similar to: a loop for boxplot graphs

I am running the sm.density.compare function amd I am getting the following error:   my code is  > sm.density.compare(LBSTRESN,COHORT,xlab="Units = umol/L"subset = LBTEST=="Creatinine")   Error in if (from == to) rep.int(from, length.out) else as.vector(c(from,  :   missing value where TRUE/FALSE needed   I do not understand the error and I have had no help when searching

Dear friends. I wanted to reproduce a sheet of paper useful for plotting the reciprocal of some biochemical variable (creatinine) over time (Bleyer Am J Kidney Dis 34:576-578,1999). Time on X axis with units years and months. On Y axis, 1/creatinine, but with the original creatinine value indicated - to make it easier for people to use it. When using the graph, the linear decrease in 1/crea over

Often, when exploring a dataset, I'd like to plot several very different Y variables against the same X variable, in panels stacked one over the other. Is there an easy way to do this? I'd like to achieve an elegant look similar to the look achieved by lattice in conditioned plots--for instance no space between panels. But unlike in straightforward conditioned plot, each panel may be on a

Thank you to Peter Dalgaard, who provided help for some of this solution. It is still not ideal since the dots which are equidistant in the x-direction ideally should respect the reciprocal scale in the y-direction. op <- par("cex"= 0.7) x<-y<-NA plot(x,y,xlim=c(0,10),ylim=1/c(2000,70), axes=F) box() axis(1)

Dear friends - We have 25 rats, 14 of these subjected to partial removal of kidney tissue, 11 to sham operation, and then followed for 6 weeks. So far we have data on 26 urine metabolites measured by NMR 7 times during the observation. I have smoothed the measurements by b.splines in fda including a roughness penalty, and inspecting the mean curves for nephrectomized and sham animals indicate

Hi all, I have a question about multiple imputation within the MICE package. I want to use passive imputation for my variable called X, because it is calculated out of multiple variables, namely Y, Z. Let's give an example with BMI. I know, that if I want to use passive imputation for BMI, I can use the following command: meth["BMI"] <- "~I(weight/(height/100)^2)"

I am running a BUGS function with following   schools.sim <-bugs(data,inits,                              parameters,                              model.file="schools.txt",                              n.chains=3,                              n.iter=1000,                              bugs.directory="E:/Rprograms")   My model.file IS in the directory

On Fri, 14 Dec 2018 15:27:27 -0500 Gilbert Soucy <gsoucy at 36pix.com> wrote: > Wow, we made some good progress. We can see the users and actually > mount a share on windows. Only issue: the uids and guids are wrong. > This is because of the backend : rip, I suppose. > > >Try adding 'winbind use default domain = yes' > >Restart Samba and then try it like

Hi all, I have a question and I do not know If I am at the right place to ask this question. But is there someone that has the formula of CKD-Epi in code in R? I have tried a lot of loops, but none of the approaches give me the right answer. Is there someone who has this formula coded? Thank you! [[alternative HTML version deleted]]

Dear friends. I have a paper (details below) examining the risk of renal failure after an operation. A logistic regression was done, and the coefficients to two regressors (age and creatinine) plus intercept with standard errors are given. These coefficients must be dependent in estimation, and when no details are given, I thought how I could most informatively get an impression as to how

Larry, You found a data set that kills coxph. I'll have to think about what to do since on the one hand it's your own fault for trying to fit a very bad model, and on the other I'd like the routine to give a nice error message before it dies. In the data set you sent me the predictor variable is very skewed: > quantile(anomaly1$CREAT, c(0, .5, .9, .999, 1)) 0% 50%

Mª Luz, si el comando de Carlos te devuelve 0 es que no hay NA's. Da igual que tus columnas sean caracteres o número. Lo que intuyo es que tienes celdas con la frase "NA" que interpretas como NA. Mandanos el summary de tu data.table y lo vemos... ----- Mensaje original ----- De: "MªLuz Morales" <mlzmrls en gmail.com> Para: "Carlos J. Gil Bellosta"

Hello, I am trying to configure samba on a Centos 7.5 linux server ( Samba 4.8.3 ) to share files stored on that server to a set of windows PCs running win7 and win10. Some facts: - the linux server is NOT the domain controller but it successfully joined the domain ( realm join ...). - we have a few linux servers on which we will want to configure samba in a similar way to share files. - the

I making an xyplot and the y label is too long and needs to be in two rows, but when I brake it there is a huge gap between the last text string and the expression, and I can't get rid of it. Any ideas? Data: structure(list(Temp = c(8L, 8L, 8L, 8L, 8L, 8L, 12L, 12L, 12L, 12L, 12L, 12L), CO2 = c(380L, 380L, 380L, 750L, 750L, 750L, 380L, 380L, 380L, 750L, 750L, 750L), Treat = structure(c(3L,

Hi! Here is some output from an str command: > str(CO2) Classes ‘nfnGroupedData’, ‘nfGroupedData’, ‘groupedData’ and 'data.frame': 84 obs. of 5 variables: $ Plant : Ord.factor w/ 12 levels "Qn1"<"Qn2"<"Qn3"<..: 1 1 1 1 1 1 1 2 2 2 ... $ Type : Factor w/ 2 levels "Quebec","Mississippi": 1 1 1 1 1 1 1 1 1 1 ... $

I found this example in an Introductory R book in the chapter on Matrices and Arrays The array is > m [,1] [,2] [,3] [,4] [,5] [1,] 0 12 13 8 20 [2,] 12 0 15 28 88 [3,] 13 15 0 6 9 [4,] 8 28 6 0 33 [5,] 20 88 9 33 0 The code is #returns the minimum value of d[i,j], i !=j and the row attaining #the minimum, for square

I making an xyplot and the y label is too long and needs to be in two rows, but when I brake it there is a huge gap between the last text string and the expression, and I can't get rid of it. Any ideas? Data: structure(list(Temp = c(8L, 8L, 8L, 8L, 8L, 8L, 12L, 12L, 12L, 12L, 12L, 12L), CO2 = c(380L, 380L, 380L, 750L, 750L, 750L, 380L, 380L, 380L, 750L, 750L, 750L), Treat = structure(c(3L,

Hello , I'm new to R and don't really understand how to use the function "apply" instead of a "for loop", particularly for a function with multiple entries. I have a big data file and would like to apply a function in multi thread to accelerate the processus. I have a data frame containing values of* CO2 in ppm (resp[i,6])* that I want to convert in umol of CO2

Dear R community, I am trying to fit an nlme model where I want to estimate the fixed effects of two treatments on the parameters on the following equation Photo~(a*(1-exp(-c*PARi/a)))-b I was able to fit a simple model without covariates following the method described in Mixed-Effects Methods and Classes for S and S-PLUS, version 3.0, but when I add the covariates, I get the error "

I have downloaded the Umacs (Universal Markov chain sampler) and submitted the following sample code from Kerman and Gelman.   s <-Sampler( J=8, sigma.y  =c(15,10,16,11,9,11,10,18),           y  =c(28, 8,-3,7,-1,1,18,12),      theta =Gibbs(theta.update,theta.init),           V =Gibbs(V.update,mu.init),         mu =Gibbs(mu.update,mu.init),         tau =Gibbs(tau.update,tau.init),