similar to: Several factors same levels

I still need to do some repetitive statistical analysis on some outcomes from a dataset. Take the following as an example; id sex hiv age famsize bmi resprate 1 M Pos 23 2 16 15 2 F Neg 24 5 18 14 3 F Pos 56 14 23 24 4 F Pos 67 3 33 31 5 M Neg 34 2 21 23 I want to know if there are statistically detectable differences in all of the continuous variables in

Dear All I am trying to include sampling weights in multilavel regression analysis using packege lme4 using following codes print(fm1 <- lmer(DC~sex+age+smoker+alcohol+fruits(1|setting), dataset,REML = FALSE), corr = FALSE) print(fm2 <- lmer(DC~sex+age+smoker+alcohol+fruits(1|setting), dataset,REML = FALSE), corr = FALSE,weights=sweight) The problem is both the

I apologize in advance if this is posted all ready I have not been able to find any information about it. I have this data frame and I want to sort smoking by retlevel. Age Gender BMI Calories Fat Fiber Alc retlevel Smoking 1 64 Female 18.87834 1828.0 63.4 14.7 0.0 Normal Non-Smoker 2 25 Female 20.64102 1517.4 59.1 5.9 0.0 Normal Smoker 3

Hi, We are seeing a frequent assertion failure on LMTP delivery with 2.3.0. This only appears to happen on CentOS/RHEL 7. Jan 24 08:30:58 smoker-devautomerge-c7-1 dovecot: lmtp(29540): Panic: file lib-event.c: line 148 (event_unref): assertion failed: (event != current_global_event) Jan 24 08:30:58 smoker-devautomerge-c7-1 dovecot: lmtp(29540): Error: Raw backtrace:

Hi Aki, Please see below: Thank you -Nick # gdb /usr/libexec/dovecot/lmtp ./3445 Using host libthread_db library "/lib64/libthread_db.so.1". Core was generated by `dovecot/lmtp'. Program terminated with signal 6, Aborted. #0 0x00007f297814d1f7 in raise () from /lib64/libc.so.6 Missing separate debuginfos, use: debuginfo-install dovecot-2.3.0-8.cp1162.x86_64 (gdb) back #0

How do I change the font size in the facet labels along the edges of the plot? For example (from the ggplot help file): p<-ggplot(tips, sex ~ smoker, aesthetics=list(x=tip/total_bill)) gghistogram(p) In this plot, the facet labels are "smoker: No", "smoker: Yes", "sex: Female", "sex: Male". What command can I use to reduce the font size of

Hi everyone, I have a certain number of samples and I want to visualize the groups those samples belong to. For example, suppose to have three variables, age, sex, and smoker/nonsmoker, and three samples, S1, S2, S3. S1 is 35, male, nonsmoker S2 is 24, female, nonsmoker S3 is 24, female, smoker at the end I have the following data frame: S1 S2 S3 age 35 24 30 sex M F F smk N N S What I

Hello there, I have two groups (men and women) and I know per group how many of them smoke or don't smoke (women 40 of 200; men 100 of 300). I would like to know how I can compare in R if men and women differ significantly in their smoking. However, because there are more men in the sample than women I cannot just compare the number of smokers and non-smokers in both groups, right?! (I would

Hi, we are trying to do structural equation modelling on R. However, one of our predictor variables is categorical (smoker/nonsmoker). Now, if we want to run the sem() command (from the sem library), we need to specify a covariance matrix (cov). However, Pearson's correlation does not work on the dichotomous variable, so instead we produced a covariance matrix using the Spearman's (or

I'm trying to do a Hosmer-Lemeshow 'goodness of fit' test on my logistic regression model. I found some code here: http://sas-and-r.blogspot.com/2010/09/example-87-hosmer-and-lemeshow-goodness.html The R code is above is a little complicated for me but I'm having trouble with my answer: Hosmer-Lemeshow: p=0.6163585 le Cessie and Houwelingen test (Design library): p=0.2843620

Full_Name: Sven Sandin Version: 2.0.0 OS: SuSE Linux 9.0 Submission from: (NULL) (81.227.17.135) Have just compiled and installed R-2.0.0.tar.gz running SuSE9.0. The function vcov do not accept "coxph" object as input any longer. The same R-program running R1.9.1 do work. R-program attached below. Exporting the coxph object from R2.0.0 to R1.9.1 I get vcov ouput in R1.9.1. Exporting

I have a data set with 3 variables V1, V2, V3. If there are 2 data points have the same values on both V1 and V2, I want to delete one of them which has smaller V3 value. i.e., in the data below, I want to delete the first observation. How can I do that ? Thanks in advance! V1 V2 V3 3 3 1 3 3 4 -- View this message in context:

Hi all, I've got this graphics question which really should be easy. I want to shade an area between bounds under a curve. A suitable beginning seems to be the following: > plot(dnorm,-4,4) > segments(-4,0,4,0) > segments(-2,0,-2,dnorm(-2)) > segments(2,0,2,dnorm(2)) It is the area between -2 and 2 which I want to shade (or something similar). Hints anyone? Robert

Hi, I have a factor and I would like to find the most frequent level. I think my current approach is a bit long winded and I was wondering if there was a more elegant way to do it: x <- factor(sample(1:0, 5,replace=TRUE)) levels(x)[ which( as.logical((table(x) == max(table(x)))) == TRUE ) ] (The length of x will always be an odd number, so I wont get a tie in max()) Thanks,

I have data in a file named hands.dat, which is given at the end of this question. (It's from a stats textbook example on anova). I'd like to do an aov on this, which I guess would be d <- read.table("~/hands.dat", header=TRUE) aov(Bacterial.Counts ~ Water + Soap + Antibacterial.Soap + Alcohol.Spray, data=d) but this fails. Do I need to break d$Method up into columns for

the data is : >table.8.3<-data.frame(expand.grid( marijuana=factor(c("Yes","No"),levels=c("No","Yes")), cigarette=factor(c("Yes","No"),levels=c("No","Yes")), alcohol=factor(c("Yes","No"),levels=c("No","Yes"))), count=c(911,538,44,456,3,43,2,279))

Hi! This is a bug in the new event code. Can you get a full gdb backtrace? Aki > On January 27, 2018 at 6:58 AM "J. Nick Koston" <nick at cpanel.net> wrote: > > > Hi, > > We are seeing a frequent assertion failure on LMTP delivery with 2.3.0. This only appears to happen on CentOS/RHEL 7. > > Jan 24 08:30:58 smoker-devautomerge-c7-1 dovecot:

On 08/13/2017 05:18 AM, ken wrote: > Also, cowboys scoff, but I always wear a grounded wrist strap when > handling electronics. It's a good idea, especially in low-humidity climates. Also noteworthy: the air moving through a hose can cause a vacuum's hose or attachment to build up a static charge, which is another reason it can be a bad idea to use a vacuum in a computer.

I can't comment on frailtypack issues, but would like to mention that coxme will handle nested models, contrary to the statement below that "frailtypack is perhaps the only .... for nested survival data". To reprise the original post's model cgd.nfm <- coxme(Surv(Tstart, Tstop, Status) ~ Treatment + (1 | Center/ID), data=cgd.ag) And a note to the poster-- you should

Hello helpers, This is probably quite simple, but I'm stuck. I want to create a summary statistics table with frequencies and summary statistics for a large number of variables. The problem here is that (1) there are two different classes of categories (sex, type of substance abuse and type of treatent) which overlap, (2) the data for different variables should be presented in different