search for: newdata2

..." & !is.na(subdata$ti_all)) new_out_c = ifelse(new_out_c == 0, NA, new_out_c) new_cma_c = ifelse(new_cma_c == 0, NA, new_cma_c) return(c(newname, NA, NA, NA, new_out_c, new_cma_c, new_c_n, NA)) } }) # recombine and sort colnames(newrows2) = colnames(mydata2) newdata2 = rbind(mydata2, newrows2) newdata2 = newdata2[order(newdata2$id), ] mydata3 <- newdata2 newrows2 <- ddply(mydata3, .(id), function(subdata) { subdata_ty = subdata[!is.na(subdata$ty_all), ] if (NROW(subdata) == 1) { r = subdata[1, ] c("v_001_arm_1", NA, NA...

...nd year (for example I need to have de b0 for industry 20 and year 2000, for industry 20 and year 2001...). Then I need to calculate the fitted values and the residuals by firm so I need to keep b0, b1 and b2 in a way that I could do something like newdata1<-transform(final3,Y'=b0+b1.X+b2.Z) newdata2<-transform(newdata1,residual=Y-Y') or another way to keep Y' and the residuals in a dataframe with the columns firm and year. Until now I have been doing this in very hard way and because I need to do it several times, I need your help to get an easier way. Thank you, Cecília Ca...

...it$linear.predictors)=0. Am I missing something here? 3. Using the fitted coeficients: # Coefficients fitc<-coef(model.fit) # Predictors basis <- ns(x2, df = 3) ; # df= 3 were used to fit the model newx2<- seq(0,30,length=60) # new data in the coords of the basis and x1=1 for all obs newdata2<-cbind(rep(1,60),predict(basis, newx2)) newpred<-newdata2%*%fitc termplot(model.fit, ylim=c(0,3)) lines(newx2, newpred) This method gives predictions that appear to be proportional to the results in termplot - but all predicted values are higher. I am missing something here? Ideally, I wo...

After fitting and pruning an rpart model, it is often the case that one or more of the original predictors is not used by any of the splits of the final tree. It seems logical, therefore, that values for these "unused" predictors would not be needed for prediction. But when predict() is called on such models, all predictors seem to be required. Why is that, and can it be easily

...i.e. column three # Find the location of the column ColumToDelete <- grep("th",colnames((mydata))) cat("The colomumn to be dropped is the column called three, which is column",ColumToDelete,"\n") ColumToDelete # Drop the column whose name starts with "th" newdata2 <- mydata[,-ColumnToDelete] cat("Data frame after droping column whose name is three\n") newdata2 I hope this helps. John ________________________________________ From: R-help <r-help-bounces at r-project.org> on behalf of Valentin Petzel <valentin at petzel.at> Sent: Sa...

Dear R Wizards, After much frustration and days of confusion I have finally broken down and am asking for help, which I don’t like doing, but I just can’t figure this one out on my own. I’ve conducted a laboratory experiment testing the effects of temperature and salinity on whether or not a biological event will occur (Go or NoGo). I’ve coded the factors temperature and salinity as factors for

...14 9 2002 3 87 10 2003 3 56 Second, I need to create a new variable that is the lagged value of x. I''ve done newdata1<-transform(data1,y=lag(x,1)) But it doesn''t work. I also need to create a new variable that is the opposite of lag(). I''ve done newdata2<-transform(data1,z=lag(x,-1)) But, of course, it doesn''t work neither. Thank you for all your help. Cecília Carmo (Universidade de Aveiro – Portugal) [[alternative HTML version deleted]]

Hello Avi, while something like d$something <- ... may seem like you're directly modifying the data it does not actually do so. Most R objects try to be immutable, that is, the object may not change after creation. This guarantees that if you have a binding for same object the object won't change sneakily. There is a data structure that is in fact mutable which are environments. For

Hi, I am trying to plot two data sets on one plot but with using a different y-axis ranges for each - preferably with one shown on each side of the graph. Is there a function that will allow me to do this. Thanks Allan McRae [[alternate HTML version deleted]]

Dear R users I have the matrix of the centres of some clusters, e.g. 20 clusters each with 100 dimentions, so this matrix contains 20 rows * 100 columns numeric values. I have collected new data (each with 100 numeric values) and would like to keep the above 20 centres fixed/'unmoved' whilst just see how my new data fit in this grouping system, e.g. if the data is close to cluster 1