If you really want your coefficient estimates to be scale-equivariant you
should test those methods for such a thing. E.g., here are functions that
let you check how scaling one predictor affects the estimated coefficients
- they should give the same results for any scale factor.
f <-
function (s
I found a fix to my problem using the fastLm() from package RcppEigen, using
the Jacobi singular value decomposition (SVD) (method 4) or a method based
on the eigenvalue-eigenvector decomposition of X'X - method 5 of the fastLm
function
install.packages("RcppEigen")
library(RcppEigen)
n_obs <-
s Applications,
Australian National University, Canberra ACT 0200.
On 29/03/2015, at 23:00,
mailto:r-help-requ...@r-project.org>>
mailto:r-help-requ...@r-project.org>> wrote:
From: Ben Bolker mailto:bbol...@gmail.com>>
Subject: Re: [R] Error in lm() with very small (close t
RiGui business.uzh.ch> writes:
>
[snip]
> I am terribly sorry for the code not being reproducible, is the
> first time I am posting here, I run the code several times before I
> posted, but...I forgot about the library used.
Thanks for updating.
> To answer to your questions:
>
>> How d
RiGui business.uzh.ch> writes:
>
[snip]
> I am terribly sorry for the code not being reproducible, is the
> first time I am posting here, I run the code several times before I
> posted, but...I forgot about the library used.
Thanks for updating.
> To answer to your questions:
>
>> How do
> On 28 Mar 2015, at 18:52 , RiGui wrote:
>
> Thank you for your replies!
>
> I am terribly sorry for the code not being reproducible, is the first time I
> am posting here, I run the code several times before I posted, but...I
> forgot about the library used.
>
> To answer to your questions:
Thank you for your replies!
I am terribly sorry for the code not being reproducible, is the first time I
am posting here, I run the code several times before I posted, but...I
forgot about the library used.
To answer to your questions:
How do you know this answer is "correct"?
What I am doing
> On 28 Mar 2015, at 18:28 , Ben Bolker wrote:
>
> peter dalgaard gmail.com> writes:
>
>>
>>
>>> On 28 Mar 2015, at 00:32 , RiGui business.uzh.ch> wrote:
>>>
>>> Hello everybody,
>>>
>>> I have encountered the following problem with lm():
>>>
>>> When running lm() with a regressor close
peter dalgaard gmail.com> writes:
>
>
> > On 28 Mar 2015, at 00:32 , RiGui business.uzh.ch> wrote:
> >
> > Hello everybody,
> >
> > I have encountered the following problem with lm():
> >
> > When running lm() with a regressor close to zero -
> of the order e-10, the
> > value of the estim
> On 28 Mar 2015, at 00:32 , RiGui wrote:
>
> Hello everybody,
>
> I have encountered the following problem with lm():
>
> When running lm() with a regressor close to zero - of the order e-10, the
> value of the estimate is of huge absolute value , of order millions.
>
> However, if I write
Hello everybody,
I have encountered the following problem with lm():
When running lm() with a regressor close to zero - of the order e-10, the
value of the estimate is of huge absolute value , of order millions.
However, if I write the formula of the OLS estimator, in matrix notation:
pseudoinv
For a single response variable tools like LASSO, LARS, ridge
regression, elasticnet, model averageing, and other penalized methods
(packages lasso2, lars, rms, elasticnet, MASS, BMA, and probably
others implement these tools) are preferred to stepwise methods. I
don't know if any of these have bee
First your response in the formula is a matrix which causes the lm
function to return an object of type 'mlm' for multivariate linear
model. Then when you run the stepAIC function it runs the addterm
function which looks for a method(function) to add terms to mlm
objects. However nobody has writt
Hi everybody
I am trying to run the next code but I have the next problem
Y1<-cbind(score.sol, score.com.ext, score.pur)
> vol.lm<-lm(Y1~1, data=vol14.df)
> library(MASS)
> stepAIC(vol.lm,~fsex+fjob+fage+fstudies,data=vol14.df)
Start: AIC=504.83
Y1 ~ 1
Error in addterm.mlm(fit, scope$add, scale
On Nov 11, 2009, at 7:14 AM, Bogaso wrote:
Hi all,
I wanted to have a seasonality study like whether a particular month
has
significant effect as compared to others. Here is my data :
0.10499 0 0 1 0 0 0 0 0 0
0 0
0.00259 0
Hi Bogaso,
Try this
vecnames<-names(test[,2:11])
fmla <- as.formula(paste("test[,1] ~ ", paste(vecnames, collapse= "+")))
res<-lm(fmla)
Regards
M
Bogaso a écrit :
Hi all,
I wanted to have a seasonality study like whether a particular month has
significant effect as compared to others. Here i
Hi all,
I wanted to have a seasonality study like whether a particular month has
significant effect as compared to others. Here is my data :
0.10499 0 0 1 0 0 0 0 0 0
0 0
0.00259 0 0 0 1 0 0 0
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