similar to: question on plot dates

Hi Anne, I would suggest to change the linear model to lm(BloodPressure~Age), as this model makes more sense in biological means (you would assume that age influences pressure, not vice versa) and also obeys the statistical assumption of weak exogeneity, that age can be measured without error, at least compared to error-prone bp measures. Cheers Am 18.04.2018 um 16:07 schrieb Gerrit Eichner:

Hello, I am trying to graph a regression line using the followings: Age <- c(39, 47, 45, 47, 65, 46, 67, 42, 67, 56, 64, 56, 59, 34, 42, 48, 45, 17, 20, 19, 36, 50, 39, 21, 44, 53, 63, 29, 25, 69) BloodPressure <- c(144, 220, 138, 145, 162, 142, 170, 124, 158, 154, 162, 150, 140, 110, 128, 130, 135, 114, 116, 124, 136, 142, 120, 120, 160, 158, 144, 130, 125, 175) SimpleLinearReg1=lm(Age ~

Hi, Anne, assign Age and Bloodpressure in the correct order to the axes in your call to plot as in: plot(y = Age, x = BloodPressure) abline(SimpleLinearReg1) Hth -- Gerrit --------------------------------------------------------------------- Dr. Gerrit Eichner Mathematical Institute, Room 212 gerrit.eichner at math.uni-giessen.de Justus-Liebig-University Giessen Tel:

Hi All, I have questions about MANOVA which I am still not sure if appropriately I should use it. For example I have a data set like this: BloodPressure (BP) Weight Height 120 115 165 125 145 198 156 99 176 I know that BloodPressure is correlated with both Weight and Height, however colinearity exists between Weight and Height. When I use BP = Weight + Height

Hi R folks, In my previous post I forgot to mention that I was new to R. I was really grateful for your quick help. I have two further questions: 1) In the graph of a regression line I would like to show one specific residual yi obs - yi pred (let's take the person whose residual is 76). How do I add a bracket to this vertical distance and name it? I'am getting stuck after the

Dear all, I have the following 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)

I do not know if I have a problem or not. The R script at the end of this email seems to run properly and a I get a boxplot that looks proper but I get the long string of messages during execution of the script looking like: ... The following object(s) are masked from dat ( position 8 ) : sugar The following object(s) are masked from dat ( position 9 ) :

Dear All, using the example from the help of summary.rms library(rms) n <- 1000 # define sample size set.seed(17) # so can reproduce the results age <- rnorm(n, 50, 10) blood.pressure <- rnorm(n, 120, 15) cholesterol <- rnorm(n, 200, 25) sex <- factor(sample(c('female','male'), n,TRUE)) label(age) <- 'Age'

Dear R-helpers, I'm having a problem in using plot.design in Design Library. Tho following example code produce the error: > n <- 1000 # define sample size > set.seed(17) # so can reproduce the results > age <- rnorm(n, 50, 10) > blood.pressure <- rnorm(n, 120, 15) > cholesterol <- rnorm(n, 200, 25) > sex <-

Hi, just making some experiments with design library i get an error if i want plot(fit) - show below from onlineHelp !? ..perhaps is here another mask problem?, but label from xtable which was my first problem is now off ! Thanks for advance & regards, Christian $ n <- 1000 # define sample size $ set.seed(17) # so can reproduce the results $ age <- rnorm(n, 50, 10)

Hi, I have what I believe is a repeated-measures dataset that I'm trying to analyze using lme(). This is *not* homework, but an exercise in my trying to self-teach myself repeated-measure ANOVA for other *real* datasets that I have and that are extremely similar to the following design. I'm fairly sure the dataset described below would work with lme() -- but it'd be great if anybody

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 I've compiled rsync 3.0.3 on OSX 10.4 PPC via the plain jane ./configure; make method (no patches). The resulting binary has then been moved to /usr/local/bin/rsync3 and copied to a OSX 10.5.4 Intel client machine as rsync3. I then run the following command: rsync3 -vvv -e ssh -avAX --progress --delete -

Hi, When running z <- plot(fit, age=NA, cholesterol=NA, perim=boundaries, method='image') Legend(z, fun=plogis, at=qlogis(c(.01,.05,.1,.2,.3,.4,.5)), zlab='Probability') And after pointing the cursor to the plot() screen in R, I obtain the following message: Using function "locator(2)" to place opposite corners of image.legend Error in

I have a plot produced by function bplot (package = rms) that is really a lattice plot (class="trellis"). It is similar to this plot produced by a very minor modification of the first example on the bplot help page: requiere(rms) n <- 1000 # define sample size set.seed(17) # so can reproduce the results age <- rnorm(n, 50, 10) blood.pressure <- rnorm(n, 120,

dear£º in the example£¨nomogram£©£¬I don't understand the meanings of the program which have been marked by red line.And how to compile the program(L <- .4*(sex=='male') + .045*(age-50) + (log(cholesterol - 10)-5.2)*(-2*(sex=='female') + 2*(sex=='male'))). n <- 1000 # define sample size set.seed(17) # so can reproduce the results age <- rnorm(n, 50, 10)

Hi Everyone, I have a dataset like this mean sd 0% 25% 50% 75% 100% n BODY TEMPERATURE 36.41099 0.4015699 35.1 36.22222 36.5 36.66667 37.1 89 DIASTOLIC BLOOD PRESSURE 73.60079 9.4656186 50.0 67.00000 73.0 80.00000 95.0 253 HEIGHT 171.94000 9.2011670 153.5 166.50000 173.0 176.25000 190.0

I''m a ferret newbie, so hopefully I''m missing something simple :) I am using ferret to index data about 36,000 products from a MySQL database. The index has one document for each product, with these important fields: id: the id (unique) of the product record in the database content: a concatenation of several bits of information from the product and associated records I

I understand that the hat matrix is a function of the predictor variable alone. So, in 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)

Hello friends, I am a newbie to asterisk , please help. I am receiving a phone from a sip server and I want to route it to another sip server. The problem is that the target sip server takes a # in the argument . I am trying to dial as exten => s,5,Dial(12345#123456789@xxx.xxx.xxx.xxx) ;;;; dial the number at the ip address. When this gets executed, I get the following eror:-

Assume that we collect below data : - subjects = 20 males + 20 females, every single individual is independence, and difference events = 1, 2, 3... n covariates = 4 blood types A, B, AB, O http://r.789695.n4.nabble.com/file/n4245397/CodeCogsEqn.jpeg ?m = hazards rates for male ?n = hazards rates for female Wm = Wn x ?, frailty for males, where ? is the edge ratio of male compare to female Wn =