similar to: How to exclude insignificant intercepts using "step" function

I have two questions about the built-in function step. Ultimately I want to apply a lm fitting and subsequent step procedure to thousands of data sets groups by a factor defined as a unique ID. Q1. The code below creates a data.frame comprising three marginally noisy surfaces. The code below fails when I use step in a function but summary seems to show the model fits are legitimate. Any ideas on

I have a large dataset grouped by a factor and I want to perform a regression on each data subset based on this factor. There are many ways to do this, posted here and elsewhere. I have tried several. However I found one method posted on the R wiki which works exactly as I want, and I like the elegance and simplicity of the solution, but I don't understand how it works. Its all in the formula

Dear Spencer and Prof. Fox, Thank you for your replies. I'll very appreciate, if you have any ideas concerning the problem described below. First, I'd like to describe the model in brief. In general I consider a model with three equations. First one is for annual GRP growth - in general it looks like: 1) GRP growth per capita = G(investment, migration, initial GRP per

Hello, I do not have any statistical background, So I shall apologise if I am asking trivial question or help. I am trying to work with R. The problem I have on hand is: I have 2 sets of data(means & SD for each sample in the group of both sets). The sample size is massive(2000+ in each grp). I have a new set of experimental data and I like to classify this to either of the grps based on

1. I did not attempt to sort through your voluminous code. But I suspect you are trying to reinvent wheels. 2. I don't understand this: "I've failed to find a solution after much searching of various R related forums." A web search on "error handling in R" **immediately** brought up ?tryCatch, which I think is what you want. If not, you should probably explain why it

tryCatch() is good for catching errors but not so good for warnings, as it does not let you resume evaluating the expression that emitted the warning. withCallingHandlers(), with its companion invokeRestart(), lets you collect the warnings while letting the evaluation run to completion. Bill Dunlap TIBCO Software wdunlap tibco.com On Tue, Mar 6, 2018 at 2:45 PM, Bert Gunter <bgunter.4567 at

You can capture warnings by using withCallingHandlers. Here is an example, its help file has more information. dataList <- list( A = data.frame(y=c(TRUE,TRUE,TRUE,FALSE,FALSE), x=1:5), B = data.frame(y=c(TRUE,TRUE,FALSE,TRUE,FALSE), x=1:5), C = data.frame(y=c(FALSE,FALSE,TRUE,TRUE,TRUE), x=1:5)) withWarnings <- function(expr) { .warnings <- NULL # warning handler will

Hi, I am trying to automate the creation of tables for some simply analyses. There are lots and lots of tables, thus the creation of a user-defined function to make and output them to excel. My problem is that some of the analyses have convergence issues, which I want captured and included in the output so the folks looking at them know how to view those estimates. I am successfully able to do

Hi, The following function does work optimize.W<-function(W,k,G,Groups,cph.call,z){ n<-length(Groups) grp.wt<-rep(0,n) for(i in 1:(length(G))){ ind<-Groups == G[i] if(G[i]!=k){ grp.wt[ind]<-W[i] } elsegrp.wt[ind]<-1 } z<-data.frame(cbind(z,grp.wt=grp.wt)) #needed to make the case weights #part of the data

Hi, I am a graduate student applying published R scripts to compare the classification accuracy of 2 predictive models, one built using discriminant function analysis and one using random forests (webpage link for these scripts is provided below). The purpose of these models is to predict the biotic integrity of streams. Specifically, I am trying to compare the classification accuracy (i.e.,

Hello, It appears there is a limit in the number of criteria that can be put into the Aggregate sum function. (It looks like it is 32). My code is; HSfirst=aggregate(count,

Hi, I am having trouble with the survival library, particualrily the coxph function. the following works coxph(jtree9$cph.call,z,rep(1,dim(z)[1])) Call: coxph(formula = jtree9$cph.call, data = z, weights = rep(1, dim(z)[1])) coef exp(coef) se(coef) z p SM 0.2574 1.294 0.0786 3.274 1.1e-03 Sex -0.1283 0.880 0.1809 -0.709

On F7, I get to where I have Postfix and Dovecot installed and configured, postfix check runs fine, but when I try to start Dovecot I'm getting a permission error. Here is the log entry: Dec 29 21:54:06 grp-01-50-90 dovecot: Dovecot v1.0.7 starting up Dec 29 21:54:06 grp-01-50-90 dovecot: Generating Diffie-Hellman parameters for the first time. This may take a while.. Dec 29 21:54:06

I am having great difficulty plotting what should be a simple graph. I have measured 1 'y' and 5 'x' variables in each of two groups. Linear regression shows significant differences in the slopes of the regression for each 'x' variable between the two groups. All that I want to do is to plot one graph that shows the scatterplot for the three groups (each group represented

Hi, I try to ask here, because I hope someone will help me understand this problem- I have fittet a glm in R with the results > glm1 <- > glm(log(claims)~log(sum)*as.factor(grp),family=gaussian(link="identity")) > summary(glm1) Call: glm(formula = log(claims) ~ log(sum) * as.factor(grp), family = gaussian(link = "identity")) Deviance Residuals: Min 1Q

Dear R-Helpers: I am using trying to use *ddply* to extract min and max of a particular column in a data.frame. I am using two different forms of the function: ## var_name_to_split is a string -- something like "var1" which is the name of a column in data.frame ddply( df, .(as.name(var_name_to_split)), function(x) c(min(x[ , 3] , max(x[ , 3]))) ## fails with an error - case 1 ddply(

Hi All, I am currently conducting a mixed model. I have 7 repeated measures on a simulated clinical trial. If I understand the model correctly, the outcome is the measure (as a factor) the predictors are clinical group and trial (1-7). The fixed factors are the measure and group. The random factors are the intercept and id and group. I tried using 2 functions to calculate mixed effects.

Dear R-helpers, the problem I'm facing today is to convince lattice to paint some areas in gray. The areas I would like to have in gray, are confidence bands I've googled around in the mailing list archives and eventually find some clues. This link is my starting point http://tolstoy.newcastle.edu.au/R/e2/help/07/04/15595.html I'm reproducing here the code for your convenience est

Hi, how can I get a more descriptive text instead of the variable names in my XY-lattice plot, according to the table below? Variable text acet = "Acetylaspartate Thalamus" chol = "Choline Thalamus" acetp = "Acetylaspartate parieoc" ino = "Inositole Thalamus" I could not find a solution. Please have a look at my syntax. Thanks a lot,

Is there a way to specify a Z matrix using the lmer function, where the model is written as y = X*Beta + Z*u + e? I am trying to reproduce smoothing methods illustrated in the paper "Smoothing with Mixed Model Software" my Long Ngo and M.P. Wand. published in the /Journal of Statistical Software/ in 2004 using the lme4 and Matrix packages. The code and data sets used can be found at