similar to: Step by significance

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 am trying to perform a backwards stepwise variable selection with an mlogit model. The usual functions, step(), drop1(), and dropterm() do not work for mlogit models. Update() works but I am only able to use it manually, i.e. I have to type in each variable I wish to remove by hand on a separate line. My goal is to write some code that will systematically remove a certain set of variables

Hello, I have a question pertaining to the stepwise regression which I am trying to perform. I have a data set in which I have 14 predictor variables accompanying my response variable. I am not sure what the difference is between the function "mle.stepwise" found in the wle package and the functions "step" or "stepAIC"? When would one use

Hi R, Does the "step" function used to perform stepwise regression has the option to specify the entry/exit significance levels for the independent variables? (This is similar to the 'slentry' and 'slstay' option in 'Proc reg' of SAS.). Or do we have any other package which does the above? Thanks. Thanks and Regards, Shubha This e-mail may

Hello all, I am trying to use stepwise procedure to select covariates in Cox model and use bootstrap to repeat stepwise selection, then record how many times variables are chosen by step() in bootstrap replications. When I use step() (or stepAIC) to do model selection, I got errors. Here is the part of my code for (j in 1:mm){ #<--mm=10 for (b in 1:nrow(reg.bs)){ #<--bootstrap 10

Dear all, I cannot seem to get the R functions step or stepAIC to perform forward or stepwise regression as I expect. I have enclosed the example data in a dataframe at the end of this mail. Note rubbish is and rnorm(17) variable which I have deliberately added to the data to test the stepwise procedure. I have used wateruse.lm<-lm(waterusage~.,data=wateruse) # Fit full model

Dear Geskus, I want to develop a prediction model. I followed your paper and analysed thro' weighted coxph approach. I can develop nomogram based on the final model also. But I do not know how to do internal validation of the model and subsequently obtain calibration plot. Is it possible to use Wolbers et al Epid 2009 approach 9 (R code for internal validation and calibration) . It is

I have a lm in R in the form model <- lm( Z ~ A*B*C*D,data=mydata) I want to run the model and include all interactions expect the 4 way (A:B:C:D) is there an easy way of doing this? I then want to step down the model eliminating the non-significant terms I understand step() does this but how would I do it by hand? -- View this message in context:

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

hello I built logistic regression model. To model check I used stepAIC. But I don't know how it is interpreted . I could not any find any explanation about it For instance which model is preferable ? What are the critarias to choose beter model I will appreciate if you give me an explanation ? models --------- > lo1.step$anova Stepwise Model Path Analysis of Deviance Table Initial

Hello everybody, I would need some help from you. I am trying to fit a logistic model to some presence absence data of animals living on river islands. I have got 12 predictor variables and I am trying to use a stepwise forward method to fit the best logistic model to my data. I am using the function STEP (stats). I have a question for you. Can I use step function if my variables have a

Hi, I am trying to understand the alternative methods that are available for selecting variables in a regression without simply imposing my own bias (having "good judgement"). The methods implimented in leaps and step and stepAIC seem to fall into the general class of stepwise procedures. But these are commonly condemmed for inducing overfitting. In Hastie, Tibshirani and Friedman

Hi. Background - I am working with a dataset involving around 750K observations, where many of the variables (8/11) are unordered factors. The typical model used to model this relationship in the literature has been a simple linear additive model, but this is rejected out of hand by the data. I was asked to model this via kernel methods, but first wanted to play with the parametric

I can only assume I'm betraying my ignorance here, but this is not what I would expect. I'm getting the following from a stepwise selection (with both step and stepAIC): > step(lm(sqrt(Grids)~ SE + Edge + NH), scope=~ (Edge + SE + NH)^2) Start: AIC= 593.56 sqrt(Grids) ~ SE + Edge + NH Df Sum of Sq RSS AIC <none> 2147.0 593.6 + Edge:NH 1

Full_Name: John E. Kolassa Version: Version 1.8.1 OS: Solaris Submission from: (NULL) (128.6.76.36) Stepwise model selection for coxph appears to fail with method="exact". The code step(coxph(Surv(1:100,rep(1,100))~factor(rep(1:4,25)),method="exact")) produces the error message Start: AIC= 733.07 Surv(1:100, rep(1, 100)) ~ factor(rep(1:4, 25)) Error in

Hello Lady or Sir, I want to do model selection with 'step' function. But there is a trouble to me. For example, there are 5 independent variables, X0,X1,X2,X3 and X4; and 1 dependent variable y. I want to run stepwise regression y ~ X0 + X1 + X2 + X3 + X4, and I want to keep X0 always in the model. Every time when I run command 'step' with 'keep', error information

Hello, I have generated 2 Poisson processes and want to plot them on a single graph in a step by step manner in order to be able to compare them. I tried plot and biplot but it does not help, I could connect two points by hand for point graph if they were 5 or 10 I have more than 200 such point to be connected and Poisson cluster makes it difficult for me to even read them properly. Can anyone

Hi Community R I need to make a stepwise using F statistics as a criteria to choose variables.I have 3 independant variables and one dependant variable, and I need to choose the best model fitting to my data using F statistics. The problem is I haven't found any package to do such operation. I have found packages to choose variables using significance level like "stepback" from

Dear R-users: Im trying (using gam package) to develop a stepwise analysis. My gam object contains five pedictor variables (a,b,c,d,e,f). I define the step.gam: step.gam(gamobject, scope=list("a"= ~s(a,4), "b"= ~s(b,4), "c"= ~s(c,4), "d"= ~s(d,4), "e"= ~s(e,4), "f"= ~s(f,4))) However, the result shows a formula containing the whole

Dear forum members, hope you can understand my unprofessionell English. I have never been working with RStudio before (even not with R), so I immediately need some help, because I want to carry out an automated multiple stepwise linear regression between temperatures and different surface parameters. Data can be found in several .xls- or .csv-files within one folder. Files are named