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Mäkinen Jussi

2002-Apr-15 12:58 UTC

[R] glm link = logit, passing arguments

Hello R-users.

I haven't use R for a life time and this might be trivial - I hope you do
not mind.

I have a  questions about arguments in the Glm-function. There seems to be
something that I cannot cope.

The basics are ok:> y <- as.double(rnorm(20) > .5)
>  logit.model <- glm(y ~ rnorm(20), family=binomial(link=logit), trace
TRUE)Deviance = 28.34255 Iterations - 1 
Deviance = 27.72554 Iterations - 2 
Deviance = 27.72527 Iterations - 3 
Warning message: 
non-integer #successes in a binomial glm! in: eval(expr, envir, enclos).

But trying to exclude intercept (or pass anything to glm.fit, especially
mustart to improve convergence if I have understand the role of mustart
correctly):

glm(as.matrix(y) ~ as.matrix(X), family=binomial(link=logit), intercept FALSE,
TRACE = TRUE)

seems not to be a correct since it returns:

Error in glm.control(...) : unused argument(s) (intercept ...)		###
Same apply to the mustart

But using instead

glm(as.matrix(y) ~ as.matrix(X), family=binomial(link=logit), intercept FALSE,
TRACE = TRUE,
			control = glm.control(epsilon=1e-04, maxit = 1000))

yields:

Call:  glm(formula = as.matrix(y) ~ as.matrix(X), family = binomial(link logit),
control = glm.control(epsilon = 1e-04, maxit = 1000), intercept
= FALSE,      TRACE = TRUE) 

Coefficients:
      (Intercept)  as.matrix(X)due14  as.matrix(X)due45  as.matrix(X)due90  
           -22.05            -305.47            -283.72            -144.07  

Degrees of Freedom: 7775 Total (i.e. Null);  7772 Residual
Null Deviance:      7959 
Residual Deviance: 3.589e-06    AIC: 8 
Warning message: 
fitted probabilities numerically 0 or 1 occurred in: (if
(is.empty.model(mt)) glm.fit.null else glm.fit)(x = X, y = Y, 

which seems to work without explicite errors. For me its a little strange
that including an argument does help to pass an other one. But trace doesn't
show anything and also intercept is still included eventhough intercept FALSE is
set up (probably unwisely but the judgement should here be users I
think). Obviously there is something wrong with my arguments passing. Is it
alright to try to pass arguments to glm.fit at all?

The estimation process seems to have hard times (because of myriad of 0s, I
guess) with data and it is not able to reach convergence (even after maxit
1000). Is there any other estimation methods available in the R-packages? Or
would you have any other idea to transform data a prior to make it possible.
Making explanatory also binary seems to be a remedy but that's something I
do not want to do (and I'm not sure is it even statistically correct way).

I have a binary response variable y: 0 1 1 1 1 1 1 0 0 0 (a sample)

and a matrix of continous (but includin a lot of zeros)  explanatory
variables X1, X2, X3:
X1, X2, X3
198 0.0045861625 0.001717838  0.0000000
199 0.0000000000 0.000000000 -0.9992498
200 0.0000000000 0.000000000 -0.9988129
201 0.0000000000 0.000000000 -0.9984882
202 0.0000000000 0.000000000 -0.9983429
203 0.0000000000 0.000000000 -0.9980252
204 0.0003933059 0.000000000 -0.9980207
205 0.0000000000 0.000000000  0.0000000
206 0.0000000000 0.000000000  0.0000000
207 0.0000000000 0.000000000  0.0000000

version
         _              
platform i386-pc-mingw32
arch     x86            
os       Win32          
system   x86, Win32     
status                  
major    1              
minor    4.1            
year     2002           
month    01             
day      30             
language R

One more basic question crossed my mind - is this iterative weighted OLS
with a link function practically same than Maximum Likelihood estimation
which I would like to use? Can you recommend any reference to the
probit/Logit within R/Splus (other than Modern Applied Statistics with
S-PLUS which is in my list anyway).

Regards and thank you for sharing your knowledge,

Jussi Makinen
Analyst
State Treasure of Finland
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Thomas Lumley

2002-Apr-15 15:11 UTC

head link

[R] glm link = logit, passing arguments

On Mon, 15 Apr 2002, [iso-8859-1] Mäkinen Jussi wrote:
> Hello R-users.
>
> I haven't use R for a life time and this might be trivial - I hope you
do
> not mind.
>
> I have a  questions about arguments in the Glm-function. There seems to be
> something that I cannot cope.
>
> The basics are ok:
> > y <- as.double(rnorm(20) > .5)
> >  logit.model <- glm(y ~ rnorm(20), family=binomial(link=logit),
trace > TRUE)
> Deviance = 28.34255 Iterations - 1
> Deviance = 27.72554 Iterations - 2
> Deviance = 27.72527 Iterations - 3
> Warning message:
> non-integer #successes in a binomial glm! in: eval(expr, envir, enclos).
Even this isn't ideal -- if your data are really binomial it would be
better to pass them as a number of successes and failures, if they aren't
really binomial then family=quasibinomial() would probably be better.
> But trying to exclude intercept (or pass anything to glm.fit, especially
> mustart to improve convergence if I have understand the role of mustart
> correctly):
>
> glm(as.matrix(y) ~ as.matrix(X), family=binomial(link=logit), intercept
> FALSE, TRACE = TRUE)
>
> seems not to be a correct since it returns:
>
> Error in glm.control(...) : unused argument(s) (intercept ...)		###
That's because intercept is not one of the arguments that glm() takes. You
can fit a model without intercept by using  y~x+0 or y~x-1 as the formula.
> Same apply to the mustart
Yes, there is no mustart argument to glm() either.
> But using instead
>
> glm(as.matrix(y) ~ as.matrix(X), family=binomial(link=logit), intercept
> FALSE, TRACE = TRUE,
> 			control = glm.control(epsilon=1e-04, maxit = 1000))
>
> yields:
><snipped>
> which seems to work without explicite errors. For me its a little strange
> that including an argument does help to pass an other one. But trace
doesn't
> show anything and also intercept is still included eventhough intercept
> FALSE is set up (probably unwisely but the judgement should here be users I
> think). Obviously there is something wrong with my arguments passing. Is it
> alright to try to pass arguments to glm.fit at all?
>
No. If you want to call glm.fit() you have to call it directly. If you
look at the code for glm() you can see that it doesn't pass the ...
arguments to glm.fit.

The ... argument is passed to glm.control() if there is no explicit
glm.control= argument -- that's what caused the error and why it went away
when you supplied the glm.control argument.  Otherwise it's there for
future expansion.
>
> One more basic question crossed my mind - is this iterative weighted OLS
> with a link function practically same than Maximum Likelihood estimation
> which I would like to use? Can you recommend any reference to the
> probit/Logit within R/Splus (other than Modern Applied Statistics with
> S-PLUS which is in my list anyway).
It is exactly maximum likelihood.

	-thomas

Thomas Lumley			Asst. Professor, Biostatistics
tlumley at u.washington.edu	University of Washington, Seattle

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Send "info", "help", or "[un]subscribe"
(in the "body", not the subject !)  To: r-help-request at
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