similar to: Probit Analysis

Hi, Thanks in advance for the help. I have a few vectors and I need to get all the combinations of the vector values. Normally I would use a few for-loops to do that. e.g x1_c(1,3,5) x2_c(2,5,6) for (i in x1) { for (j in x2) { ... }} My problem is that I don't know how many vectors there are ahead of time, so I don't know how many for-loops I need. Any

Dear all, I have a question on the dose-response estimation with clustered/ correlated binary data. I would like to estimate the hit rate for a certain test at various concentration levels. The test is used on 5 subjects, and each subject is tested 20 times. If we assume that the 100 samples are independent, the hit rate estimate is unbiased, but the variance is under-estimated. The other

Hello everyone, Does anyone know if I can do Breslow Day Test for the homogeneity of odds ratio in R? Thanks! - Jacqueline

Classification: UNCLASSIFIED Caveats: NONE A similar question has been posted in the past but never answered. My question is this: for probit analysis, how do you program a 95% confidence interval for the LD50 (or LC50, ec50, etc.), including a heterogeneity factor as written about in "Probit Analysis" by Finney(1971)? The heterogeneity factor comes into play through the chi-squared

 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

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

Hi all, I am trying to run the k-means cluster analysis using the function kmeans in the package cluster. The data are: x = c(-0.26, -0.23, -0.05, -0.20, 0.30, -0.84, -0.10, -0.12, 0.10, -0.31, -0.19, 0.18, -0.26, -0.23, -0.37, -0.23) I've got two different solutions when I ran this function over a few times: kmeans(x, centers=2) The first solution gives the following: $cluster [1]

Hello all I have done some fitting of pnorm functions to dose-response data, so I could calculate EC50 values (dose where the response is 0.5). I used the nlm function for this, so I did not get any information about the confidence intervals of the fitted parameters. What would be a good way to do such a probit fit, or is there a package which I could use? Best regards Johannes Ranke

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

Dear list, I have a simple question. If I need to run linear regression on a subset of the dataset, will the following two commands give the same output: lm(y ~ as.factor(x1) + as.factor(x2), data, subset = z==Z[1]) and lm(y ~ as.factor(x1) + as.factor(x2), data[data$z==Z[1],] ) I have got different results running the two commands. Am I missing something here, or it there a bug on the

I just ran into this strange behavior. y <- rnorm(1000) x <- seq(0, length=length(y)) plot(x, y, type='l', lty=1) - instantaneous plot(x, y, type='l', lty=2) - 18s plotting lines + 15s plotting axes = 33s plot(x, y, type='l', lty=3) - 76s ,, + 75s ,, = 151s plot(x, y, type='l', lty=4) -

Hi all, Suppose I have a vector Fruits <- c('Apple','Orange','Pear','Banana','Mango') I want to print the statement "The fruits in the basket are: Apple, Orange, Pear, Banana, Mango" If I do: print(paste('The fruits in the basket are:',Fruits)) The output is: [1] "The fruits in the basket are: Apple" "The fruits in

Is there any way to process the "vignette" documantation files? I have been looking at the bioconductor packages and most of them have *.Rnw files in doc directories. They look like regular TeX but have sections showing R code that seem to choke TeX. Thanks, Andy __________________________________ Andy Jaworski Engineering Systems Technology Center 3M Center, 518-1-01 St. Paul, MN

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

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 All, I'm trying to run a multinomial probit on a dataset with 28 data points and five levels (0,1,2,3,4) in the latent choice involving response variable. I downloaded the latest mnp package to run the regression. It starts the calculation and then crashes the rpogram. I wish I could give the error message but it literally shuts down R without a warning. I'm using the R

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, 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

If I wanted to fit a logit model and account for clustering of observations, I would do something like: library(Design) f <- lrm(Y1 ~ X1 + X2, x=TRUE, y=TRUE, data=d) g <- robcov(f, d$st.year) What would I do if I wanted to do the same thing with a probit model? ?robcov says the input model must come from the Design package, but the Design package appears not to do probit? Thanks very