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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible c
ta <- data[subset, ]
> fit <- some_regression_tool(formula, data = data)
>
> ##
>
> data_after_processing
> }
>
> with subset = subY == subY.val.
>
> Best,
>
> Philippe
>
>> On 02 Dec 2015, at 06:11, Brant Inman wrote:
>>
>>
I am trying to build a function that can accept variables for a regression. It
would work something like this:
---
# Y = my response variable (e.g. income)
# X = my key predictor variable (e.g. education)
# subY = a subsetting variable for Y (e.g. race)
# subY.val = the value of the subsetting v
a faster matching algorithm. I am pretty
certain that R functions can be used to do this but I do not have a good grasp
of how to make it work.
Brant Inman
[[alternative HTML version deleted]]
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R-help@r-project.org mailing list -- To U
were trying to help me). This error is not
reversible by converting y.fac back into a numeric variable. Interestingly,
the ordgee function from the same geepack package handles the factor response
variable without issue and appears to give results that best mimic the textbook
example.
Thanks to
want to check out the repolr and multgee packages as well
>
> Duncan
>
> Duncan Mackay
> Department of Agronomy and Soil Science
> University of New England
> Armidale NSW 2351
> Email: home: mac...@northnet.com.au
>
>
>
> -Original Message-
> From: r-help-boun...
R-helpers:
I am getting an error when trying to fit a GEE model. Below is code
reproducing the error.
###
library(foreign)
muscatine <-
read.dta('http://www.hsph.harvard.edu/fitzmaur/ala2e/muscatine.dta')
muscatine$gender <- as.factor(muscatine$gender)
muscatine$y <- as.factor(muscati
and 14. Is there a function in nlme or lme4 that does
this?
Brant
On Feb 28, 2011, at 02:24 AM, Ben Bolker wrote:
Brant Inman mac.com> writes:
>
> R-helpers:
>
> I would like to measure the correlation coefficient between the repeated
measures of a single variable
> that i
R-helpers:
I would like to measure the correlation coefficient between the repeated
measures of a single variable that is measured over time and is unbalanced. As
an example, consider the Orthodont dataset from package nlme, where the model
is:
fit <- lmer(distance ~ age + (1 | Subject), dat
R-helpers,
Below is a simple example of some output that I am getting while trying to work
with a data frame in R 2.12.1 for Mac.
-
> testdat <- data.frame(matrix(ncol=10, nrow=10))
> colnames(testdat) <- c('a','b','c','d','e','f','g','h','i','j')
> testdat[seq(1,10,3),] <- c(1,0,0,0,0,0,0,0
NANA
[6,] 7.788784NANA
There appears to be a problem with the predict.drc function. This code
previous generated confidence and prediction intervals in columns 2 and 3 of
the prediction matrices but now fails for reasons unknown to me. Anyone have
an idea of what
rror: package 'pcvsuite' was built before R 2.10.0: please re-install it
>
Anybody have an idea of how I can use the pcvsuite package running on R 2.12.0
for Mac?
Thank you in advance for any solutions.
Brant Inman
Duke University
[[altern
R-helpers,
I am using the package "drc" to fit a 4 parameter logistic model. When I
use the predict function to get prediction on a new dataset, I am not
getting the requested confidence or prediction intervals. Any idea what
is going on? Here is code to reproduce the problem:
---
library(
rstand why.
Brant
On May 5, 2009, at 11:43 PM, Rolf Turner wrote:
On 6/05/2009, at 3:00 PM, Brant Inman wrote:
R-experts:
In 2002, Hans Van Houwelingen et al. published a tutorial on how to
do
meta-regression in Statistics in Medicine. They used the classic BCG
dataset of Colditz to d
pointed out my
error with models f3 and f4 and why they do not seem to be correct.
Incidentally, other sources (ex: Egger/Altman book on systematic
reviews) report results on this dataset similar to Van Houwelingen, so
I think that my code is definitely the problem.
Thanks,
Brant Inm
ant
-
On Sep 4, 2008, at 2:59 PM, Prof Brian Ripley wrote:
Try another mirror: this is not the first time we've seen problems
at www.ibiblio.org.
On Thu, 4 Sep 2008, Brant Inman wrote:
R-helpers:
I just updated from R 7.0 to R 7.2.2 today. I am using MAC OS X
version
t(fmt, domain = domain), ...) :
argument is missing, with no default
The first part of the procedure seemed to work, then came page after
page of error messages. Can someone tell me what is going on here and
how I can fix the problem? I found a similar report
as a predictor in
the fake model in fit1 (stage being represented by the 3 dummy
variables stage2, stage3 and stage4), would the LR test calculated in
the last line of code be valid? Given the pseudolikelihood used in
the crr fit, I am not sure if this is a valid statistical test.
Thanks for any c
quot;, i, sep="") <- input.value[i]
}
Of course this loop will not work since the paste function returns a
character string that cannot be a variable. How can I construct a loop to
create sequential variables and assign a value to them in the same spirit of
the loop above?
Brant Inman
Mayo Cli
*As usual, the R-helpers were bang on. Some package must have modified my
digits option without me noticing.*
**
*--*
**
*> test <- matrix(c(7,47,4,38,20,96,1,14,10,48,2,101,12,161,
+ 1,28,1,19,22,49,25,162,31,200,9,39,22,193,
+ 0.5,45.5,31,131,4,75,31,220,7,55,3,91,14.5,
+ 25.5,3,
he problems in rows 15, 21, 37 and 43. They should read [0.5, 45.5],
[14.5, 21.5], etc... The matrix function seems to be rounding the second
column up to the next integer. Why would this occur? Can I do something to
prevent this?
I would very much appreciate any comments.
Thanks,
Bran
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