similar to: R stepping through multiplie interactions

I would like test AICc as a criteria for model selection for a glm using stepAIC() from MASS package. Based on various information available in WEB, stepAIC() use extractAIC() to get the criteria used for model selection. I have created a new extractAIC() function (and extractAIC.glm() and extractAIC.lm() ones) that use a new parameter criteria that can be AIC, BIC or AICc. It works as

Hello, I'm having some difficulty understanding the useage of the "dropterm()" function in the MASS library. What exactly does it do? I'm very new to R, so any pointers would be very helpful. I've read many definitions of what dropterm() does, but none seem to stick in my mind or click with me. I've coded everything fine for an interaction that runs as follows: two sets

Hi everybody I am trying to run the next code but I have the next problem Y1<-cbind(score.sol, score.com.ext, score.pur) > vol.lm<-lm(Y1~1, data=vol14.df) > library(MASS) > stepAIC(vol.lm,~fsex+fjob+fage+fstudies,data=vol14.df) Start: AIC=504.83 Y1 ~ 1 Error in addterm.mlm(fit, scope$add, scale = scale, trace = max(0, trace - : no addterm method implemented for

Dear all, I have been trying to investigate the behaviour of different weights in weighted regression for a dataset with lots of missing data. As a start I simulated some data using the following: library(MASS) N <- 200 sigma <- matrix(c(1, .5, .5, 1), nrow = 2) sim.set <- as.data.frame(mvrnorm(N, c(0, 0), sigma)) colnames(sim.set) <- c('x1', 'x2') # x1 & x2 are

Dear R fans, I have got a difficult sounding problem. For fitting a linear model using continuous response and then for re-fitting the model after excluding every single variable, the following functions can be used. library(MASS) model = lm(perf ~ syct + mmin + mmax + cach + chmin + chmax, data = cpus) dropterm(model, test = "F") But I am not sure whether any similar functions is

Hello Tyler, I want to bring to your attention the following document: "What happens if you omit the main effect in a regression model with an interaction?" (https://stats.idre.ucla.edu/stata/faq/what-happens-if-you-omit-the-main-effect-in-a-regression-model-with-an-interaction). This gives a useful review of the problem. Your example is Case 2: a continuous and a categorical regressor.

To compare two different models, I've compared the result of using dropterm() on both. Single term deletions Model: growth ~ days + I(days^0.5) Df Sum of Sq RSS AIC <none> 2.8750 -0.2290 days 1 4.8594 7.7344 4.6984 I(days^0.5) 1 0.0234 2.8984 -2.1722 AND Single term deletions Model: growth ~ days + I(days^2) Df Sum

R gurus, I'm working on data analysis for a small project. My response variable is total vines per tree (median = 0, mean = 1.65, min = 0, max = 24). My predictors are two categorical variables (four sites and four species) and one continuous (tree diameter at breast height (DBH)). The main question I'm attempting to answer is whether or not the species identity of a tree has

Hello Tyler, Thank you for searching for, and finding, the basic description of the behavior of R in this matter. I think your example is in agreement with the book. But let me first note the following. You write: "F_j refers to a factor (variable) in a model and not a categorical factor". However: "a factor is a vector object used to specify a discrete classification"

Does anybody know how to perform a step function by significance of p instead of AIC? I want to perform a forward-backward stepwise logistic regression and want to compare the results obtained by steps of significance and by steps of AIC. Thank you -- View this message in context: http://n4.nabble.com/Step-by-significance-tp1754237p1754237.html Sent from the R help mailing list archive at

Hi all,   I’m using "step" and "stepAIC" for stepwise regression. After each step, I would like to make an additional calculation based on the independent variables that have been selected until this step and their corresponding weights. Where do I have to add this calculation?   And a second question: Is it possible, to define a certain limit of factors for the regression,

I'm having a bit of difficulty using the stepwise model-building tools in a glm context. Here, for example is one problem I have had using add1, where the abbreviation "." does not work as I expected it to do. I someone could point me towards some examples involving the interactive building of glm models I would be grateful. The data set that I am using is the

Hi, I encountered a problem when I am trying to write my own function which contains another function. To simplify a problem, I tried the following simplified function, hope someone can idenfity the problem for me. I have a simple data frame called "testdata" as following: >

Hi, I have sent this message to this list the July, 7th. It was about a problem in MASS package. Until now there is no change in the devel version. As the problem occurs in a package and not in the R-core, I don't know if the message should have been sent here. Anyway, I have added a copy to Pr Ripley. I hope it could have been fixed. Sincerely Marc Le 09/07/2017 ? 16:05, Marc Girondot via

Hello Tyler, I rephrase my previous mail, as follows: In your example, T_i = X1:X2:X3. Let F_j = X3. (The numerical variables X1 and X2 are not encoded at all.) Then T_{i(j)} = X1:X2, which in the example is dropped from the model. Hence the X3 in T_i must be encoded by dummy variables, as indeed it is. Arie On Thu, Nov 2, 2017 at 4:11 PM, Tyler <tylermw at gmail.com> wrote: > Hi

I am trying to replicate the first example from stepAIC from the MASS package with my own dataset but am running into error. If someone can point where I have gone wrong, I would appreciate it very much. Here is an example : set.seed(1) df <- data.frame( x1=rnorm(1000), x2=rnorm(1000), x3=rnorm(1000) ) df$y <- 0.5*df$x1 + rnorm(1000, mean=8, sd=0.5) # pairs(df); head(df) lo <-

dear R users, I have fit the lm() on a mtrix of responses. i.e M1 = lm(cbind(R1,R2)~ X+Y+0). When i use summary(M1), it shows details for R1 and R2 separately. Now i want to use stepAIC on these models. But when i use stepAIC(M1) an error message comes saying that dropterm.mlm is not implemented. What is the way out to use stepAIC in such cases. regards,

Hi there - I am using R1.5.1 on WinNT and the latest MASS (Venables and Ripley) library. Running the following code: >minimod<-glm(miniSF~gtbt*f.batch+log(mxjd),data=gtbt,family="binomial") >summary(minimod,cor=F) Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 0.91561 0.32655 2.804 0.005049 ** gtbtgt 0.47171

Hi, I'm working on fitting a glm model to my data using Gamma error structure and reciprocal link. I've been using dropterm (MASS) in the model simplification process, but the F values from analysis of deviance tables reported by dropterm and anova functions are different - sometimes significantly so. However, the reported residual deviances, degrees of freedom, etc. are not different.

Hello Tyler, You write that you understand what I am saying. However, I am now at loss about what exactly is the problem with the behavior of R. Here is a script which reproduces your experiments with three variables (excluding the full model): m=expand.grid(X1=c(1,-1),X2=c(1,-1),X3=c("A","B","C")) model.matrix(~(X1+X2+X3)^3-X1:X3,data=m)