Select the parameters in COAP models

Select the number of factors and the rank of coefficient matrix in the covariate-augmented overdispersed Poisson factor model

chooseParams(
  X_count,
  Adj_sp,
  H,
  Z = matrix(1, nrow(X_count), 1),
  offset = rep(0, nrow(X_count)),
  q_max = 15,
  r_max = 24,
  threshold = c(0.1, 0.01),
  verbose = TRUE,
  ...
)

Arguments

X_count

a count matrix, the observed count matrix with shape n-by-p.

Adj_sp

a sparse matrix, the weighted adjacency matrix;

H

a n-by-d matrix, the covariate matrix with low-rank regression coefficient matrix;

Z

an optional matrix, the fixed-dimensional covariate matrix with control variables; default as a full-one column vector if there is no additional covariates.

offset

an optional vector, the offset for each unit; default as full-zero vector.

q_max

an optional string, specify the upper bound for the number of factors; default as 15.

r_max

an optional integer, specify the upper bound for the rank of the regression coefficient matrix; default as 24.

threshold

an optional 2-dimensional positive vector, specify the the thresholds that filters the singular values of beta and B, respectively.

verbose

a logical value, whether output the information in iteration.

...,

other arguments passed to the function SpaCOAP.

Value

return a named vector with names `hr` and `hq`, the estimated rank and number of factors.

Details

The threshold is to filter the singular values with low signal, to assist the identification of underlying model structure.

References

None

See also

SpaCOAP

Examples

width <- 20; height <- 15; p <- 300
d <- 20; k <- 3; q <- 6; r <- 3
datlist <- gendata_spacoap(width=width, height=height, p=p, d=d, k=k, q=q, rank0=r)
#> Loading required package: MASS
#> Loading required package: Matrix
set.seed(1)
para_vec <- chooseParams(X_count=datlist$X, Adj_sp=datlist$Adj_sp, H= datlist$H, Z = datlist$Z, r_max=6)
#> Calculate initial values...
#> Loading required package: irlba
#> Model fitting...
#> iter = 2, ELBO= 11862281583.710794, dELBO=6.523805 
#> iter = 3, ELBO= 11862294363.620056, dELBO=0.000001 
#> iter = 4, ELBO= 11862300321.562973, dELBO=0.000001 
#> iter = 5, ELBO= 11862303727.445311, dELBO=0.000000 
#> iter = 6, ELBO= 11862306064.884951, dELBO=0.000000 
#> iter = 7, ELBO= 11862307506.121174, dELBO=0.000000 
#> iter = 8, ELBO= 11862308363.670418, dELBO=0.000000 
#> iter = 9, ELBO= 11862309356.043238, dELBO=0.000000 
#> iter = 10, ELBO= 11862309518.625141, dELBO=0.000000 
#> iter = 11, ELBO= 11862310038.486944, dELBO=0.000000 
#> iter = 12, ELBO= 11862310411.646021, dELBO=0.000000 
#> iter = 13, ELBO= 11862310032.750786, dELBO=0.000000 
#> iter = 14, ELBO= 11862310199.577761, dELBO=0.000000 
#> iter = 15, ELBO= 11862310301.618076, dELBO=0.000000 
print(para_vec)
#> hr hq 
#>  2  6