package is now available on CRAN. This function fits very general mixed-effects Cox models using an lmer-like syntax. For instance here is a model that fits a random treatment effect, using data from a multi-study collaboration. fit <- coxme(Surv(pg.time, pg.stat) ~ stage + trt + (trt|site) + stata(site), data=

Dear All, We are happy to announce, after a long gestation, the release of the new version of FRAILTYPACK (version 2.2-9.5) which is now available from CRAN. The package fit general frailty models using penalized likelihood estimation, for clustered or recurrent events. For instance : -- ADDITIVE FRAILTY MODELS for proportional hazards models with two correlated random effects (intercept

Hi, I am very confused with constructing the wilcox.test in R. I have two populations 'original' and 'test'. I want to know if the 'test' is generally 'lower' than original. I use alpha of 0.05. So do I write the function as wilcox.test(original, test, alternative="l")? or wlcox.test(original, test, alternative = "g")? or wilcox.test(test,

Does R, or will R, be integrating the changes to the Chambers/Lucent S language under their version 5.0? If not already, then when? John Thaden Little Rock, Arkansas, USA -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe"

I noticed that the 3.2.1 release cycle is about to start. Is there any chance that this fix will make it into the next version of R? This bug is fairly serious: getting the wrong variance estimate leads to the wrong log-likelihood and the wrong AIC, BIC etc, which can and does lead to suboptimal model selection. If it's not fixed, this issue will affect every student taking our time series

Dear All, I'am looking for examples showing that correlation does not imply causality, the targeted audience consists of undergraduate students (their first year at the university but in the BioMathStat track). All practicals are under R. I was able to extract this from R datasets: ### begin data(sunspots) data(lynx) spots <- window(sunspots, freq = 1, start = 1880, end = 1900) lnx <-