similar to: Psychometric curves, two altnerative force choice, glm, and budbworms

Hi, I want to fit some psychophysical data with cumulative gaussians. There is quite a convenient toolbox for matlab called 'psignifit' (formerly known as 'psychofit'). It allows the lower bound of the sigmoid to vary slightly from zero, aswell as the upper bound to vary from one. with these two free parameters, the fitted function is less sensitive to noisy data and outliers.

Dear all, How to obtain the odds ratio (OR) and 95% confidence interval (CI) with 1 standard deviation (SD) change of a continuous variable in logistic regression? for example, to investigate the risk of obesity for stroke. I choose the happening of stroke (positive) as the dependent variable, and waist circumference as an independent variable. Then I wanna to obtain the OR and 95% CI with

Hi, What package would you recommend for analyzing the validity/reliability of multiple choice tests. Doing things such as classical test analysis, factor analysis, item response theory. I've used psychometric (item.exam), MiscPsycho (alpha.Summary), and ltm (rcor.test). MiscPsycho reported the numbers most similar to what I get in SPSS: corrected point biserial correlations,

Dear R-Experts, I was wondering 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

Dear all: "predict.glm" provides an example to perform logistic regression when the response variable is a tow-columned matrix. I find some paradox about the degree of freedom . > summary(budworm.lg) Call: glm(formula = SF ~ sex * ldose, family = binomial) Deviance Residuals: Min 1Q Median 3Q Max -1.39849 -0.32094 -0.07592 0.38220 1.10375

I am working with Probit regression (I cannot switch to logit) can anybody help me in finding out how to obtain with R Finney's fiducial confidence intervals for the levels of the predictor (Dose) needed to produce a proportion of 50% of responses(LD50, ED50 etc.)? If the Pearson chi-square goodness-of-fit test is significant (by default), a heterogeneity factor should be used to calculate

> From: Ross Darnell > > Liaw, Andy wrote: > >>From: Liaw, Andy > >> > >> > >>>From: Ross Darnell > >>> > >>>A good point but what is the value of storing a large set of > >>>predicted > >>>values when the values of the explanatory variables are lost > >>>(predicted >

I have a two-dimensional loess fit, and need to calculate the gradient field from it. Even after looking at loess.c and loess.f, I don't understand the meaning of the returned polynomial coefficients. Or is the brute force method of using a tangential approx to the fitted values the way to go? Dieter Menne --------------------------------------- Dr. Dieter Menne Biomed Software 72074

Dear R-supporters, I have results of lme similar to those shown in Fig. 1.21, p.51 of Pinheiro/Bates. However, In my data set, one of the panels is missing, leading to an ugly frame shift of the following panels. How can I replace one of the panels by an empty one to restore the raster? Dieter Menne --------------------------------------- Dr. Dieter Menne Biomed Software 72074 T?bingen Tel

Is there an easy way to remove data frame columns by name instead of by index? The following gives the idea remove<-c("subj","drug") data[-remove] I found a solution with a few evals and substitutes, similar to that used in reshapeLong, but there must be an easier way out. Dieter --------------------------------------- Dr. Dieter Menne Biomed Software 72074 T?bingen Tel

Dear R's, I have a survey where customers rank a set of 5 packages for a product, so the response variable looks like a d b c a c d b d b a c Predictors variables are 4 socio-economic parameters. I have modelled the FIRST choice of each subject as a multinomial model, similar to the housing example in MASS ch7.3, , but I would prefer to use the whole rank-set instead. Can someone give me a

I have would like to use logistic regression to analyze the percentage of correct responses in a 2 alternative forced choice task. The question is whether one needs to take into account the fact expected probabilities for the percentage of correct responses ranges between 0.5 and 1 in this case and how to adjust the link function accordingly in R (see details below). Gabriel Subjects were asked

Using a formula converted with as.formula with lme leads to an error message. Same works ok with lm, and with lme and a fixed formula. # demonstrates problems with lme and as.formula demo<-data.frame(x=1:20,y=(1:20)+rnorm(20),subj=as.factor(rep(1:2,10))) demo.lm1<-lme(y~x,data=demo,random=~1|subj) print(summary(demo.lm1)) newframe<-data.frame(x=1:5,subj=rep(1,5))

deriv seems to have problems with a minus-sign before a bracket. Below are four examples of the same function, the top one is wrong, all others are correct (hopefully). Rest of expression not shown, it is the same for all versions. _ platform i386-pc-mingw32 arch x86 os Win32 system x86, Win32 status major 1 minor 3.0 year 2001 month 06 day 22 language R

Dear Mixed Effect Friends, Somehow, R(1021, Windows) seem to run differently from S Plus: The soybean example from Pinheiro/Bates on page 290 fails in R. (Soybean1 is Soybean with the NA and "critical" case removed. Same procedure with full Soybean). > fm1Soy.lis<-nlsList(weight~SSlogis(Time,Asym,xmid,scal),data=Soybean1) > fm1Soy.nlme<-nlme(fm1Soy.lis) Error: Singularity

I try to display the grouping variable in coplot. It work, but it's special solution and rather ugly. Any better idea? # Simulate my data frame data(state) x77<-data.frame(state.x77) x77$region<-state.region coplot(Life.Exp ~ Income | region, data=x77, show.given=F, subscripts=T, panel = function(x, y,subscripts, ...) { panel.smooth(x, y, span = 1., ...)

Dear friends of nlme, Running quinModel (Pinheiro/Bates page 380) on R (current release, windows) gives: Nonlinear mixed-effects model fit by maximum likelihood Model: conc ~ quinModel(Subject, time, conc, dose, interval, lV, lKa, lCl) Data: Quinidine Log-likelihood: -497 Fixed: lV + lKa + lCl ~ 1 lV lKa lCl 5.382 -0.273 2.470 Random effects: Formula: list(lV ~ 1, lCl ~ 1)

I am confused by the logic of renaming: # Rename individual list items? Empl<-list(employee="Anna",spouse="Fred") names(Empl)<-c("empl","spo") names(Empl) #[1] "empl" "spo" # worked like a charm... but names(Empl[1])<-"newempl" # no error message, yet .... names(Empl) #[1] "empl" "spo" #

I have problem getting the concept of a nested fixed variable into the nlme scheme. I fear the question is very stupid. In the past I had asked this before, and never got a reply (in other cases, the response was within hours). I also checked the S-list, where several similar enquiries of other people are orphaned. We have a cross-over design, where patient are treated two weeks with placebo,

Thanks to Dieter Menne and Spencer Graves I started to get my way through lsoda() Now I need to use it in with nls() to assess parameters I have a go with a basic example dy/dt = K1*conc I try to assess the value of K1 from a simulated data set with a K1 close to 2. Here is (I think) the best code that I've done so far even though it crashes when I call nls()