Stephen Choularton
2005-Jun-16 21:39 UTC
[R] logistic regression - using polys and products of features
Hi I can get all my features by doing this:> logistic.model = glm(similarity ~ ., family=binomial, data cData[3001:3800,])I can get the product of all my features by this: logistic.model = glm(similarity ~ . ^ 2, family=binomial, data cData[3001:3800,]) I don't seem to be able to get polys by doing this: logistic.model = glm(similarity ~ poly(.,2), family=binomial, data cData[3001:3800,]) Error in poly(., 2) : Object "." not found>How can I get polys? What do the warnings mean when I do this:> logistic.model = glm(similarity ~ . + . ^ 2, family=binomial, data cData[3001:3800,])Warning messages: 1: Algorithm did not converge in: glm.fit(x = X, y = Y, weights weights, start = start, etastart = etastart, 2: fitted probabilities numerically 0 or 1 occurred in: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart,>How can I do this? logistic.model = glm(similarity ~ . + . ^ 2 + poly(.,2), family=binomial, data = cData[3001:3800,]) Thanks Stephen -- Internal Virus Database is out-of-date. Checked by AVG Anti-Virus. [[alternative HTML version deleted]]
ronggui
2005-Jun-17 17:05 UTC
[R] logistic regression - using polys and products of features
On Fri, 17 Jun 2005 07:39:30 +1000 Stephen Choularton <mail at bymouth.com> wrote:> Hi > > I can get all my features by doing this: > > > logistic.model = glm(similarity ~ ., family=binomial, data > cData[3001:3800,]) > > > I can get the product of all my features by this: > > logistic.model = glm(similarity ~ . ^ 2, family=binomial, data > cData[3001:3800,]) > > I don't seem to be able to get polys by doing this: > > logistic.model = glm(similarity ~ poly(.,2), family=binomial, data > cData[3001:3800,]) > Error in poly(., 2) : Object "." not found > > > > How can I get polys? > > What do the warnings mean when I do this: > > > logistic.model = glm(similarity ~ . + . ^ 2, family=binomial, data > cData[3001:3800,]) > Warning messages: > 1: Algorithm did not converge in: glm.fit(x = X, y = Y, weights > weights, start = start, etastart = etastart, > 2: fitted probabilities numerically 0 or 1 occurred in: glm.fit(x = X, y > = Y, weights = weights, start = start, etastart = etastart,.^2 means all the 2-order and 1-order terms,so .+.^2 is meaningless.> How can I do this? > > logistic.model = glm(similarity ~ . + . ^ 2 + poly(.,2), > family=binomial, data = cData[3001:3800,])> > Thanks > > Stephen > > > > > -- > Internal Virus Database is out-of-date. > Checked by AVG Anti-Virus. > > > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help at stat.math.ethz.ch mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html-- Department of Sociology Fudan University,Shanghai Blog:http://sociology.yculblog.com