search for: alcohol

...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 each category, with boolean entries? Or is there a way to do this more cleanly? Data file (hands.dat) ============ Bacterial.Counts Method 74 Water 84 Soap 70 Antibacterial.Soap 51 Alcohol...

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)) >fit3<-glm(count~.^3,poisson,table.8.3) >sumary(fit3) ... Residual deviance: -1.5543e-15 on 0 degrees of freedom AIC: 65.043 > drop1(fit3,.~.,test="Chisq&quo...

...et a tie in max()) Thanks, ------------------------------------------------------------------- Rajarshi Guha <rxg218 at psu.edu> <http://jijo.cjb.net> GPG Fingerprint: 0CCA 8EE2 2EEB 25E2 AB04 06F7 1BB9 E634 9B87 56EE ------------------------------------------------------------------- Alcohol, an alternative to your self - 'Alcohol' by the Bare Naked Ladies

...quot;, "Threitol", "Galactinol", "Galactitol") Class<-c("aminos","aminos","aminos","aminos","aminos","sugars","sugars","sugars","glycerols","glycerols","sugar alcohols","sugar alcohols","sugar alcohols") set.seed(5) Ratio<-rnorm(13, 0.5, 3) df<-data.frame(Compound, Class,Ratio) library(lattice) P<-barchart(data=df,Ratio~Compound|Class) However, I would like to have the bars attached to an imaginary y=0-line so that they go...

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" not meant...

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","...

...ies (sex, type of substance abuse and type of treatent) which overlap, (2) the data for different variables should be presented in different ways -- sometimes with relative frequencies, other times with mean values. The table would finally look something like: All Male Female Alcohol Drug ... Age (mean) (mean) ... Sex (% male) (freq) (freq) ... Alcohol CS (mean) (mean) ... ... ... Data is in a data frame with quite a lot of columns (variables) and each row represents a single case. I have found out that part of this can be done with tapply, fo...

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

..."X45.54..06." "X55.64..06." > [16] "X65.74..06." "X75.84..06." "X85.94..06." > [19] "X95.and.more..06." > > head(mort$X) > [1] Accidental poisoning > [2] Addiction to prescription medication > [3] Alcohol abuse and alcohol-related psychosis > [4] Asthma > [5] Blood and hematopoietic disorders > [6] Cerebrovascular disease > 65 Levels: 1109 1869 36 ... Viral hepatitis > > En cambio mira el dataset "mortality": > > names(mortality) > [1] "15-24 (79)"...

Hi useRs, Many studies of the link between red meat and colorectal cancer use Cox proportional hazards with (among other things) a gender covariate. If it is true that men eat more red meat, drink more alcohol and smoke more than women, and if it is also true that alcohol and tobacco are known risk factors then why does it make sense to "adjust" for gender? I would think that in this case some of the risk that should be properly attributed to the bad habits will actually end up being attribut...

I need to compare gene expression differences between multiple line pairs of alcohol preferring and non-preferring rat lines. I have 5 such line pairs, 3 are unrelated but two were derived independently from the same parent stock. For each line, there are 10 samples. I'll be testing multiple genes, but for simplicity assume just one gene whose expression is measures as geneExpr...

...73 23.11 1 49 25.3 97.2 52 1 2 144 0.01 4.41 28.61 0 55 28.87 2.06 63 1 > dimnames(heart.data$x)[[2]] [1] "sbp" "tobacco" "ldl" "adiposity" "famhist" "typea" [7] "obesity" "alcohol" "age" Note that the printed variable names do not use the column names of the matrix. In contrast, in S-PLUS the names are used; the printout begins: x.sbp x.tobacco x.ldl x.adiposity x.famhist x.typea x.obesity x.alcohol --please do not edit the information below--...

...13] "X35.44..06." "X45.54..06." "X55.64..06." [16] "X65.74..06." "X75.84..06." "X85.94..06." [19] "X95.and.more..06." > head(mort$X) [1] Accidental poisoning [2] Addiction to prescription medication [3] Alcohol abuse and alcohol-related psychosis [4] Asthma [5] Blood and hematopoietic disorders [6] Cerebrovascular disease 65 Levels: 1109 1869 36 ... Viral hepatitis En cambio mira el dataset "mortality": > names(mortality) [1] "15-24 (79)" "25-34 (79)" "3...

...cp /tmp/sample-call-file\ > /var/spool/asterisk/outgoing/${RANDOM} > done > sleep 10 > > Here's what's wrong with this snippet: > > 1) I don't know why I chmod the 'template.' No idea whatsoever. > Alcohol may have been involved. > > 2) I hate single character variable names. I love alcohol. > > 3) cp is ill advised. For a testing script, it was easy. For a > production application, use mv. > > In use, I would execute it specifying how many call files to create, > like 50. T...

Steve, would you be willing to share that "quick bash script"? James Cass <http://goog_987864563> jcass78 at gmail.com On Wed, Aug 19, 2015 at 12:11 PM, Steve Edwards <asterisk.org at sedwards.com> wrote: > On Wed, 19 Aug 2015, Dominique Haeber wrote: > > Hi Barry Flanagan, >> >> Barry Flanagan <barryf-lists at flanagan.ie> schrieb am Mit, 19.

Estimados Queremos con el paquete FactoMineR hacer este tipo de tabla de mortalidad que lea los datos desde de una tabla csv Realizamos lo que viene en la ayuda y es muy interesante, sin embargo cuando mandamos a leer desde la tabla csv original de los autores no hace el análisis porque algo falta y no nos percatamos de que es. Adjunto tabla original Saludos cordiales #ESTO ES LO QUE

...e easy. > do(2) * lm( length ~ width + shuffle(sex), data=KidsFeet ) Intercept width sexG sigma r-squared 1 9.646822 1.693137 -0.3057453 1.026824 0.4246224 2 11.416739 1.453416 0.4860068 1.013323 0.4396534 > tally( ~ sex & substance, HELPrct ) substance sex alcohol cocaine heroin Total female 36 41 30 107 male 141 111 94 346 Total 177 152 124 453 > tally( ~ sex | substance, HELPrct ) # auto switch to proportions > for conditional distributions substance sex alcohol cocaine h...

...e easy. > do(2) * lm( length ~ width + shuffle(sex), data=KidsFeet ) Intercept width sexG sigma r-squared 1 9.646822 1.693137 -0.3057453 1.026824 0.4246224 2 11.416739 1.453416 0.4860068 1.013323 0.4396534 > tally( ~ sex & substance, HELPrct ) substance sex alcohol cocaine heroin Total female 36 41 30 107 male 141 111 94 346 Total 177 152 124 453 > tally( ~ sex | substance, HELPrct ) # auto switch to proportions > for conditional distributions substance sex alcohol cocaine h...

...its minimum and maximum values would be used as the knots beyond which regression would be linear rather than to know that the function automatically calculates boundary knots which are not appropriate and so he needs to override them. Just so that I am clear, an example. Assume that my variable *alcohol*? has values from 1 to 100 and I want to specify natural splines at knots 20,40 and 60, expecting linearity would hold below 20 and above 60. Currently, I have to specify knots as 40 and boundary knots as 20 and 60 as?ns(alcohol , knots = c(40), Boundary.knots = c(20,60)) If I incorrectly assume t...