Description Usage Arguments Details Value References See Also Examples
Given a matrix of covariates, this function estimates the underlying factors and computes data residuals after regressing out those factors.
1 2 
X 
an n x p data matrix with each row being a sample. 
K.factors 
a optional number of factors to be estimated. Otherwise estimated internally. K>0. 
robust 
a boolean, specifying whether or not to use robust estimators for mean and variance. Default is TRUE. 
cv 
a boolean, specifying whether or not to run crossvalidation for the tuning parameter. Default is FALSE. Only used if 
tau 

verbose 
a boolean specifying whether to print runtime updates to the console. Default is TRUE. 
For details about the method, see Fan et al.(2017).
Using robust = TRUE
uses the Huber's loss to estimate parameters robustly. For details of covariance estimation method see Fan et al.(2017).
Number of rows and columns of the data matrix must be at least 4 in order to be able to calculate latent factors.
Number of latent factors, if not provided, is estimated by the eignevalue ratio test. See Ahn and Horenstein(2013). The maximum number is taken to be min(n,p)/2. User can supply a larger number is desired.
The tuning parameter = tau * sigma * optimal rate
where optimal rate
is the optimal rate for the tuning parameter. For details, see Fan et al.(2017). sigma
is the standard deviation of the data.
A list with the following items
residual 
the data after being adjusted for underlying factors 
loadings 
estimated factor loadings 
factors 
estimated factors 
nfactors 
the number of (estimated) factors 
Ahn, S. C., and A. R. Horenstein (2013): "Eigenvalue Ratio Test for the Number of Factors," Econometrica, 81 (3), 1203<e2><80><93>1227.
Fan J., Ke Y., Wang K., "Decorrelation of Covariates for High Dimensional Sparse Regression." https://arxiv.org/abs/1612.08490
1 2 3 4 5 6 7 8 9 10 11 12 13 14  set.seed(100)
P = 200 #dimension
N = 50 #samples
K = 3 #nfactors
Q = 3 #model size
Lambda = matrix(rnorm(P*K, 0,1), P,K)
F = matrix(rnorm(N*K, 0,1), N,K)
U = matrix(rnorm(P*N, 0,1), P,N)
X = Lambda%*%t(F)+U
X = t(X)
output = farm.res(X) #default options
output$nfactors
output = farm.res(X, K.factors = 10) #inputting factors
names(output) #list of output

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