search for: newdataset

...:nrow(dataset) testindex <- sample(index, trunc(length(index)*30/100)) trainset <- dataset[-testindex,] testset <- dataset[testindex,-1] So I have the index information, how could I re-combine the data using that back into a single df? I tried what I thought might work, but failed with: newdataset[testindex] = testset[testindex] object 'dataset' not found newdataset[-testindex] = trainset[-testindex] object 'dataset' not found Brian

...nding for global variable 'top' acfMod : onOK: no visible binding for global variable 'top' acfMod: no visible binding for global variable 'buttonsFrame' aggConv: no visible binding for global variable 'top' aggConv : onOK: no visible binding for global variable 'newDataSet' aggConv : onOK: no visible global function definition for 'newDataSet' aggConv : onOK: no visible binding for global variable 'alternativeVariable' aggConv : onOK: no visible binding for global variable 'alternativeaVariable' aggConv : onOK: no visible binding for g...

...ow splits a dataset by "target_species" and then further splits each target species group by year. Finally the sum of a variable called "total_hook_num" is calculated and then assigned a name based on (paste(a$target_species[1],a$year[1],sep=""). newsplit<-split(newdataset,list(newdataset$target_species)) for(i in newsplit){ by.year<-split(i,list(i$year)) for(a in by.year){ assign(paste(a$target_species[1],a$year[1],sep=""),sum(a$total_hook_num)) } } The end result is a single value for the number of hooks in a particular year for a particular species...

...n R code ... OK * checking S3 generic/method consistency ... OK * checking replacement functions ... OK * checking foreign function calls ... OK * checking R code for possible problems ... NOTE xbara: no visible binding for global variable ?top? xbara : onOK: no visible binding for global variable ?newDataSet? xbara : onOK: no visible global function definition for ?newDataSet? xbara: no visible binding for global variable ?buttonsFrame? * checking Rd files ... OK * checking Rd cross-references ... OK * checking for missing documentation entries ... OK * checking for code/documentation mismatches ... OK...

Hi, I've written a script that checks all bivariate correlations for variables in a matrix. I'm now trying to run a logistic regression on each pair (x,y) where y is a factor with 2 levels. I don't know how (or whether I want) to try to fathom what's up with glm. What I wrote is attached. Here's what I get. *****************************************************

...has about 4,000 values. Dataset1 grpnum x1 x2 A 343 334 B 313 898 C 4076 4077 D 275 276 Dataset2 unique num uniqueid 343 GHU82RK02HD7D6 4076 GHU82RK02IXPC7 4077 GHU82RK02HICZ2 261 GHU82RK02FLOFU 4100 GHU82RK02IDO7K 4101 GHU82RK02HQYQL 4102 GHU82RK02GADV1 4103 GHU82RK02G7RQH 4104 GHU82RK02HQD2E NewDataset grpnum x1 x2 a GHU82RK02HD7D6 Na b Na Na c GHU82RK02IXPC7 GHU82RK02HICZ2 d Na Na Thanks for your time and thoughts. I hope someone can help! -- View this message in context: http://r.789695.n4.nabble.com/Replacing-variables-in-one-dataset-with-those-from-another-tp3566552p3566552.html Sent...

...l is built from all variables that are solely significant in the cox PH model. Well, the stepAIC command works for this particular dataset but it crashes when I bootstrap and I come up with a different model set. I get another dataset in the following way: samp = sample(1:117, 117, replace =T) newdataset = dataset[samp,] The error I get is Error in fitter(X, Y, strats, offset, init, control, weights = weights, : NA/NaN/Inf in foreign function call (arg 6) The dataset that is inputted into the model is itself rid from all NA values. The initial model put into the stepAIC fun...

Dear List, I have two questions about how to do predictions using lrm, specifically how to predict the ordinal response for each observation *individually*. I'm very new to cumulative odds models, so my apologies if my questions are too basic. I have a dataset with 4000 observations. Each observation consists of an ordinal outcome y (i.e., rating of a stimulus with four possible

Hello to all, I recently downloaded R to my PC and am enjoying getting acquainted with it. Thank you to everyone involved in the R-project! I am interested in doing a log-linear analysis with R on a data set with dichotomous variables. There are 11 variables (columns) and around 1000 subjects (rows). How do I aggregate my data, i.e. how do I make a new dataset that includes the variable giving