a linear function of speed! Hence
> cor(cars$dist,fitted.right)^2 and cor(x=cars$dist,y=fitted.wrong)^2 must be
> the same.
>
> HTH
> d
>
> Feladó: R-help [r-help-boun...@r-project.org] ; meghatalmazó: Jonathan
> Thayn [jth...@il
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
This is exactly what I was looking for. Thank you.
Jonathan Thayn
On Oct 3, 2014, at 10:32 AM, David L Carlson wrote:
> You can reconstruct the data from the first component. Here's an example
> using singular value decomposition on the original data matrix:
>
>> d <
p. It's been a long time since I've played with PCA.
Jonathan Thayn
On Oct 2, 2014, at 4:59 PM, David L Carlson wrote:
> I think you want to convert your principal component to the same scale as d1,
> d2, d3, and d4. But the "original space" is a 4-dimensional space
I have four time-series of similar data. I would like to combine these into a
single, clean time-series. I could simply find the mean of each time period,
but I think that using principal components analysis should extract the most
salient pattern and ignore some of the noise. I can compute com
Hello all
I've been performing a series of multidimensional optimizations (3 variables)
using the optima() function. Recently, I noticed that the solution is rarely
unimodal. Is there a package or function that handles multimodal
multidimensional optimizations? I really appreciate any suggestio
Sometimes the p.value returned by t.test() is the same that I calculate using
pt() and sometimes it's not. I don't understand the difference. I'm sure there
is a simple explanation but I haven't been able to find it, even after looking
at the code for t.test.default. I apologize if this is a bas
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