Displaying 2 results from an estimated 2 matches for "coef_i".
2008 Jul 15
2
meaning of tests presented in anova(ols(...)) {Design package}
...027662261037
conc * sand -0.0157426 0.0076013 -2.07 0.0384966958735
conc * sand^2 0.0003419 0.0001989 1.72 0.0857381555491
conc * sand^3 -0.0000027 0.0000015 -1.77 0.0777025949762
Looking at what I 'think' are "marginal p-values" i.e. results of a
test against coef_i != 0, there are several terms with non-significant
coefficients (at p<0.05). Does a non-significant coefficient warrant
removal from the model, or perhaps a mention in the discussion?
Compared to the above example, what tests are performed when calling
anova() on this object? Here is the output...
2002 Aug 20
0
Re: SVM questions
..._i is the i-th support vector, y_i the corresponding label, a_i
the corresponding coefficiant, and K is the kernel (in your case, the
linear one, so
K(u,v) = u'v).
Now, ``libsvm'' actually returns a_i * y_i as i-th coefficiant and the
*negative* rho, so in fact uses the formula:
Sum(coef_i * K(x_i, n)) - rho
i
where the training examples (=training data) are labeled {1,-1}.
A simplified R function for prediction with linear kernel would be:
svmpred <- function (m, newdata, K=crossprod) {
## this guy does the computation:
pred.one <- function (x)
sign(sum(sapply(1:m...