get_data {MOFA2} | R Documentation |
Fetch the input data
get_data( object, views = "all", groups = "all", features = "all", as.data.frame = FALSE, add_intercept = TRUE, denoise = FALSE, na.rm = TRUE )
object |
a |
views |
character vector with the view name(s), or numeric vector with the view index(es). Default is "all". |
groups |
character vector with the group name(s), or numeric vector with the group index(es). Default is "all". |
features |
a *named* list of character vectors. Example: list("view1"=c("feature_1","feature_2"), "view2"=c("feature_3","feature_4")) Default is "all". |
as.data.frame |
logical indicating whether to return a long data frame instead of a list of matrices. Default is |
add_intercept |
logical indicating whether to add feature intercepts to the data. Default is |
denoise |
logical indicating whether to return the denoised data (i.e. the model predictions). Default is |
na.rm |
remove NAs from the data.frame (only if as.data.frame is |
By default this function returns a list where each element is a data matrix with dimensionality (D,N)
where D is the number of features and N is the number of samples.
Alternatively, if as.data.frame
is TRUE
, the function returns a long-formatted data frame with columns (view,feature,sample,value).
Missing values are not included in the the long data.frame format by default. To include them use the argument na.rm=FALSE
.
A list of data matrices with dimensionality (D,N) or a data.frame
(if as.data.frame
is TRUE)
# Using an existing trained model on simulated data file <- system.file("extdata", "model.hdf5", package = "MOFA2") model <- load_model(file) # Fetch data data <- get_data(model) # Fetch a specific view data <- get_data(model, views = "view_0") # Fetch data in data.frame format instead of matrix format data <- get_data(model, as.data.frame = TRUE) # Fetch centered data (do not add the feature intercepts) data <- get_data(model, as.data.frame = FALSE) # Fetch denoised data (do not add the feature intercepts) data <- get_data(model, denoise = TRUE)