1] -3.281849
David L. Carlson
Department of Anthropology
Texas A&M University
-Original Message-
From: R-help [mailto:r-help-boun...@r-project.org] On Behalf Of Jonathan Thayn
Sent: Sunday, February 22, 2015 12:01 AM
To: Kehl Dániel
Cc: r-help@r-project.org
Subject: Re: [R] Correlat
stu.edu]
> Küldve: 2015. február 21. 22:42
> To: r-help@r-project.org
> Tárgy: [R] Correlation question
>
> I recently compared two different approaches to calculating the correlation
> of two variables, and I cannot explain the different results:
>
> data(cars)
> m
: Jonathan
Thayn [jth...@ilstu.edu]
Küldve: 2015. február 21. 22:42
To: r-help@r-project.org
Tárgy: [R] Correlation question
I recently compared two different approaches to calculating the correlation of
two variables, and I cannot explain the different results:
data(cars)
model <- lm(dist~sp
I recently compared two different approaches to calculating the correlation of
two variables, and I cannot explain the different results:
data(cars)
model <- lm(dist~speed,data=cars)
coef(model)
fitted.right <- model$fitted
fitted.wrong <- -17+5*cars$speed
When using the OLS fitted values, the
Hi everyone,
Thanks for the help.
On Thu, 9 Sep 2010, Peter Ehlers wrote:
The first thing to do when you get results that you don't expect is
to check the help page. The page for cor clearly states that its
input is to a *numeric* vector, matrix or data frame (my emphasis).
I would not be happ
On 2010-09-09 11:53, Stephane Vaucher wrote:
Hi Josh,
Initially, I was expecting R to simply ignore non-numeric data. I guess I
was wrong... I copy-pasted what I observe, and I do not get an error when
The first thing to do when you get results that you don't expect is
to check the help page.
Hi Stephane,
According to the NEWS file, as of 2.11.0: "cor() and cov() now test
for misuse with non-numeric arguments, such as the non-bug report
PR#14207" so there is no need for a new bug report.
Here is a simple way to select only numeric columns:
# Sample data
dat <- data.frame(a = 1:10L, b
Hi Josh,
Initially, I was expecting R to simply ignore non-numeric data. I guess I
was wrong... I copy-pasted what I observe, and I do not get an error when
calculating correlations with text data. I can also do cor(test.n$P3,
test$P7) without an error.
If you have a function to select only
Hi Stephane,
When I use your sample data (e.g., test, test.number), cor() throws an
error that x must be numeric (because of the factor or character
data). Are you not getting any errors when trying to calculate the
correlation on these data? If you are not, I wonder what version of R
are you us
Thank you Dennis,
You identified a factor (text column) that I was concerned with.
I simplified my example to try and factor out possible causes. I
eliminated the recurring values in columns (which were not the columns
that caused problems). I produced three examples with simple data sets.
1
Did you try taking out P7, which is text? Moreover, if you get a message
saying ' the standard deviation is zero', it means that the entire column is
constant. By definition, the covariance of a constant with a random variable
is 0, but your data consists of values, so cor() understandably throws a
Hi everyone,
First of all, thanks for the quick responses. I appreciate the help.
Before answering questions, I wanted to mention that I tested this
behaviour on 2.3.1 and 2.10.1 on a x86_64 linux arch, and on version 2.9.0
on a 32 bit arch.
Now for the answers (batch version):
1/ I receive
Hi,
Does your data have missing values? I am not sure it would change
anything, but perhaps try adding:
cor(test2, method = "spearman", use = "pairwise.complete.obs")
or something of the like. I am not sure what R does by default. My
reasoning stems from this particular passage in the documen
Hi everyone,
I'm observing what I believe is weird behaviour when attempting to do
something very simple. I want a correlation matrix, but my matrix seems to
contain correlation values that are not found when executed on pairs:
test2$P2
[1] 2 2 4 4 1 3 2 4 3 3 2 3 4 1 2 2 4 3 4 1 2 3 2 1 3
Dear R-users please ignore my most recent posting..
Found the solution.. Thanks to David Winsemius..
Thanks,
Santosh
On Tue, Jul 14, 2009 at 9:14 PM, Santosh wrote:
> Dear R-users..
>
> I hope the following scenario is more explanatory of my question..
>
> Continuous variables: AGE, WEIGH
Dear R-users..
I hope the following scenario is more explanatory of my question..
Continuous variables: AGE, WEIGHT, HEIGHT
Categorical variables: Group, Sex, Race
I would like to find a correlation between WEIGHT and AGE, grouped by
"Group","Sex", and "Race".
Is the following formula correct?
t
On Jul 14, 2009, at 10:34 PM, Santosh wrote:
Hi R-users,
Was wondering if there is a way to quickly compute correlations
between
continuous variables grouped by some categorical variables?
What function do I use?
?tapply
?by
Thanks much in advance.
Regards,
Santosh
[[alterna
Hi R-users,
Was wondering if there is a way to quickly compute correlations between
continuous variables grouped by some categorical variables?
What function do I use?
Thanks much in advance.
Regards,
Santosh
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