Displaying 20 results from an estimated 3000 matches similar to: "spurious warning in ave()"
2018 May 01
4
issue with model.frame()
A user sent me an example where coxph fails, and the root of the failure is a case where
names(mf) is not equal to the term.labels attribute of the formula -- the latter has an
extraneous newline. Here is an example that does not use the survival library.
# first create a data set with many long names
n <- 30? # number of rows for the dummy data set
vname <- vector("character",
2010 Nov 11
3
Evaluation puzzle
The survexp function can fail when called from another function. The "why" of
this has me baffled, however.
Here is a simple test case, using a very stripped down version of survexp:
survexp.test <- function(formula, data,
weights, subset, na.action, rmap,
times, cohort=TRUE, conditional=FALSE,
ratetable=survexp.us, scale=1, npoints, se.fit,
2010 Oct 07
1
model.frame deficiency
The model.frame function has trouble with a certain type of really long
formula. Here is a test:
tname <- paste('var', 1:50, sep='')
tmat <- matrix(rnorm(500), ncol=50, dimnames=list(NULL, tname))
tdata <- data.frame(tmat)
temp1 <- paste( paste(tname, tname, sep='='), collapse=', ')
temp2 <- paste("~1 + cbind(", temp1, ")")
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
2015 Nov 06
4
Puzzled by eval
I am currently puzzled by a seach path behavior. I have a library of a dozen routines
getlabs(), getssn(), getecg(), ... that interface to local repositories and pull back
patient information. All have a the first 6 arguments in common, and immediately call a
second routine to do initial processing of these 6. The functions "joe" and "fred" below
capture the relevant
2010 Feb 16
3
converting character vector "hh:mm" to chron or strptime 24 clock time vectors
Hi All,
I am attempting to work with some data from loggers. I have read in a
.csv exported from MS Access that already has my dates and times (in 24
clock format), (with StringsAsFactors=FALSE).
> head(tdata)
LogData date time
1 77.16 2008/04/24 02:00
2 61.78 2008/04/24 04:00
3 75.44 2008/04/24 06:00
4 89.43 2008/04/24
2013 Apr 17
1
Bug in VGAM z value and coefficient ?
Dear,
When i multiply the y of a regression by 10, I would expect that the
coefficient would be multiply by 10 and the z value to stay constant. Here
some reproducible code to support the case.
*Ex 1*
library(mvtnorm)
library(VGAM)
set.seed(1)
x=rmvnorm(1000,sigma=matrix(c(1,0.75,0.75,1),2,2))
2008 Mar 03
1
Problem plotting curve on survival curve
Calum had a long question about drawing survival curves after fitting a Weibull
model, using pweibull, which I have not reproduced.
It is easier to get survival curves using the predict function. Here is a
simple example:
> library(survival)
> tfit <- survreg(Surv(time, status) ~ factor(ph.ecog), data=lung)
> table(lung$ph.ecog)
0 1 2 3 <NA>
63 113 50 1
2009 Feb 03
1
How to show variables used in lm function call?
Hello R users,
I am new to R and am wondering if anyone can help me out
with the following issue: I wrote a function to build ts models using
different inputs, but when R displays the call for a model, I cannot tell
which variables
it is using because it shows the arguments instead of the real variables
passed to the function.
(e.g
Call:
lm(formula = dyn(dep ~ lag(dep, -1) + indep)) --->
2009 Feb 07
3
Output results to a single postscript document
Hello R users,
I have been trying to output all my results (text, plots, etc) into the same
postscript file as
one document, but have been unable to...Can anyone help me improve my code
below so that I can
accomplish this? Currently I have to output them separately then piece them
back together into
one document..
Thanks in Advance for any help!
options (scipen=999, digits=7)
2005 Nov 02
2
Orientation of tickmarks labels in boxplot/plot
Hi,
I have been trying draw tickmark labels along
the y - axis perpendicular to the y axis while
labels along the x - axis parallel to x axis
while making box plot.
Here is my test dataset.
TData
ID Ratio
1 0 7.075
2 0 7.414
3 0 7.403
4 0 7.168
5 0 6.820
6 0 7.294
7 0 7.238
8 0 7.938
9 1 7.708
10 1 8.691
11 1 8.714
12 1 8.066
13 1 8.949
14 1 8.590
15 1 8.714
16 1
2016 Apr 23
2
Data Frame Column Name Attribute
I am attempting to add a calculated column to a data frame. Basically,
adding a column called "newcol2" which are the stock closing prices from 1
day to the next.
The one little hang up is the name of the column. There seems to be an
additional data column name included in the attributes (dimnames?). So
when i run HEAD(DATAFRAMENAME) i get the column name = "Open". but
2015 Jun 15
2
Different behavior of model.matrix between R 3.2 and R3.1.1
Terry - your example didn't demonstrate the problem because the variable
that interacted with strata (zed) was not a factor variable.
But I had stated the problem incorrectly. It's not that there are too
many strata terms; there are too many non-strata terms when the variable
interacting with the stratification factor is a factor variable. Here
is a simple example, where I have
2015 Jun 15
2
Different behavior of model.matrix between R 3.2 and R3.1.1
Terry - your example didn't demonstrate the problem because the variable
that interacted with strata (zed) was not a factor variable.
But I had stated the problem incorrectly. It's not that there are too
many strata terms; there are too many non-strata terms when the variable
interacting with the stratification factor is a factor variable. Here
is a simple example, where I have
2007 Aug 23
1
Estimate Intercept in ARIMA model
Hi, All,
This is my program
ts1.sim <- arima.sim(list(order = c(1,1,0), ar = c(0.7)), n = 200)
ts2.sim <- arima.sim(list(order = c(1,1,0), ar = c(0.5)), n = 200)
tdata<-ts(c(ts1.sim[-1],ts2.sim[-1]))
tre<-c(rep(0,200),rep(1,200))
gender<-rbinom(400,1,.5)
x<-matrix(0,2,400)
x[1,]<-tre
x[2,]<-gender
fit <- arima(tdata, c(1, 1, 0), method = "CSS",xreg=t(x))
2010 Nov 02
1
Setting the names of a data.frame
I have tData as below. I need to set the names with the headers from the
first row in sHeaders
Sorry .. forgot how to set the names from row in another data frame .. pls
advise.
names(tData) = sHeaders[1,] does not work correctly
Also, why doesn't drop.levels(sHeaders) not work?
dput(tData)
structure(list(V1 = structure(c(3L, 1L, 1L, 2L), .Label = c("P H Ravi
Kumar",
"Rahul
2005 Jan 07
3
Basic Linear Algebra
I don't normally have to go anywhere near this stuff , but it seems to me that this should be a straight-forward process in R.
For the purposes of this enquiry I thought I would use something I can work out on my own.
So I have my matrix and the right hand results from that matrix
tdata <- matrix(c(0,1,0,-1,-1,2,0,0,-5,-6,0,0,3,-5,-6,1,-1,-1,0,0),byrow = T,ncol = 5)
sumtd <-
2010 Nov 03
4
Drawing circles on a chart
Dear Group,
I have the following data matrix which is a timeseries.
> dput(tData)
structure(list(A = c(0.2, 0.13, 0.05, 0.1, 0.02, 0.18, 0.09,
0.06, 0.13), B = c(0.15, 0.06, 0.09, 0.02, 0.03, 0.12, 0.01,
0.15, 0.06), C = c(-0.1, 0, -0.07, -0.06, -0.05, -0.05, -0.06,
-0.08, -0.07), D = c(-0.15, -0.05, -0.1, -0.03, -0.13, -0.04,
-0.1, -0.04, -0.15), E = c(-0.17, -0.16, -0.08, -0.07, -0.09,
2012 Mar 14
4
Merging fully overlapping groups
Hi,
I have data on individuals (B) who participated in events (A). If ALL
participants in an event are a subset of the participants in another event I
would like to remove the smaller event and if the participants in one event
are exactly similar to the participants in another event I would like to
remove one of the events (I don't care which one). The following example
does that however it
2003 Oct 01
1
question about predictions with linear models
Hi,
this question is probably very obvious but I just cant see where I
might be going wrong.
I'm using the lm() function to generate a linear model and then make
predictions using a different set of data.
To generate the model I do (tdata & pdata are matrices of observations
and parameters, tdepv, pdepv are response vectors)
x <- as.data.frame(tdata)
x$tdepv <- tdepv