search for: fpca

...unctional time series using the multivariate time series data(hourly time series data). Sir? i am using FAR model more than one order for which no statistical package is available in R, so for this i convert my data into functional form and obtained the functional principle component and from those FPCA i extract their corresponding? FPCscores. Know i use the VAR model on those FPCscores for the forecasting of each 24 hours through the VAR model, but the 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 r...

...he functional time series using themultivariate time series data(hourly time series data). Sir? i am usingFAR model more than one order for which no statistical package is available inR, so for this i convert my data into functional form and obtained thefunctional principle component and from those FPCA i extract theircorresponding? FPCscores. Know i use the VAR model on those FPCscores forthe forecasting of each 24 hours through the VAR model, but the VAR 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 resul...

...4)? 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)? #center the data? mu = mean.fd(fdata1)? data = center.fd(fdata1)? #fPCA? fpca = pca.fd(data,nharm=D)? scores = fpca$scores[,1:d.hat](4)?# to avoid warnings from vars predict function below? ? ? colnames(scores) <- as.character(seq(1:d.hat))? ? ? VAR.pre= predict(VAR(scores, p.hat), n.ahead=1, type="const")$fcst after this I need help first how to transform...

...4)? 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)? #center the data? mu = mean.fd(fdata1)? data = center.fd(fdata1)? #fPCA? fpca = pca.fd(data,nharm=D)? scores = fpca$scores[,1:d.hat](4)?# to avoid warnings from vars predict function below? ? ? colnames(scores) <- as.character(seq(1:d.hat))? ? ? VAR.pre= predict(VAR(scores, p.hat), n.ahead=1, type="const")$fcst after this iIneed help first how i transform...

...everal methods used in psychometry (kappa, icc, cronbach, screeplot (with simulations), non linear mapping, etc.) A bug has been fixed in function wkappa (weighted kappa): in particular circumstances, the 2*2 table presented levels in an order different to what was suggested in the help file. The fpca function (PCA plot with pvalues and compatible with dependant/independant variables dichotomy) has been extended. As usual, any kind of feedback is the most welcome Bruno ------------------------------------------------------------------------ Bruno Falissard Dpartement de sant publique...

...everal methods used in psychometry (kappa, icc, cronbach, screeplot (with simulations), non linear mapping, etc.) A bug has been fixed in function wkappa (weighted kappa): in particular circumstances, the 2*2 table presented levels in an order different to what was suggested in the help file. The fpca function (PCA plot with pvalues and compatible with dependant/independant variables dichotomy) has been extended. As usual, any kind of feedback is the most welcome Bruno ------------------------------------------------------------------------ Bruno Falissard Dpartement de sant publique...