similar to: Fitting binomial data to a probit distribution without an equation

Hola a tod@s, Hace poco en la lista se discutieron algunas aproximaciones para determinar la distribución de probabilidad que potencialmente podrían haber generado los datos (ver [1]) y una de ellas fué el AIC. Haciendo uso del programa enviado por Pablo Verde (ver [1]), estos son los resultados para mis datos: weibull , AIC = 69839.44 exponential , AIC = 79488.77 gaussian , AIC = 69413.03

Hello. I am not certain even how to search the archives for this particular question, so if there is an obvious answer, please smack me with a large halibut and send me to the URLs. I have been experimenting with fitting curves by using both maximum likelihood and maximum spacing estimation techniques. Originally, I have been writing distribution-specific functions in 'R' which work

Dear all, I am trying to find a suitable R-function for parametric proportional hazard regressions. The package survival contains the coxph() function which performs a Cox regression which leaves the base hazard unspecified, i.e. it is a semi-parametric method. The package Design contains the function pphsm() which is good for parametric proportional hazard regressions when the underlying base

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

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:

> 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

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 have had some weird results using the optim() function. I wrote a probit likelihood and wanted to run it with optim() with simulated data. I did not include a gradient at first and found that optim() would not even iterate using BFGS and would only occasionally work using SANN. I programmed in the gradient and it iterates fine but the estimates it returns are wrong. The simulated data work

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:

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 List I am trying to fit a simutaneous equation logit model. i.e., the response variables of the structured equations are binomial, I am not sure if systemfit can do this job. A google search doesn't yield too much helpful information. Your knowledge on any other packages or codes are appreciated. Thanks will

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.

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

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 <-

Howdy everyone I?m trying to get Odds ratio and OR confidence intervals using a probit model, but I'm not getting. Do you think you can help me? I?m new with R L naive = summary(glm(pcr.data[,7]~boldBeta_individual+pcr.data$age,family=binomial(link=probit))) naive_answer = c(naive$coefficients[,1:3]) #naive estimates for

Estimation of spatial probit models is as far as I know not implemented functions in the spdep package of R. I am looking for a scripting that handles both endogeneity and heteroscedasticity in spatial probit models. This estimator was first suggested by Case (1992). If someone could provide me with such an R-script I would be most grateful. Moreover I would be interested in obtaining script for

Deal all: i want to do the probit with sample selection estimation, the following is my code: probit with sample selection can be done by stata :heckprob The heckprobll is the likelihood function shown in W.H. Greene 5th p714 ¡´ The question is the convergence is very slow compare with Stata using likellihood only. ¡´ Second i did the similar way in matlab using fminsearch , the estimated

hello all, I am a beginner at R and not exactly a statistician either. I'm messing around with a probit model on an enconomic time series. I can get the model estimated but I have not been able to get it to give me predictions out of sample data. I'm using the predict function but getting errors. Here is my code: #predict a recession 12 months out #k=12 means 12 months forward #dates

Dear All, I have some questions about probit regressions. I saw a nice introduction at http://bit.ly/bU9xL5 and I mainly have two questions. (1) The first is almost about data manipulation. Consider the following snippet ################################################## mydata <- read.csv(url("http://www.ats.ucla.edu/stat/r/dae/binary.csv")) names(mydata) <-

hi, people How can we compare two probit models brought out from the same data? Let me use the example used in "An Introduction to R". "Consider a small, artificial example, from Silvey (1970). On the Aegean island of Kalythos the male inhabitants suffer from a congenital eye disease, the effects of which become more marked with increasing age. Samples of islander males