search for: ogiv

...vector for argument ?nodes? when simulating from a non hierarchical compound model. But really, one should use ?rcompound? for such cases. ? New alias ?rcomphierarc? for ?simul? that better fits within the usual naming scheme of random generation functions. ? Functions ?grouped.data? and ?ogive? now accept individual data in argument. The former will group the data using ?hist? (therefore, all the algorithms to compute the number of breakpoints available in ?hist? are also available in ?grouped.data?). ?ogive? will first create a grouped data object and then compute the ogive....

...vector for argument ?nodes? when simulating from a non hierarchical compound model. But really, one should use ?rcompound? for such cases. ? New alias ?rcomphierarc? for ?simul? that better fits within the usual naming scheme of random generation functions. ? Functions ?grouped.data? and ?ogive? now accept individual data in argument. The former will group the data using ?hist? (therefore, all the algorithms to compute the number of breakpoints available in ?hist? are also available in ?grouped.data?). ?ogive? will first create a grouped data object and then compute the ogive....

...ot;Comparing the Three Estimation Methods for the Four Parametric Logistic (4PL) Item Response Model". While I am looking for a package about Markov chain Monte Carlo (MCMC) method for 4PL model in this R forum, I found an sirt package of an MCMC method but only for the three parametric normal ogive (3PNO) item response model. However, my study focus on 4PL model. In line with this, I would like to know if there exist a function of MCMC method for 4PL model in R language. I am asking for your help to inform me if such function exist. I highly appreciate your response on this matter. Thank you...

...;least squares" fit on artificial data, and it is not a problem at the level I'm doing this. (In fact, I have a suspicion that it is never a problem, but let's put that aside.) The trouble is that nls rarely converges. When I plot the data graphically, they look OK. Nice, monotonic, ogive-like plots (using categories on the horizontal axis). To make it concrete, here is a typical call to nls. logit <- function(x) {exp(x)/(1+exp(x))} nls(score[,1]~logit((mscore-A)/B)+D)),start=list(A=.5,B=.1,C=.2,D=.8) The variable mscore is the mean score, ranging 0-1, and score[,1] is 0/1, m...

...ot;Comparing the Three Estimation Methods for the Four Parametric Logistic (4PL) Item Response Model". While I am looking for a package about Markov chain Monte Carlo (MCMC) method for 4PL model in this R forum, I found an sirt package of an MCMC method but only for the three parametric normal ogive (3PNO) item response model. However, my study focus on 4PL model. In line with this, I would like to know if there exist a function of MCMC method for 4PL model in R language. I am asking for your help to inform me if such function exist. I highly appreciate your response on this matter. Thank you...

...ies to store and manipulate grouped data (stored in an interval-frequency fashion). Function grouped.data() creates a grouped data object similar to a data frame. Methods of "[", "[<-", mean() and hist() created for objects of class "grouped.data". o Function ogive() --- with appropriate methods of knots(), plot(), print() and summary() --- to compute the ogive of grouped data. Usage is in every respect similar to ecdf(). o Function elev() to compute the empirical limited expected value of a sample of individual or grouped data. o Function emm() to c...

...ies to store and manipulate grouped data (stored in an interval-frequency fashion). Function grouped.data() creates a grouped data object similar to a data frame. Methods of "[", "[<-", mean() and hist() created for objects of class "grouped.data". o Function ogive() --- with appropriate methods of knots(), plot(), print() and summary() --- to compute the ogive of grouped data. Usage is in every respect similar to ecdf(). o Function elev() to compute the empirical limited expected value of a sample of individual or grouped data. o Function emm() to c...

...In addition, contracts without data are now supported like in other credibility models. o Argument 'right' for grouped.data() to allow intervals closed on the right (default) or on the left. o Method of quantile() for grouped data objects to compute the inverse of the ogive. USER-VISIBLE CHANGES o cm() no longer returns the values of the unbiased estimators when method = "iterative". o Specification of regression models in cm() has changed: one should now provide the regression model as a formula and the regressors in a separate matr...

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...In addition, contracts without data are now supported like in other credibility models. o Argument 'right' for grouped.data() to allow intervals closed on the right (default) or on the left. o Method of quantile() for grouped data objects to compute the inverse of the ogive. USER-VISIBLE CHANGES o cm() no longer returns the values of the unbiased estimators when method = "iterative". o Specification of regression models in cm() has changed: one should now provide the regression model as a formula and the regressors in a separate matr...

..., however I am not sure. I would like to find that function and use it in "mde" instead of ecdf for method"CvM". you can see the part I would like to modify in mde function. .... if (measure == "CvM") { G <- fn Gn <- if (grouped) ogive(x) else ecdf(x) if (is.null(weights)) weights <- 1 Call$x <- knots(Gn) Call$par <- start } ...... I'd appreciate if you could suggest a way to create the empirical distribution function after the 0.1-quantile till 0.9-quantile. S...

...ng how to fit a cumulative gaussian to a set of empirical data using R. On the R website as well as in the mail archives, I found a lot of help on how to fit a normal density function to empirical data, but unfortunately no advice on how to obtain reasonable estimates of m and sd for a gaussian ogive function. Specifically, I have data from a psychometric function relating the frequency a subject's binary response (stimulus present / absent) to the strength of a physical stimulus. Such data is often modeled using a cumulative gaussian function. I have tried to implement such a fitting...

Please forgive if this question has been asked recently in this list. I've scanned some of the recent archives and the FAQ and haven't found the answer to my question. I'm taking a class on Elementary Stats, basic stuff, and I found R while looking for stats programs that run on Linux. The documentation seems to be fairly complete on the functions contained within R, but to be honest,

I have some Bernoulli data something like this: x<-sort(runif(100,1,20)) p<-pnorm(x,10,3) y<-as.numeric(runif(x)<p) plot(x,y) lines(x,p) This plot is not very satisfactory because the ogive does not visually fit the (0,1) points very well, and also because the points tend to fall on top of one another. The second problem can be eliminated by adding vertical jitter. However I was thinking about the following plot. Instead of plotting all the 0,1 points, instead divide the x axis into...