search for: nmatrix

...g newdata, which is a data.frame with all variables being "numeric", as str() shows, R tells me the following: ar1.xpred.test.pred <- predict(ar1.xpred.fitted, regdata.test, se.fit = FALSE) Fehler: variable 'lag(ret1)' was fitted with type "numeric" but type "nmatrix.1" was supplied Zus?tzlich: Warnmeldung: 'newdata' had 23 rows but variable(s) found have 89 rows Estimating a model from the test-data and examining, the resulting lm-object I found out that the "data classes" switched to "nmatrix.1" - while they were "nume...

...unique(rownames(rpartModel$splits)); When later I apply the rpart model to predict the new data I strip the input data from unneccessary columns and only use X columns that exist in colnamesUsed. Unfortunately I get error message like this Error: variable 'X' was fitted with type "nmatrix.3522" but type "nmatrix.19" was supplied The error message is correct. In the documentation it clearly specifies that the predictors referred to in the right side of formula (object) must be present by name in newdata, but I wonder why, if they are not used? Thanks DK ________...

Hi, I work with hyperspectral remote sensing data and I try to built a pls model with this data. I already built the model but if I try to calculate the RMSEP and R2 with a test data set I get the following error message: Error: variable 'subX' was fitted with type "nmatrix.501" but type "nmatrix.73" was supplied The problem is that I don't get the message for the pls models. Thank you very much for your help. /Thomas -- *^*^*^*^*^*^*^*^*^*^*^*^*^*^*^*^*^*^*^*^*^*^*^* Thomas Möckel, PhD student Department of Earth and Ecosystem Science Lund U...

...7 8 0.6228353 0.5752811 0.5319524 0.4957381 0.4695269 0.4562077 0.4586691 0.4798001 9 10 0.5224893 0.5896255 - thats the same as last time. Extrapolate? > predict(mmn[[3]],newdata=data.frame(x=c(9,10,11))) Error: variable 'poly(x, i)' was fitted with type "nmatrix.3" but type "nmatrix.5" was supplied In addition: Warning message: In Z/rep(sqrt(norm2[-1L]), each = length(x)) : longer object length is not a multiple of shorter object length it falls over. I can't see the difference between the objects, summary() looks the same. Is somethi...

...working, rather than it being a bug. Would anyone care to enlighten me? > Data1=data.frame(xx=1:10, yy=2.1:12) > Data1$xx=scale(Data1$xx) > > reg1=lm(yy~xx, data=Data1) > New=data.frame(xx=2:4) > b=predict(reg1, New, se.fit=T) Error: variable 'xx' was fitted with nmatrix.1 but numeric was supplied > > New=data.frame(xx=scale(2:4, center=5.5, scale=3.02)) > b=predict(reg1, New, se.fit=T) Error: variable 'xx' was fitted with nmatrix.1 but numeric was supplied > Bob -- Bob O'Hara Department of Mathematics and Statistics P.O. Box 68 (Gust...

...nd of the email. Here is an example of the error: > poly.fit <- polyModelSelection(x,y,10,F) > for (d in 1:4) { + lm.model <- poly.fit$models[[d]] + curve(predict(lm.model,data.frame(x)),add=T,lty=d+1) + } Error: variable 'poly(x, d, raw = T)' was fitted with type "nmatrix.1" but type "nmatrix.10" was supplied Does anyone have any ideas? Thanks, Harold ############################################################################ polyModelSelection <- function(x,y,maxd,add.dim=F) { dim.mult <- 0 if (add.dim) { dim.mult = 1...

Hallo, I'll need a matrix with n rows of the an identical vector. > h [1] 3 3 3 3 2 2 2 The nmatrix should look like this: > x<-rbind(h,h,h) > x [,1] [,2] [,3] [,4] [,5] [,6] [,7] h 3 3 3 3 2 2 2 h 3 3 3 3 2 2 2 h 3 3 3 3 2 2 2 but I need n rows which must be variable. Can anyone help me? thanks Andreas [[alternative...

...ase forgive me just sending this report without offering any patches. You find my test code below, in case you like to have a look on it. I would appreciate your comments very much. Best regards Jens Oehlschl?gel # This example shows the slowdown (where as.ref and deref are defined as below) nmatrix <- 1000 nloop <- 100 m <- matrix(nrow=nmatrix, ncol=nmatrix) rm <- as.ref(m) # the following is by order of magnitudes (factor 20 to 80) slower # under R 1.8, R 1.9 (11.45 seconds) # compared to R 1.7, 1.6, 1.5 ( 0.12 seconds) system.time( for(i in 1:nloop) dere...

...match error. > df$out = df$x+df$y+df$yy + rnorm(3) > df x y yy out 1 1 1 1 3.066348 2 2 2 2 5.516017 3 3 3 3 11.073452 > glmout = glm(out~x+y+yy,data=df) > predict(glmout,newdata=data.frame(x=1:3,y=1:3,yy=1:3)) Error: variable 'yy' was fitted with type "nmatrix.1" but type "numeric" was supplied > > predict(glmout,newdata=data.frame(x=1:3,y=1:3,yy=matrix(1:3))) Error: variable 'yy' was fitted with type "nmatrix.1" but type "numeric" was supplied > predict(glmout,newdata=df[,-4]) 1 2...

...")= int 1 .. .. ..- attr(*, "response")= int 1 .. .. ..- attr(*, ".Environment")=<environment: 0x0bd89124> .. .. ..- attr(*, "predvars")= language list(estimates, lambda, I(lambda^2)) .. .. ..- attr(*, "dataClasses")= Named chr [1:3] "nmatrix.2" "numeric" "numeric" .. .. .. ..- attr(*, "names")= chr [1:3] "estimates" "lambda" "I(lambda^2)" ..$ model :'data.frame': 5 obs. of 3 variables: .. ..$ estimates : num [1:5, 1:2] -13.27 -11.71 -10.47 -9.46 -8....

...[,2]        [,3]       [,4] [24,] -0.06046868 0.002829377 -0.01569903 0.03599957   I am pretty troubled by what I see here. So if anyone would offer me some condolence, as well as helpful advice, I am greatly grateful. Thanks a lot. Here are the code and the data.   Thanks a lot.   Wei   nTotal = nMatrix + 1 BuildMod <- function(x){  L1 = matrix(0,nFactor,nFactor)   L1[upper.tri(L1,T)] <- x[1:nMatrix]  return(dlm(   m0  = rep(0,nFactor),   C0  = diag(nFactor)*10,   FF  = matrix(1,1,nFactor),   GG  = diag(nFactor),   V   = tail(x,1)^2,   W   = crossprod(L1),   JFF = matrix(1:4,nr=1),   X   = X...

...quot;)) && exists(".checkMFClasses", envir=NULL)) .checkMFClasses(cl, m) to be backwards compatible.) The exact nature of the `classes' is tricky because of inheritance. I have implemented logical, ordered, factor (not ordered), numeric (not matrix), nmatrix.n and other: nmatrix.n is a numeric matrix of n columns (as used by poly() and bs(), for example). Let me know if you see a need for other categories. Brian -- Brian D. Ripley, ripley@stats.ox.ac.uk Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ Universit...

I am fittling a spline to a variable in a regression model, I am then using the predict.glm funtion to make some predictions. When I use bs to fit the spline I don't have any problems using the predict.glm function however when I use ns I get the following error: Error in model.frame(formula, rownames, variables, varnames, extras, extranames, : variable lengths differ (found for

...regdata, use = "lm") > ar1.xpred.train.pred <- predict(ar1.xpred.fitted, regdata, se.fit = FALSE) > ar1.xpred.test.pred <- predict(ar1.xpred.fitted, regdata.test, se.fit = FALSE) Fehler: variable 'lag(ret1)' was fitted with type "numeric" but type "nmatrix.1" was supplied Zus?tzlich: Warnmeldung: 'newdata' had 23 rows but variable(s) found have 89 rows >

...21.4 18.7 18.1 14.3 24.4 22.8 19.2 ... ## $ x : num 2.62 2.88 2.32 3.21 3.44 ... ## $ NA: num 110 110 93 110 175 105 245 62 95 123 ... This means attempts to use this data structure in predict() fail: str(predict(m2, get_all_vars(m2))) ## Error: variable 'x' was fitted with type "nmatrix.2" but type "numeric" was supplied The correct structure needs to resemble following in order for that to succeed: newdat <- data.frame(y = y) newdat$x <- x str(newdat) ## 'data.frame': 32 obs. of 2 variables: ## $ y: num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8...

Hi list, How to save a list content into a text file? Please consider example below, I have two numeric matrices that I bundle into a list & give each list element a name Example: > matrixA <- matrix(0,5,4) > matrixB <- matrix(1,7,13) > matrixList <- list(matrixA,matrixB) > names <- c("Matrix A","Matrix B") > names <-

Hello, I am working with a sparse matrix that is approx. 13,900 by 14,100. My goal is to select a row out of the matrix and create a new matrix with that row repeated 13,900 times without having to do any looping. Example: Starting Matrix: exampleMatrix 3 x 4 sparse Matrix of class "dgCMatrix" [1,] 1 . . . [2,] . 1 . 0.5 [3,] . . 1 .. New Matrix:.. newExampleMatrix 3 x 4 sparse

I have the following questions about the variance of the random effects in the survreg() function in the survival package: 1) How can I extract the variance of the random effects after fitting a model? For example: set.seed(1007) x <- runif(100) m <- rnorm(10, mean = 1, sd =2) mu <- rep(m, rep(10,10)) test1 <- data.frame(Time = qsurvreg(x, mean = mu, scale= 0.5, distribution =

I'm trying to write a formula method for canonical correlation analysis, that could be called similarly to lm() for a multivariate response: cancor(cbind(y1,y2,y3) ~ x1+x2+x3+x4, data=, ...) or perhaps more naturally, cancor(cbind(y1,y2,y3) ~ cbind(x1,x2,x3,x4), data=, ...) I've adapted the code from lm() to my case, but in this situation, it doesn't make sense to include an