metagen: Inference: Analysis of the data set In metagen: Inference in Meta Analysis and Meta Regression

Description

Runs all implemented methods and combines them in a neat summary.

Usage

 ```1 2 3``` ``` metagen(y, d, x, sgnf, s = NULL, n, method = list("univariate", "multivariate"), adjusted = FALSE) ```

Arguments

 `y` k-vector of responses. `d` k-vector of heteroscedasticities. `x` design k-p-matrix. `sgnf` vector of significance levels. `s` k-vector of study responses. Default is NULL. If 'adjusted=TRUE', this value needs to be given. `n` draws from the pivotal distribution. `method` Default is 'list("univariate", "multivariate")'. `adjusted` : TRUE or FALSE

Value

The same return type as the skeleton 'metagenEmpty()'.

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 26``` ```bcg <- bcgVaccineData() bcg_y <- bcg\$logrisk bcg_d <- bcg\$sdiv bcg_x <- cbind(1,bcg\$x) sgnf_lev <- c(0.01, 0.025, 0.05, 0.01) set.seed(865287113) # for reproducibility # Runs a standard analysis, use n=1000 in an actual # analysis instead! m1 <- metagen(y=bcg_y, d=bcg_d, x=bcg_x, sgnf=0.025, n=50) m2 <- metagen(y=bcg_y, d=bcg_d, x=bcg_x, sgnf=sgnf_lev, n=50) # Runs the methods based on generalised principles via an # adjustment for the unknown heteroscedasticity. Use # n=1000 in an actual analysis instead!! bcg_s <- bcg\$size m3 <- metagen(y=bcg_y, d=bcg_d, x=bcg_x, sgnf=0.025, s=bcg_s, n=50, adj=TRUE) m4 <- metagen(y=bcg_y, d=bcg_d, x=bcg_x, sgnf=sgnf_lev, s=bcg_s, n=50, adj=TRUE) if (!all(names(m1) == names(metagenEmpty()))) stop("Name clash") if (!all(names(m2) == names(metagenEmpty()))) stop("Name clash") if (!all(names(m3) == names(metagenEmpty()))) stop("Name clash") if (!all(names(m4) == names(metagenEmpty()))) stop("Name clash") ```

Example output

```Warning message: