Hello, Paulito,
first, I think you haven't received an answer yet because you did not
"provide commented, minimal, self-contained, reproducible code" as the
posting guide does request it from you.
Second, see inline below.
On Wed, 11 Sep 2013, Paulito Palmes wrote:
Hi,
I have a data.frame with dimension 336x336 called *training*, and
another one called *observation* which is 336x1. I combined them as one
table using table=data.frame(training, observation). table now has
336x337 dimension with the last column as the observation to learn using
the training data of the rest of the column in the table. For
prediction, i combined the testing data and observation and pass it like
predict(model,testingWTesingObservation)
I've used the formula: rpart(table[,337] ~ ., data=table) or
svm(table[,337] ~ ., data=table).
I am not familiar with rpart() nor with svm() but "table[,337] ~ ., data =
table" has the consequence that table[,337] is also in the right hand side
of the formula, so that your "observations" are also in the "training"
data. That doesn't seem to make sense to me, and is different from the
call to svm() below.
Hth -- Gerrit
I recently discovered that this formulation produces different model
from the: svm(training, observation) formulation. Which is correct and
why one of them is not correct? I thought that syntactically, both are
the same. I hope that R should be able to detect the error in one of the
formulation to avoid the possibility of using it.
Regards,
Paul
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
