search for: nhanes

Dear R enthusiasts, I would like to announce nhanesA, a package that enables easy retrieval of the data tables that are available at the National Health and Nutritional Examination Survey (NHANES). NHANES data are used in over 10,000 peer-reviewed journal publications every year. In addition to easy table download, nhanesA features several functions...

Dear R enthusiasts, I would like to announce nhanesA, a package that enables easy retrieval of the data tables that are available at the National Health and Nutritional Examination Survey (NHANES). NHANES data are used in over 10,000 peer-reviewed journal publications every year. In addition to easy table download, nhanesA features several functions...

...y(latticeExtra) if possible) and also single hexbinplots with two regression lines (panel.lmlines) corresponding to the two groups to facilitate data comparison. Tick marks and labels should be off in most panels, and the scale in each row will be different. I have some code written for the NHANES dataset in library(hexbin) which almost does this job, but leaves blank paper up the middle of the two columns of panels. I have been exploring lattice for this which almost gets me there but not quite. The code I have written is as follows. I would be ever so grateful for any advice an...

...abriefs/db92.htm This is the National Health and Nutrition Examination Survey, produced by the US CDC. The CDC has published a bunch of syntax examples for generating age-adjusted statistics with SUDAAN, SAS, and Stata. Here's a link with all of that syntax: http://www.cdc.gov/nchs/tutorials/NHANES/NHANESAnalyses/agestandardization/age_standardization_intro.htm I've provided my detailed code below that gets to the point of the age adjustment, and then fails. However, when I export my R data frame near the end of the process and run a few Stata commands, I get the correct numbers easily...

I am a physician examining an NHANES dataset available at the NCHS website: http://www.cdc.gov/nchs/about/major/nhanes/nhanes2005-2006/demo_d.xpt http://www.cdc.gov/nchs/about/major/nhanes/nhanes2005-2006/hdl_d.xpt http://www.cdc.gov/nchs/about/major/nhanes/nhanes2005-2006/tchol_d.xpt Thank you to the R authors and the foreign p...

Hello all - I am sorry if this simple question is addressed in somewhere else. But, I couldn't find it. It's been for about a week using R. . . My problem is: Reading matrix data with "scan" does not seem to allow me to incorporate matrix labels (columns, and rows). If I use read.table, I can import the lables. But, the problem is, I don't know how I make this data as

I need a picture like this: http://rwiki.sciviews.org/doku.php?id=graph_gallery:graph38 http://rwiki.sciviews.org/doku.php?id=graph_gallery:graph38 but when I try compile it require("hexbin") data(NHANES)# pretty large data set! good <- !(is.na(NHANES$Albumin) | is.na(NHANES$Transferin)) NH.vars <- NHANES[good, c("Age","Sex","Albumin","Transferin")] # extract dependent variables and find ranges for global binning x <- NH.vars[,"Albumin"]...

Dear there, I am working on the NHANES survey data, and want to apply quantile regression on these complex survey data. Does anyone know how to do this? Thank you in advance, Yiling Cheng Yiling J. Cheng MD, PhD Epidemiologist CoCHP, Division of Diabetes Translation Centers for Disease Control and Prevention 4770 Buford Highway, N.E. M...

Hello All, I am still a beginner with R, and I only have a week or two under my belt so far. I'm working with NHANES anthropometric data on stature. I have two sets of data that have different statistical weights. I'm trying to combine those two data sets into one "mega-set" while retaining each data point's individual weight. If you have any idea as to how this is done, please let me know o...

...h) for children with diagnosis that impacts weight/length. But we we don't know how to use R for producing growth charts. We are collection data of Age, Weight and Length. The data are used to produce diagnosis-specific Growth charts (like the CDC Growth Charts: http://www.cdc.gov/nchs/data/nhanes/growthcharts/set1clinical/cj41l017.pdf) Data-example is in the UsingR package as data(kid.weights). In the CDC-papers a LMS approch is described. Cole has programmed the methology (I guess) in a <R 2.0.0 package called lmsqreg: """ > library(lmsqreg) Error in library(lmsqreg)...

...tring at 10000 bytes > > I can open the xpt using wordpad and there is a header but it seems to be just text. I really don't know what constitutes an " > SAS xport file header" I'm not sure why Peter deleted my copy of a sample of a SAS xport header that I took from an NHANES data distribution. He seemed to think I was confused about the function you had been using. The reason I mentioned that `read.xport` was from the 'foreign' package is that one generally loads that package to make the function available, while it appears you were using a different package, S...

Dear R users I want to combine multiply imputed data-sets generated from mice to do classfication. However, I have various questions regarding the use of mice library. For example suppose I want to predict the class in this data.frame: data(nhanes) mydf=nhanes mydf$class="pos" mydf$class[sample(1:nrow(mydf), size=0.5*nrow(mydf))]="neg" mydf$class=factor(mydf$class) First I impute: imp=mice(mydf) I want to use randomForest to do my analysis, not the inbuilt glm.mids functions. In a previous post it was suggested to subst...

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 fine with(imp, lm(bmi~hyp+chl)) #Creating a formula object and than passing it as argument does not work: form.obj <- formula(bmi~hyp+chl) with(imp, lm(form.obj...

Hello, I'm using a complex survey dataset and my goal is to simply spit out a bunch of probability-weighted outcome variable means for the different levels of covariate. So I first define the structure of the study design (I'm using the CDC's NHANES data): dhanes <- svydesign(id=~PSU, strat=~STRATA, weight=~lab_weight, data=final, nest=TRUE) No problem there. Now I use the "svyby" function as follows: svyby(~outcome, ~covariate, design=dhanes, svymean, na.rm=T) -> haha print(haha) covariate outcome se.outcome 1...

Dear folks ? I have a large (26 gig) ASCII flat file in fixed-width format with about 10 million observations of roughly 400 variables. (It is 51 years of Current Population Survey micro data from IPUMS, roughly half the fields for each record). The file was produced by automatic process in response to a data request of mine. The file is not accompanied by a human-readable file giving the

-------- Original Message ---------- From: WRAY NICHOLAS <nicholas.wray at ntlworld.com> To: peter dalgaard <pdalgd at gmail.com> Date: 14 April 2018 at 20:18 Subject: Re: [R] Reading xpt files into R Well yesterday I'd downloaded the "foreign" package and tried to open the xpt file using that: library(foreign) read.xport("test.xpt") I got the following

Hello, I am utilizing linear quantile regression models to analyze health survey data. The survey (NHANES) is a multi-stage complex survey and I want to incorporate survey sampling weights in generating my quantile estimates. To my knowledge, this is currently not possible in SAS or STATA. Is there any way to do this in R? If so, how? Thanks! -Kish

...)) barchart(BP.FN.RATE~ gender|eth,data=a1c.cast[a1c.cast$eth!="Other"&a1c.cast$bmi.cat.reordered!="Other",], groups =bmi.cat.reordered , par.settings = list(superpose.polygon = list(col=c("blue","cyan","red","yellow" )) ), sub=" NHANES-99:06; n = 12,514 " ,ylab= list("Error Rate (%)",cex=1.5), xlab=list("Gender",cex=1.25),auto.key=list(title=" FPG VS A1C for DM Diagnosis: Missed BP",columns=2,cex=1.25), col=c("blue","cyan","red","yellow")) -- Chetty Prof...

...any way to calculate a likelihood ratio or the McFadden-pseudoR2 from the results of the logistic model? I mean, as it is possible to extract pooled averaging and odds ratio... Thank you in advance, Antonello Here an example of logistic regression on imputed data: library(mice) imp <- mice(nhanes) # logistic regression on the imputed data fit <- glm.mids((hyp==2)~bmi+chl, data=imp, family = binomial) summary(fit) summary(pool(fit)) ### pool averaging across all imputed dataset summary(pool(fit, method = "rubin1987")) ### pool across all imputed dataset ### odds ratio...

hello, I am trying to do exploratory factor analysis with BRFSS dataset ( http://www.cdc.gov/brfss/technical_infodata/surveydata/2008.htm) for a couple of days, but I was not able to do that and got frustrated. Can anybody help me with step by step guide? BRFSS dataset provides ASCII or SAS format. Thank you. chloe [[alternative HTML version deleted]]