similar to: lm and glm

Hello list, I am running a regression using lm(Y~A+B+log(C)+log(D)) Now, I would like to test if glm can produce similar results. So the code was revised as glm(Y~A+B+C+D, family=poisson) (code 1) However, I found some example using glm for lm. It suggests that the code should be revised like glm(Y~A+B+log(C)+log(D), family=poisson) (code 2) Please kindly advise which code is correct.

Hi Folks, This tip is probably lurking somewhere already, but I've just discovered it the hard way, so it is probably worth passing on for the benefit of those who might otherwise hack their way along the same path. Say (for example) you want to do a logistic regression of a binary response Y on variables X1, X2, X3, X4: GLM <- glm(Y ~ X1 + X2 + X3 + X4) Say there are 1000 cases in the

Dear List, Some independent variable were missing in calculation using lm and glm (gaussian). (X= Y1+Y2+…..+Y16, Independent number: 16 variable) However, those variables did work well in cor(X, Y) respectively. str(dataframe) was also run to ensure that the variables were all numbers. Moreover, the missing variables were different in lm and glm. In lm, 3 factors were not taken into

Hello all, I need to tap into the collective wisdom of the group re an issue of efficiency. A sketch of the situation: Let's say 4000 observations in variables Y, X1, X2 , X3 and X4. I would like to feed various combinations of this expression Y ~ X1+X2+X3+X4 + I(X1^2)+I(X2^2)+I(X3^2)+I(X4^2) + X1*X2 + X1*X3 + X1*X4 + X2*X3 + X2*X4 + X3*X4 repeatedly to glm(). (I really have little

Dear List, I want to ask a AIC question based on package library(MuMIn) The relative importance of 16 explanatory variables are assessed using delta AIC in a generalized linear model. Please kindly advise if it is possible to show models with any two only certain variables. Thank you. Elaine I asked a similar question and got a great help for models with only one variable as below.

Dear, I want to compute coefficient of determination (R-squared) to complement AIC for model selection of multivariable GLM. However, I found this is not a built-in function in glm. neither is it available through reviewing the question in the R-help archive. Please kindly help and thanks a lot. Elaine [[alternative HTML version deleted]]

Hello guys, i looked over the archive files and found nothing about this kind of error. I have a database of 33 elements described in 8 variables, i'm using the Leave-One-Out iterative process to take one of the elements to be the test element and make a regression with the other 32 and then I try to predict the clas of the element out. I'm using this call as a part of a Leave-One-Out

Hi, Using S=1000 and simdata <- replicate(S, generate(3000)) #If you want both "m1" and "m0" #here the missing values are 0 res1<-sapply(seq_len(ncol(simdata.psm1)),function(i) {x1<-merge(simdata.psm0[,i],simdata.psm1[,i],all=TRUE); x1[is.na(x1)]<-0; x1}) res1[,997:1000] #????? [,1]???????? [,2]???????? [,3]???????? [,4]??????? #x1??? Numeric,3000 Numeric,3000

Hi dear R users, 1) I would like to know if there is a simple way to define a vglm family which would be a mix of poisson variables and bernoulli variables (0/1 response) for idea this would be invoked like this: vglm(...,family=mixpoissonmultinom(npoisson,n01response)) where the n's give the number of each type of response. 2) and a simpler question : How to use constraints in rrvglm?

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 am looping through various models with different combinations of independent variables which are stored as columns in x glm(y ~ ??, data=x) How can I pass the colnames of the selected columns of x into ?? seperating them with a + ie I want to generate glm(y ~ x1 + x2, data=x) glm(y ~ x2 + x3, data=x) glm(y ~ x1 + x2 + x4, data=x) ... I have tried the sedit function

Members of this mailing list may be interested in the following new book: Beginner's Guide to GLM and GLMM with R. - A frequentist and Bayesian perspective for ecologists - Zuur AF, Hilbe JM and Ieno EN This book is only available from: http://www.highstat.com/BGGLM.htm This book presents Generalized Linear Models (GLM) and Generalized Linear Mixed Models (GLMM) based on both

Hi, I would like to use constrained splines in a GLM model (Poisson link) to take into account the nonlinear effect of some covariate. The constraints I need are described below. I have several variables that I need concurrently in the same model. I looked at package mgcv but I do not know if/how I can use it in GLM (not GAM) : I could not manage to adapt the mono.con(mgcv) example to GLM. The

I am trying to find methods for testing and visualizing calibration to Cox models with time-depended coefficients. I have read this nice article <http://journals.sagepub.com/doi/10.1177/0962280213497434>. In this paper, we can fit three models: fit0 <- coxph(Surv(futime, status) ~ x1 + x2 + x3, data = data0) p <- log(predict(fit0, newdata = data1, type = "expected")) lp

Hello, I am using package MuMIn to calculate AIC for a full model with 10 explanatory variables. Thanks in advance in sharing your experience. Q1 In the AIC list of all models, each model is differentiated by model number. Please kindly advise if it is possible to find the corresponding explanatory variable(s) for the model number. Q2 error message I tried to display sub-model with only

Hi again! Following up my previous posting below (to which no response as yet), I have located a report which situates this type of question in a longitudinal modelling context. http://www4.stat.ncsu.edu/~dzhang2/paper/glm.ps Generalized Linear Models with Longitudinal Covariates Daowen Zhang & Xihong Lin (This work seems to originally date from around 1999). They consider an outcome Y,

Hi, I am experiencing a baffling behaviour of stepAIC(), and I hope to get any advice/help on what went wrong or I'd missed. I greatly appreciate any advice given. I am using stepAIC() to, say, select a model via stepwise selection method. R Version : 1.7.1 Windows ME Many thanks and best regards, Siew-Leng ***Issue : When stepAIC() is placed within a function, it seems

First, as others have said please obey the mailing list rules and turn of First, as others have said please obey the mailing list rules and turn off html, not everyone uses an html email client. Here is your code, formatted and with line numbers added. I also fixed one error: "y" should be "status". 1. fit0 <- coxph(Surv(futime, status) ~ x1 + x2 + x3, data = data0) 2. p

Hi, I would like to estimate coefficients using poisson regression and then get standard errors that are adjusted for heteroskedasticity, using a complex sample survey data. Then I will calculate prevalence ratio and confidence intervals. Can sandwich estimator of variance be used when observations aren?t independent? In my case, observations are independent across groups (clusters), but

Hi, I am experiencing a baffling behaviour of stepAIC(), and I hope to get any advice/help on this. Greatly appreciate any kind advice given. I am using stepAIC() to, say, select a model via stepwise selection method. R Version : 1.7.1 Windows ME Many thanks! ***Issue : When stepAIC() is placed within a function, it seems that stepAIC() cannot detect the data matrix, and the program is