The package contains several factor analysis methods, with emphasis to count data. The main methods rely on copulas to estimate the residual covariance, not explained by the latent structure. Additionally, the package contains several data sets for empirical evaluation. The methods use RStan for inference.
Download the contents of the package to a local folder. Then use
devtools::install("path", quick = TRUE)
where path is the path to the folder where you saved the contents of the package. The parameter quick = TRUE allows to install already compiled 64-bit models in the package. If you want to re-compile the models on your computer, you can install the package directly from git with
devtools::install_github("bstatcomp/gfac")
If you are installing this way, you may have trouble installing the 32-bit and 64-bit versions on Windows. In that case only install one version with
devtools::install_github("bstatcomp/gfac", INSTALL_opts = c("--no-multiarch"))
which will automatically install the arch that is used in your current RStudio session. If it still does not install, you may need to check your system Path, where the compiler for the desired version should be before the compiler for the other version.
The main functionality of the package is training and predicting with different factor analysis methods. For that, you only need to use functions starting with train_ and pred_. Additionally, there are 20 data sets that can be used for training, evaluation, and latent structure extraction.
For example, to train, predict, and evaluate the statics negative binomial factor analysis with copula on EPL data, use
data(EPL_data)
train_ind <- EPL_data$splits[ ,1] == 2
test_ind <- EPL_data$splits[ ,1] == 1
train_dat <- EPL_data$fa_data[train_ind, ]
train_grp <- EPL_data$group[train_ind]
test_dat <- EPL_data$fa_data[test_ind, ]
test_grp <- EPL_data$group[test_ind]
my_model <- train_CgNBFA(train_dat, train_grp)
my_eval <- pred_CgNBFA(my_model, test_grp, test_dat)
These methods are an extension of static factor models. The latent factors are further modeled with smooth locally periodic Gaussian processes. Below is an example of fitting gNBGPFA and gNBGPFAC to a toy data set.
data(toy_A)
train_ind <- toy_A$splits[ ,1] == 0
# gNBGPFA
trained_mod <- train_gNBGPFA_LP(X = toy_A$df[train_ind, ],
ts = toy_A$ts[train_ind],
gp = toy_A$gp[train_ind],
nfac = 3,
nchain = 1,
nit = 2000,
period_length = 2,
prior_r2 = c(4.6, 22),
prior_r3 = c(4.6, 22),
prior_phi_type = 1,
prior_phi = c(1, 0.01),
transform_t = function (t) return (t))
predicted_mod <- pred_gNBGPFA_LP(mod = trained_mod,
X_test = toy_A$df[!train_ind, ],
ts_test = toy_A$ts[!train_ind],
gp_test = toy_A$gp[!train_ind],
ts_train = toy_A$ts[train_ind],
gp_train = toy_A$gp[train_ind],
transform_t = function (t) return (t),
period_length = 2)
# gNBGPFAC
# Set initial values for gNBGPFAC
ext <- rstan::extract(trained_mod$samps)
init_list <- list( # initial values from gNBGPFA
simp = apply(ext$simp, c(2,3), mean),
fn_pri = apply(ext$fn_pri, c(2,3), mean),
base = apply(ext$base, c(2,3), mean),
phi = apply(ext$phi, 2, mean),
a2 = apply(ext$a2, c(2,3), mean),
r2 = apply(ext$r2, c(2,3), mean),
r3 = apply(ext$r3, c(2,3), mean),
L = diag(1, nrow = 8)
)
trained_mod2 <- train_gNBGPFAC_LP(X = toy_A$df[train_ind, ],
ts = toy_A$ts[train_ind],
gp = toy_A$gp[train_ind],
nfac = 3,
nchain = 1,
nit = 2000,
period_length = 2,
prior_r2 = c(4.6, 22),
prior_r3 = c(4.6, 22),
prior_phi_type = 1,
prior_phi = c(1, 0.01),
transform_t = function (t) return (t),
init_list = init_list)
predicted_mod2 <- predict_gNBGPFAC_LP(mod = trained_mod2,
X_test = toy_A$df[!train_ind, ],
ts_test = toy_A$ts[!train_ind],
gp_test = toy_A$gp[!train_ind],
ts_train = toy_A$ts[train_ind],
gp_train = toy_A$gp[train_ind],
transform_t = function (t) return (t),
period_length = 2)