similar to: problems with axis

I'm trying to produce a barchart plot with groups, in which each group is placed in a particular time scale in x-axis. If I use barchart directly it does not take the time scale. I've tried with xyplot and adding a panel.barchart, I have the bars in the right place, but not the three groups I'm trying to produce. I've tried defining panel and panel.group, but can't get it to

Is there a function to compute the derivative of the probit (qnorm) function in R, or in any of the packages? Thanks, -Andrew [[alternative HTML version deleted]]

Hi, I would like to plot a graph with one axis in log scale and the other in normal probability scale using R. I cannot change the axis scale to probability scale. What can be a solution? Thanks. CT

Hi there, I'm a bit confused concerning the axis tck setting in the lattice package as the ticks on left sided axis aren't drawn at all with the following setting: dados <- data.frame(varsep = factor(rep(1:2,10)), i = runif(20)) library(lattice) my.theme <- list( axis.components = list(left = list(tck = 1, pad1 = 1, pad2 = 2), top = list(tck = 0, pad1

Hello, I am getting some results from my Probit estimation in R that are in the opposite direction of what I hypothesized. In sas, the default is probability that y=0 (instead of 1) so one needs to type the word "descending" to get P(y=1). Is the same true for R? Is the default set to P(0)? Thank you in advance. Nita Umashankar [[alternative HTML version deleted]]

Dear R users, I need to add minor axis ticks to my graph. In traditional R this is easily achievable by simply adding a second axis with the minor ticks. But how to do that in trellis? I am already out of ideas. Any suggestions will be appreciated. Best regards, Martin ----------------------------------------------------------------- ?????????? ??????????? ? ?????? ?? ??????????

Hi all, I want the y-axis label to be ( in symbols) g(sigma given alpha) where given is the conditional sign. I've tried ylab=expression(g(sigma|alpha))) but it gave me g(|(sigma,alpha)) where the sigma and alpha are in greek but the conditional sign is misplaced (before the bracket) Any help would be appreciated. Maram. [[alternative HTML version deleted]]

Hello, I'm attempting to alter the location of text in my axis labels in lattice plots and have been unsuccessful so far. For example, the y-axis labels are always right-justified, but I would like them to be horizontally centered. Here's an example: library(lattice); # create fake dataset to plot to.plot <- data.frame( x = 1:5, y = c("1\nAAA", "2\nBBB",

Not real data. It was gererated randomly. The original codes are the following: par(mfrow=c(2,1)) n <- 500 ######################### #DATA GENERATING PROCESS# ######################### x1 <- rnorm(n,0,1) x2 <- rchisq(n,df=3,ncp=0)-3 sigma <- 1 u1 <- rnorm(n,0,sigma) ylatent1 <-x1+x2+u1 y1 <- (ylatent1 >=0) # create the binary indicator ####################### #THE

I am using lattice for a bar plot, having a little trouble removing ticks, tick labels from x-axis, but keeping them on the y-axis. I looked around quite a bit (http://tolstoy.newcastle.edu.au/R/e7/help/09/06/1733.html, help pages, etc), tried variations of "scales = list(alternating = c(0,0)", "scales = list(alternating = c(0,0), tck = c(0,0))" and others, couldn't quite

Dear all, I have a very simple set of data and I would like to analyze them with probit analysis. dose event trial 0.0 3 15 1.1 4 15 1.3 4 15 2.0 3 15 2.2 5 15 2.8 4 15 3.7 5 15 3.9 9 15 4.4 8 15 4.8 11 15 5.9 12 15 6.8 13 15 The dose should be transformed with log10(). I use glm(y ~ log10(dose), family=binomial(link=probit)) to do probit analysis, however, I have to exclude the

Hi, I'm trying to make a new S4 object with a slot for a "link-glm" object. R doesn't like me have a slot of class "link-glm" > class(make.link("probit")) [1] "link-glm" > setClass("a",representation(item="link-glm")) [1] "a" Warning message: undefined slot classes in definition of "a": item(class

Hello all! I have a problem to draw a polygon with R. My data is like this> Year Nb.series Perc.pos Perc.neg Nature RGV_mean RGV_sd neg poz 1 1901 1 0.00 0.00 0 4.29 NA 0.00 0.00 2 1902 1 100.00 0.00 1 16.47 NA 0.00 100.00 3 1903 1 100.00 0.00 1 31.31 NA 0.00 100.00 4 1904 1 0.00 0.00 0 -9.62 NA 0.00 0.00 5 1905 1 0.00 100.00 -1 -22.55 NA -100.00 0.00 6 1906 1 0.00 100.00 -1 -12.09 NA

> I make a study in health econometrics and have a categorical > dependent variable (take value 1-5). I would like to fit an ordered > probit or ordered logit but i didn't find a command or package who > make that. Does anyone know if it's exists ? R is very fancy. You won't get mundane things like ordered probit off the shelf. (I will be very happy if someone will show

I am working with Probit regression (I cannot switch to logit) can anybody help me in finding out how to obtain with R Finney's fiducial confidence intervals for the levels of the predictor (Dose) needed to produce a proportion of 50% of responses(LD50, ED50 etc.)? If the Pearson chi-square goodness-of-fit test is significant (by default), a heterogeneity factor should be used to calculate

Hi - I am fitting a probit model using glm(), and the deviance and residual degrees of freedom are different depending on whether I use a binary response vector of length 80 or a two-column matrix response (10 rows) with the number of success and failures in each column. I would think that these would be just two different ways of specifying the same model, but this does not appear to be the case.

Hi, I have used the heckit function in micEcon. Now I would like to evaluate the fit of the probit part of the model but when I enter AIC(sk$probit) I get this error Error in logLik(object) : no applicable method for "logLik" How can I then get the AIC for this model? Side question: If you know - from the top of your head - some link to readings dealing with evaluating the

Hi, I am a stata user trying to transition to R. Typically I compute marginal effects plots for (example) probit models by drawing simulated betas by using the coefficient/standard error estimates after I run a probit model. I then use these simulated betas to compute first difference marginal effects. My question is, can I do this in R? Specifically, I was wondering if anyone knows how R

R users, I got a problem to analyze with probit model. What package contains the algorithm to do probit model. Lawrence N.M Kazembe Mathematical Sciences Department Chancellor College University of Malawi P.O. Box 280 Zomba Malawi Tel: (265) 524 222 ext 284 Fax: (265) 524 046 e-mail: lkazembe at chirunga.sdnp.org.mw url: kazembe.cjb.net kazembe.tsx.org

Hi to all of you, I'm fitting an full factorial probit model from an experiment, and I've the independent variables as factors. The model is as follows: fit16<-glm(Sube ~ as.factor(CE)*as.factor(CEBO)*as.factor(Luz), family=binomial(link="probit"), data=experimento) but, when I took a look to the results I've obtained the following: glm(formula = Sube ~ CE * CEBO *