similar to: glm and plot.effects

I am attempting to estimate LC50 (analogous to LD50, but uses exposure concentration rather than dose) by fitting a Weibull model; but I can't seem to get it to work. From what I can gather, I should be using survreg() from the survival package. The survreg() function relies on time-to-event data; my data result from 96 h exposures (i.e., dead or alive after a fixed period; 96 h). I've

I've been looking into the effects package and it seems to be a great tool for plotting the probabilities of the response variable by the predictors. However, I'm wonder if I can use the effects package to plot the probabilities on the y axis and one predictor on the x axis, with the curve having the info for another predictor. So let's say our response variable is win, a binary

Hello! I am trying to put two math expressions in the title of a plot. As you can see below, I can place correctly one expression at a time, but not both. Ideally I would like to have them separated by a comma. Any suggestions? > k <- 1 > n.eff <- c(20, 30) > ### this works > plot(0,0, main = substitute(n == k, list(k = k))) > ### this works > plot(0,0, main =

Hello list, I've seen the recent discussions documenting problems with lm. I have encountered the following problem. I use WinXP Pro with service pack 1, and R 1.9.0, on a XEON 2GHz, with 1GB of RAM. > eff.fro std.dev mean NSTRDSP 7.403749e-01 1.215686e-01 CPFGEP 9.056763e+00 1.815686e+00 WSWOLF 4.703588e+05 1.112832e+05 NPILGRIM 1.017640e+06 2.134335e+05

Two friend reported me a problem, which I can't solve: (I run R-1.0.0, Debian Linux) They hava a function "corr.matrix" (see end of mail), and when they create a 173x173 matrix with this function V <- corr.matrix(0.3, n=173) V1 <- qr.solve(V) reports: Error in qr(a, tol = tol) : NA/NaN/Inf in foreign function call (arg 1) For n < 173, qr.solve returns the correct

hi, - big and ugly mail. If you don't like them, delete it now :-) - I have collected and classified some information of: http://home.earthlink.net/~rwhron/kernel/bigbox.html And I observed that ext3 performance is worse than previous kernels(2.4.19...). -ac and -aa are here only as reference. Complete information is in the upper URL. dbench: Performance is worse. dbench (Numbers are in

############################################################################### Before reporting 4 problems with windows(rescale=) I want to congrat on R1.2 and to thank r-developers for quickly adding the rescale workaround to the windows version. Happy New Year Jens Oehlschlaegel ###############################################################################

Dear all, This problem came up initially while debugging a function, but it seems to be a more general problem of R. I hope I'm wrong, but I can't find another explanation. Let me illustrate with the raster package. For an object "RasterLayer" (which inherits from Raster), there is a method xyValues defined with the signature

R-helpers, I am hoping to find someone who uses both R and program Stata for GLMs. I am a beginner R user, finding my own way through; learning code etc. at the same time as learning the statistics I need to complete my project. What I have is the code from Stata and am trying to reproduce the same analysis in R - my program of choice. . glm count md ms rf sg, family(poisson)

In the course of exploring response prediction, I stumbled upon a small discrepancy between the CIs produced by predict.lm() and all.effects() require(mlmRev) require(effects) hsb.lm <- lm(mAch ~ minrty * sector, Hsb82) hsb.new <- data.frame( minrty = rep(c('No', 'Yes'), 2), sector = rep(c('Public', 'Catholic'), each = 2)) hsb.eff <-

Hi All, I am trying to use optim() to minimize a function with a penalty function term. This is a simple model bioeconomic model of a fishery. The penalty function constrains the amount of effort (f) at 9. This works fine. The code is: ********** nfleets<-2 M<-1 M<-array(M,dim=c(nfleets)) N<-1000 cost<-c(30,30) cost<-array(cost,dim=c(nfleets)) Price<-2

Hi, I have some values in a list format generated by the following: Path_Number <- 0010 ID.Path <- formatC(0001:Path_Number, width=4, flag=0) # Make vector of ID's. No_of_Effectors <- sample(1:550, length(ID.Path), replace=TRUE) # Define Number of Effectors each individual gets. Effectors <- split(sample(1:10000, sum(No_of_Effectors), replace=TRUE), rep(ID.Path, No_of_Effectors))

After an upgrade to Asterisk 12, I'm "collecting" channels. When I enter and then exit a conference room, I see: -- <CBAnn/207-0000067f;1> Playing 'confbridge-leave.slin' (language 'en') -- Channel CBAnn/207-0000067f;2 joined 'softmix' base-bridge <5edb1920-3774-4ba3-8c4d-23e8fd04519c> -- Channel CBAnn/207-0000067f;2 left

I am not sure whether it is a design decision or just an oversight. When I ask for the standard errors of the predictions with predict(budwm.lgt,se=TRUE) where budwm.lgt is a logistic fit of the budworm data in MASS, I got Error in match.arg(type) : ARG should be one of response, terms If one is to construct a CI for the fitted binomial probability, wouldn't it be more natural to do

Greetings. I'm trying to replicate an analysis I did a few years ago, then in SPSS, using the SPSS GLM command: GLM n_diffpt WITH age_i inc_i join_i work_i educ_i give_i cs_i eff_i age_a inc_a join_a work_a educ_a give_a cs_a eff_a /METHOD = SSTYPE(3) /INTERCEPT = INCLUDE /PRINT = PARAMETER ETASQ RSSCP GEF /CRITERIA = ALPHA(.05) /DESIGN = age_i*age_a inc_i*inc_a

I am a true R novice aonly using it for this function ;) I am trying to use color2D.matplot to form a image of my data using the following conditions color2D.matplot(fi1, c(dr), c(dg), c(db), nslices=7, ylab='Species', xlab="gene", show.legend=TRUE) where fi1 is my matrix. I have a matrix with 36 columns and 130 rows. most entries are 1 or 0 and I am trying to get this

Hi R-users, I would like to find the goodness of fit using Chi-suare test for my data below: xobs=observed data, xtwe=predicted data using tweedie, xgam=predicted data using gamma > xobs <- c(223,46,12,5,7,17) > xtwe <- c(217.33,39,14,18.33,6.67,14.67) > xgam <- c(224.67,37.33,12.33,15.33,5.33,15) > chisq.test(xobs, xtwe = xtwe, rescale.p = TRUE) Error in chisq.test(xobs,

I suspect the weights argument is not having any effect. Package: Matrix Version: 0.995-2 Date: 2006-01-19 Beginning with this: Browse[1]> resp.lmer <- lmer(SensSSC ~ Block + Season + (1 | Plot) + (1 | Ma) + (1 | Pa) + + (1 | MaPa), weights = SensSSC.N, data = xx) I group the output into a table with my ran.eff function and get this:

Dear UseRs, I wrote following function in order to solve Data Envelopment Analysis. Reason for posting is that the function is slow when nrow(dat) is large. I wonder if other functions could substitute the for() loop in the code, such as mapply(). Can anybody help to rewrite the dea() function as efficiently as possible? The code is as follows:

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