gtx (version 0.0.3)

est.moments2: Estimate regression coefficients using quadratic approximation to likelihood function.

Description

To make a quadratic approximation to the likelihood function, the score and information are obtained from a pre-built matrix of weighted second moments. This allows a parameter estimate to be obtained by one iteration of weighted least squares, or equivalently a score test. Typically the weights used to construct the pre-built matrix correspond to the MLE under a chosen null model.

Usage

est.moments2(xtwx, leftvar, rightvars, n = NULL, vscale = NULL)

Arguments

xtwx

a matrix of weighted second moments, typically built using make.moments2 with the weightvar argument set.

leftvar

name of the response variable (the left hand side of the formula).

rightvars

name(s) of the explanatory variables (the right hand side of the formula).

n

sample size, only needed for the normal linear model if there is not a single intercept ONE for all individuals.

vscale

parameter, set to NULL for normal linear model and 1 for logistic regression.

Value

• A list with slots for the effect size estimates, standard errors, and a precision matrix.

Details

Non-identifiable variables in rightvars are removed, with preference for keeping variables that occur earlier rather than later in rightvars.

When the vscale argument is NULL this function assumes that the xtwx argument was calculated with unit weights and therefore that a linear model fit is required with error variance estimated from the data. For this application it is preferred to call lm.moments2, which is a wrapper for this function with vscale=NULL.

When the vscale argument is set equal to 1 this function assumes that the xtwx argument was calculated with weights calculated such that a correct likelihood function can be recovered and therefore that a generalised linear model fit is required.

Values other than NULL or 1 for the vscale parameter may not be what you think. Do not use other values unless you are absolutely sure what you understand what are doing. See the manuscript for details.

Examples

data(mthfrex)
mthfrex <- gls.approx.logistic(mthfrex, "HTN", c("SexC", "Age"))
xtwx <- make.moments2(mthfr.params, c("HTNstar", "SexC", "Age"), mthfrex,
                      weightvar = "weight")
est.moments2(xtwx, "HTNstar", c("ONE", "rs6668659_T", "rs4846049_T",
                                "rs1801133_G", "SexC", "Age"), vscale=1)
## Compare against results from glm
## Note have to use coded alleles in original data
glm(HTN ~ rs6668659_G+rs4846049_G+rs1801133_A+Sex+Age,
    family="binomial", data = mthfrex$data)
## Note in results Sex factor coded differently than SexC
## Look at pairwise correlations
cor(subset(mthfrex$data, select = c("rs6668659_G", "rs4846049_G",
                                    "rs1801133_A")))^2