similar to: Diabetes Software

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Se podrá seguir el taller en la red a través de videoconferencia. Objetivos del Taller: La elaboración de gráficos de calidad y, en su caso, destinados a la publicación requiere una reflexión sobre los métodos, tipos y soluciones que pueden emplearse desde R. En este taller discutiremos los aspectos relacionados con los procedimientos para realizar gráficos de

Hola, El próximo 26-septiembre comenzamos la nueva temporada de reuniones del "Grupo de Usuarios de R de Madrid". Esta temporada trae algunas novedades, una de ellas es que intercambiaremos el lugar de las reuniones entre la Facultad de Ciencias de la UNED y ahora el bar S10 en el que esperamos mantener unas reuniones más próximas y si cabe informales. Por ahora tenemos un par de

Hello, new here. I am a Centos/Fedora user, and for the past 3 years have been running my mail server using Postfix/MySQL/CourierMail/Squirrelmail on Fedora. The current incantation of the tutorial is at: http://www.howtoforge.com/virtual-users-and-domains-with-postfix-courier-mysql-and-squirrelmail-centos-6.2-x86_64 Well it is time to rebuild my mailserver; this time on Centos 6.3. My goal

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

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

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

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

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 R users - I have a list of patient identifiers and diagnoses from inpatient admissions. I would like to reorganize the list, presently in a long format to a wide format in reshape, but in the absence of a "time" element, I am uncertain how to do this - any help greatly appreciated. ID Dx A nausea A diabetes A kidney failure A heart attack A fever B fever B

Hi, I am trying a linear regression model where the dependent variable is the size of the heart corrected for the patient's height and weight. This is labelled as LAVI. The independent variables are race (european or non-eurpoean), age, sex (male or female) of the patient and whether they have diabetes and high blood pressure. sample size 2000 patients selected from a community. when I

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 <-

Dear R-Sig-Jobs members, For its Executive Office in Brussels, The International Diabetes Federation (IDF) is looking to hire a Data Analyst & Coordinator with significant R experience. This person will join the Epidemiology and Public Health unit that sits within the Policy & Programmes department. They will be responsible for the management of IDF?s high-profile Diabetes Atlas. They

Dear R-Sig-Jobs members, For its Executive Office in Brussels, the International Diabetes Federation (IDF) is looking to hire a Statistician and Data Analyst to join the Policy & Programmes department. This person will be responsible for the management of the high-profile IDF Diabetes Atlas (www.diabetesatlas.org). They will coordinate the collection, analysis, interpretation and presentation

Hi, I am trying to fit a ordinal GEE model using ordgee {geepack}. In order to check the validity of the function, I specified the correlation structure as independence (i.e. constr = "independence") and compared the result with that using polr {MASS}. Because a GEE model with an independent working correlation structure is equivalent to an ordinary GLM model, we would expect the same

Hello Everyone, I was analysing big survey data using survey packages on RStudio. Survey package allows survey data analysis with the design effect.The survey package included functions for all other statistical analysis except two-proportion z tests. I was trying to calculate the difference in prevalence of Diabetes and Prediabetes between the year 2011 and 2017 (with 95%CI). I was able to

Hi I want to create a nice picture about my result of k -the nearest neighbours algorithm. Here is my easy 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)

hello I tried to use lars() but neither with my own data nor with the sample data it works. I get in both cases the following error prompt: > data(diabetes) > par(mfrow=c(2,2)) > attach(diabetes) > x<-lars(x,y) Error in one %*% x : requires numeric matrix/vector arguments > x<-lars(x,y, type="lasso") Error in one %*% x : requires numeric matrix/vector arguments