Displaying 20 results from an estimated 10000 matches similar to: "plot methods for summary of rms objects"
2018 Jan 03
1
summary.rms help
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'
2012 May 25
1
Multiple rms summary plots in a single device
I would like to incorporate multiple summary plots from the rms
package into a single device and to control the titles, and also to
open a new device when I reach a specified number of plots. Currently
I am only getting a single "plot(summary(" graph in the upper left-
hand corner of each successive device. However, in the rms
documention I see instances of a loop being used with
2011 May 17
2
can not use plot.Predict {rms} reproduce figure 7.8 from Regression Modeling Strategies (http://biostat.mc.vanderbilt.edu/wiki/pub/Main/RmS/course2.pdf)
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
2012 Apr 30
1
question on jitter in plot.Predict in rms
Dear colleagues,
I have a question regarding controlling the jitter when plotting
predictions in the rms package. Below I've simulated some data that
reflect what I'm working with. The model predicts a continuous variable
with an ordinal score, a two-level group, and a continuous covariate. Of
primary interest is a plot of the group by score interaction, where the
score is the ordinal
2011 Aug 25
1
survplot() for cph(): Design vs rms
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 <-
2012 Oct 20
1
rms plot.Predict question: swapping x- and y- axis for categorical predictors
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 <-
2011 Jun 23
2
Rms package - problems with fit.mult.impute
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
2010 Jan 21
1
Simple effects with Design / rms ols() function
Hi everyone,
I'm having some difficulty getting "simple effects" for the ols()
function in the rms package. The example below illustrates my
difficulty -- I'll be grateful for any help.
#make up some data
exD <- structure(list(Gender = structure(c(1L, 2L, 1L, 2L, 1L, 1L, 1L,
2L, 1L, 2L, 2L, 2L, 1L, 2L), .Label = c("F", "M"), class = "factor"),
2010 Aug 14
1
How to add lines to lattice plot produced by rms::bplot
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,
2012 Apr 09
3
how to add 3d-points to bplot {rms} figure?
Hello!
I have created a bplot-figure using this code:
*file <- "2dcali_red.ttt"
ux<-as.matrix(read.table(file, dec = ","))
mode(ux)<-'numeric'
vel<-ux[,1]
ang<-ux[,2]
x<-ux[,3]
y<-ux[,4]
dat<- data.frame(ang=ang, x=x,y=y)
require(rms)
ddist2 <- datadist(dat)
options(datadist="ddist2")
fitn <- lrm(ang ~ rcs(x,4) +
2013 Jun 24
2
Nomogram (rms) for model with shrunk coefficients
Dear R-users,
I have used the nomogram function from the rms package for a logistic
regresison model made with lrm(). Everything works perfectly (r version
2.15.1 on a mac). My question is this: if my final model is not the one
created by lrm, but I internally validated the model and 'shrunk' the
regression coefficients and computed a new intercept, how can I build a
nomogram using that
2011 Aug 06
1
help with predict for cr model using rms package
Dear list,
I'm currently trying to use the rms package to get predicted ordinal
responses from a conditional ratio model. As you will see below, my
model seems to fit well to the data, however, I'm having trouble
getting predicted mean (or fitted) ordinal response values using the
predict function. I have a feeling I'm missing something simple,
however I haven't been able to
2010 Jan 27
1
control of scat1d tick color in plot.Predict?
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
2009 Dec 07
1
multiple plots using summary in rms package
Dear All,
I wonder if someone can point me in the right direction here. I'm working
with the rms library, R 2.9.2 under Windows XP.
I'm trying to arrange two plots side by side for a colleague. mfrow or
mfcol do not seem to work, however, so I am obviously missing something
important. I know that there have been changes in the graphics from Design
to rms, but am just not sure where to
2012 Mar 22
2
Summary values from Glm function (rms package)
Dear fellow R-users,
I?m using the Glm function (gamma family of distributions) from the rms
package to compare 2 groups on costs data. Although the summary function
does provide the mean cost difference and standard errors, I believe these
values were in the (natural) log ratio format. Is there a function to
express these values into the original scale of the response variable (i.e.,
dollars)
2011 Feb 08
1
Error in example Glm rms package
Hi all!
I've got this error while running
example(Glm)
library("rms")
> example(Glm)
Glm> ## Dobson (1990) Page 93: Randomized Controlled Trial :
Glm> counts <- c(18,17,15,20,10,20,25,13,12)
Glm> outcome <- gl(3,1,9)
Glm> treatment <- gl(3,3)
Glm> f <- glm(counts ~ outcome + treatment, family=poisson())
Glm> f
Call: glm(formula = counts ~
2013 Apr 19
2
NAMESPACE and imports
I am cleaning up the rms package to not export functions not to be called
directly by users. rms uses generic functions defined in other packages.
For example there is a latex method in the Hmisc package, and rms has a
latex method for objects of class "anova.rms" so there are anova.rms and
latex.anova.rms functions in rms. I use:
2010 Feb 17
1
strangeness in Predict() {rms}
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))
2010 Oct 04
2
i have aproblem --thank you
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
2009 Oct 26
1
Cbind() on the right-side of a formula in xYplot()
Hi,
Using the latest rms package I am able to make nice plots of model predictions
+/- desired confidence intervals like this:
# need this
library(rms)
# setup data
d <- data.frame(x=rnorm(100), y=rnorm(100))
dd <- datadist(d)
options(datadist='dd')
# fit model
l <- ols(y ~ rcs(x), data=d)
# predict along original limits of data
l.pred <- Predict(l)
# plot of fit and