Displaying 20 results from an estimated 5000 matches similar to: "update.formula has 512 char buffer?"
2005 Aug 16
4
as.character and a formula
Dear list,
given this formula:
> fmla <- formula(y1 ~ spp1 + spp2 + spp3 + spp5)
> fmla[[3]]
spp1 + spp2 + spp3 + spp5
is this the intended behaviour of as.character:
> as.character(fmla[[3]])
[1] "+" "spp1 + spp2 + spp3" "spp5"
? Where does the extra "+" come from?
> as.character(fmla)
[1] "~"
2012 Jan 25
4
formula error inside function
I want use survfit() and basehaz() inside a function, but it doesn't work.
Could you take a look at this problem. Thanks for your help. Following is my
codes:
library(survival)
n <- 50 # total sample size
nclust <- 5 # number of clusters
clusters <- rep(1:nclust,each=n/nclust)
beta0 <- c(1,2)
set.seed(13)
#generate phmm data set
Z <- cbind(Z1=sample(0:1,n,replace=TRUE),
2003 Oct 02
4
using a string as the formula in rlm
Hi,
I am trying to build a series of rlm models. I have my data frame and
the models will be built using various coulmns of the data frame.
Thus a series of models would be
m1 <- rlm(V1 ~ V2 + V3 + V4, data)
m2 <- rlm(V1 ~ V2 + V5 + V7, data)
m3 <- rlm(V1 ~ V2 + V8 + V9, data)
I would like to automate this. Is it possible to use a string in place
of the formula?
I tried doing:
fmla
2006 Apr 20
1
A question about nlme
Hello,
I have used nlme to fit a model, the R syntax is like
fmla0<-as.formula(paste("~",paste(colnames(ldata[,9:13]),collapse="+"),"-1"))
> fmla1<-as.formula(paste("~",paste(colnames(ldata[,14:18]),collapse="+"),"-1"))
>
2012 May 29
3
trouble automating formula edits when log or * are present; update trouble
Greetings
I want to take a fitted regression and replace all uses of a variable
in a formula. For example, I'd like to take
m1 <- lm(y ~ x1, data=dat)
and replace x1 with something else, say x1c, so the formula would become
m1 <- lm(y ~ x1c, data=dat)
I have working code to finish that part of the problem, but it fails
when the formula is more complicated. If the formula has log(x1)
2012 Apr 19
2
ANOVA in quantreg - faulty test for 'nesting'?
I am trying to implement an ANOVA on a pair of quantile regression models in
R. The anova.rq() function performs a basic check to see whether the models
are nested, but I think this check is failing in my case. I think my models
are nested despite the anova.rqlist() function saying otherwise. Here is an
example where the GLM ANOVA regards the models as nested, but the quantile
regression ANOVA
2005 Jul 01
1
scope argument in step function
Thanks a lot for help in advance. I am switching from matlab to R and I guess I need some time to get rolling. I was wondering why this code :
> fit.0 <- lm( Response ~ 1, data = ds3)
> step(fit.0,scope=list(upper=~.,lower=~1),data=ds3)
Start: AIC= -32.66
Response ~ 1
Call:
lm(formula = Response ~ 1, data = ds3)
Coefficients:
(Intercept)
1.301
is not working
2006 May 24
1
Problem with pasteing formulas (PR#8897)
Hi,
If I create a formula with say 100 terms and then paste it:
xnam <- paste("x", 1:100, sep="")
fmla <- as.formula(paste("y ~ ", paste(xnam, collapse= "+")))
paste(fmla)
The result seems to cut off everything after the first 500 characters
and gives no warning message.
I have the most recent version of R from the R website and the problem
occurs
2003 Apr 02
8
lm with an arbitrary number of terms
Hello folks,
Any ideas how to do this?
data.frame is a data frame with column names "x1",...,"xn"
y is a response variable of length dim(data.frame)[1]
I want to write a function
function(y, data.frame){
lm(y~x1+...+xn)
}
This would be easy if n was always the same.
If n is arbitrary how could I feed the x1+...+xn terms into lm(response~terms)?
Thanks
Richard
--
Dr.
2019 Sep 05
2
ARM vectorized fp16 support
Hi,
I'm trying to compile half precision program for ARM, while it seems
LLVM fails to automatically generate fused-multiply-add instructions
for c += a * b. I'm wondering whether I did something wrong, if not,
is it a missing feature that will be supported later? (I know there're
fp16 FMLA intrinsics though)
Test programs and outputs,
$ clang -O3 -march=armv8.2-a+fp16fml
2011 Dec 19
1
pls help to print out first row of terms(model) output in example program
Greetings.
I've written a convenience function for multicollinearity diagnosis.
I'd like to report to the user the formula that is used in a
regression. I get output like this:
> mcDiagnose(m1)
[1] "The following auxiliary models are being estimated and returned in a list:"
[1] "`x1` ~ ."
formula(fmla)()
[1] "`x2` ~ ."
I'd like to fill in the period
2017 Jun 17
3
Prediction with two fixed-effects - large number of IDs
Dear all,
I am running a panel regression with time and location fixed effects:
###
reg1 <- lm(lny ~ factor(id) + factor(year) + x1+ I(x1)^2 + x2+ I(x2)^2 ,
data=mydata, na.action="na.omit")
###
My goal is to use the estimation for prediction. However, I have 8,500 IDs,
which is resulting in very slow computation. Ideally, I would like to do
the following:
###
reg2 <-
2006 May 05
1
A question about linear optimizaton
Dear all,
I am trying to find a solution satisfying the below equations
in R.
Set up the problem
9 X1+ X2 + X3 = 2
X1+ X2 + X3 = 1
which is subjected to
0 < X1 < X2 < X3 < 2.
I have downloaded the packages \'linprog\' and \'lpSolve\' but can
not see how to solve the question.
Thank you for your help.
With
2019 Sep 05
2
ARM vectorized fp16 support
Thanks for reply. I was using LLVM 8.0. Let me try trunk and will let
you know if it works.
On Wed, Sep 4, 2019 at 11:19 PM Sjoerd Meijer <Sjoerd.Meijer at arm.com> wrote:
>
> Hi,
> Which version of Clang are you using? I do get a "vfma.f16" with a recent trunk build. I haven't looked at older versions and when this landed, but we had an effort to plug the remaining
2017 Jun 17
0
Prediction with two fixed-effects - large number of IDs
I have no direct experience with such horrific models, but your formula is a mess and Google suggests the biglm package with ffdf.
Specifically, you should convert your discrete variables to factors before you build the model, particularly since you want to use predict after the fact, for which you will need a new data set with the exact same levels in the factors.
Also, your use of I() is
2008 Aug 29
1
nls() fails on a simple exponential fit, when lm() gets it right?
Dear R-help,
Here's a simple example of nonlinear curve fitting where nls seems to get
the answer wrong on a very simple exponential fit (my R version 2.7.2).
Look at this code below for a very basic curve fit using nls to fit to (a)
a logarithmic and (b) an exponential curve. I did the fits using
self-start functions and I compared the results with a more simple fit
using a straight lm()
2016 May 13
2
A question about AArch64 Cortex-A57 subtarget definition
Hello everybody,
I'm reading the .td files defining the Cortex-A57 processor,
which is a subtarget of AArch64 target, and there is something
confusing me in the `AArch64SchedA57.td` file.
In the top of `AArch64SchedA57.td`, various processor resource are
defined, as follows
```
def A57UnitB : ProcResource<1>; // Type B micro-ops
def A57UnitI : ProcResource<2>; // Type
2005 Jul 20
1
aregImpute in Hmisc
Hi,
I have a dataframe ds1.2 - 503 categorial variables
and 1 continuous response variables. I ran aregImpute
to deal with NA's and got the followig error:
> fmla = terms( Response ~ . ,data=ds1.2)
> ds.i = aregImpute(fmla,data=ds1.2)
Error in matrix(as.double(1), nrow = n, ncol = p,
dimnames = list(rnam, :
length of dimnames [2] not equal to array
extent
Could you explain
2003 Mar 02
1
model.frame.default problem in function definition
Could someone point me in the right direction for the following issue:
A function is defined as follows:
tfun <- function(dat)
{
fmla <- as.formula("y~x+z")
dat2 <- dat
mdl <- lm(fmla,dat2)
mdl <- step(mdl)
}
Then the following code
dat <- data.frame(x=1:10,z=1:10,y=(1:10)^2+10*(1:10))
tfun(dat)
generates the output
Start: AIC= 43.67
2005 Jun 24
1
"Error in contrasts" in step wise regression
Hi,
I have a problem in getting step function work. I am getting the following error:
> fit1 <- lm(Response~1)
> fmla <- as.formula(paste(" ~ ",paste(colnames,collapse="+")))
> sfit <- step(fit1,scope=list(upper= fmla,lower= ~1),k=log(nrow(dat)))
Start: AIC= -1646.66
Response ~ 1
Error in "contrasts<-"(`*tmp*`, value =