Displaying 4 results from an estimated 4 matches for "0.2649".
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0.264
2007 Jul 25
2
using contrasts on matrix regressions (using gmodels, perhaps)
Hi,
I want to test for a contrast from a regression where I am regressing the columns of a matrix. In short, the following.
X <- matrix(rnorm(50),10,5)
Y <- matrix(rnorm(50),10,5)
lm(Y~X)
Call:
lm(formula = Y ~ X)
Coefficients:
[,1] [,2] [,3] [,4] [,5]
(Intercept) 0.3350 -0.1989 -0.1932 0.7528 0.0727
X1 0.2007 -0.8505 0.0520
2005 Jan 25
1
CODA vs. BOA discrepancy
Dear List:
the CODA and BOA packages for the analysis of MCMC output yield different
results on two dignostic test of convergence: 1) Geweke's convergence
diagnostic; 2) Heidelberger and Welch's convergence diagnostic. Does that
imply that the CODA and BOA packages implement different ``flavors'' of
the same test?
I paste below an example.
Geweke's test
2006 Feb 02
0
crossvalidation in svm regression in e1071 gives incorrect results (PR#8554)
Full_Name: Noel O'Boyle
Version: 2.1.0
OS: Debian GNU/Linux Sarge
Submission from: (NULL) (131.111.8.96)
(1) Description of error
The 10-fold CV option for the svm function in e1071 appears to give incorrect
results for the rmse.
The example code in (3) uses the example regression data in the svm
documentation. The rmse for internal prediction is 0.24. It is expected the
10-fold CV rmse
2006 Feb 02
0
crossvalidation in svm regression in e1071 gives incorre ct results (PR#8554)
1. This is _not_ a bug in R itself. Please don't use R's bug reporting
system for contributed packages.
2. This is _not_ a bug in svm() in `e1071'. I believe you forgot to take
sqrt.
3. You really should use the `tot.MSE' component rather than the mean of
the `MSE' component, but this is only a very small difference.
So, instead of spread[i] <- mean(mysvm$MSE), you