similar to: Is there any function can be used to compare two probit models made from same data?

Respected Sir/Madam, I have a question regarding calculation of LD50 (Lethal Dose) and IC50 (50% inhibitory concentration) of an antimicrobial experiment. I have used a compound isolated from a plant and observed its effect on the fungus *Fusarium oxysporum* by the food poisoning method. Solutions of the compound at concentrations of 0, 50, 100, 150, 200 and 250µg/ ml were added to

Hi, people I am just new to R, when writing my loop functions, I meet a problem. There are some data frames, their names is randomized, ( e.g. a1,b3,c7, etc), what should I write it when I want to see them printed together in a loop function? I have tried : > for ( df in 'a1','b3','c7') { + print(print(df)) + } the

Hello, all. I'm investigating the rate at which skeletal joint surfaces pass through a series of ordered stages (changes in morphology). Current statistical methods in this type of research use various logit or probit regression techniques (e.g., proportional odds logit/probit, forward/backward continuation ratio, or restricted/unrestricted cumulative probit). Data typically include the

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

> 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

Hello. I have yet to receive a response to my previous post, so I may have done a poor job asking the question. So, here is the general question: how can I run a run a multivariate (more than one non-independent, response variables) ordered probit regression model? I've had success doing this in the univariate case using the vglm() function in the VGAM package. For example:

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

Hello, I want to start testing using the MNP probit function in stead of the lrm function in my current experiment. I have one dependant label and two independent varaibles. The lrm is simple model <- lrm(label ~ val1 + val2) I tried the same thing with the mnp function and got an error that I don't understand model <- mnp(label ~ val1 + val2) I get back an immediate error that

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]]

Hi, If you plot a cumulative histogram of a gausian distribution, using a log scale on the x-axis and a probit scale on the y-axis, you get a straight line. My question is whether it is possible in R to use a "probit" scale in a "plot". For example on the following webpage you can see an application of how I would like to use a probit scale:

Hi, I have a problem with the ordered probit model -polr function (library MASS). My independent variables are countinuos. I am not able to understand two main points: a) how to calculate marginal effects b) how to calculate the relative importance of each independent variables If required i will attach my model output. Thanks Franco

Dear R-community, I am currently replicating a study and obtain mostly the same results as the author. At one point, however, I calculate marginal effects that seem to be unrealistically small. I would greatly appreciate if you could have a look at my reasoning and the code below and see if I am mistaken at one point or another. My sample contains 24535 observations, the dependent variable

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

What is the current state of the R-art for ordered probit models, and more esoterically is there any available R strategy for ordered cauchit models, i.e. ordered multinomial alternatives with a cauchy link function. MCMC is an option, obviously, but for a univariate latent variable model this seems to be overkill... standard mle methods should be preferable. (??) Googling reveals that spss

 Hello! > I have a very simple set of data and I would like to analyze > them with probit analysis. > The data are: X    Event    Trial > 1210  8        8 > 121  6        8 > 60.5  6        8 > I want to estimate the value of X that will give a 95% hit > rate (Event/Trial) and the corresponding 95% CI. > you can help me? Thanks!! > Trinh  [[alternative HTML version

Hey everyone, I've been reading an old scientific paper (well, not that old, about 15 years) and I want to verify the authors' statistical results. The paper is fairly unclear about what exactly they did, and none of the relatively simple commands I'm familiar with are producing results similar to theirs. The data is dose-response, recorded as binomial data: structure(list(X1 =

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

Hi, I am new in R. I would like to do a ordered probit regression using clm2 (in the ordinal package). My dependent variable y is the way of payment in M&A: y=0 if the deal is financed by stock only, y=1 if the deal is financed by a mix of cash and stock and y=2 if it is by cash only. My independent variables are CollateralB, Cashavailable and Leverage. This is the code I wrote: >

Dear everybody! Are there algorithms for multinominal logit/probit available for R? Is it my fault that I cannot find these in CRAN? Has somebody programmed these? with best wishes Ott Toomet Ott.Toomet at mail.ee -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info",

Hello, I'm trying to do a probit plot but am a little confused about how to do this. Sample data: > dat <- data.frame(dose=c(0.18,0.48,0.78,0.18,0.48,0.78),n=c(103,120,123,60,110,100),r=c(19,53,83,14,54,81), + sample=c("s","s","s","t","t","t")) > temp <-