search for: coefficent

Hello all, Does anyone know how to constrain/force specific coefficients when running lm()? I need to run recresid() {strucchange package} on the residuals of forecast.lm, but forecast.lm's coefficients must be determined by parameter.estimation.lm I could estimate forecast.lm without lm() and use some other kind of optimisation, but recresid() requires an object with class lm.

...nd for my training set. So basically what I would like is to fit an ARMA-GARCH model but with predetermined coefficients. If you have any ideas please share them with me...I will be very grateful. Thanks, G -- View this message in context: http://r.789695.n4.nabble.com/fGarch-garchFit-with-fixed-coefficents-tp2289304p2289304.html Sent from the R help mailing list archive at Nabble.com.

...variables? I start out with 2 tables: Table1 123123 124351 ... 626773 Table2 Count,IS_DEAD,IS_BURNING 1231,T,F 4521,F,T ... 3321,T,T Everything looks fine when I import the data. then we get a oh_crap <- lm(table1 ~ Count + IS_DEAD + IS_BURNING, table2) Magically when I look at my oh_crap coefficents they get turned into Count, IS_DEADTRUE, IS_BURNINGTRUE I get it that it finds them to be factors by how in the name of all that is holy do I prevent them from doing that crap since later after a stepwise removal I go into one of the models grab what coefficents were kept (IS_BURNINGTTRUE now) a...

Hi all, I am trying to teach myself to use and program R (How else do you do it? lol) Anyway, I wrote a piece of code to compute coefficent alpha for a scale. As I am neither a statistician nor a programmer, I wanted people's feedback. The code appears to work (Win 95 with R 1.0.1) and I have verified my result with SPSS and it was correct (much to my astonishment!). Nonetheless, I am sure there are better ways to do things than...

Why are my coefficients getting appended with a 1? It borks a match I do later against the original list that doesn't have the random 1 added to the end. > linearModel[[1]] Call: lm(formula = modelSource ~ +UNITBUILD + UNITDB + ITBUILD + ITDB + UATBUILD + UATDB + HOGANCODE + RCF + ReleaseST1 + ReleaseST2 + ReleaseBLA + Small.Bank.Acquisitions + HLY.NewYear + HLY.MLK + HLY.PRES +

I have formulae with coefficents that I would like to update. However, I get some strange results. For example, see the following: For the formula y ~ d+ 3*r+t I want to add a variable p, so > update(y~d+0*r+t, .~.+p) produces y ~ d + t + p - 1 If the coefficient is not 0, but rather, something else - say, 3, I get the...

Thanks to the help of many on this list, I am now an R user and have been able to write some functioning code to do matching estimation. I have two for loops (i in 1:3, and j in 0:2). Within the loops, I had been creating matrices of relevant estimation coefficents in order to make lots of LaTeX tables. Well, now I want to be able to combine the results of many different estimations from within the loops into one larger table outside the loops. How can I save the estimation results from each iteration of the estimation within a loop for later use outside t...

...dratic regressions are significantly different. I was advised to make the test using step 1 bootstrapping both quadratic regressions and get their slope coefficients. (Let's call the slope coefficient *â*^1 and *â*^2) step 2 use the slope difference *â*^1-*â*^2 and bootstrap the slope coefficent step 3 find out the sampling distribution above and calculate the % =0 step 4 multiple the % by 2 However, I am new to the package boot. I wrote a code for step 1 and got a error message. Please kindly advise how to modify it and thank you. Elaine code require(boot) function.fit2 <- fun...

Hi I have the problem with fitting curve to data with lm and glm. When I use polynominal dependiency, fitted values from model are OK, but I cannot recive proper values when I use coefficents to caltulate this. Let me present simple example: I have simple data.frame: (dd) a: 1 2 3 4 5 6 b: 3 5 6 7 9 10 I try to fit it to model: model=glm(b~poly(a,3),data=dd) I have following data fitted to model (as I expected) > fitted(model) 1 2 3 4...

...spline(foo.curve) fits4foo <- predict(spline.model)$y spline4foo <- spline(1:500, fits4foo) lines(spline4foo$x, spline4foo$y, col = "red", lwd = 2, lty = "dashed") # I originally generated the data I needed by using a nls model and # adding some noise to the mean of the coefficents from those fits. # However, I've been told to try and do this using splines for arcane reasons. # So, if you had 20 splines like spline.model above and wanted to generate some similar data what would you do? # Thanks in advance. Promote security and make money with the Hushmail Affil...

Hi, I would like to compute the inverse FFT of the MDF coefficients. I have noticed that some coefficients are obviously missing since I do not see the mirror effect, neither find the purely real frequency 0 coefficent that have to be found for a "real" signal. I guess the frequency 0 coefficient is 0 (average energy 0), but how should I do the mirroring and how many coefficients should I expect ? Thanks, David _________________________________________________________________ Votre correspondant a c...

...50. my dataframe is like this: Settimana ALIMENTI..ALTRI. ALIMENTI.APROTEICI 1 1 3 19 2 2 2 0 3 3 1 22 4 4 2 6 I computed correlation coefficents among categories having a correlation matrix (53X53). Now I will extract from this matrix only significative correlations, or, in alternative correlations >0.5 and <-0.5, excluding the other, and put this coefficients in a dataframe. I'm looking for significative correlations among cat...

Hi, Given a data frame: df1: application id (appid), project id (pid), person months (pm), function points (fp) How do I produce linear modelling results at the appid level. That is, I would like to find the coefficent and intercept for the formula "pm ~ fp" for each application. Thanks

...t = etastart, ... 2: fitted probabilities numerically 0 or 1 occurred in: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart, ... ########################################################### For those cases for which I got the warning messages, shouldn't I rely on coefficents ? It looks like I can still extract coefficients from R outputs Are there any ways to avoid these warning messages ? or are these due to the problems with my data (e.g., perfect separation) Any help or advice would be appreciated Thank you TM

...n on the following problem. :( Suppose I have a dataframe with two predictor variables (x1,x2) and one depend binary variable (y). How is it possible to define a glm object (family="binomial") with a user defined logistic function like p(y) = exp(a + c1*x1 + c2*x2) where c1,c2 are the coefficents which I define. So I would like to do no fitting of the coefficients. Still, I would like to define a GLM object because I could then easily use other functions which need a glm object as argument (e.g. I could use the anova, summary functions). Thank you very much! Greetings J?rgen -- ----...

Dear R-user: After the fitting the Tobit model using zelig, if I use the following command then I can get the regression coefficents: beta=coefficients(il6.out) > beta (Intercept) apache 4.7826 0.9655 How may I extract the "Naive SE" from the following output please? > summary(il6w.out) Call: zelig(formula = il6.data$il6 ~ il6.data$apache, model = "tobit", data = il6.data, robu...

...UM.1 + S1Q6_NUM.1 + S1Q10_NUM.1 + S1Q12_BURG.1 + S1Q12_CD.1 + S1Q4.1 + S1Q12_OTHVIOL.1 + S1Q8.1 + S1Q12_GBH.1 + S1Q11.1 + S1Q7.1 + S1Q12_THEFT.1 + S1Q12_DRIV.1 + S1Q5.1 + S1Q9.1 + S1Q12_DRUG.1, family = binomial, data = moddata) But when I run step() on the resulting model, some of the coefficents are doubled when it comes back, with a "2" at the end, e.g. like this: mymodel = step(sectionmodel, direction="backward", test="F") summary(mymodel) returns this: Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -4.58519 0....

Hello R users I like to extract z values for x1 and x2. I know how to extract coefficents using model$coef but I don't know how to extract z values for each of independent variable. I looked around using names(model) but I couldn't find how to extract z values. Any help would be appreciated. Thanks TM ######################################################### >summary(m...

...t to check out the LDheatmap() package which can generate the plots you describe. The help indicates that it accepts a matrix of pair-wise linkage disequilibrium measures, one of which is R^2 (the correlation coefficient between loci), but I suspect you could simply pass it a matrix of correlation coefficents. Hope that helps, Neil -- "In mathematics you don't understand things. You just get used to them." - Johann von Neumann Email - nshephard at gmail.com / n.shephard at sheffield.ac.uk Website - http://slack.ser.man.ac.uk/ Photos - http://www.flickr.com/photos/slackline/

...re any package that assists in saving and reconstituting glm and nls fits without bringing along the accompanying data? A quick search on CRAN didn't turn up anything. If not, how do other people deal with saving the coefficients of model fits? For example, I've run a glm fit that has 23 coefficents on data set that had 193,008 rows, by the time the fit was called. When I save the resulting fit object, I get a 491 MB object, which suggests that it's pulling along all sorts of junk in the environment, as 23*193k*8 is only 34 MB. Even so, I would prefer to only save the coefficients and t...