similar to: How to combine two learned regression models?

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

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

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 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]]

Hello all, I would like to fit a mixture model whose components are normal distributions confined in a closed interval. Since there are already several packages for EM, I would like to extend one of these instead of writing a new script from scratch. What would be the best way to customize such an existing package for doing that? Which one is the best in terms of extensibility? (e.g mixtools,

Hello, I would like to fit a mixture of two Weibull distributions to my data, estimate the model parameters, and compare the fit of the model to that of a single Weibull distribution. I have used the mix() function in the 'mixdist' package to fit the mixed distribution, and have got the parameter estimates, however, I have not been able to get the log-likelihood for the fit of this model

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 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

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 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

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 =

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 R-help, I am doing clustering via finite mixture model. Please suggest some packages in R to find clusters via finite mixture model with continuous variables. And also I wish to verify the distributional properties of the mixture distributions by fitting the model with lognormal, gamma, exponentials etc,. Thanks in advance,  warm regards,Ms.Karunambigai M PhD Scholar Dept. of Biostatistics

Hello I am trying to find an automated way of fitting a mixture of normal and log-normal distributions to data which is clearly bimodal. Here's a simulated example: x.1<-rnorm(6000, 2.4, 0.6)x.2<-rlnorm(10000, 1.3,0.1)X<-c(x.1, x.2) hist(X,100,freq=FALSE, ylim=c(0,1.5))lines(density(x.1), lty=2, lwd=2)lines(density(x.2), lty=2, lwd=2)lines(density(X), lty=4) Currently i am using

Dear all, I am attempting to model some one-dimensional data using a mixture model of non-central Student's t distributions. However, I haven't been able to find any R package that provides this functionality. Could there be a way to "manipulate" the EM algorithms from the mixdist or mixtools package to fit the model, or do you have any other suggestions? If anyone could help

I am trying to install mixtools on Debian Etch and get the following error dell-2 /usr/lib # R CMD INSTALL mixtools_0.1.0.tar.gz * Installing *source* package 'mixtools' ... ** libs gcc -I/usr/share/R/include -I/usr/share/R/include -fpic -g -O2 -std=gnu99 -c new_svalues.c -o new_svalues.o gfortran -fpic -g -O2 -c sphericaldepth.f -o sphericaldepth.o make: gfortran: Command not