similar to: Is there any design based two proportions z test?

Dear Md Kamruzzaman, To answer your second question first, you could just use the svychisq() function. The difference-of-proportion test is equivalent to a chisquare test for the 2-by-2 table. You don't say how you computed the confidence intervals for the two separate proportions, but if you have their standard errors (and if not, you should be able to infer them from the confidence

Dear Md Kamruzzaman, I've copied this response to the r-help list, where you originally asked your question. That way, other people can follow the conversation, if they're interested and there will be a record of the solution. Please keep r-help in the loop See below: On 2024-01-17 9:47 p.m., Md. Kamruzzaman wrote: > > Caution: External email. > > > Dear John >

I'm resending this message because I did not include a subject line in my first posting. Apologies for the inconvenience! Tanja > Hello, > > I'm trying to fit a generalized linear mixed model to estimate diabetes prevalence at US county level. To do this I'm using the glmer() function in package lme4. I can fit relatively simple models (i.e. few covariates) but when

Well, something like: LAP <- ifelse(gender =='male', (WC-65)*TG, (WC-58)*TG) The exact code depends on whether your variables are in a data frame or list or whatever, which you failed to specify. If so, ?with may be useful. Cheers, Bert On Fri, Nov 3, 2023 at 3:43?AM Md. Kamruzzaman <mkzaman.m at gmail.com> wrote: > Hello Everyone, > I have three variables: Waist

Hello Everyone, I have three variables: Waist circumference (WC), serum triglyceride (TG) level and gender. Waist circumference and serum triglyceride is numeric and gender (male and female) is categorical. From these three variables, I want to calculate the "Lipid Accumulation Product (LAP) Index". The equation to calculate LAP is different for male and females. I am giving both

Hello, I'm trying to fit a generalized linear mixed model to estimate diabetes prevalence at US county level. To do this I'm using the glmer() function in package lme4. I can fit relatively simple models (i.e. few covariates) but when expanding the number of covariates I usually encounter the following error message. gm8 <-

df$LAP <- with(df, ifelse(G=='male', (WC-65)*TG, (WC-58)*TG)) That will do both calculations and merge the two vectors appropriately. It will use extra memory, but it should be much faster than a 'for' loop. Regards, Jorgen Harmse. ------------------------------ Message: 8 Date: Fri, 3 Nov 2023 11:10:49 +1030 From: "Md. Kamruzzaman" <mkzaman.m at gmail.com>

Just a minor point in the suggested solution: df$LAP <- with(df, ifelse(G=='male', (WC-65)*TG, (WC-58)*TG)) since WC and TG are not conditional, would this be a slight improvement? df$LAP <- with(df, TG*(WC - ifelse(G=='male', 65, 58))) -----Original Message----- From: R-help <r-help-bounces at r-project.org> On Behalf Of Jorgen Harmse via R-help Sent: Friday,

Yes, that will halve the number of multiplications. If you?re looking for such optimisations then you can also consider ifelse(G=='male', 65L, 58L). That will definitely use less time & memory if WC is integer, but the trade-offs are more complicated if WC is floating point. Regards, Jorgen Harmse. From: avi.e.gross at gmail.com <avi.e.gross at gmail.com> Date: Friday,

Dear all, I have been stuck on this problem, am rather struggling and would appreciate some advice if anyone can help. I apologise if this is a bit long-winded, but I've tried to limit it to the bare essentials, but don't know how to make it more generic! I have some slightly odd real world data that I'm looking at representing number of positive diagnoses for different diseases,

Hello list, I want to make a large rulebased algorithm, to provide decision support for drug prescriptions. I have defined the algorithm in a function, with a for loop and many if statements. The structure should be as follows: 1. Iterate over a list of drug names. For each drug: 2. Get some drug related data (external dataset). Row of a dataframe. 3. Check if adaptions should be made to

I am trying to calculate Bayesian Credible Intervals for a proportion (disease prevalence values to be more specific) and am having trouble using R to do this. I am working with ncredint() function but have not had success with it. Please help! Example: Positive samples = 3 Total sampled = 10 Prevalence = 0.3 pvec <- seq(1,10,by=1) npost = dbinom(pvec,10,prob=0.3, log=FALSE) ncredint(pvec,

Greetings, I checked the Indian diabetes data again and get one tree for the data with reordered columns and another tree for the original data. I compared these two trees, the split points for these two trees are exactly the same but the fitted classes are not the same for some cases. And the misclassification errors are different too. I know how CART deal with ties --- even we are using the

My laboratory is measuring the abundance of various proteins in the blood from either healthy individuals or from individuals with various diseases. I would like to determine which proteins, if any, have significantly different abundances between the healthy and diseased individuals. Currently, one of my colleagues is performing an ANOVA on each protein with MS Excel. I would like to analyze

Hi, I am wondering if there is a way to specify the prevalence of events in logistic regression using lrm() from Design package? Linear Discriminant Analysis using lda() from MASS library has an argument "prior=" that we can use to specify the prevalent of events when the actual dataset being analyzed does not have a representative prevalence. How can we incorporate this information in

Greetings, I am using rpart for classification with "class" method. The test data is the Indian diabetes data from package mlbench. I fitted a classification tree firstly using the original data, and then exchanged the order of Body mass and Plasma glucose which are the strongest/important variables in the growing phase. The second tree is a little different from the first one. The

Hey everyone, I'm working on a contour plot depicting asymptomatic prevalence at varying durations of infectiousness and force of infection. I've been able to work everything out except for this one - my legend title keeps getting cut off. Here's what I have: filled.contour(x=seq(2,30,length.out=nrow(asym_matrix)), y=seq(1,2,length.out=ncol(asym_matrix)), asym_matrix, color =

Hi everybody! I want to find a closer neighbourins observation. This is my code: ########################## library(klaR) library(ipred) library(mlbench) data(PimaIndiansDiabetes2) dane=na.omit(PimaIndiansDiabetes2)[,c(2,5,9)] dane[,2]=log(dane[,2]) dane[,1:2]=scale(dane[,1:2]) zbior.uczacy=sample(1:nrow(dane),nrow(dane)/2,F)

Hi All, I'm hoping someone can help me with a relatively simple problem. Take the following dataset: ID Diabetes ESRD HIV Contact 1 0 0 NA 0 2 1 0 NA 0 3 NA 1 0 0 4 0 NA 0 1 5 1 1 1 0 I want to generate a

Dear all, Could you please help me how to get the output as I described in the following example? x<-c(543, 543, 543, 543, 551 , 551 ,1128 ,1197, 1197) diff<-x-lag(x) diff [1] NA 0 0 0 8 0 577 69 0 How to index the occasions in x repeatedly if the diff<15? if diff>=15, it will give a new index. I want the output be like y. y<-c(1,1,1,1,1,1,2,3,3) Thank you so