similar to: function to convert lm model to LaTeX equation

Dear R users: I am not entirely convinced that clogit gives me the correct result when I use pspline() and maybe you could help correct me here. When I add a constant to my covariate I expect only the intercept to change, but not the coefficients. This is true (in clogit) when I assume a linear in the logit model, but the same does not happen when I use pspline(). If I did something similar

Hi all, I try to make a split-plot with poisson errors using glmmPQL, but I have some doubts about the model simplification. Look my system: Block = 3 blocks Xvar1 = 2 levels Xvar2 = 13 levels Yvar = Count data Response I need know about the behaviour of Var1, Var2 and interaction Var1:Var2. Look the levels: > levels(Xvar1) [1] "A" "B" > levels(Xvar2) [1]

Hi there, I have following equations to be solved for a and b: a/(a+b) = x1 ab/((a+b)^2 (a+b+1)) = x2 Is there any direct function available to solve them without disentangling them manually? Thanks for your help.

I have constructed the function mml2 (below) based on the likelihood function described in the minimal latex I have pasted below for anyone who wants to look at it. This function finds parameter estimates for a basic Rasch (IRT) model. Using the function without the gradient, using either nlminb or optim returns the correct parameter estimates and, in the case of optim, the correct standard

I'm trying to solve a system of 3 equations as part of a sub-routine in R, ie first eqn a/x-b*sqrtx+c=log(1/dx+1/e(sqrtx); snd eqn (f*y)/z-g/y-h=-log(2/x+(z/y)/(i*x) and third eqn is of the form zz=x/(j-k(z/y) where a..k inclusive are constants, x,y,z and zz are inputs. How can this be done in R? [[alternate HTML version deleted]]

Friends, I'm trying to pass an equation as an argument to a function. The idea is as follows. Let us say i write an independent function Ideal Situation: ifunc <- function(x) { return((x*x)-2) } mainfunc <- function(a,b) { evala <- ifunc(a) evalb <- ifunc(b) if (evala>evalb){return(evala)} else return(evalb) } Now I want to try and write this entire program in a single

Hello, I would like, if it is possible, to compare the effect of a variable across regression models. I have looked around but I haven't found anything. Maybe someone could help? Here is the problem: I am studying the effect of a variable (age) on an outcome (local recurrence: lr). I have built 3 models: - model 1: lr ~ age y = \beta_(a1).age - model 2: lr ~ age + presentation

Hi all, I am using package lavaan and have created a structural equation model with two exogenous and seven endogenous variables with the following relationships #specify the model m1 = ' # regressions D ~ ma + hs + b4 + b5 + b15 + b16 ma ~ hs + b4 + b5 + b15 + b16 hs ~ b4 + b5 + b15 + b16 b4 ~ el + la b5 ~ el + la

dear R users, I am using sweave to generate report for my data analysis. I recently updated R ro 2.8.0, and now I have the following results when compile the the tex file generated from R. This is pdfTeXk, Version 3.141592-1.40.3 (Web2C 7.5.6) %&-line parsing enabled. entering extended mode (./Lajos.tex LaTeX2e <2005/12/01> Babel <v3.8h> and hyphenation patterns for english,

Hello R users, I have a puzzle with the VGAM package, on my first excursion into generalized additive models, in that this very nice package seems to want to do either more or less than what I want. Precisely, I have a 4-component outcome, y, and am fitting multinomial logistic regression with one predictor x. What I would like to find out is, is there a single nonlinear function f(x) which acts

Dear R-users, I want to give a try to Sweave and Latex but I am having some problems compiling my .Rnw files within Kile. I have followed the recommendations given in http://tolstoy.newcastle.edu.au/R/e5/help/08/10/4277, but they do not seem to address my particular problem. I am using R 2.11.0 and Kile v 2.0.83 on an OpenSUSE11.2 installation (KDE 4.3 environment). According to the log (see

Hi r-users, I would like to solve system of nonlinear equation using BBsolve function and below is my code.  I have 4 parameters and I have 4 eqns. mgf_gammasum <- function(p) { t  <- rep(NA, length(p)) mn <- 142.36 vr <- 9335.69 sk <- 0.8139635 kur <- 3.252591 rh  <- 0.896 # cumulants k1 <- p[1]*(p[2]+p[3]) k2 <- p[1]*(2*p[2]*p[3]*p[4] +p[2]^2+p[3]^2) k3 <-

I'm currently working on writing up some documentation for some of my code, but am having the darndest time coding in equations. For example, the equation in the following: \details{ Calculated the R Squared for observed endogenous variables in a structural equation model, as well as several other useful summary statistics about the error in thoe variables. R Squared values are

I have data (each Y_i is a vector) in the form of Y_i = X_i \beta_i + Z_i b_i + epsilon_i Were it not for the measurement error (the epsilon_i) it's a very simple model --- nice and balanced, compound symmetry, and I'd just use lme(y ~ x1 + x2, random=~1|subj, ...) but the measurement error is throwing me off. Because the Y_i are actually derived from other data, I am able

Hi R Graphics Gurus I am unable to figure out this issues with unevaluated expressions. I'm trying to create a graphic where I calculate the residual from a regression and want to mark each residual with its observation number. So something like plot(0,0, type = "n", xlim = c(0,10)) for(i in 1:10){ text(i, 0, substitute(paste(epsilon[i]))) } except that i end up pasting

I'm interested in doing multinomial logistic regression with equality constraints on some of the parameter values. For example, with categorical outcomes Y_1 (baseline), Y_2, and Y_3, and covariates X_1 and X_2, I might want to impose the equality constraint that \beta_{2,1} = \beta_{3,2} that is, that the effect of X_1 on the logit of Y_2 is the same as the effect of X_2 on the

*Dear R-help: My name is Rodrigo and I have a question with nlme package in R to fit a mixed beta regression model. The details of the model are: Suppose that:* *j in {1, ..., J}* *(level 1)* *i in {1, ..., n_j}* *(level 2)* *y_{ij} ~ Beta(mu_{ij} * phi_{ij}; (1 - mu_{ij}) * phi_{ij}) y_{ij} = mu_{ij} + w_{ij} * *with* *logit(mu_{ij}) = Beta_{0i} + Beta_{1i} * x1_{ij} + b2 * x2_{ij}

*Dear R-help: My name is Rodrigo and I have a question with nlme package in R to fit a mixed beta regression model. I'm so sorry. In the last email, I forgot to say that W is also a unknown parameter in the mixed beta regression model. In any case, here I send you the correct formulation. ** Suppose that:* *j in {1, ..., J}* *(level 1)* *i in {1, ..., n_j}* *(level 2)* *y_{ij} ~

Dear Deepayan Sarkar, I have (again) a question concerning "panel" and my function "SplomT", see attachments. Some time ago you helped me to write this function, thanks again. I have used it to great advantage in my statistics instructions. Now the problem I encounter is that the .pdf figure generated in Sweave consists of one extra empty page at the start. This prevents

Hello, I was wondering if someone knows the formula used by the function lm to compute the t-values. I am trying to implement a linear regression myself. Assuming that I have K variables, and N observations, the formula I am using is: For the k-th variable, t-value= b_k/sigma_k With b_k is the coefficient for the k-th variable, and sigma_k =(t(x) x )^(-1) _kk is its standard deviation.