similar to: inv.logit (package boot) (PR#2394)

Dear subscribers of R-devel I am experiencing that R crashes (further details are given below) in some complex matrix calculations when EISPACK=TRUE has been specified in eigen(). I discovered the behaviour some months ago just after the release of R-2.2.0, and it has been lying on my desk since. I apologise for not having nailed the problem down to a simple function call, but I thought I

I submit the following matrix to both MATLAB and R x= 0.133 0.254 -0.214 0.116 0.254 0.623 -0.674 0.139 -0.214 -0.674 0.910 0.011 0.116 0.139 0.011 0.180 MATLAB's inv(x) provides the following 137.21 -50.68 -4.70 -46.42 -120.71 27.28 -8.94 62.19 -58.15 6.93 -7.89 36.94 8.35 11.17 10.42 -14.82 R's solve(x) provides: 261.94 116.22 150.92 -267.78 116.22 344.30 286.68

Hi, We're using stats4 for a logistic regression. The code is chdreg.logit2 <- glm(chd ~ age + sex, family = binomial) summary(chdreg.logit2) oddsratios <- coef(chdreg.logit2) exp(oddsratios) # Calculate model predicted values pred <- predict(chdreg.logit2,type="response") The glm part runs fine, and up to now so has the predict function. However, now we're

I started out with a real vector b and then obtained its Fourier transform thus B<-fft(b) When I did F<-fft(B, inv=TRUE) I expected that F would be the inverse FT of B but it still has imaginary components. Should the inverse FT not be purely real? Am I missing something? Thanks, Peter.

Hello, I am looking for a routine to do a logistic regression. In the book "Modern Applied Statistics with S-PLUS" a function is described which uses the 'ms' command. Is there an analogue for R, or an alternative approach that can accomplish the same thing? Thanks, John. -- ========================================== John Janmaat Department of Economics Acadia

I know this has been dealt with before on this list, but the previous messages lacked detail, and I haven't figured it out yet. The model is: \x_{ij} = \mu + \alpha_i + \beta_j \alpha is a random effect (subjects), and \beta is a fixed effect (condition). I have a link function: p_{ij} = .5 + .5( 1 / (1 + exp{ -x_{ij} } ) ) Which is simply a logistic transformed to be between .5 and 1.

ALERT Buy Tuesday September 11th 2007 ww Energy Inc. Sym: wwng This stock is going to once again, EXPLODE! Real company, real earnings, not a pump and dump! Current: $.01 Expected: $.54 #1 pick this week. Check 07/10/07. 2 months ago. This Stock exploded, traded over 7 million shares. It's going to do it again. Get in on the money train now! Company info: WW Energy, Inc., through its

ALERT Buy Tuesday September 11th 2007 ww Energy Inc. Sym: wwng This stock is going to once again, EXPLODE! Real company, real earnings, not a pump and dump! Current: $.01 Expected: $.54 #1 pick this week. Check 07/10/07. 2 months ago. This Stock exploded, traded over 7 million shares. It's going to do it again. Get in on the money train now! Company info: WW Energy, Inc., through its

Hi, I've found the Vertical Service Codes / vservices.inc of Julian in the cache of google. It's an very extended extensions include with all the *21 *67 etc services implemented so it is stored to ODBC or if you replace it to Dbget/put etc. I'm wondering if somebody has the macro/agi for using these extensions once stored in the Asterisk db or ODBC. Or am I missing something? And

Greetings all, I have two logistic plots coming from two calls to plogis. The code is .x <- seq(-7.6, 7.6, length=100) plot(.x, plogis(.x, location=0, scale=1), xlab="x", ylab="Density", main="Logistic Distribution: location = 0, scale = 1", type="l") abline(h=0, col="gray") .y <- seq(-7.6, 7.6, length=100) plot(.x, plogis(.x,

Hi all R users I did a logistic regression with my binary variable Y (0/1) and 2 explanatory variables. Now I try to draw my nomogram with predictive value. I visited the help of R but I have problem to understand well the example. When I use glm fonction, I have a problem, thus I use lrm. My code is: modele<-lrm(Y~L+P,data=donnee) fun<- function(x) plogis(x-modele$coef[1]+modele$coef[2])

Dear list, I'm a bit perplexed why the 95% confidence bands for the predicted probabilities for units where x=0 and x=1 overlap in the following instance. I've simulated binary data to which I've then fitted a simple logistic regression model, with one covariate, and the coefficient on x is statistically significant at the 0.05 level. I've then used two different methods to

I added "plogis" to the derivative table in the development version of R; the patch against yesterday's R-devel src/deriv/main.c is available at http://www.zoology.ufl.edu/bolker/deriv_patch.txt . I pretty much followed the framework of the other symbols; here was my incantation - } else if (CAR(expr) == PlogisSymbol) { - ans = simplify(TimesSymbol, -

Hello all, How do I actually use the output of predict.lm(..., type="terms") to predict new term values from new response values? I'm a chromatographer trying to use R (2.15.1) for one of the most common calculations in that business: - Given several chromatographic peak areas measured for control samples containing a molecule at known (increasing) concentrations, first

I'm a week-old R user, and have become stuck trying to create usable CSV outputs for post-processing. I am using the package Rioja, which provides small datasets of results. I am running several analyses in a loop and iteratively adding the results to a *list* ("combined"). Within each iteration I use the following: > combined[[i]] <- performance(fit) With two iterations I

Dear All, I'm trying to use waldtest to test poolability (parameter stability) between two logistic regressions. Because I need to use robust standard errors (using sandwich), I cannot use anova. anova has no problems running the test, but waldtest does, indipendently of specifying vcov or not. waldtest does not appear to see that my models are nested. H0 in my case is the the vector of

dear£º in the example£¨nomogram£©£¬I don't understand the meanings of the program which have been marked by red line.And how to compile the program(L <- .4*(sex=='male') + .045*(age-50) + (log(cholesterol - 10)-5.2)*(-2*(sex=='female') + 2*(sex=='male'))). n <- 1000 # define sample size set.seed(17) # so can reproduce the results age <- rnorm(n, 50, 10)

Dear Spencer and Prof. Fox, Thank you for your replies. I'll very appreciate, if you have any ideas concerning the problem described below. First, I'd like to describe the model in brief. In general I consider a model with three equations. First one is for annual GRP growth - in general it looks like: 1) GRP growth per capita = G(investment, migration, initial GRP per

Dear R users this may be a simple question - but i would appreciate any thoughts does anyone know how you would get one lower and one upper confidence interval for a set of data that consists of proportions. i.e. taking a usual confidence interval for normal data would result in the lower confidence interval being negative - which is not possible given the data (which is constrained between

Caching computation in rails? Simple example: factorial modulus a large number input: integer x output: factorial( x ) % 12345678901234567 I want it so that if it computes factorial of N once, it will not have to compute for N again. code: class SiteController < ApplicationController caches_action :factorial, :inv def examine @inv = @params[''inv''] @outv =