Re: [R] Logistic Regression with 200K features in R?
Dear Eik,
thank you so much for your help!
best Regards,
Romeo
On 12/12/2013 12:51 PM, Eik Vettorazzi wrote:
I thought so (with all the limitations due to collinearity and so on),
but actually there is a limit for the maximum size of an array which is
independent of your memory size and is due to the way arrays are
indexed. You can't create an object with more than 2^31-1 = 2147483647
elements.
https://stat.ethz.ch/pipermail/r-help/2007-June/133238.html
cheers
Am 12.12.2013 12:34, schrieb Romeo Kienzler:
ok, so 200K predictors an 10M observations would work?
On 12/12/2013 12:12 PM, Eik Vettorazzi wrote:
it is simply because you can't do a regression with more predictors than
observations.
Cheers.
Am 12.12.2013 09:00, schrieb Romeo Kienzler:
Dear List,
I'm quite new to R and want to do logistic regression with a 200K
feature data set (around 150 training examples).
I'm aware that I should use Naive Bayes but I have a more general
question about the capability of R handling very high dimensional data.
Please consider the following R code where "mygenestrain.tab" is a 150
by 20 matrix:
traindata <- read.table('mygenestrain.tab');
mylogit <- glm(V1 ~ ., data = traindata, family = "binomial");
When executing this code I get the following error:
Error in terms.formula(formula, data = data) :
allocMatrix: too many elements specified
Calls: glm ... model.frame -> model.frame.default -> terms ->
terms.formula
Execution halted
Is this because R can't handle 200K features or am I doing something
completely wrong here?
Thanks a lot for your help!
best Regards,
Romeo
__
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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 code.
__
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and provide commented, minimal, self-contained, reproducible code.
Re: [R] Logistic Regression with 200K features in R?
ok, so 200K predictors an 10M observations would work?
On 12/12/2013 12:12 PM, Eik Vettorazzi wrote:
it is simply because you can't do a regression with more predictors than
observations.
Cheers.
Am 12.12.2013 09:00, schrieb Romeo Kienzler:
Dear List,
I'm quite new to R and want to do logistic regression with a 200K
feature data set (around 150 training examples).
I'm aware that I should use Naive Bayes but I have a more general
question about the capability of R handling very high dimensional data.
Please consider the following R code where "mygenestrain.tab" is a 150
by 20 matrix:
traindata <- read.table('mygenestrain.tab');
mylogit <- glm(V1 ~ ., data = traindata, family = "binomial");
When executing this code I get the following error:
Error in terms.formula(formula, data = data) :
allocMatrix: too many elements specified
Calls: glm ... model.frame -> model.frame.default -> terms -> terms.formula
Execution halted
Is this because R can't handle 200K features or am I doing something
completely wrong here?
Thanks a lot for your help!
best Regards,
Romeo
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Re: [R] Logistic Regression with 200K features in R?
On 12/12/2013 7:08 AM, Eik Vettorazzi wrote:
thanks Duncan for this clarification.
A double precision matrix with 2e11 elements (as the op wanted) would
need about 1.5 TB memory, that's more than a standard (windows 64bit)
computer can handle.
According to Microsoft's "Memory Limits" web page (currently at
http://msdn.microsoft.com/en-us/library/windows/desktop/aa366778%28v=vs.85%29.aspx#memory_limits,
but these things tend to move around), the limit is 8 TB for virtual
memory.(The same page lists a variety of smaller physical memory
limits, depending on the Windows version, but R doesn't need physical
memory, virtual is good enough. )
R would be very slow if it was working with objects bigger than physical
memory, but it could conceivably work.
Duncan Murdoch
Cheers.
Am 12.12.2013 13:00, schrieb Duncan Murdoch:
> On 13-12-12 6:51 AM, Eik Vettorazzi wrote:
>> I thought so (with all the limitations due to collinearity and so on),
>> but actually there is a limit for the maximum size of an array which is
>> independent of your memory size and is due to the way arrays are
>> indexed. You can't create an object with more than 2^31-1 = 2147483647
>> elements.
>>
>> https://stat.ethz.ch/pipermail/r-help/2007-June/133238.html
>
> That post is from 2007. The limits were raised considerably when R
> 3.0.0 was released, and it is now 2^48 for disk-based operations, 2^52
> for working in memory.
>
> Duncan Murdoch
>
>
>>
>> cheers
>>
>> Am 12.12.2013 12:34, schrieb Romeo Kienzler:
>>> ok, so 200K predictors an 10M observations would work?
>>>
>>>
>>> On 12/12/2013 12:12 PM, Eik Vettorazzi wrote:
it is simply because you can't do a regression with more predictors
than
observations.
Cheers.
Am 12.12.2013 09:00, schrieb Romeo Kienzler:
> Dear List,
>
> I'm quite new to R and want to do logistic regression with a 200K
> feature data set (around 150 training examples).
>
> I'm aware that I should use Naive Bayes but I have a more general
> question about the capability of R handling very high dimensional
> data.
>
> Please consider the following R code where "mygenestrain.tab" is a 150
> by 20 matrix:
>
> traindata <- read.table('mygenestrain.tab');
> mylogit <- glm(V1 ~ ., data = traindata, family = "binomial");
>
> When executing this code I get the following error:
>
> Error in terms.formula(formula, data = data) :
> allocMatrix: too many elements specified
> Calls: glm ... model.frame -> model.frame.default -> terms ->
> terms.formula
> Execution halted
>
> Is this because R can't handle 200K features or am I doing something
> completely wrong here?
>
> Thanks a lot for your help!
>
> best Regards,
>
> Romeo
>
> __
> R-help@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>>>
>>
>
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Re: [R] Logistic Regression with 200K features in R?
thanks Duncan for this clarification.
A double precision matrix with 2e11 elements (as the op wanted) would
need about 1.5 TB memory, that's more than a standard (windows 64bit)
computer can handle.
Cheers.
Am 12.12.2013 13:00, schrieb Duncan Murdoch:
> On 13-12-12 6:51 AM, Eik Vettorazzi wrote:
>> I thought so (with all the limitations due to collinearity and so on),
>> but actually there is a limit for the maximum size of an array which is
>> independent of your memory size and is due to the way arrays are
>> indexed. You can't create an object with more than 2^31-1 = 2147483647
>> elements.
>>
>> https://stat.ethz.ch/pipermail/r-help/2007-June/133238.html
>
> That post is from 2007. The limits were raised considerably when R
> 3.0.0 was released, and it is now 2^48 for disk-based operations, 2^52
> for working in memory.
>
> Duncan Murdoch
>
>
>>
>> cheers
>>
>> Am 12.12.2013 12:34, schrieb Romeo Kienzler:
>>> ok, so 200K predictors an 10M observations would work?
>>>
>>>
>>> On 12/12/2013 12:12 PM, Eik Vettorazzi wrote:
it is simply because you can't do a regression with more predictors
than
observations.
Cheers.
Am 12.12.2013 09:00, schrieb Romeo Kienzler:
> Dear List,
>
> I'm quite new to R and want to do logistic regression with a 200K
> feature data set (around 150 training examples).
>
> I'm aware that I should use Naive Bayes but I have a more general
> question about the capability of R handling very high dimensional
> data.
>
> Please consider the following R code where "mygenestrain.tab" is a 150
> by 20 matrix:
>
> traindata <- read.table('mygenestrain.tab');
> mylogit <- glm(V1 ~ ., data = traindata, family = "binomial");
>
> When executing this code I get the following error:
>
> Error in terms.formula(formula, data = data) :
> allocMatrix: too many elements specified
> Calls: glm ... model.frame -> model.frame.default -> terms ->
> terms.formula
> Execution halted
>
> Is this because R can't handle 200K features or am I doing something
> completely wrong here?
>
> Thanks a lot for your help!
>
> best Regards,
>
> Romeo
>
> __
> R-help@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>>>
>>
>
--
Eik Vettorazzi
Department of Medical Biometry and Epidemiology
University Medical Center Hamburg-Eppendorf
Martinistr. 52
20246 Hamburg
T ++49/40/7410-58243
F ++49/40/7410-57790
--
Besuchen Sie uns auf: www.uke.de
_
Universitätsklinikum Hamburg-Eppendorf; Körperschaft des öffentlichen Rechts;
Gerichtsstand: Hamburg
Vorstandsmitglieder: Prof. Dr. Christian Gerloff (Vertreter des Vorsitzenden),
Prof. Dr. Dr. Uwe Koch-Gromus, Joachim Prölß, Rainer Schoppik
_
SAVE PAPER - THINK BEFORE PRINTING
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Re: [R] Logistic Regression with 200K features in R?
On 13-12-12 6:51 AM, Eik Vettorazzi wrote:
I thought so (with all the limitations due to collinearity and so on),
but actually there is a limit for the maximum size of an array which is
independent of your memory size and is due to the way arrays are
indexed. You can't create an object with more than 2^31-1 = 2147483647
elements.
https://stat.ethz.ch/pipermail/r-help/2007-June/133238.html
That post is from 2007. The limits were raised considerably when R
3.0.0 was released, and it is now 2^48 for disk-based operations, 2^52
for working in memory.
Duncan Murdoch
cheers
Am 12.12.2013 12:34, schrieb Romeo Kienzler:
ok, so 200K predictors an 10M observations would work?
On 12/12/2013 12:12 PM, Eik Vettorazzi wrote:
it is simply because you can't do a regression with more predictors than
observations.
Cheers.
Am 12.12.2013 09:00, schrieb Romeo Kienzler:
Dear List,
I'm quite new to R and want to do logistic regression with a 200K
feature data set (around 150 training examples).
I'm aware that I should use Naive Bayes but I have a more general
question about the capability of R handling very high dimensional data.
Please consider the following R code where "mygenestrain.tab" is a 150
by 20 matrix:
traindata <- read.table('mygenestrain.tab');
mylogit <- glm(V1 ~ ., data = traindata, family = "binomial");
When executing this code I get the following error:
Error in terms.formula(formula, data = data) :
allocMatrix: too many elements specified
Calls: glm ... model.frame -> model.frame.default -> terms ->
terms.formula
Execution halted
Is this because R can't handle 200K features or am I doing something
completely wrong here?
Thanks a lot for your help!
best Regards,
Romeo
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Re: [R] Logistic Regression with 200K features in R?
I thought so (with all the limitations due to collinearity and so on),
but actually there is a limit for the maximum size of an array which is
independent of your memory size and is due to the way arrays are
indexed. You can't create an object with more than 2^31-1 = 2147483647
elements.
https://stat.ethz.ch/pipermail/r-help/2007-June/133238.html
cheers
Am 12.12.2013 12:34, schrieb Romeo Kienzler:
> ok, so 200K predictors an 10M observations would work?
>
>
> On 12/12/2013 12:12 PM, Eik Vettorazzi wrote:
>> it is simply because you can't do a regression with more predictors than
>> observations.
>>
>> Cheers.
>>
>> Am 12.12.2013 09:00, schrieb Romeo Kienzler:
>>> Dear List,
>>>
>>> I'm quite new to R and want to do logistic regression with a 200K
>>> feature data set (around 150 training examples).
>>>
>>> I'm aware that I should use Naive Bayes but I have a more general
>>> question about the capability of R handling very high dimensional data.
>>>
>>> Please consider the following R code where "mygenestrain.tab" is a 150
>>> by 20 matrix:
>>>
>>> traindata <- read.table('mygenestrain.tab');
>>> mylogit <- glm(V1 ~ ., data = traindata, family = "binomial");
>>>
>>> When executing this code I get the following error:
>>>
>>> Error in terms.formula(formula, data = data) :
>>>allocMatrix: too many elements specified
>>> Calls: glm ... model.frame -> model.frame.default -> terms ->
>>> terms.formula
>>> Execution halted
>>>
>>> Is this because R can't handle 200K features or am I doing something
>>> completely wrong here?
>>>
>>> Thanks a lot for your help!
>>>
>>> best Regards,
>>>
>>> Romeo
>>>
>>> __
>>> R-help@r-project.org mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-help
>>> PLEASE do read the posting guide
>>> http://www.R-project.org/posting-guide.html
>>> and provide commented, minimal, self-contained, reproducible code.
>
--
Eik Vettorazzi
Department of Medical Biometry and Epidemiology
University Medical Center Hamburg-Eppendorf
Martinistr. 52
20246 Hamburg
T ++49/40/7410-58243
F ++49/40/7410-57790
--
Besuchen Sie uns auf: www.uke.de
_
Universitätsklinikum Hamburg-Eppendorf; Körperschaft des öffentlichen Rechts;
Gerichtsstand: Hamburg
Vorstandsmitglieder: Prof. Dr. Christian Gerloff (Vertreter des Vorsitzenden),
Prof. Dr. Dr. Uwe Koch-Gromus, Joachim Prölß, Rainer Schoppik
_
SAVE PAPER - THINK BEFORE PRINTING
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Re: [R] Logistic Regression with 200K features in R?
it is simply because you can't do a regression with more predictors than
observations.
Cheers.
Am 12.12.2013 09:00, schrieb Romeo Kienzler:
> Dear List,
>
> I'm quite new to R and want to do logistic regression with a 200K
> feature data set (around 150 training examples).
>
> I'm aware that I should use Naive Bayes but I have a more general
> question about the capability of R handling very high dimensional data.
>
> Please consider the following R code where "mygenestrain.tab" is a 150
> by 20 matrix:
>
> traindata <- read.table('mygenestrain.tab');
> mylogit <- glm(V1 ~ ., data = traindata, family = "binomial");
>
> When executing this code I get the following error:
>
> Error in terms.formula(formula, data = data) :
> allocMatrix: too many elements specified
> Calls: glm ... model.frame -> model.frame.default -> terms -> terms.formula
> Execution halted
>
> Is this because R can't handle 200K features or am I doing something
> completely wrong here?
>
> Thanks a lot for your help!
>
> best Regards,
>
> Romeo
>
> __
> R-help@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
--
Eik Vettorazzi
Department of Medical Biometry and Epidemiology
University Medical Center Hamburg-Eppendorf
Martinistr. 52
20246 Hamburg
T ++49/40/7410-58243
F ++49/40/7410-57790
--
Besuchen Sie uns auf: www.uke.de
_
Universitätsklinikum Hamburg-Eppendorf; Körperschaft des öffentlichen Rechts;
Gerichtsstand: Hamburg
Vorstandsmitglieder: Prof. Dr. Christian Gerloff (Vertreter des Vorsitzenden),
Prof. Dr. Dr. Uwe Koch-Gromus, Joachim Prölß, Rainer Schoppik
_
SAVE PAPER - THINK BEFORE PRINTING
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
