similar to: testing hipotheses using GAMLSS package

Hi everyone, I'm trying to perform a linear regression y = b1x1 + b2x2 + b3x3 + b4x4 + b5x5 while constraining the coefficients such that -3 <= bi <= 3, and the sum of bi =1. I've searched R-help and have found solutions for constrained regression using quadratic programming (solve.QP) where the coefficients are between 0 and 1 and sum to 1, but unfortunately do not understand

Good day R-listers, My question is more a statistical question than an R related question, so please bear with me i'm currently applying the chow test of poolability in fact i'm working with panel N=17 T=5 , and my model looks like this : Yit= a0+B1X1+B2X2+B3X3+B4X4+eit My question is the following when i'm Testing for the equality of the coefficients of the unpooled data (the

Hi all, Can you please tell me what is the problem here. My regression eq is y = B0 + B1X1 +B2X2 +e And i am interested in coefficient B1 I am doing regression with two cases: 1) reg<-lm(y ~ X1 + X2, sam) where sam is the data 2) reg<-lm(y ~ X1 + X2, sam, na.action= na.exclude) . I have missing values in X1 but the values of coefficient is not consistent in two cases. Actually B1 in

Hi everyone: ¡¡I am new to R and I have a really basic question. I have already got a generalized linear model from some dataset, say y=b0 + b1X1 + b2X2. Then I want to get the value of y provided, say, X1=1, X2=2. And the confidence Intervals of this y. I know I can just calculate that since I know the model already. But is there some code that can calculate those automatically?

Forgive me if this is a trivial question, but I couldn't find it an answer in former forums. I'm trying to reproduce some SAS results where they set two parameters equal. For example: y = b1X1 + b2X2 + b1X3 Notice that the variables X1 and X3 both have the same slope and the intercept has been removed. How do I get an estimate of this regression model? I know how to remove the intercept

Dear All, I am currently working with the coxph function within the package survival. I have the model h_ij = h_0(t) exp(b1x1 + b2x2) where the indicator variables are as follows: x1 x2 VPS 0 0 LTG 1 0 TPM 0 1 [[alternative HTML version deleted]]

Dear all, I would like to estimate a system of regression equations of the following form: y1 = a1 + b1 x1 + b2x2 + e1 y2 = a2 + c1 y1 + c2 x2 + c3 x3 + e2 Specifically the dependent variable in Equation 1 appears as an independent variable in Equation 2. Additionally some independent variables that appear in Equation 1 are also included in Equation 2. I assume that I cannot estimate these two

Hello With due respect, have a nice time. I would like to ask some command in R. It is regarding variable selection in linear regression. In R, there is one rebuild function called "step" which selecting variables according to AIC. let say i have data [y, x1,x2,x3,x4] we start with y~b0 i compute the partial F test and choose the variable with maximum partial F to enter the

I have a logit model with about 10 predictors and I am trying to plot the probability curve for the model. Y=1 = 1 / 1+e^-z where z=B0 + B1X1 + ... + BnXi If the model had only one predictor, I know to do something like below. mod1 = glm(factor(won) ~ as.numeric(bid), data=mydat, family=binomial(link="logit")) all.x <- expand.grid(won=unique(won), bid=unique(bid)) y.hat.new

Hello, I am trying to learn how to perform Multiple Regression Analysis in R. I decided to take a simple example given in this PDF: http://www.utdallas.edu/~herve/abdi-prc-pretty.pdf I created a small CSV called, students.csv that contains the following data: s1 14 4 1 s2 23 4 2 s3 30 7 2 s4 50 7 4 s5 39 10 3 s6 67 10 6 Col headers: Student id, Memory span(Y), age(X1), speech rate(X2) Now

Hello! I've just build one-inflated beta regression model using package GAMLSS. It all worked very nicely but now I want to make prediction using it. I use typical function predict() give all necessary arguments (my new data is in data frame and all relevant columns have same names as before). Unfortunately it ends with following error: Error in nrow(x) : (subscript) logical subscript too

Dear R Help, I'd like to be able to use gamlss to generate distributions that are both truncated and have censoring. It doesn't look as though it is possible to do this at the moment: > gen.trun(par=c(0),family="NO",name="tr",type="left") A truncated family of distributions from NO has been generated and saved under the names: dNOtr pNOtr qNOtr rNOtr

Hi, I'm not sure how use curve(dexGAUS(… None of the following four works: rt<- rexGAUS(100, mu=300, nu=100, sigma=35) m1<-gamlss(rt~1, family=exGAUS) curve(dexGAUS(rt=x, mu=300 ,sigma=35,nu=100), 100, 600, main = "The ex- GAUS density mu=300 ,sigma=35,nu=100") curve(dexGAUS(x=rt, mu=300 ,sigma=35,nu=100), 100, 600, main = "The ex- GAUS density mu=300

Dear All, I'd like to set up a loop whereby successive parameter values are used as start values in gamlss (yes I know this isn't usually necessary ! - unfortunately for my truncated data it is), to return the estimated parameters etc. giving the lowest AIC value. I notice that mu.start can take a vector of values - but I can't find any information as to what the function actually

Dear Nik Try the following code loo_predict.mu <- function(model.obj, input.data) { yhat <- foreach(i = 1 : nrow(input.data), .packages="gamlss", .combine = rbind) %dopar% { updated.model.obj <- update(model.obj, data = input.data[-i, ]) predict(updated.model.obj, what = "mu", data = input.data[-i, ], newdata = input.data[i,], type =

Hello: I'd like to know how and if the GLM convergence problems are addressed in gamlss. For simplicity, let's focus on Normal and Negative Binomial with log link. The convergence issues of the glm() function were alleviated in 2011 when glm2 package was released. Package gamlss was released in 2012, so it might still use the glm-like solution or call glm() directly. Is that the case or

Hi everyone, I''m trying to use function vcov to extract the covariance matrix from a GAMLSS object. But I''m getting some strange errors and I was hoping someone could help me out? Vcov works with the same model for lm and glm objects, but not gamlss objects. I''ve searched various help sites to no avail. Its very possible the reason is that vcov failed though,

Dear All: I have succeeded in fitting a GAMLSS.dist model to growth data I am working with it. My aim is to create a matrix of predicted percentiles and the corresponding the fitted model's sigma mu nu by agebins. Q: How do it generate these parameters as in L M S per Cole and Green 1992? Here are my working codes. Name of fitted model is gamlssfit > Agebin<-seq(6,36,6)

Hi, I'm playing around with gamlss and don't entirely understand the sigma result from an attempted lognormal fit. In the example below, I've created lognormal data with mu=10 and sigma=2. When I try a gamlss fit, I get an estimated mu=9.947 and sigma=0.69 The mu estimate seems in the ballpark, but sigma is very low. I get similar results on repeated trials and with Normal and

Hi, I am running a negative binomial model using Gamlss and when I try to include random effect, I get the following message: Warning messages: 1: In vcov.gamlss(object, "all") : addive terms exists in the mu formula standard errors for the linear terms maybe are not appropriate 2: In vcov.gamlss(object, "all") : addive terms exists in the sigma formula standard