search for: glucose

Hi, I am new to R so forgive me if the following query is somewhat simple.  I have a small tab-separated file with n records of glucose values, one record per day, seven measurements per day. It looks like this: date    sober    no    vm    nm    va    na    vs 20091229    NA    6.8    NA    2.7    11.7    2.7    6.2 I'd like to make a graph on which the glucose day curves are plotted separately for each day. I'm sure I...

...2L, 2L, 2L, 2L), .Label = c("NO", "YES" ), class = "factor"), Event_name = c(8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30), Glucose.n = c(26, 3, 0, 26, 1, 25, 26, 25, 25, 26, 26, 25, 23, 23, 24, 24, 26, 26, 25, 25, 26, 26, 24, 21, 7, 12, 4, 0, 0, 4, 0, 4, 4, 4, 4, 4, 4, 4, 3, 4, 4, 4, 3, 3, 2, 2, 2, 1, 1), Glucose.m = c(92.5, 90.3333333333333, NaN, 97.2307692307692, 116, 97.84, 107.653846153846, 105.32, 102.6, 94.6538461538462,...

...g output generated by linear regression. Since there is only one regression intercept and one slope for one set of data, what is the meaning of std. error for intercept and that of slope? Thanks in advance. Sincerely, Minghua > data(thuesen) > attach(thuesen) > lm(short.velocity~blood.glucose) Call: lm(formula = short.velocity ~ blood.glucose) Coefficients: (Intercept) blood.glucose 1.09781 0.02196 > summary(lm(short.velocity~blood.glucose)) Call: lm(formula = short.velocity ~ blood.glucose) Residuals: Min 1Q Median 3Q Max -0.40141 -0...

...nd I cannot find any examples on the internet. There are some examples for comparisons of variances but not linear models. If I take the hellung data set, which is the example in Dalgaard's book. I know var.test works fine but I want to learn how to use the Bartlett's test. > hellung$glucose <- factor(hellung$glucose, labels=c("Yes","No")) > attach(hellung) > tethym.gluc <- hellung[glucose=="Yes",] > tethym.nogluc <- hellung[glucose=="No",] > lm.nogluc <- lm(log10(diameter)~log10(conc), data=tethym.nogluc) > lm.glu...

...at.wmich.edu/mckean/Stat666/Pkgs/npsmReg2_0.1.1.tar.gz') #install.packages("DMwR") library(robustbase) library(MASS) library(quantreg) library(RobPer) library(hbrfit) library("devtools") library("DMwR") bmi=c(23,43,12,1,23,4,3,4,5,5,6,5,5,34,67,87,32,12,23,19) glucose=c(98,34,21,13,2,43,16,43,28,93,11,14,16,43,23,65,42,16,54,32) crp=c(56,34,42,32,1,2,54,47,67,65,38,95,45,76,67,87,35,41,34,54) newdata=data.frame(bmi,glucose,crp) ? reg1 <- lm( crp ~ bmi+glucose,data=newdata) DMwR::regr.eval(newdata$crp,reg1$fitted.values) reg <- lmrob( crp ~ bmi+glucose,dat...

...et 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 <-c(34,32,12,76,54,34,21,18,92,32,11,13,45,46,56,57,67,87,12,13) glucose <-c(23,54,11,12,13,21,32,12,45,54,65,87,21,23,12,12,23,23,43,54) # Create a list to store the results lst<-list() # Numbers of bootstrap samples nboot=100 bootstrap.MedAESQ =rep(NA,nboot) for(i in 1?:nboot) { fit <- rq( crp ~ bmi+glucose, tau = 0.5) ypred=predict(fit) y=new$crp bootst...

...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) klasyfikatorKNN=ipredknn(diabetes~glucose+insulin,data=dane,subset=zbior.uczacy,k=3) oceny=predict(klasyfikatorKNN,dane[-zbior.uczacy,],"class") #data frames with my result from klasyfikatorKNN df=data.frame(glucose=c(klasyfikatorKNN$learn$X[,1]),insulin=klasyfikatorKNN$learn$X[,2],diabetes=c(klasyfikatorKNN$learn$y)) #And pic...

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$crp median(y - ypred)^2 } dat <- data.frame(crp, bmi, glucose) nboot <- 100 medse <- boot(dat, bootMedianSE, R = nboot) medse$t0 mean(medse$t) # This is the value you want Hope this helps, Rui Barradas On 5...

...f 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$crp > ??? median(y - ypred)^2 > } > > dat <- data.frame(crp, bmi, glucose) > nboot <- 100 > > medse <- boot(dat, bootMedianSE, R = nboot) > > medse$t0 > mean(medse$t)??? # This is...

Hi! I'm a bit lost while performing multiple comparisons on a lme model of that type: lmeglu=lme(glucose~Ath*tim,random=~1|Vol,na.action=na.omit,data=data) multc = glht(lmeglu, linfct = mcp(Ath = "Tukey", tim = "Tukey")) This works fine for identifying the effect of each factor. However, when I look for their interactions, l only obtain error messages. For example this leads to a...

...the following example why do the values on the diagonal of the hat matrix change when I go from an unweighted fit to a weighted fit? Is the function hatvalues giving me something other than what I think it is? library(ISwR) data(thuesen) attach(thuesen) fit <- lm(short.velocity ~ blood.glucose) summary(fit) hatvalues(fit) W <- 1/blood.glucose fit.w <- lm(short.velocity ~ blood.glucose,weights=W) summary(fit.w) hatvalues(fit.w) Thanks for the help. Tom [[alternative HTML version deleted]]

..., I'm trying to figure out how anova works in R by translating the examples in Sokal And Rohlf's (1995 3rd edition) Biometry. I've hit a snag with planned comparisons, their box 9.4 and section 9.6. It's a basic anova design: treatment <- factor(rep(c("control", "glucose", "fructose", "gluc+fruct", "sucrose"), each = 10)) length <- c(75, 67, 70, 75, 65, 71, 67, 67, 76, 68, 57, 58, 60, 59, 62, 60, 60, 57, 59, 61, 58, 61, 56, 58, 57, 56, 61, 60, 57, 58, 58, 59, 58, 61, 57,...

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 misclassification tables are different too. I did not change the data, but why the results are so different? Does anyone know how rpart deal with ties? Here is the codes...

...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) KNN=ipredknn(diabetes~glucose+insulin,data=dane,subset=zbior.uczacy,k=3) KNN=ipredknn(diabetes~glucose+insulin,data=dane,subset=zbior.uczacy,k=3) b= ifelse((a==1),'red','blue' ) a=as.numeric(KNN$learn$y) plot(KNN$learn$X[,1], KNN$learn$X[,2],pch=a,col = ifelse((a==1),'red','blue' )) ##########...

...e used commands like the following to be certain that I have an active com port: dmesg | grep ttyS rm com1 ln -s /dev/ttyS0 com1 So I suspect the solution lies somewhere in WINE. If all else fails I will get a USB to SERIAL converter cable and try that. My wife has an Abbott Labs Freestyle Lite Glucose meter which uploads to a program called COPILOT via RS232. I was only able to get it to work on ONE of our three Windows XP machines. I tried all day to get it to work on a Dell XP converted to Ubuntu (Gnu/Linux) which d...oes have an RS232 and TERMINAL commands indicate that the system recognizes...

Hi All, I have about one hundred patients and all the patients had their glucose measured on three different days. The days are all the same for all he patients. So I have three measurement for each patient . I want to know whether the day when the glucose was measured has an effect on the measurements. I was thinking to use a single factor analysis of variance but I am not sur...

Hi there, >library(faraway) >pima pregnant glucose diastolic triceps insulin bmi diabetes age test 1 6 148 72 35 0 33.6 0.627 50 1 2 1 85 66 29 0 26.6 0.351 31 0 >data(pima) >pima pregnant glucose diastolic triceps insulin bmi diabetes age test 1 6...

Hello, Right! I copied from the OP's question without thinking about it. Corrected would be 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) } Sorry, rui Barradas On 5/22/2018 11:32 AM, Daniel Nordlund wrote: > On 5/22/2018 2:32 AM, Rui Barradas wrote: >> bootMedianSE <- function(data, indices){ >> ????? d <- data[in...

Hello, I'm trying to plot a graph of blood glucose versus date. I also record conditions, such as missing the previous night's medications, and missing exercise on the previous day. My data looks like: > b2[68:74,] # A tibble: 7 ? 5 Date Time bg missed_meds no_exercise <date> <time> <dbl> <lgl>...

...ean. An interprestion or suggestion would be greatly appreciated. Running on Kubuntu using R version 2.6.2 (2008-02-08) PS: I'm obviously a newbie and this script is my first serious attempt at using R. R text follows: -------------------- source("dbi-start.R") am_pm<-"PM Glucose for" fc_year<-2008 bfc_head<-"select blood.sugar from blood where" bfc_PM<-"blood.time>\"22:00:00\" and blood.time<=\"23:59:00\"" bfc_tail<-"order by blood.date,blood.time" bfc<-"blood.date" nmon<-c(2:12,1) ls...