similar to: Arguments of a function

Dear R users/developers I requested help to solve the problem of formulating Multivariate Sample selection model by using Full Information Maximum Likelihood (FIML)estimation method. I could not get any response. I formulated the following code of FIML to analyse univariate sample selection problem. Would you please advise me where is my problem library (sem) library(nrmlepln) Selection

Hello, I've got to compute a minimization equation under an equality constraint (Min g(x1,x2,x3) with x1+x2=const). The Constroptim function does not authorize an equality condition but only inequality conditions. Which function can I use instead? Thank you very much for your help. Gael Robert - +33 1 42 14 27 96 ****************************************************************** This

Hello, I am having a problem figuring out how to model a continuous outcome (y) given a continuous predictor (x1) and two levels of nested categorical predictors (x3 nested in x2). The data are observational, not from a designed experiment. There are about 15 levels of x2 and between 3 and 14 levels of x3 nested within each level of x2. There are between 6 and 50 x1,y observations for each unique

Hello all! I want to estimate parameters in a MIMIC model. I have one latent variable (ksi), four reflexive indicators (y1, y2, y3 and y4) and four formative indicators (x1, x2, x3, x4). Is there a way to do it in R? I know there is the SEM library, but it seems not to be possible to specify formative indicators, that is, observed exogenous variables which causes the latent variable. Thanks,

Hi All, I've been successfully using the with function for analyses and the transform function for multiple transformations. Then I thought, why not use "with" for both? I ran into problems & couldn't figure them out from help files or books. So I created a simplified version of what I'm doing: rm( list=ls() ) x1<-c(1,3,3) x2<-c(3,2,1) x3<-c(2,5,2)

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

Dear R-users, I am facing problem with integrating in R a likelihood function which is a function of four parameters. It's giving me the result at the end but taking more than half an hour to run. I'm wondering is there any other efficient way deal with. The following is my code. I am ready to provide any other description of my function if you need to move forward.

Hi I have a data frame, including x1, x2, x3, and y. I use lm() to fit second-order linear model, like the following: ft <- lm(y ~ x1 + x2 + x3 + I(x1 * x1) + I(x1 * x2) + I(x1 * x3) + I(x2 * x2) + I(x2 * x3) + I(x3 * x3), mydata) if the independent variable number is large, the formula will be very long. Is there a easy way to write formula like the above one? I have read the R

Hello! I am getting the following errors when running optim() [I tried optim() with 3 different methods as you can see]: Error in optim(c(0.66, 0.999, 0.064), pe, NULL, method = "L-BFGS-B") : objective function in optim evaluates to length 6 not 1 > out <- optim( c(0.66, 0.999, 0.064), pe, NULL, method = "Nelder-Mead") Error in optim(c(0.66, 0.999, 0.064),

Hi everyone, I admit I am a bit of an R novice, and I was hoping someone could help me with this error message: Warning message: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : extra arguments weigths are just disregarded. My equation is: lm( Y ~ X1 + X2 + X3, weigths = seq(0.1, 1, by = 0.1)) -- View this message in context:

Hi, Subject: Using R for Multiple Regression I am new to statistic but am interested in applying mathematical models to solve biological problems. I have used a linear model to generate the test data. When using this data I expect R to correctly identify the model but that does not seem to be the case. I am certain that I am doing something wrong but not able to figure it out. Model: Y = m1x1 +

Hi, I have the response variable 'Y' and four predictors say X1, X2, X3 and X4. Assuming all the assmptions like Y follows normal distribution etc. hold and I want to run linear multiple regression. How do I run the stepwise regression (forward as well as the backward regression). >From other software (i.e. minitab), I know only X1 and X2 are significant so my regression equation

Hi, I would like to minimize the value of x1-x2, x2 is a fixed value of 0.01, x1 is the quantile of normal distribution (0.0032,x) with probability of 0.7, and the changing value should be x. Initial value for x is 0.0207. I am using the following codes, but it does not work. fr <- function(x) { x1<-qnorm(0.7,0.0032,x) x2=0.01 x1-x2 } xsd <- optim(0.0207, fr,

Hi there, I hope to build a model Y ~ X1 + X2 + X3 + X4 with X1 has two instrumental variable A and B, and X2 has one instrumental variable A. I have searched the R site and mailling list, and known that the tsls() from sem package and ivreg() from AER package can deal with instrumental variable regression, however, I don't know how to formula the model. Any suggestion will be really

I am trying to estimate a demand function: Y=K * X1^b1 * X2^b2 * X3 ^(-1-b1) in log form: ln Y = ln K + b1 ln X1 + b2 ln X2 + (-1-b1) ln X3 As the regression coefficients are related for 2 of the regressors, I am not sure of the appropriate methodology or function in R to handle this. Any hints? thx, Tarun [[alternative HTML version deleted]]

Hi All, I'm trying to do the following constrained optimization example. Maximize x1*(1-x1) + x2*(1-x2) + x3*(1-x3) s.t. x1 + x2 + x3 = 1 x1 >= 0 and x1 <= 1 x2 >= 0 and x2 <= 1 x3 >= 0 and x3 <= 1 which are the constraints. I'm expecting the answer x1=x2=x3 = 1/3. I tried the "constrOptim" function in R and I'm running into some issues. I first start off

Hi Has anyone used R to conduct confirmatory factor analysis? This email pertains to use of SEM. For context consider an example: the basic idea is that there are a bunch of observables variables (say study habbits, amount of time reading in the bus, doing homework, helping other do homework, doing follow-up on errors etc.) and one believes that all these variables maybe measured by two or

I want to get some "simple" logic regression examples to work before exploring a hard problem. I can get results, but I'm having some problems using "cat" to save the logic regression equation to a disk file. Consider this: # Simple Logic Regression Example # efg, 23 Feb 2005 library(LogicReg) # Create simulated data with known logic equation: # "noise"

Hi I am running a structural equation model with R using the sem command; am getting the following error: "Error in sem.default : The model has negative degrees of freedom = -4" My model is as follows: s_model = specify.model() x1->m1, b1,NA x2->m1, b2,NA x3->m2, b3,NA x4->m2, b4,NA x5->m2, b5,NA x6->m2, b6,NA m1->y, a1,NA m2->y, a2,NA m1<->m1, v1,NA

Hi, can anybody tell me how the Hausman test for endogenty works? I have a simulated model with three correlated predictors (X1-X3). I also have an instrument W for X1 Now I want to test for endogeneity of X1 (i.e., when I omit X2 and X3 from the equation). My current approach: library(systemfit) fit2sls <- systemfit(Y~X1,data=data,method="2SLS",inst=~W) fitOLS <-