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Dear All, I have a bunch of data points as follows: x  100 y  200 z  300 ... where 100, 200, 300 are the values. I would like to know the distribution of my data? how can I fit my data into a distribution? Thanks a lot, Andra [[alternative HTML version deleted]]

Hi All, When modeling with glm and family = binomial (link = logit) and response values of 0 and 1, I get the predicted probabilities of assigning to my class one, then I would like to compare it with my vector y which does have the original labels. How should I change the probabilities into values of zero and 1 and then compare it with my vector y to find out about the accuracy of my

Hi All, I am trying to fit my data with glm model, my data is a matrix of size n*100. So, I have n rows and 100 columns and my vector y is of size n which contains the labels (0 or 1) My question is: instead of manually typing the model as glm.fit = glm(y~ x[,1]+x[,2]+...+x[,100], family=binomial()) I have a for loop as follows that concatenates the x variables as follows: final_str=NULL for

Hello All, I have a question about glm in R. I would like to fit a model with glm function, I have a vector y (size n) which is my response variable and I have matrix X which is by size (n*f) where f is the number of features or columns. I have about 80 features, and when I fit a model using the following formula,? glmfit = glm(y ~ x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9 + x10 + x11 + x12 + x13

Hi All,  I would like to create a matrix in R but I dont know the size of my matrix. I only know the size of the columns but not the size of the rows. So, is there any way to create a dynamic matrix of size NULL by n_cols? and then add to that matrix? I know for a vector, I can do this: x= NULL but is there any way to do the same for a matrix as well? Thanks a lot,Andra [[alternative HTML version

Hi All,  I am trying to run cv.glmnet(x,y,family="multinomial", nfolds =4) and I only have 8 observations and the number of features I have is 1000, so my x matrix is 8 by 1000 and when I run the following, I get this error, I am not sure what is causing this problem.  Error in predmat[which, , seq(nlami)] = preds :   number of items to replace is not a multiple of replacement length Can

Hi All,  After fitting a model with glm function, I would like to do the model selection and select some of the features and I am using the "step function" as follows:  glm.fit <- glm (Y ~ . , data = dat, family = binomial(link=logit)) AIC_fitted = step(glm.fit, direction = "both") I was wondering is there any way to select the features based on BIC rather than AIC? is there

Hi All,  I am plotting different lines in my graph and the problem I have is that if the first plot has smaller y value than the second plot, I will not be able to see the the top part of the graph. I do the following:plot(p1, avg="vertical", lwd=3, col="red", main ="all graphs")plot(p2, avg="vertical", lwd=3, col="pink", add = TRUE) where p1 and

Hi All, I have a data frame as follow: user_id time age location gender ..... and I learn a logistic regression to learn the weights (glm with family= (link = logit))), my response value is either zero or one. I would like to group the users based on user_id and time and see the y values and predicted y values at the same time. Or plot them some how. Is there any way to somehow group them

Hello All, I have a data frame consisting of 4 columns (id1, id2, y, pred) where pred is the predicted value based on the glm function and my data frame is called "all". "data" is another data frame that has all data but I want to put together some important columns from my original data frame (data) into another data frame (all) as follows and I would like them to be sorted

Hi All, I have a fitted model called glm.fit which I used glm and data dat is my data frame pred= predict(glm.fit, data = dat, type="response") to predict how it predicts on my whole data but obviously I have to do cross-validation to train the model on one part of my data and predict on the other part. So, I searched for it and I found a function cv.glm which is in package boot.

Hi All, I have a set of features of size p and I would like to separate my feature space into two sets so that p = p1 + p2, p1 is a set of features and p2 is another set of features and I want to fit a glm model for each sets of features separately. Then I want to combine the results of two glm models with a parameter beta. For example, beta * F(p1) + (1-beta) * F(p2) where F(p1) is a learned

Hi All,  I have been trying to use glmnet package to do LASSO linear regression. my x data is a matrix n_row by n_col and y is a vector of size n_row corresponding to the vector data. The number of n_col is much more larger than the number of n_row. I do the following: fits = glmnet(x, y, family="multinomial")I have been following this

...cv entering PAM file ======= # Disallows other than root logins when /etc/nologin exists # (Replaces the `NOLOGINS_FILE' option from login.defs) auth requisite pam_nologin.so auth required pam_env.so auth [authinfo_unavail=ignore auth_err=done success=done default=ok] /isan/lib/libpam_aaa_auth.so # Standard Un*x authentication. The "nullok" line allows passwordless # accounts. auth required pam_unix.so nullok likeauth try_first_pass account required pam_unix.so se...

Hi All,  In order to compare two different logistic regressions, I think I need to compare them based on their BIC values, but I am not sure if the smaller BIC would mean a better model or the reverse is true? Thanks a lot,Andra [[alternative HTML version deleted]]

Hello All,  I have used logistic regression glm in R and I am evaluating two models both learned with glm but with different predictors. model1 <- glm (Y ~ x4+ x5+ x6+ x7, data = dat, family = binomial(link=logit))model2 <- glm (Y~ x1 + x2 +x3 , data = dat, family = binomial(link=logit))  and I would like to compare these two models based on the prediction that I get from each model: pred1 =

...balloon memory which is useful for an assigned device. Requires telling host first. Also knowing how much memory there is in a balloon would be useful for admin. There could be other uses. > > I guess filesystem does not currently get a callback > > before page is reclaimed but this isan implementation detail - > > maybe this can be fixed? > > I do not follow this question. Assume we want to tell host before use. Can you implement this on top of your patch? > > > > Also can you pls answer Avi's question? > > How is overcommit managed? > >...

...balloon memory which is useful for an assigned device. Requires telling host first. Also knowing how much memory there is in a balloon would be useful for admin. There could be other uses. > > I guess filesystem does not currently get a callback > > before page is reclaimed but this isan implementation detail - > > maybe this can be fixed? > > I do not follow this question. Assume we want to tell host before use. Can you implement this on top of your patch? > > > > Also can you pls answer Avi's question? > > How is overcommit managed? > >...

A month or two ago I sent an email to this list proposing to expand the list of "standard" tags for Ogg Vorbis. No tag would be required, but if you wanted to encode certain types of information about a file, you could use a standard tag. I went through the whole discussion, and revised my proposal in light of all the comments from everyone. Here is the updated proposal. This

This implementation of a virtio balloon driver uses the page cache to "store" pages that have been released to the host. The communication (outside of target counts) is one way--the guest notifies the host when it adds a page to the page cache, allowing the host to madvise(2) with MADV_DONTNEED. Reclaim in the guest is therefore automatic and implicit (via the regular page reclaim).