search for: bmy

I am doing a reanalysis of results that have previously been published. My hope was to demonstrate the value of adoption of more modern regression methods in preference to the traditional approach of univariate stratification. I have encountered a puzzle regarding differences between I thought would be two equivalent analyses. Using a single factor, I compare poisson models with and without

Hi I was wondering if there is a permutation test available in R for linear models with continuous dependent covariates. I want to do a test like the one shown here. bmi<-rnorm(100,25) x<-c(rep(0,75),rep(1,25)) y<-rnorm(100)+bmi^(1/2)+rnorm(100,2)*x+bmi*x H0<-lm(y~1+x+bmi) H1<-lm(y~1+x+bmi+x*bmi) anova(H0,H1) summary(lm(y~1+x+bmi)) But I want to use permutation testing to

Im doing a project using Rcommander. I have a dataset with 5 variables, BMI being one of them. I would like to find the variance, standard deviation, range, median, etc of BMI. However, everytime I type a command into the script window, (such as var(BMI$BMI,na.rm=TRUE) )an error message pops up saying Object "BMI" not found. I can graph it, work it, do t-test, etc,. It's listed as

Hello, Say I have 2 columns, bmi and gender, the first being all the values and the second being male or female. How would I subset this into males only and females only? I have searched these fora and read endlessly about select[] and split() functions but to no avail. Also the table is not ordered. bmi gender -> bmi gender + bmi gender 1 24.78 male

Hola a todos, -Estoy estudiando el efecto de dos genotipos (~tratamientos) en la aparición de síndrome metabólico (MetS) con datos longitudinales recogidos a tiempo 0,7,10,15,20 y 25 años. -He hecho un dataframe con las siguientes variables MetS: Síndrome Metabólico (Si=1,No=0) bmi: Indice de masa corporal (IMC) cuando se produce la conversión a MetS+ . Para los que permancen MetS-, esta variable

Dear R-helpers, First I apologize if my question is quite simple I have a large datasets which more 100 variables. For a research I need to change all name of variables with add one or more letters on each variables. For example, > data(Pima.tr) > Pima.tr[1:5,] npreg glu bp skin bmi ped age type 1 5 86 68 28 30.2 0.364 24 No 2 7 195 70 33 25.1 0.163 55 Yes 3 5

Hi. I am new here, and I am writing this Winbugs code with BRugs. n=length(bi.bmi) Lagegp=13 Lgen=2 Lrace=5 Lstra=15 Lpsu=2 #model gen x race bi.bmi.model=function(){ # likelihood for (i in 1:n){ bi.bmi[i]~ dbern(p[i]) logit(p[i])<- a0 + a1[agegp[i]]+a2[gen[i]]+a3[race[i]] + a12[agegp[i], gen[i]] + gam[stra[i]]+ u[psu[i],stra[i]] } # constraints for a1, a2, a3, a12 a1[1]<-0.0

hi everyone, wondering if you could help me with a novice problem. I have a data frame called subjects with a height and weight variable and want to calculate a bmi variable from the two. i have tried: attach(subjects) bmi <- (weight)/((height/100)^2) but it comes up with the error: Warning messages: 1: In Ops.factor(height, 100) : / not meaningful for factors 2: In Ops.factor((weight),

Hello, I'm not sure if I'm in the right place with my question... I'm running R on Windows and wrote a function and saved it as .R file. It looks like this: bmi <- function(weight, height) { bmi <- weight / height^2 bmi } If I want to use this function, I have to mark everything and then press Ctrl-R. But then everything single line is executed on the command line,

Hi All, I have a small dataset named das (43 cases) in which I am trying to create a binary outcome (1/0) based on the following code: if (das$age>65 && das$bmi>30) {das$danger<-1} else das$danger<-0 I am setting a flag called 'danger' to 1 of the subject is over 65 and has a BMI > 30. I find that my statement evaluates the first record in the data.frame and

Dear R-experts, I guess I have a problem with my fast function (fast tau estimator) here below. Indeed, zero errors look highly suspicious. I guess there is a bug in my R code. How could I correct my R code ? # install.packages( "robustbase" ) # install.packages( "MASS" ) # install.packages( "quantreg" ) # install.packages( "RobPer" ) #

Dear R-experts, I am trying to bootstrap (and average) the median squared error evaluation metric for a robust regression. I can't get it. What is going wrong ? Here is the reproducible example. ############################# install.packages( "quantreg" ) library(quantreg) crp <-c(12,14,13,24,25,34,45,56,25,34,47,44,35,24,53,44,55,46,36,67) bmi

Hello, How would you go about handling the following situation? This is on R 2.12.0 on Ubuntu 32-bit. I have a wrapper function to lm. I want to pass in a subset argument. First, I just thought I'd use "...". ## make example reproducible set.seed(123) df1 <- data.frame(age = rnorm(100, 50, 10), bmi = rnorm(100, 30, sd = 2)) ## create a wrapper using

I am new to R, and I am trying to cut a continuous variable BMI into different categories and can't figure out how to use it. I would like to cut it into four groups: <20, 20-25, 25-30 and >= 30. I am having difficulty figuring the code for <20 and >=30? Please help. Thank you. -- View this message in context:

Hello I would like to pass a model formula as an argument to the with.mids function from the mice package. The with.mids functon fits models to multiply imputed data sets. Here's a simple example library(mice) #Create multiple imputations on the nhanes data contained in the mice package. imp <- mice(nahnes) #Fitting a linear model with each imputed data set the regular way works

....Equity 7 -0.003419938 AAPL.UW.Equity 8 0.009316770 BHI.UN.Equity 9 -0.007564103 BHI.UN.Equity 10 -0.040175688 BHI.UN.Equity 11 -0.017900404 BHI.UN.Equity 12 -0.010278197 BHI.UN.Equity 13 -0.034339518 BHI.UN.Equity 14 -0.006739317 BHI.UN.Equity 15 -0.013637913 BMY.UN.Equity 16 -0.015900449 BMY.UN.Equity 17 0.004566210 BMY.UN.Equity 18 -0.002097902 BMY.UN.Equity 19 0.014716188 BMY.UN.Equity 20 0.006560773 BMY.UN.Equity 21 0.000343053 BMY.UN.Equity 22 0.010712869 COP.UN.Equity 23 -0.012823868 COP.UN.Equity 24 -0.000132556...

Hello, If you want to bootstrap a statistic, I suggest you use base package boot. You would need the data in a data.frame, see how you could do it. library(boot) bootMedianSE <- function(data, indices){ d <- data[indices, ] fit <- rq(crp ~ bmi + glucose, tau = 0.5, data = d) ypred <- predict(fit) y <- d$crp median(y - ypred)^2 } dat <-

Hi:   I am using the following code to read a data set in txt in R and using the follwoing model. But it seems to give me an error ' expected variable name error pos 134022'. Any help is greatly appreciated.   Code: anal.data <- read.table("nausea.txt", header=T, sep="\t") list(names(anal.data)) attach(anal.data) n.samples <- dim(anal.data) [1] # number of data

I forgot, you should also set.seed() before calling boot() to make the results reproducible. Rui Barradas On 5/22/2018 10:00 AM, Rui Barradas wrote: > Hello, > > If you want to bootstrap a statistic, I suggest you use base package boot. > You would need the data in a data.frame, see how you could do it. > > > library(boot) > > bootMedianSE <- function(data,

Hello, I’m not sure if this is the right place to ask beginner questions. If not, please direct me to the appropriate place. I’m writing my first llvm program and I’m trying to call an intrinsic, but failing. So far this is what I have: declare ccc i32 @llvm.x86.bmi.pdep.32(i32, i32) @.str2 = private unnamed_addr constant [4 x i8] c"%d\0A\00", align 1 declare i32 @printf(i8*, ...)