I think what you have done should be fine. read.table() will return a
data frame, which cor() can handle happily. For example:
my.data <- read.table("file.csv", header = TRUE, row.names = 1,
sep=",", strip.white = TRUE) # assign your data to "my.data"
cor(my.data) # calculate the correlation ma
>
> If you read each of your 230,000 variables in separately, you can
> combine them into a matrix or dataframe using cbind(variablename1, 2,
> etc.).
>
> HTH,
I used read.table("file.csv", header = TRUE, row.names = 1, sep=",",
strip.white = TRUE)
to read it in but I could easily only read in th
Hello Vincent,
The command to correlate two variables and a set is the same (see
?cor). How have you read the data in? If it is a matrix or data
frame, you should be able to just use cor(name_of_your_matrix) and it
will return the correlation matrix for all variables in your matrix or
data frame
Just another opinion about R^2 coming from the field of US Psychology
research and business:
The first and foremost technique taught in Psychology Departments in
subfields where experimental designs are rarely possible (i.e., social
psychology, personality psychology, developmental psychology,
Indu
Dieter Menne wrote
>I noted the "and" was misleading. Read: Good journals like Lancet,
>New English and many British Journal of XXX really help you to do
>better.
I am one of the statistical editors for PLoS Medicine, and I try to help
people do better; often, the people take my advice. Somet
Dieter Menne menne-biomed.de> writes:
> q(paper) = 10* n(pvalues) + 5*n(R^2) + 3.5*n(Error Bars)
>
> Values above 300 qualify for immediate acceptance, and Journals
> like Lancet, New English and British Journal of XXX provide
> professional advice.
>
I noted the "and" was misleading. Read: Goo
Dieter Menne wrote:
Bert Gunter gene.com> writes:
Martin's reply provides an appropriate response, so nothing to add. But my
questions dig deeper: Why do so many (presumably nonstatisticians, but ?)
belong to this R^2 religion? Is it because:
1) This is what they are taught in their Stat 1
Bert Gunter gene.com> writes:
> Martin's reply provides an appropriate response, so nothing to add. But my
> questions dig deeper: Why do so many (presumably nonstatisticians, but ?)
> belong to this R^2 religion? Is it because:
>
> 1) This is what they are taught in their Stat 101 courses by st
2009 8:22 AM
> To: Benedikt Niesterok
> Cc: r-help@r-project.org
> Subject: Re: [R] correlation coefficient
>
> >>>>> "BN" == Benedikt Niesterok
> >>>>> on Tue, 28 Apr 2009 15:33:02 +0200 writes:
>
> BN> Hello,
>
> "BN" == Benedikt Niesterok
> on Tue, 28 Apr 2009 15:33:02 +0200 writes:
BN> Hello,
BN> I would like to get a correlation coefficient (R-squared) for my model.
{{ arrrgh... how many people think they "need" an R^2 when they
fit a model ?? }}
BN> I don't kno
You could use the qqnorm function to obtain the correlation, as:
> qqp=qqnorm(rstudent(regrname))
> cor(qqp$x,qqp$y)
If you do not want see the plot (as the qq.plot is richer):
> qqp=qqnorm(rstudent(regrname), plot.it=F)
domenico vistocco
Tom Fitzhugh wrote:
> Hi,
>
> I am trying to figure
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