similar to: Questions about Probit Analysis

Hi, I try to minimize the sum of the sum of sce. The following program has been created but it only takes in consideration the last kinetic and not the first ones. I think that I have forget a subscrib but I don't know where. so if you can help me, it will be great.... I have try an other program where y and x are directly calculate in sce function but the time of

Hi, I try to fit a non linear regression by minimising the sum of the sum of squares. The model is number[2]-(x/number[1])^number[3] Number [2] and number [1] change as the data changes but for all the set of data number[3] must be identical. I have 3 set of data (x1,y1), (x2,y2), (x3,y3). x_a<-c(0,0.5,1,1.5,2,3,4,6) y_a<-c(5.4,5,4.84,4.3,4,2,1.56,1.3)

R'istas: I am trying to plot a marginal distribution on the plane behind a persp() plot. My existing code is: library(MASS) X <- mvrnorm(1000,mu=c(0,0),Sigma=matrix(c(1,0,0,1),2)) X.kde <- kde2d(X[,1],X[,2],n=25) # X.kde is list: $x 1*n, $y 1*n, $z n*n persp(X.kde,phi=30,theta=60,xlab="x_b",ylab="x_a",zlab="f") ->res Any suggestions are very

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 All I am kind of stuck up with a code a part of which seems to be causing a problem, or at least I think so. May be the community can help me. It’s simple but I suppose I am missing something. I generate a data matrix X, say of order n*p, where n represents independent row-vectors and p correlated col vectors. Let the row representation be X = (X’_1, . . ., X’_n)’. I generate the

Hello all I have been working to fix this for weeks now, It should be simple to fix. Please help Let me explain what I am doing, I have a data set for 65 countries over a period of 9 years (2000-2008). Each country has on an average say 2000 interviews, so that the total set has roughly 65*9*2000 data points/observations (of course there are missing vales as well). Now let me explain how are the

I can get this summary of a model that I am running: summary(myprobit) Call: glm(formula = Response_Slot ~ trial_no, family = binomial(link = "probit"), data = neg_data, na.action = na.pass) Deviance Residuals: Min 1Q Median 3Q Max -0.9528 -0.8934 -0.8418 1.4420 1.6026 Coefficients: Estimate Std. Error z value Pr(>|z|)

Hi I have been trying so many different things to get my Inverse Mills Ratio going for a Two stage Heckman Model, I have tried the following so far (the commands are listed below till teh point where I get an error), I get an error in the last sentence (marked in bold below), if this were successful then I could have used the IMR as a control in my OLS (which would be the OLS for the outcome

Dear R community, I would like to import data saved with Stata and then run a Probit model using R. My data comes from the World Values Surveys and in the Probit model I want to control for countries. So far I figured out that I should put "convert.factors = FALSE" when using stata.get() in order to import numeric values instead of label mappings, which is what I want for most of the

Hi I am working on a panel data, my data are clustered/grouped by the variable "yearctry", I am running the regression below, but I cant make the regression recognise "yearctry" as the panel variable in the regression myProbit<- glm(s ~ age + gender + gemedu + gemhinc + es_gdppc + imf_pop + estbbo_m, family = binomial(link = "probit"), data = adpopdata) Can

Hi Saurav! On Sun, Jul 12, 2009 at 6:06 PM, Pathak, Saurav<s.pathak08 at imperial.ac.uk> wrote: > I am new to R, I have to do a 2 step Heckman model, my selection equation is > below which I was successful in running but I am unable to proceed further, > > > > I have so far used the following command > > glm(formula = s ~ age + gender + gemedu + gemhinc + es_gdppc +

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