similar to: releveling a numeric by factor interaction in a lm

Can anyone help me with the necessary code to relevel a numeric*factor interaction term in a linear model? I would like to report the estimate, std. error and t-value for the reference factor. First, I estimated a linear model with dummy variables and was able to retrieve model estimates for the reference factor using relevel. for example: > summary(update(mod.mod, . ~ . - dummy + +

Something that bit me: The function relevel takes a factor, and a reference level to be promoted to the first place. If ?ref? is a character this level is promoted, if it?s a numeric the ?ref?-th level is promoted. Which turns out to be very confusing if you have factor with numeric values (e.g. when reading in a csv with some dirty numeric columns and stringsAsFactors TRUE) For example:

Dear all, I am a relative novice with R, so please forgive any terrible errors... I am working with a GLM that describes a response variable as a function of a categorical variable with three levels and a continuous variable. These two predictor variables are believed to interact. An example of such a model follows at the bottom of this message, but here is a section of its summary table:

Dear list, I am running R code which produces a data.frame with variable rows. For plotting purposes I need to relevel the column called "names" but since the dimension of the data.frame can vary I do not know how to dynamically revel the data.frame. Here is an example data.frame called df to illustrate what I try to achieve: > df <-

To the R developers, The discussion below reminded me that I think it might be a good idea to take the Relevel function from the Lexis package and replace relevel in stats with it. This is really nothing special for epidemiology. It is fully compatible with the existing relevel (it actually contains the relevel code almost verbatim as a subset), but it has the extra functionality of combining

Hi All, I'm perplexed by the way the unclass function displays a factor whose labels have been swapped with the relevel function. I realize it won't affect any results and that the relevel did nothing useful in this particular case. I'm just doing it to learn ways to manipulate factors. The display of unclass leaves me feeling that the relevel had failed. I've checked three books

Dear list, I'm having second thoughts after solving a very trivial problem: I want to extend the relevel() function to reorder an arbitrary number of levels of a factor in one go. I could not find a trivial way of using the code obtained by getS3method("relevel","factor"). Instead, I thought of solving the problem in a recursive manner (possibly after reading

Dear list members, I have problems to interpret the coefficients from a lm model involving the interaction of a numeric and factor variable compared to separate lm models for each level of the factor variable. ## data: y1 <- rnorm(20) + 6.8 y2 <- rnorm(20) + (1:20*1.7 + 1) y3 <- rnorm(20) + (1:20*6.7 + 3.7) y <- c(y1,y2,y3) x <- rep(1:20,3) f <- gl(3,20,

Dear colleagues, How can I do to "relevel" the intercept? I need that the treatment "Db" be the intercept, and have p-values for the comparisons with the others treatments. I used the function "relevel" but it did not work out to have what I want. Thanks for your help, Xavier T1 <- read.table(file.choose(), h=T) > head(T1) treatment replicate Time

Hello, I am trying to create a function that will move through each record of a data frame, find the value in the "HUC" column, then randomly select another observation from the dataframe with the same value in "HUC" column, as well as the same value in "Yr" column as the first observation. I want the function to produce a list of the RchID of the first observation,

Hi, I have a question about how I should report the results for a linear mixed effects model where the model includes as predictors three factors (facA, facB and facC), one of which (facA) interacts with the other two. facA and facB have two levels and facC has 3 levels. There are also several other continuous predictors (e.g. varA, varB, varC). My mixed model is specified with the following

Dear All, I got a warning message "X matrix deemed to be singular" in Cox model with a time dependent coefficient. In my analysis, the variable "SEX" is a categorical variable which violate the PH assumption in Cox. I first used the survSplit() function to break the data set into different time intervals, and then fit the model. The procedures can be described as follows:

I was surprised to find that just changing the base level of a factor variable changed the number of significant coefficients in the solution. I was surprised at this and want to know how I should choose the order of the factors, if the order affects the result. Here is the small example. It is taken from 'The R Book', Crawley p. 365. The data is at

Dear Forum, Which functions and packages should be installed to make work the function "relevel"? treatment<-revel(treatment,ref="Db") Error: no se pudo encontrar la funci?n "revel" Thank you very much for your help, Xavier Chiriboga M. PhD Candidate Fundamental and Applied Research in Chemical Ecology Lab. Institute of Biology University of Neuchatel

I've spent several days compiling the following code (I apologize in advance - this code is very inelegant, and I'm sure could be written much more efficiently, but I've stuck with whatever method I could get to work - sometimes the more efficient code I just couldn't get to work without an error, because of my R inexperience). My main motivation for writing the code is that

I would like to define the order of the levels of a factor. The relevel function would work but since I have 20 levels I would prefer to declare the order explicitly. Using a smaller example levels(oldfactor) "b1" "b2" "r1" "r2" nufactor <- order(oldfactor,order=c("b1","r1","b2","r2")) # my fabricated function

Dear R Users, Am using lm() with contrasts as below. If I skip the contrasts() statement, I get the coefficient names to be > names(results$coef) [1] "(Intercept)" "VarAcat" "VarArat" "VarB" which are much more meaningful than ones based on integers. Can anyone tell me how to get R to keep the coefficient names based on the factor levels

Full_Name: J. R. M. Hosking Version: 1.5 OS: Windows 2000 Submission from: (NULL) (198.81.209.17) Here is a test case: # Some arbitrary data # v1<-1:12 v2<-cumsum(v1) v3<-cumsum(v2) f<-factor(rep(c("a","b","c"),4)) y<-c(1,4,2,7,5,8,7,9,6,10,12,10) print(cbind(y,f,v1,v2,v3)) # # Fit a regression model with no intercept, and different slopes # for each

Hi all: I'm attempting to create a faceted plot with ggplot2 and I'm having issues with a factor's order that is used to define the facet_grid(). The factor (named total.density) has three levels - 8, 16, and 32 - and I would like them presented in that order. Running order(levels(total.density)) yields the incorrect order of the facet grid - 2 3 1, corresponding with 16, 32, and 8.

Dear all, I have a model with simple terms, quadratic effects, and interactions. I am wondering what to do when a variable is involved in a significant interaction and in a non-significant quadratic effect. Here is an example d = data.frame(a=runif(20), b=runif(20)) d$y = d$a + d$b^2 So I create both an simple effect of a and a quadratic effect of b. m = lm(y ~ a + b + I(a^2) + I(b^2) +