search for: smoker

...Excuse my English. I've installed Samba+OpenLDAP as a PDC. Everything works fine but Samba ignores completely group information. Linux is ok. Any clue? I'm going crazy here! Here's the sittuation: user: fish1 home dir: /home/reaml/swim/fish1 primary group: swimmers other groups: smokers Directory of smoker's group: /home/realm/smokers Here's an 'ls -l' on smoker's parent dir: drwxrws--- 19 cigarr smokers 2208 Jul 27 2010 smokers Here's the share: [smokers] comment = Smoking path = /home/realm/smokers valid users = @smokers...

...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 50 Female 20.40345 1902.9 72.9 35.4 7.3 Normal Non-Smoker 4 32 Female 35.97525 3328.4 163.3 20.0 4.1 High Smoker 5 43 Female 25.58279 2501.6 121.1 19.5 0.0 High Smo...

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: /usr/lib64/dovecot/libdovecot.so.0(+0xc8ec4) [0x7f226d036ec4] -> /usr/li...

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 these labels? In lattice terminology, cex is used to scale...

...ull 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: 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: /usr/lib64/dovecot/libdovecot.so.0(+0xc8ec4) [0x7f226d036ec4] -> /u...

...; 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: 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: /usr/lib64/dovecot/libdovecot.so.0(+0xc8ec4) [0x7f226d036ec4] -&gt...

...en) 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 not really know how to do that either, to be honest :(). I have done this: gensmoke <- table(data$gender,data$smoke) #save table old.digits = options("digits") # store the number of digits options(digits=3)...

Dear UseRs, Suppose I have data regarding smoking habits of a prospective cohort and wish to determine the risk ratio of colorectal cancer in the smokers compared to the non-smokers. What do I do at the end of the study with people who die of heart disease? Can I just censor them exactly the same as people who become uncontactable or who die in a plane crash? If not, why not? I'm thinking that heart disease isn't independent of smoking e...

Dear All I am trying to include sampling weights in multilavel regression analysis using packege lme4 using following codes print(fm1 &lt;- lmer(DC~sex+age+smoker+alcohol+fruits(1|setting), dataset,REML = FALSE), corr = FALSE) print(fm2 &lt;- lmer(DC~sex+age+smoker+alcohol+fruits(1|setting), dataset,REML = FALSE), corr = FALSE,weights=sweight) The problem is both the codes&nbsp;giving&nbsp;me exactly the same results.is this "weights" n...

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 would like is to see this represented in a matrix with colors representing th...

This is probably very simple but I'm new to R so apologies for being stupid. I have some data with No coded as 0 and yes coded as 1. e.g. id sex alcohol smoker 1 M 0 1 2 F 1 0 3 M 0 0 I realise I can covert the numerical variable back to a factor by falcohol<-factor(alcohol,levels=0:1) levels<-c("No","Yes") fsmoker<-factor(smoker,levels=0:1) levels<-c("No","Yes&quo...

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 Kendalls) correlation meth...

I'm having some problems getting CentOS to serve up my Smokeping pages ( http://oss.oetiker.ch/smokeping/) However, I'm having problems getting the pages to serve up correctly. Here is a quick run down of what I've done. (1) wget and untar to /usr/local/smokeping (2) chown -R root:root /usr/local/smokeping (3) modifying to the following files to reflect my environment (not sure if

...harlottesville, Virginia ------------------ ######################################################## # Compute the Hosmer-Lemeshow 'goodness-of-fit' test cd.full_model = glm(formula = Collaterals ~ CHF + Age + CABG + relevel (as.factor (num.obst.vessels),"one") + Current.smoker + DM + HTN + ace.inhibitor + MI, family = binomial(link = "logit")) hosmerlem = function(y, yhat, g=10) { cutyhat = cut(yhat, breaks = quantile(yhat, probs=seq(0, 1, 1/g)), include.lowest=TRUE) obs = xtabs(cbind(1 - y, y) ~ cutyhat) expect = xtabs(cbind(1 - yhat, yhat...

...35, 45, 55, 65, 75, 85) par(mfrow=c(2,2)) #Chart 1 options( survey.lonely.psu = "adjust" ) svyhist (~age_p, subset (nhis, xsmoke=='Current SMK'), breaks=MyBreaks, ylim = c(0,0.040), main= " ", col="red", xlab="Current Smoker's Age ") lines (svysmooth(~age_p, bandwidth=5,subset(nhis, xsmoke=='Current SMK')), lwd=2) #Chart 2 options( survey.lonely.psu = "adjust" ) svyhist (~age_p, subset (nhis, xsmoke=='Former SMK'), breaks=MyBreaks, ylim = c(0,0.040), main...

.... I'm trying to impute data, where a fictitional dataset is defined as... set.seed(110) n <- 500 test <- data.frame(smoke_status = rbinom(n, 2, 0.6), smoke_amount = rbinom(n, 2, 0.5), rf1 = rnorm(n), rf2 = rnorm(n), outcome = rbinom(n, 1, 0.3)) # smoke_status (0, 1, 2) is c("non-smoker, "ex-smoker", "current_smoker"), and # smoke_amount (0, 1, 2) is c("light", "moderate", "heavy") # rf1 and rf2 are two other risk factors (for illustration purposes - real data set has more risk factors) # artificially NA some of these values tes...

...ake a set of numbers and aggregate them like you can in SPSS? For example, could you calculate the percentage of people who smoke based on a dataset like the following: smoke = 1 non-smoke = 2 variable 1 1 1 2 2 1 1 1 2 2 2 2 2 2 When aggregated, SPSS can tell you what percentage of persons are smokers based on the frequency of 1's and 2's. Can R statistical package do a similar thing? Thanks, Nat

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

...trying to read a .txt file, do a couple of select if statements on my data, and then finally use the ?table function to get frequency counts on the data. Specifically, i am looking at answering the following question: What is the frequency of Grade 7 students in the province of Alberta who are smokers? I am having some problems: 1)i cannot get the column names to show up when print to screen 2)I cannot seem to skip variables properly when i choose certain other variables 3)i cannot get the combination of Select If statements to work to produce a different table with my new criteria Here...

Hi, I've written code to extact a pumf file in R, subset it, and weight it like you would do in SPSS. My code is below & it works great. My question is: how do i then calculate the frequencies of smokers (1) versus non-smokers (2) after having weighted my file? or even the process that SPSS is going through to aggregate the data? Thanks, Nat Here is my code: myfile<-("c:/test2.txt") mysubset<-myfile mysubset$Y_Q02 <-mysubset$DVSELF <-NULL mysubset2<-mysubset mysubs...