OptDim(Obj, criteria = c("PC1", "PC2", "PC3", "BIC3", "IC1", "IC2", "IC3", "IPC1","IPC2", "IPC3", "ABC.IC1", "ABC.IC2", "KSS.C", "ED", "ER", "GR"), standardize = FALSE, d.max, sig2.hat, spar, level = 0.01, c.grid = seq(0, 5, length.out = 128), T.seq, n.seq)TRUE the input variable will be standardized. Default is FALSE.d.max=NULL) yields to an internal selection of
d.max.d.max. The default (sig2.hat=NULL) yields to an internal estimation.NULL, which leads to internal computation."ABC.IC1" and "ABC.IC2". It specifies the grid interval in which the scaling parameter of the penalty terms in "ABC.IC1" and "ABC.IC2" are calibrated. Default is c.grid =seq(0, 5, length.out = 128)."ABC.IC1" and "ABC.IC2". It can be a vector containing different dimensions for T or an integer indicating the length of the sequence to be considered in calibrating "ABC.IC1" and "ABC.IC2". If it is left unspecified, the function determines internally a sequence of the form seq((T-C), T), where C is the square root of min{T,900}. "ABC.IC1" and "ABC.IC2". It can be a vector containing different dimensions for n or an integer indicating the length of the sequence to be considered in calibrating "ABC.IC1" and "ABC.IC2". If it is left unspecified, the function determines internally a sequence of the form seq((n-D), n), where D is the square root of min{n,900}.criteria a table is returned with the optimal dimension, the empirical standard deviation of the residuals, and some other informations required internally by the criterion, such as d.max and/ or sig2.hat.criteria a table is returned with the optimal dimension, the empirical standard deviation of the residuals, and some other informations required internally by the criterion, such as d.max and/ or sig2.hat.criteria a table is returned with the optimal dimension, the empirical standard deviation of the residuals, and some other informations required internally by the criterion, such as d.max and/ or sig2.hat.criteria a table is returned with the optimal dimension, the empirical standard deviation of the residuals, and some other informations required internally by the criterion, such as d.max.criteria a table is returned with the optimal dimension, the empirical standard deviation of the residuals, and some other informations required internally by the criterion, such as d.max.criteria a table is returned with the optimal dimension, the empirical standard deviation of the residuals, and some other informations required internally by the criterion, such as d.max.criteria a table is returned with the optimal dimension, the empirical standard deviation of the residuals, and some other informations required internally by the criterion, such as d.max and/ or sig2.hat.criteria a table is returned with the optimal dimension, the empirical standard deviation of the residuals, and some other informations required internally by the criterion, such as d.max and/ or sig2.hat.criteria a table is returned with the optimal dimension, the empirical standard deviation of the residuals, and some other informations required internally by the criterion, such as d.max and/ or sig2.hat.criteria a table is returned with the optimal dimension, the empirical standard deviation of the residuals, and some other informations required internally by the criterion, such as d.max and/ or sig2.hat.criteria a table is returned with the optimal dimension, the empirical standard deviation of the residuals, and some other informations required internally by the criterion, such as d.max.criteria a table is returned with the optimal dimension, the empirical standard deviation of the residuals, and some other informations required internally by the criterion, such as d.max.criteria a table is returned with the optimal dimension, the empirical standard deviation of the residuals, and some other informations required internally by the criterion, such as d.max.Obj' given to the function OptDim().## See the example in 'help(Cigar)' in order to take a look at the
## data set 'Cigar'
##########
## DATA ##
##########
data(Cigar)
N <- 46
T <- 30
## Data: Cigarette-Sales per Capita
l.Consumption <- log(matrix(Cigar$sales, T,N))
## Calculation is based on the covariance matrix of l.Consumption
OptDim(l.Consumption)
## Calculation is based on the correlation matrix of l.Consumption
OptDim(l.Consumption, standardize = TRUE)
## Display the magnitude of the eigenvalues in percentage of the total variance
plot(OptDim(l.Consumption))
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