Le mercredi 06 mars 2013 à 09:18 -0800, Jonas125 a écrit :
> Length(Datasplit) = 7100
>
> I did a regression for Datasplit[[1]] and the calculated columns --> the
> object size is 70 MB. Quite large
7100*70/1024 = 485 (GB)
No wonder why you run out of memory quite fast.
You probably do not n
Length(Datasplit) = 7100
I did a regression for Datasplit[[1]] and the calculated columns --> the
object size is 70 MB. Quite large
Assuming that R cannot handle inf values in regressions (didn't have the
time to google it)
How can I avoid the calculation of infinite values? Like "If the deno
Le mercredi 06 mars 2013 à 08:31 -0800, Jonas125 a écrit :
> The datatable (and the split obviously) only contain characters and numeric
> data.
>
> I found that 4 regression in a row work if I don't use the calculated
> columns as variables but 2 of the original columns.
> RAM usage stays below
The datatable (and the split obviously) only contain characters and numeric
data.
I found that 4 regression in a row work if I don't use the calculated
columns as variables but 2 of the original columns.
RAM usage stays below 3GB!
--> Why does R has such problems with the calculated columns? Thei
On Wed, Mar 6, 2013 at 9:51 AM, Jonas125 wrote:
> Hello,
>
> I am a rather unexperienced r-user (learned the language 1 month ago) and
> run into the following problem using a local computer with 6 cores & 24 GB
> RAM and R 2.15 64-bit. I didn't install any additional packages
>
> 1. Via the read.
Hello,
I am a rather unexperienced r-user (learned the language 1 month ago) and
run into the following problem using a local computer with 6 cores & 24 GB
RAM and R 2.15 64-bit. I didn't install any additional packages
1. Via the read.table command I load a data table (with different data
types)
Smells like homework to me. If so, we don't do homework on this list.
-- Bert
On Thu, Feb 14, 2013 at 3:55 PM, email wrote:
> Hello:
>
> I have a 4-column dataset: Crime, Education, Urbanization, Age. I want to
> construct a multiple linear regression to find the effect of Education,
> Urbanizat
>
>
You could also run it and find out.
There are many R tutorials free online.
I presume crime is continuous, as well...?
~Nicole Ford
Graduate Instructor
Department of Government and International Affairs
University of South Florida
office: SOC 012M
e: nmhi...@mail.usf.edu
http://gi
You will also need to specify/ name your model:
Mod <- lm(Crime~.
~Nicole Ford
Graduate Instructor
Department of Government and International Affairs
University of South Florida
office: SOC 012M
e: nmhi...@mail.usf.edu
http://gia.usf.edu/student/nford/
Sent from my iPhone
On Feb 14, 201
Hi,
Did you read the help file? Particularly the section "Details".
?lm
Regards,
Pascal
Le 15/02/2013 08:55, email a écrit :
Hello:
I have a 4-column dataset: Crime, Education, Urbanization, Age. I want to
construct a multiple linear regression to find the effect of Education,
Urbanization,
Hello:
I have a 4-column dataset: Crime, Education, Urbanization, Age. I want to
construct a multiple linear regression to find the effect of Education,
Urbanization, and Age on Crime"
lm(Crime ~ Education + Urbanization + Age)
If I use + in above statement, does it mean it will build a model to
Can you create a small example that replicates your problem? If not,
at least send the code you are running right before R throws an error
(e.g., the exact call to lm) and some basic information on your data
like the class of each variable and the number of observations.
Is your outcome variable,
I found one error in in the code, however, I still receive errors.
Error in storage.mode(y) <- "double" :
invalid to change the storage mode of a factor
Moreover: Warning message:
In model.response(mf, "numeric") :
using type="numeric" with a factor response will be ignored
>From my unders
Hi,
It probably means that somehow your data is stored incorrectly. Try
using str(name of the dataframe) and see if everything is named what
you expect and is the class you think it is with equal numbers of
observations.
HTH,
Josh
On Fri, Nov 26, 2010 at 3:39 AM, effeesse wrote:
>
> Hi! My r
Hi! My regression is lm(Y ~ A + B + C + D + E + F + G), where each covariate
"A--G" is of the kind "as.factor(column of the dataframe)".
I receive an error for the first explanatory variable "A": Error in column
of the dataframe: wrong number of dimensions. What does it mean?
--
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