Data Container for Normal-Block Models — NormalBlockData • normalblockr

R6 class holding the responses and design matrix used to fit a normal-block model.

Public fields

Y: the matrix of responses (rescaled column-wise if `scale = TRUE`)
Y_scale: the per-column standard deviation Y was divided by (all 1's if `scale = FALSE`)
X: the matrix of covariates
X0: the matrix of zero-inflation covariates, if applicable
formula: describes the relationship between Y and X, and X0 if applicable, useful if not all of X's or X0's covariates should be used, should be formatted ~ X1 + X2... | Z1 + Z2... with the Normal formula before the | and the ZI formula after the |
n: sample size
d: number of covariates
d0: number of zero-inflation covariates, if applicable
p: number of variables
XtX: useful for inference in some cases
XtXm1: inverse of XtX, useful for inference
XtY: useful for inference
npY: total number of non zeros in Y
nY: total number of non zeros for each column/variable in Y
zeros: where are the zero in Y
zeros_bar: where are the non-zeros in Y

Methods

`NormalBlockData$new()`

Create a new [`NormalBlockData`] object.

Usage

NormalBlockData$new(
  Y,
  X,
  X0 = NULL,
  formula = NULL,
  scale = TRUE,
  zeros = NULL
)

Arguments

Y: the matrix of responses (called Y in the model).
X: design matrix (called X in the model).
X0: zero-inflation design matrix, if applicable.
formula: describes the relationship between Y and X, useful if not all of X's covariates should be used.
scale: whether to rescale each column of Y by its own standard deviation (no centering). Default TRUE, see the class-level documentation for the rationale and its limits.
zeros: an optional 0/1 matrix of structural zeros, overriding the default `Y == 0`.

`NormalBlockData$ols_fit()`

Ordinary-least-squares fit of Y on X, with its residuals and their covariance. Computed once and memoized.

Usage

NormalBlockData$ols_fit()

Returns

a list with `B` (d x p), `R` (n x p residuals) and `Sigma` (p x p residual covariance)

`NormalBlockData$zi_ols_fit()`

Masked counterpart of `ols_fit()`: a per-variable weighted least-squares fit of B under the zero-inflation mask, with its inverse residual variances and residuals (see `zi_weighted_fit()`). Computed once and memoized.

Usage

NormalBlockData$zi_ols_fit()

Returns

a list with `B` (d x p), `dm1` (p) and `R` (n x p masked residuals)

`NormalBlockData$zi_fit()`

Zero-inflation component: `p` independent logistic regressions of each variable's zero pattern on `X0`, and the fixed contribution they make to the log-likelihood. The (V)EM never revisits these, so they are a property of the data rather than of a model.

Usage

NormalBlockData$zi_fit()

Returns

a list with `B0` (d0 x p), `kappa` (n x p zero-inflation probabilities) and `ZI_cond_mean` (a scalar)

`NormalBlockData$clone()`

The objects of this class are cloneable with this method.

Usage

NormalBlockData$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Examples

ex <- generate_normal_block_var_data(n = 50, p = 20, d = 1, q = 3)
data <- NormalBlockData$new(ex$Y, ex$X)
c(n = data$n, p = data$p, d = data$d)
#>  n  p  d 
#> 50 20  1