similar to: R formula

Hi there, I am really new to statistics in R and statistics itself as well. My situation: I ran a lot of OLS regressions with different independent variables. (using the lm() function). After having done that, I know there is endogeneity due to omitted variables. (or perhaps due to any other reasons). And here comes the Hausman test. I know this test is used to identify endogeneity. But what I

I use code ( actually its code from vars package and its directly below ) to do a sequence of lm calls and the data I use from the matrix depends on restrictions. for(i in 1:K){ datares <- datasub[, which(x$restrictions[i, ] == 1),drop=FALSE] y <- yendog[, i] lmres <- lm(y ~ -1 + ., data=datares) # x$varresult[[i]] <- lmres # x$resid[, i] <-

Hello !   I’m trying to estimate a system of equation (demand and supply) using the systemfit package.  My program is:   library(systemfit) demand <- tsyud ~ tsyud1 + tsucp + tspo + tssn supply <- tscn ~ tsyn + tsqn + tsksn + tsucp system <- list(demand=eqdemand, learning = eqsupply) labels <- list(demand="eqdemand", learning="eqsupply") inst <- ~ tsupp1 + tsupp2

Surprisingly, I played around with some test code and below actually creates equations that look correct. tempmat<-matrix(10,nrow=6,ncol=6) restrictmat<-diag(6) colnames(tempmat)<-c("AUD.l1","CHF.l1","CAD.l1","GBP.l1","EUR.l1","JPY.l 1")

Hi everyone, I am looking to do some manual maximum likelihood estimation in R. I have done a lot of work in Stata and so I have been using output comparisons to get a handle on what is happening. I estimated a simple linear model in R with lm() and also my own maximum likelihood program. I then compared the output with Stata. Two things jumped out at me. Firstly, in Stata my coefficient

Hello to everyone, I have 2 problems and would be very pleased if anyone can help me: 1) When I use the package "systemfit" for SUR regressions, I get two different variance-covariance matrices when I firstly do the SUR regression ("The covariance matrix of the residuals used for estimation") and secondly do the OLS regressions. In the manual for "systemfit" on page

Greetings R Users, I have a system of equations for which I would like to output all the R-squares. Assume there are four equations in my system, the only way I found to output all the R-squares is by calling them out one by one as this: summary(fitSUR$eq[[1]])$r.squared summary(fitSUR$eq[[2]])$r.squared summary(fitSUR$eq[[3]])$r.squared summary(fitSUR$eq[[4]])$r.squared But isn't there a

I am confused by the following description in http://www.maths.lth.se/help/R/.R/library/systemfit/html/hausman.systemfit.html what does the "Not run" mean? if we do not load systemfit, how can we run the following code? ## Not run: library( systemfit ) data( kmenta ) attach( kmenta ) ... I install the package of systemfit, and run the code. I got the warning: > library( systemfit

The 'systemfit' function in systemfit 0.7-3 CRAN package seems to have a problem with formulas that contain transformed (eg. log) variables. If I have my data in a data frame, apparently systemfit doesn't "pass" the information of where the variables should be taken to the transforming function. I'm not entirely sure if this is a bug or just a limitation, I was just

I am having problem using output of lm() function for further analysing using systemfit package. Basicaly, the problem s following - I generate several formulas using lm() > fo1 <- lm(r98[,2] ~ f98[,1] + f98[,2] + ... + f98[,43]) > fo2 <- lm(r98[,1] ~ f98[,1] + f98[,2] + ... + f98[,43]) and than I want to estimate a general model using package systemfit. > fitsur <-

I've been working on a new package and I have a few questions regarding the behaviour of the nlm function. I've been (for better or worse) using the nlm function to fit a linear model without suppling the hessian or gradient attributes in the objective function. I'm curious as to why the nlm requires 31 iterations (for the linear model), and then it doesn't work when I try to add

Hi List I want to carry out structural mode. Following Example l have taken from Basic Econometrics- Damodar Gujarati : Advertising intensity function: Ad/S = a0 + a1M + a2 (CD/S) + a3C + a4C2 + a5Gr + a6Dur – (1) Concentration function: C = b0 + b1 (Ad/S) + b2 (MES/S) -(2) Price-cost margin function: M = c0 + c1(K/S) + c2Gr + c3C + c4GD + c5(Ad/S) + c6 (MES/S)

dear experts, i am a novice and have been trying to use the anderson-darling test on a simple text file with one column of data. i have followed the example in the manual to read from a file into a vector(mm). i am able to see the summary stats with >summary(mm) however, when i try to use the ad.test package, it keeps coming up with the following error messages, > ad.test(mm) Error in

I have a large number of troff help files which are automatically converted to .Rd files. Many of these have examples which contain the printed output of the examples. This makes example(functionname) not work. Is there a way to avoid removing all this output by delimiting it in some way such that the output will not appear in R-ex? Secondly, it would be nice if there was a place where output

The Identifiers section in the LLVM language manual states: "The "\01" prefix can be used on global variables to suppress mangling." Is this for global variables only, or global values in general, such as functions also? In implementation LLVM seems to have this behavior of suppressing mangling even for functions and aliases. Thanks, Gautam

I've been trying to figure out how to get the names of the parameter vector variables when inside the function that nlm calls to return the objective function value: knls <- function( theta, eqns, data, fitmethod="OLS", instr=NULL, S=NULL ) { ## print( names( theta ) ) # returns NULL ## get the values of the parameters for( i in 1:length( theta ) )

Dear R programming folks: I'm trying to finish off a package for non-linear simultaneous system estimation and I've been trying to figure out how to get the names of the parameter vector variables when inside the function that nlm calls to return the objective function value: knls <- function( theta, eqns, data, fitmethod="OLS", instr=NULL, S=NULL ) { ## print(

A friend of mine sent me below so I am posting below. If it is not enough information, please just let me know and I will tell him. Thanks. -----Original Message----- Sent: Friday, April 13, 2007 3:58 PM To: Leeds, Mark (IED) Subject: R question I am using the "predict" function after I have done a simultaneous estimation of a system using "systemfit". fitsur is the output

Dear all, For estimating Cobb-Douglad production Function [ Y = ALPHA * (L^(BETA1)) * (K^(BETA2)) ], i want to use nls function (without linearizing it). But how can i get initial values? ------------------------------------ > options(prompt=" R> " ) R> Y <- c(59.6, 63.9, 73.5, 75.6, 77.3, 82.8, 83.6, 84.9, 90.3, 80.5, 73.5, 60.3, 58.2, 64.4, 75.4, 85, 92.7, 85.4,

Hello. I am still a newbie in R. Excuse me if I am asking something obvious. My efforts to get an answer through browsing the mailing archives failed. I want to perform an augmented Dickey-Fuller test and to obtain AIC and BIC and to be able to impose some linear restrictions on the ADF regression so as to decide the correct order of autoregression. However I could find no obvious way to impose