search for: alcoholics

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, 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,

Dear R users, i am using the following code to produce barcharts with lattice: Compound<-c("Glutamine", "Arginine", "Glutamate", "Glycine", "Serine", "Glucose", "Fructose", "Raffinose", "Glycerol", "Galacglycerol", "Threitol", "Galactinol", "Galactitol")

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

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")

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

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

Hola. Como te dijo Carlos, el problema está en los nombres de las columnas y en los nombres de las filas. Cuando hice la importación (con dd<-read.csv('mortality.csv'), tuve problemas con las filas de nombre: - Malignant tumour of the larynx trachea bronchus and lungs - Malignant tumour of the lip pharynx and mouth - Other endocrinological metabolic and nutritional conditions

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

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 geneExpression. Alcohol

library(glmpath) data(heart.data) # heart.data is a list, $y a vector, $x a matrix data <- data.frame(x=I(heart.data$x), y = heart.data$y) > data[1:2,] x.1 x.2 x.3 x.4 x.5 x.6 x.7 x.8 x.9 y 1 160 12 5.73 23.11 1 49 25.3 97.2 52 1 2 144 0.01 4.41 28.61 0 55 28.87 2.06 63 1 > dimnames(heart.data$x)[[2]] [1] "sbp"

Hola, El problema está con el nombre de las columnas y las filas del fichero que importas. Mira este detalle tras hacer la importación: > names(mort) [1] "X" "X15.24..79." "X25.34..79." [4] "X35.44..79." "X45.54..79." "X55.64..79." [7] "X65.74..79." "X75.84..79."

Am 19.08.2015 um 19:07 schrieb Steve Edwards: > Please don't top post. > > On Wed, 19 Aug 2015, James Cass wrote: > >> Steve, would you be willing to share that "quick bash script"? > > There's no magic in the script, but here it is, embarrassing myself: > > cp sample-call-file /tmp/ > chmod +x /tmp/sample-call-file >

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

One of the products of Project MOSAIC (funded by an NSF CCLI grant) has been the development of an R package with the goal of making it easier to use R, especially in teaching situations. We're not quite ready to declare that we've reached version 1.0, but version 0.4 does represent a fairly large step in that direction. You can find out more about the package on CRAN or by installing

One of the products of Project MOSAIC (funded by an NSF CCLI grant) has been the development of an R package with the goal of making it easier to use R, especially in teaching situations. We're not quite ready to declare that we've reached version 1.0, but version 0.4 does represent a fairly large step in that direction. You can find out more about the package on CRAN or by installing

Dear Maintainer, Thanks for the excellent package splines. I am writing this email to request you to consider a suggestion I have with regards to the function ns. While trying to rework an example from a textbook, I couldn't call ns with appropriate arguments to reproduce the results. The package documentation also couldn't help me find the problem. Finally, I found a stack exchange