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...installing inst files installing data files preparing package fda for lazy loading Loading required package: splines Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from package:base : as.Date.numeric Error in basisfd(type = type, rangeval = rangeval, nbasis = nbasis, params = params, : could not find function "use.proper.basis" Error: unable to load R code in package 'fda' Execution halted make[2]: *** [lazyload] Error 1 make[1]: *** [all] Error 2 make: *** [pkg-fda] Error 2 *** Installation of fda failed ***...

.... Can you suggest corrections? library(fda) domain<-matrix(c(1,1.1,1.2,2,2.1,2.1,3,3.1,3.2,4,4.1,4.2,5,5.1,5.2), nrow = 5, ncol=3, byrow=TRUE) range<-sin(domain)+matrix(rnorm(15,sd=0.1),nrow=5, ncol=3) myfd<-Data2fd(argvals=domain, y=range) #Error in create.bspline.basis(argvals) : # rangeval is not a vector; class(rangeval) = matrix mybasis<-create.bspline.basis(rangeval=c(0.5,5.2), nbasis=6) myfd<-Data2fd(argvals=domain, y=range,basisobj=mybasis) #Error in smooth.basis(argvals, y, fdP, wtvec = w, fdnames = fdnames) : # 'y' is not the same length as 'argvals'...

...VAR give me the forecasted value for all 23hours? when i put phat=23, but whenever i put phat=24 i.e want to predict each 24 hours its give the results in the form of NA. the code is given below fdata<- function(mat){? nb = 27 # number of basis functions for the data? fbf = create.fourier.basis(rangeval=c(0,1), nbasis=nb) # basis for data? args=seq(0,1,length=24)? fdata1=Data2fd(args,y=t(mat),fbf) # functions generated from discretized y? return(fdata1)}prediction.ffpe = function(fdata1){? n = ncol(fdata1$coef)? D = nrow(fdata1$coef)? #center the data? #mu = mean.fd(fdata1)? data = center.fd(fda...

...give me theforecasted value for all 23hours? when i put phat=23, but whenever i putphat=24 i.e want to predict each 24 hours its give the results in the form ofNA. the code is given below ? fdata<- function(mat){ ? nb = 27 # number of basis functions for the data ? fbf = create.fourier.basis(rangeval=c(0,1), nbasis=nb) #basis for data ? args=seq(0,1,length=24) ? fdata1=Data2fd(args,y=t(mat),fbf) # functions generatedfrom discretized y ? return(fdata1) } prediction.ffpe = function(fdata1){ ? n = ncol(fdata1$coef) ? D = nrow(fdata1$coef) ? #center the data ? #mu = mean.fd(fdata1) ? da...

...al data(2) the functional principle components and the corresponding scores(3) I use functional final prediction error for the selection of the parameters on the VAR model(4) Using VAR for the analysis and forecasting .(1)?nb = 23 # number of basis functions for the data? fbf = create.fourier.basis(rangeval=c(0,1), nbasis=nb) # basis for data? args=seq(0,1,length=24)? fdata1=Data2fd(args,y=t(mat),fbf) # functions generated from discretized y(2)?ffpe = fFPE(fdata1, Pmax=10)? d.hat = ffpe[1] #order of the model? p.hat = ffpe[2] #lag of the model (3) n = ncol(fdata1$coef)? D = nrow(fdata1$coef)? #cente...

...al data(2) the functional principle components and the corresponding scores(3) I use functional final prediction error for the selection of the parameters on the VAR model(4) Using VAR for the analysis and forecasting .(1)?nb = 23 # number of basis functions for the data? fbf = create.fourier.basis(rangeval=c(0,1), nbasis=nb) # basis for data? args=seq(0,1,length=24)? fdata1=Data2fd(args,y=t(mat),fbf) # functions generated from discretized y(2)?ffpe = fFPE(fdata1, Pmax=10)? d.hat = ffpe[1] #order of the model? p.hat = ffpe[2] #lag of the model (3) n = ncol(fdata1$coef)? D = nrow(fdata1$coef)? #cente...