similar to: Cbind() on the right-side of a formula in xYplot()

Hi, Running the following example from ?Predict() throws an error I have never seen before: set.seed(1) x1 <- runif(300) x2 <- runif(300) ddist <- datadist(x1,x2); options(datadist='ddist') y <- exp(x1+ x2 - 1 + rnorm(300)) f <- ols(log(y) ~ pol(x1,2) + x2) p1 <- Predict(f, x1=., conf.type='mean') Error in paste(nmc[i], "=", if (is.numeric(x))

dear professor: thank you for your help,witn your help i develop the nomogram successfully. after that i want to do the internal validation to the model.i ues the bootpred to do it,and then i encounter problem again,just like that.(´íÎóÓÚerror to :complete.cases(x, y, wt) : ²»ÊÇËùÓеIJÎÊý¶¼Ò»Ñù³¤(the length of the augment was different)) i hope you tell me where is the mistake,and maybe i have

Dear all, I search the mail list about this topic and learn that no simple way is available to get "lsmeans" in R as in SAS. Dr.John Fox and Dr.Frank E Harrell have given very useful information about "lsmeans" topic. Dr. Frank E Harrell suggests not to think about lsmeans, just to think about what predicted values wanted and to use the predict

dear professor: thank you for your help,witn your help i develop the nomogram successfully. after that i want to do the internal validation to the model.i ues the bootpred to do it,and then i encounter problem again,just like that.(´íÎóÓÚerror to :complete.cases(x, y, wt) : ²»ÊÇËùÓеIJÎÊý¶¼Ò»Ñù³¤(the length of the augment was different)) i hope you tell me where is the mistake,and maybe i have

dear professor: thank you for your help,witn your help i develop the nomogram successfully. after that i want to do the internal validation to the model.i ues the bootpred to do it,and then i encounter problem again,just like that.(???error to :complete.cases(x, y, wt) : ???????????(the length of the augment was different)) i hope you tell me where is the mistake,and maybe i have chosen the

Hi, is it possible to reverse the order in which panel.lmline() or panel.smooth() operation in xyplot() ? This type of situation might occur when plotting some variable with depth, but the relation we want to describe is variable ~ depth, and not depth ~ variable, as the plotting formula would suggest. # an example: d <- 1:100 v <- d * rnorm(100) xyplot(d ~ v, ylim=c(100,0),

Hi, I was recently splitting some massive phylo class objects with extract.clade() and noticed what appears to be a bug in how tip labels are copied from the full tree to the pruned tree. This possible bug was also mentioned here: http://www.mail-archive.com/r-sig-phylo at r-project.org/msg00537.html An example: library(ape) set.seed(5) x <- matrix(rnorm(100), ncol=10) p <-

Hi, I have been trying to use the new .parallel argument with the most recent version of plyr [1] to speed up some tasks. I can run the example in the NEWS file [1], and it seems to be working correctly. However, R will only use a single core when I try to apply this same approach with ddply(). 1. http://cran.r-project.org/web/packages/plyr/NEWS Watching my CPUs I see that in both cases

Hi, I have fit a series of ols() models, by group, in this manner: l <- ols(y ~ rcs(x, 4)) ... where the series of 'x' values in each group is the same, however knots are not always identical between groups. The result is a table of 'coefs' derived from the ols objects, by group: group Intercept top top' top'' 1 6.864 0.01 2.241 -2.65

Hi, I have fit a model using the rms package with the Gls() function. Is there a way to get the model estimates, std errors, and p-values (i.e. what you get with print(fit)) into latex format? I have tried: f <- Gls(...) latex(f, file='') ... but I get the following error Error in replace.substring.wild(s, old, new, test = test, front = front, : does not handle > 1 * in

I'm trying to do a Hosmer-Lemeshow 'goodness of fit' test on my logistic regression model. I found some code here: http://sas-and-r.blogspot.com/2010/09/example-87-hosmer-and-lemeshow-goodness.html The R code is above is a little complicated for me but I'm having trouble with my answer: Hosmer-Lemeshow: p=0.6163585 le Cessie and Houwelingen test (Design library): p=0.2843620

Hello ALL! I have a problem to plot factor (lets say gender) as a line, or at least both line and point, from ols model: ols1 <- ols(Y ~ gender, data=dat, x=T, y=T) plot(ols1, gender=NA, xlab="gender", ylab="Y", ylim=c(5,30), conf.int=FALSE) If I convert gender into discrete numeric predictor, and use forceLines=TRUE, plot is not nice and true, since it shows values

Hi, I am trying to transition a system based on dynamic image generation (via R) from our development system to a production environment. Our R script functions as expected when run by a regular user. However the script dies when calling the png() function, when started by the webserver user. Here are some details >sessionInfo() R version 2.9.2 (2009-08-24) i686-pc-linux-gnu locale: C

Hello all, I'm trying to plot the effects of variables estimated by a regression model fit individually, and for categorical predictors, the independent variable shows up on the y-axis, with the dependent variable on the x-axis. Is there a way to prevent this reversal? Sample code with dummy data: # make dummy data set.seed(1) x1 <- runif(200) x2 <- sample(c(1,2),200, TRUE) x3 <-

Dear R-users, I am using R 2.13.0 and rms 3.3-0 , but can not reproduce figure 7.8 of the handouts *Regression Modeling Strategies* ( http://biostat.mc.vanderbilt.edu/wiki/pub/Main/RmS/course2.pdf) by the following code. Could any one help me figure out how to solve this? setwd('C:/Rharrell') require(rms) load('data/counties.sav') older <- counties$age6574 + counties$age75

dear professor: I have a problem about the nomogram.I have got the result through analysing the dataset "exp2.sav" through multinominal logistic regression by SPSS 17.0. and I want to deveop the nomogram through R-Projject,just like this : > n<-100 > set.seed(10) > T.Grade<-factor(0:3,labels=c("G0", "G1", "G2","G3")) >

Hi, in Design package, a plot of survival probability vs. a covariate can be generated by survplot() on a cph object using the folliowing code: n <- 1000 set.seed(731) age <- 50 + 12*rnorm(n) label(age) <- "Age" sex <- factor(sample(c('male','female'), n, TRUE)) cens <- 15*runif(n) h <- .02*exp(.04*(age-50)+.8*(sex=='Female')) dt <-

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'

Hi All, I have a quick question about using plot.Predict now that the rms package uses lattice. I'd like to add tick marks along the regression line, which is given by data=llist(variablename) in the plot call. The ticks show up fine, but I'd like to alter the color. I know the ticks are produced by scat1d, but after spending a fair bit of time going through documentation, it still

Hi! Does anyone know how to do the test for goodness of fit of a logistic model (in rms package) after running fit.mult.impute? I am using the rms and Hmisc packages to do a multiple imputation followed by a logistic regression model using lrm. Everything works fine until I try to run the test for goodness of fit: residuals(type=c("gof")) One needs to specify y=T and x=T in the fit. But