similar to: Plotting question

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

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 =

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, 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

I use ROCR to plot multiple runs' performance. Using the sample code as example: # plot ROC curves for several cross-validation runs (dotted # in grey), overlaid by the vertical average curve and boxplots # showing the vertical spread around the average. data(ROCR.xval) pred <- prediction(ROCR.xval$predictions, ROCR.xval$labels) perf <- performance(pred,"tpr","fpr")

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

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]]

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 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 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

Hola a todos, Estoy intentando utilizar este script para hacer un plot con valores x ,y,z; representando los valores TP(y) y FP(x) y en funcion de la velocidad que seria el factor alpha. Y me da este en el ultimo punto de hacer el plot, alguien sabe que significa? library(ROCR) data(ROCR.simple) pred <- prediction( ROCR.simple$predictions, ROCR.simple$labels ) perf <- performance( pred,

Hello all, I have two problems with the ROCR package. First Problem: the add=TRUE option does not work for plotting performance objects The following code is taken from the reference manual (example for ROCR.hiv, page2) data(ROCR.hiv) attach(ROCR.hiv) pred.svm <- prediction(hiv.svm$predictions, hiv.svm$labels) perf.svm <- performance(pred.svm, 'tpr', 'fpr') pred.nn <-

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

All, I'm trying again with a slightly more generic version of my first question. I can extract the plotted values from hist(), boxplot(), and even plot.randomForest(). Observe: # get some data dat <- rnorm(100) # grab histogram data hdat <- hist(dat) hdat #provides details of the hist output #grab boxplot data bdat <- boxplot(dat) bdat #provides details of the boxplot

I am doing calculations in a loop and then plotting the results by adding a point to each of 2 charts at the end of the loop. Its very informative as you can see the progression through time. My problem is, if I have 2 plots, I don't know how to get the focus back to the first plot. layout(matrix(c(1,2))) plot(iris[,1],col="red",) #plot1 plot(iris[,3],col="blue") #plot2

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

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

Dear fellow R users, I would like to draw a "sunflowerplot" because I have data (decade by month) that plots multiple times on the same x-y co-ordinates. Further I would like to colour each of the points/sunflower leaves on the plot according to the group they belong to (i.e. which type of event each represents within that decade and month). I thought that this would be relatively