| Type: | Package |
| Title: | High-Dimensional Spatial Covariate-Augmented Overdispersed Poisson Factor Model |
| Version: | 1.3 |
| Date: | 2025-03-27 |
| Author: | Wei Liu [aut, cre], Qingzhi Zhong [aut] |
| Maintainer: | Wei Liu <liuwei8@scu.edu.cn> |
| Description: | A spatial covariate-augmented overdispersed Poisson factor model is proposed to perform efficient latent representation learning method for high-dimensional large-scale spatial count data with additional covariates. |
| License: | GPL-3 |
| URL: | https://github.com/feiyoung/SpaCOAP |
| BugReports: | https://github.com/feiyoung/SpaCOAP/issues |
| Imports: | LaplacesDemon, stats, methods, Matrix, MASS,Rcpp (≥ 1.0.10) |
| Depends: | irlba, R (≥ 3.5.0) |
| Suggests: | knitr, rmarkdown |
| LinkingTo: | Rcpp, RcppArmadillo |
| VignetteBuilder: | knitr |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.2 |
| NeedsCompilation: | yes |
| Packaged: | 2025-03-27 13:55:40 UTC; 10297 |
| Repository: | CRAN |
| Date/Publication: | 2025-03-27 14:30:01 UTC |
Fit the SpaCOAP model
Description
Fit the spatial covariate-augmented overdispersed Poisson factor model
Usage
SpaCOAP(
X_count,
Adj_sp,
H,
Z = matrix(1, nrow(X_count), 1),
offset = rep(0, nrow(X_count)),
rank_use = 5,
q = 15,
epsELBO = 1e-08,
maxIter = 30,
verbose = TRUE,
add_IC_inter = FALSE,
seed = 1,
algo = 1
)
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. |
rank_use |
an optional integer, specify the rank of the regression coefficient matrix; default as 5. |
q |
an optional string, specify the number of factors; default as 15. |
epsELBO |
an optional positive vlaue, tolerance of relative variation rate of the envidence lower bound value, defualt as '1e-8'. |
maxIter |
the maximum iteration of the VEM algorithm. The default is 30. |
verbose |
a logical value, whether output the information in iteration. |
add_IC_inter |
a logical value, add the identifiability condition in iterative algorithm or add it after algorithm converges; default as FALSE. |
seed |
an integer, set the random seed in initialization, default as 1; |
algo |
an optional integer taking value 1 0r 2, select the algorithm used, default as 1, representing variational EM algorithm. |
Details
None
Value
return a list including the following components:
-
F- the predicted factor matrix; -
B- the estimated loading matrix; -
bbeta- the estimated low-rank large coefficient matrix; -
alpha0- the estimated regression coefficient matrix corresponing to Z; -
invLambda- the inverse of the estimated variances of error; -
eta- the estimated spatial autocorrelation parameter; -
S- the approximated posterior covariance for each row of F; -
ELBO- the ELBO value when algorithm stops; -
ELBO_seq- the sequence of ELBO values. -
time_use- the running time in model fitting of SpaCOAP;
References
Liu W, Zhong Q. High-dimensional covariate-augmented overdispersed poisson factor model. Biometrics. 2024 Jun;80(2):ujae031.
See Also
None
Examples
width <- 20; height <- 15; p <- 100
d <- 20; k <- 3; q <- 6; r <- 3
datlist <- gendata_spacoap(width=width, height=height, p=p, d=20, k=k, q=q, rank0=r)
fitlist <- SpaCOAP(X_count=datlist$X, Adj_sp = datlist$Adj_sp,
H= datlist$H, Z = datlist$Z, q=6, rank_use=3)
str(fitlist)
Select the parameters in COAP models
Description
Select the number of factors and the rank of coefficient matrix in the covariate-augmented overdispersed Poisson factor model
Usage
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 |
Details
The threshold is to filter the singular values with low signal, to assist the identification of underlying model structure.
Value
return a named vector with names 'hr' and 'hq', the estimated rank and number of factors.
References
None
See Also
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)
set.seed(1)
para_vec <- chooseParams(X_count=datlist$X, Adj_sp=datlist$Adj_sp,
H= datlist$H, Z = datlist$Z, r_max=6)
print(para_vec)
Generate simulated data
Description
Generate simulated data from spaital covariate-augmented Poisson factor models
Usage
gendata_spacoap(
seed = 1,
width = 20,
height = 30,
p = 500,
d = 40,
k = 3,
q = 5,
rank0 = 3,
eta0 = 0.5,
bandwidth = 1,
rho = c(10, 1),
sigma2_eps = 1,
seed.beta = 1
)
Arguments
seed |
a postive integer, the random seed for reproducibility of data generation process. |
width |
a postive integer, specify the width of the spatial grid. |
height |
a postive integer, specify the height of the spatial grid. |
p |
a postive integer, specify the dimension of count variables. |
d |
a postive integer, specify the dimension of covariate matrix with low-rank regression coefficient matrix. |
k |
a postive integer, specify the dimension of covariate matrix as control variables. |
q |
a postive integer, specify the number of factors. |
rank0 |
a postive integer, specify the rank of the coefficient matrix. |
eta0 |
a real between 0 and 1, specify the spatial autocorrelation parameter. |
bandwidth |
a real positive value, specify the bandwidth in calculating the weighted adjacency matrix. |
rho |
a numeric vector with length 2 and positive elements, specify the signal strength of loading matrix and regression coefficient, respectively. |
sigma2_eps |
a positive real, the variance of overdispersion error. |
seed.beta |
a postive integer, the random seed for reproducibility of data generation process by fixing the regression coefficient matrix beta. |
Details
None
Value
return a list including the following components:
-
X- the high-dimensional count matrix; -
Z- the low-dimensional covariate matrix with control variables. -
H- the high-dimensional covariate matrix; -
Adj_sp- the weighted adjacence matrix; -
alpha0- the regression coefficient matrix corresponing to Z; -
bbeta0- the low-rank large regression coefficient matrix corresponing to H; -
B0- the loading matrix; -
F0- the laten factor matrix; -
rank0- the true rank of bbeta0; -
q- the true number of factors; -
eta0- spatial autocorrelation parameter; -
pos- spatial coordinates for each observation.
References
None
See Also
Examples
width <- 20; height <- 15; p <- 100
d <- 20; k <- 3; q <- 6; r <- 3
datlist <- gendata_spacoap(width=width, height=height, p=p, d=20, k=k, q=q, rank0=r)
str(datlist)