similar to: order of effects plot panels

Searched for this and found some help, but I still can't figure it out. I have trying to enlarge the x and y labels on my box plot. I understand that you can do this using "cex.lab", but it does not seem to be working for me. I must be adding it in the wrong spot. Any help would be greatly appreciated. Here is my code: bwplot(hr~Herd, data=telemetry, notch=T, ylab="Home

I have a process where I am creating a effects plot similar to the cowles effect example. I would like to add the point estimates to the effects plot, can someone show me the correct syntax. I have included the "R" effects example, so you can show me the correct syntax. Thanks mod.cowles <- glm(volunteer ~ sex + neuroticism*extraversion, data=Cowles, family=binomial)

Hello, I am not having success in a simple task. Using the effects package, I would like to add reference lines at probability values of 0.1 – 0.6 on a plot of the effects. The plot command works, but following up with an abline command produces the message “plot .new has not been called yet”, and of course the reference lines were not added. Looking through past R help lists, there was a

Dear John Fox and everyone, I have been using the effects library with glms and have found it very useful. Now I'm trying it with lms and I'm not sure if the results of the allEffects() are as expected. I've got a model that looks like this: mymodel = lm(formula = A ~ B + C + D + B:D + C:D) Residuals: Min 1Q Median 3Q Max -3.80156 -0.73486 -0.09792

Dear List, I find Effects package very useful, but I believe I have found a bug in allEffects function. Please consider the following code: test <- data.frame(tries= round(runif(40, 5, 300)), tra = gl(4, 10, labels = c("V", "D", "C", "L")), prop= runif(40, 0, 1)) test$success <- round(with(test, tries*prop)) test$prop <- with(test,

Dear R users, I have some trouble using the allEffects() function to compute and display effect plots for a linear model. My data is quite simple, it concerns effects of 3 treatments on the tumoral volume of mice. vTum codes for the qualitative initial volume, from small to big, temps is the time in month since beginning of treatment, and S?rie codes for the batch. Data is unbalanced. >

Thanks in advance for any help. I am attempting to add points to a plot using the allEffects command in the effects package. When I try to add the points I get the following error message: Error in plot.xy(xy.coords(x, y), type = type, ...) : plot.new has not been called yet Strangely, using the code I've pasted below this has worked for me in the past however figuring out what has

Dear everybody! Need help with graphics. I am runnig a simple lm and then using allEffects from 'effects' package: require(effects) model<-lm(Y~A+B, data=mydataframe) I am trying to build (for each predictor - A and then B) a plot of means on Y. I was successful doing it like this - in one swoop: ml.eff<-allEffects(ml1, se=F) plot(ml.eff,ylab="Title of Y") Is it

Dear R devel I've been wondering about this for a while. I am sorry to ask for your time, but can one of you help me understand this? This concerns duplicated labels, not levels, in the factor function. I think it is hard to understand that factor() fails, but levels() after does not > x <- 1:6 > xlevels <- 1:6 > xlabels <- c(1, NA, NA, 4, 4, 4) > y <- factor(x,

Dear list, I would like to expand a DF with all the missing levels of a variable. a <- c(2,2,3,4,5,6,7,8,9) a.cut <- cut(a, breaks=c(0,2,6,9,12), right=FALSE ) (x <- data.frame(a, a.cut)) # In 'x' the level "[0,2)" is "missing". AddMissingLevel <- function(xdf) { xfac <- factor( c("[0,2)", "[2,6)", "[6,9)",

Hi, Some try: > names(mi$xlevels) [1] "f" > all.vars(mi$formula) [1] "D" "x" "f" "Y" > names(mx$xlevels) [1] "f" > all.vars(mx$formula) [1] "D" "x" "f" When offset is indicated out of the formula, it does not work... Marc Le 07/03/2018 ? 06:20, Bendix Carstensen a ?crit?: > I would like

I'm working on predict.survreg and am confused about xlevels. The model.frame method has the argument, but none of the standard methods (model.frame.lm, model.frame.glm) appear to make use of it. The documentation for model.matrix states: xlev: to be used as argument of model.frame if data has no "terms" attribute. But the terms attribute has no xlevels information in it, so I

all.vars works fine, EXCEPT, it give a bit too much. I only want the regression variables, but in the following example I also get "k" the variable holding the chosen knots. Any machinery to find only "real" regression variables? cheers, Bendix library( splines ) y <- rnorm(100) x <- rnorm(100) k <- -1:1 ml <- lm( y ~ bs(x,knots=k) ) mg <- glm( y ~

# Your mailer is set to "none" (default on Windows), # hence we cannot send the bug report directly from R. # Please copy the bug report (after finishing it) to # your favorite email program and send it to # # r-bugs@r-project.org # ###################################################### The multcomp functions work on "lm" objects as anticipated. They do not work on

library(effects) mod <- lm(Measurement ~ Age + Sex, data=d) e <-effect("Sex",mod) The effect is evaluated at the mean age. > e Sex effect Sex F M 43.33083 44.48531 > > e$model.matrix (Intercept) Age SexM 1 1 130.5859 0 23 1 130.5859 1 To evaluate the effect at Age=120 I tried: e

Hello, Everybody: This may not be a "bug", but for me it is an unexpected outcome. A factor variable's levels do not retain their ordering after the levels function is used. I supply an example in which a factor with values "BC" "AD" (in that order) is unintentionally re-alphabetized by the levels function. To me, this is very bad behavior. Would you agree? #

Dear All, probably a simple enough solution but don;t seem to be able to get my head around it...example based on a publicly available data set: mydata <- read.csv("https://stats.idre.ucla.edu/stat/data/binary.csv") mylogit <- glm(admit ~ gre + gpa + rank, data = mydata, family = "binomial") library(effects) plot(allEffects(mylogit) ? ?

>>>>> Paul Johnson <pauljohn32 at gmail.com> >>>>> on Wed, 14 Jun 2017 19:00:11 -0500 writes: > Dear R devel > I've been wondering about this for a while. I am sorry to ask for your > time, but can one of you help me understand this? > This concerns duplicated labels, not levels, in the factor function. > I think it

Dear R-devel list members, I've discovered an inconsistency in how lm() and similar functions handle logical predictors as opposed to factor or character predictors. An "lm" object for a model that includes factor or character predictors includes the levels of a factor or unique values of a character predictor in the $xlevels component of the object, but not the FALSE/TRUE values

Dear Abby, > On Aug 30, 2019, at 8:20 PM, Abby Spurdle <spurdle.a at gmail.com> wrote: > >> I think that it would be better to handle factors, character predictors, and logical predictors consistently. > > "logical predictors" can be regarded as categorical or continuous (i.e. 0 or 1). > And the model matrix should be the same, either way. I think that