similar to: lm() and interactions in model formula for x passed as matrix

Hi, You might need to check library(boot).? I have never used that before.? So, I can't comment much.? It is better to post on R-help list.? I had seen your postings on Nabble in the past.? Unfortunately those postings were not accepted in R-help.? You have to directly post at ? r-help at r-project.org after registering at: https://stat.ethz.ch/mailman/listinfo/r-help ?

Hi, probably just a quick question: can I somehow change the formula used with predict? E.g., the regression was run on "y ~ u + v + w" but for the prediction the term v should be removed from the formula contained in the regression object and only "y ~ u + w" be used. I could use model.matrix etc. to do the predictions but it would be very helpful to know a simpler way.

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

Hi all, I'm trying to model some data where the y is defined by y = summation[1 to 50] B1 * x + B2 * x^2 + B3 * x^3 Hopefully that reads clearly for email. Anyway, if it wasn't for the summation, I know I would do it like this lm(y ~ x + x2 + x3) Where x2 and x3 are x^2 and x^3. However, since each value of x is related to the previous values of x, I don't know how to do this.

Hi, I would like to set a constraint on the fixed effect estimates in a GLM model, such as b1=b2. Is this possible in the glm package? Similarly I would like to set some to equal zero too. I have tried searching the information with this package, but I can't find anything for this. Thanks in advance for any help! David -- View this message in context:

Hello, I have a simple theorical question about regresion... Let's suppose I have this: Model 1: Y = B0 + B1*X1 + B2*X2 + B3*X3 and Model 2: Y = B0 + B2*X2 + B3*X3 I.E. Model1 = lm(Y~X1+X2+X3) Model2 = lm(Y~X2+X3) The Ajusted R-Square for Model1 is 0.9 and the Ajusted R-Square for Model2 is 0.99, among many other significant improvements. And I want to do the anova test to choose the best

Hi, Using S=1000 and simdata <- replicate(S, generate(3000)) #If you want both "m1" and "m0" #here the missing values are 0 res1<-sapply(seq_len(ncol(simdata.psm1)),function(i) {x1<-merge(simdata.psm0[,i],simdata.psm1[,i],all=TRUE); x1[is.na(x1)]<-0; x1}) res1[,997:1000] #????? [,1]???????? [,2]???????? [,3]???????? [,4]??????? #x1??? Numeric,3000 Numeric,3000

Dear Mr/Mrs I am Lili Puspita Rahayu, student from magister third level of Statistics in Bogor Agriculture University. Mr/ Mrs, now I'm analyzing the Zero inflated Poisson (ZIP), which is a solution of the Poisson regression where the response variable (Y) has zero excess. ZIP now I was doing did not use real data, but using simulated data in R. Simulations by generating data on variables

Dear R users, How do I drop multiplication terms from a formula using update? e.g. forml=as.formula("Surv(time, status) ~ x1+x2+A*x3+A*x4+B*x5+strata(sex)") #I would like to drop all instances of variable A (the main effect and its interactions). The following: updated.forml=update(forml, ~ . -A) #gives me this: #Surv(time, status) ~ x1 + x2 + x3 + x4 + B + x5 + strata(sex) + A:x3 +

i need to know how to estimate a linear regression whose coefficients sum to zero

Hallo Could anybody please help. I have a simple linear regression model with 5 predictors. I want to use "bootstrap residuals" to make inferences regarding beta(2)hat. After fitting the model y=b0+b1+b2+b3+b4+b5 I tried the following: mod <- lm(y ~ x1+x2+x3+x4+x5) res <- resid(mod) pred <- predict(mod) Now, I have tried boot(res, lm(res+pred ~ x1+x2+x3+x4+x5)$coef[3],

Hi all, I have the following x1<-paste("A", 1:6, sep = "") x2<- round(rgamma(6,2,1)) x3<-paste("B", 1:6, sep = "") x4<- round(rgamma(6,2,1)) data1 <- data.frame(x1,x2,x3,x4) I would like to get data2 <- c(A1=4, A2=1, A3=0,...) Is there any standard for such a case? Thank you very much in advance, Diego

Hey List, I did a multiple regression and my final model looks as follows: model9<-lm(calP ~ nsP + I(st^2) + distPr + I(distPr^2)) Now I tried to predict the values for calP from this model using the following function: xv<-seq(0,89,by=1) yv<-predict(model9,list(distPr=xv,st=xv,nsP=xv)) The predicted values are however strange. Now I do not know weather just the model does not fit

I have a project due in my Linear Regression class re: regression on a data set & my professor gave us a hint that there were *exactly *2 sig interactions. The data set is attached. We have to find which predictors are significant, & which 2 interactions are sig. Also, I nedd some guidance for this & selecting the best model. I tried the `full' model, that being:

Hi R users, I have a data frame that contains 10K obs and 200 variables where I am trying to format the numeric columns to look like the output table below (format to 2 decimal places) but I am having no luck.. Can someone tell me the best way to accomplist this? Thanks in advance for any help! str(ad.test) 'data.frame': 10,000 obs. of 200 variables: $ ID : Factor

Hi Gabor, Thanks a lot for your help! I tried to implement your nonlinear least squares solver on my data set. I was just wondering about the argument start. If I would like to force all my coefficients to be inside an interval, let?s say, between 0 and 1, what kind of starting values are normally recommended for the start argument (e.g. Using a 4 factor model with b1, b2, b3 and b4, I tried

Dear R-experts, I have a question on the formulas used in the gam function of the mgcv package. I am trying to understand the relationships between: y~s(x1)+s(x2)+s(x3)+s(x4) and y~s(x1,x2,x3,x4) Does the latter contain the former? what about the smoothers of all interaction terms? I have (tried to) read the manual pages of gam, formula.gam, smooth.terms, linear.functional.terms but

Dear R-experts, I have a question on the formulas used in the gam function of the mgcv package. I am trying to understand the relationships between: y~s(x1)+s(x2)+s(x3)+s(x4) and y~s(x1,x2,x3,x4) Does the latter contain the former? what about the smoothers of all interaction terms? I have (tried to) read the manual pages of gam, formula.gam, smooth.terms, linear.functional.terms but

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

Good Day: When I used: multinom(formula = Y ~ X1 + X2 + X3 + X1:X2 + X1:X3 + X3:X2 + X1^2 + X2^2 + X3^2, data = DATASET), I get estimates and AIC for the model containing main effects and interactions only (no squared terms)...and FYI, all predictors are continuous. Is this "normal" behavior? If I run this in S-Plus I get estimates and AIC for the model containing all terms(including