glm.approx: Model fitting using weighted least squares or a GLM approach.

In zrxing/smash: Smoothing by Adaptive Shrinkage in R

Model fitting using weighted least squares or a GLM approach.

Description

Fit the model specified in documentation, using either a weighted least squares approach or a generalized linear model approach, with some modifications. This function fits many "simple" logistic regressions (ie zero or one covariate) simultaneously, allowing for the possibility of small sample sizes with low or zero counts. In addition, an alternative model in the form of a weighted least squares regression can also be fit in place of a logistic regression.

Usage

glm.approx(
  x,
  g = NULL,
  minobs = 1,
  pseudocounts = 0.5,
  all = FALSE,
  eps = 1e-08,
  center = FALSE,
  repara = FALSE,
  forcebin = FALSE,
  lm.approx = FALSE,
  disp = c("add", "mult"),
  bound = 0.02
)

Arguments

x	A matrix of N (# of samples) by 2*B (B: # of WCs or, more precisely, of different scales and locations in multi-scale space); Two consecutive columns correspond to a particular scale and location; The first column (the second column) contains # of successes (# of failures) for each sample at the corresponding scale and location.
g	A vector of covariate values. Can be a factor (2 groups only) or quantitative. For a 2-group categorical covariate, provide g as a 0-1 factor instead of a 0-1 numeric vector for faster computation.
minobs	Minimum number of non-zero required for each model to be fitted (otherwise NA is returned for that model).
pseudocounts	A number to be added to counts when counts are zero (or possibly extremely small).
all	Bool, if TRUE pseudocounts are added to all entries, if FALSE (default) pseudocounts are added only to cases when either number of successes or number of failures (but not both) is 0.
center	Bool, indicating whether to center g. If g is a 2-group categorical variable and centering is desired, use center=TRUE instead of treating g as numeric and centering manually to avoid slower computation.
repara	Bool, indicating whether to reparameterize alpha and beta so that their likelihoods can be factorized.
forcebin	Bool, if TRUE don't allow for overdipersion. Defaults to TRUE if nsig=1, and FALSE otherwise.
lm.approx	Bool, indicating whether a WLS alternative should be used. Defaults to FALSE.
disp	A string, can be either "add" or "mult", indicating the form of overdispersion assumed when lm.approx=TRUE.
bound	Numeric, indicates the threshold of the success vs failure ratio below which pseudocounts will be added.

Value

A matrix of 2 (or 5 if g is provided) by T (# of WCs); Each row contains alphahat (1st row), standard error of alphahat (2nd), betahat (3rd), standard error of betahat (4th), covariance between alphahat and betahat (5th) for each WC.

zrxing/smash documentation built on July 12, 2024, 6:31 a.m.