# formulaR: Regression coefficients: formulaR In metagen: Inference in Meta Analysis and Meta Regression

## Description

Calculate pivotal quantities for the regression coefficients using the method: formulaR form the dissertation.

## Usage

 `1` ``` formulaR(y, d, h, g, x) ```

## Arguments

 `y` k-vector of responses. `d` k-vector of heteroscedasticity. `h` scalar of heterogeneity. `g` p-vector of some p-variate Gaussian draw. `x` design k-p-matrix.

## Details

Algorithm for calculating a single generalised pivotal quantity for the regression coefficients for given generalised pivotal quantities for the heterogeneity using the multivariate version of the pivotal formula.

A p-vector.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25``` ```bcg <- bcgVaccineData() bcg_y <- bcg\$logrisk bcg_d <- bcg\$sdiv bcg_x <- cbind(1,bcg\$x) # When, for example, using the Mandel-Paule estimate: bcg_h <- pfunc(y=bcg_y, d=bcg_d, x=bcg_x)(dim(bcg_x)[1] - dim(bcg_x)[2]) set.seed(51351) # for reproducibility random_g <- rnorm(dim(bcg_x)[2]) formulaR(y=bcg_y, d=bcg_d, h=bcg_h, g=random_g, x=bcg_x) # The function can also be used when planing to perform # a meta regression with no intercept, and only a singel # covariate (i.e. dim(x) = 1). In this case, # the design matrix can simply be provided by a vector. set.seed(51351) # for reproducibility random_g <- rnorm(1) formulaR(y=bcg_y, d=bcg_d, h=bcg_h, g=random_g, x=bcg\$x) # When performing a meta analysis, provide the function # with a vector of 1s. formulaR(y=bcg_y, d=bcg_d, h=bcg_h, g=random_g, x=rep(1, length(bcg_y))) ```

metagen documentation built on May 29, 2017, 7:13 p.m.