drawMeta.metabinM: Plot Function for 'metabinM' Objects
In pmur002/metaplot: Meta-analysis Forest Plots

Description Usage Arguments Details Value See Also Examples

Function to create forest plots for objects of class 'metabinM'.

## S3 method for class 'metabinM'
drawMeta(matrix, plotCol = NCOL(matrix$Matrix) + 1, 
        plotHead = "", xlab = NULL, 
        refLine = 0, plotWidth = unit(0.3, "npc"), plotPar = metaPar(), 
        xlog = TRUE, xticks = NULL, boxSize = NULL, align = NULL, 
        clip = log(c(0.05,6)), newpage=TRUE, fit = TRUE, abbreviate = FALSE, 
        vpName = "Forest", ...)

`matrix`	An object of class `'metabinM'`.
`plotCol`	Numeric column the confidence interval graph goes into.
`plotHead`	Heading for the confidence interval graph.
`xlab`	Vector of length 2 specifying direction of effect as x-axis labels.
`refLine`	x-axis coordinate for no effect line.
`plotWidth`	Width of confidence interval graph.
`plotPar`	Parameters for confidence interval graph, see `metaPar`.
`xlog`	If `TRUE`, x-axis tick marks are exponentiated.
`xticks`	Optional user-specified x-axis tick marks. Specify `NULL` to use the defaults, `numeric(0)` to omit the x-axis.
`boxSize`	Override the default box size based on precision.
`align`	Vector giving alignment `("l","r","c")` for columns of table.
`clip`	Lower and upper limits for clipping confidence intervals to arrows.
`newpage`	Draw plot on a new page and overwrites current device.
`fit`	Fit plot into current viewport.
`abbreviate`	Abbreviate names of graphical objects.
`vpName`	Name of the forest plot viewport.
`...`	Not used.

This function is more flexible but contains fewer arguments than the forest.meta method for metabin objects in the meta package. It requires the user to first produce a dataframe using meta2DF.metabin and convert that into a text matrix using metaDF2Matrix.metabinDF. This process can be done with minimal input from the user, such that only the meta-analysis object is needed to produce the plot.

If more flexibility is required, customisations to the plot can be made at the different stages of the process. Changing the order of studies by rows and adding extra columns from external sources can be done using meta2DF.metabin. Reordering of columns and making new columns from existing columns in the data frame can be done using metaDF2Matrix.metabinDF. Changing the position of the confidence interval graph relative to the other text columns can be done in drawMeta.metabinM.

None

meta2DF.metabin, metaDF2Matrix.metabinDF

### simple example
library(meta)
data(Olkin95)
meta1 <- metabin(event.e, n.e, event.c, n.c, data = Olkin95, 
                 subset = c(41,47,51,59), sm = "RR", method = "I")        
Data <- meta2DF(meta1)                 
matrix <- metaDF2Matrix(Data,  
                        order = c("study", "effect", "w.random"),     
                        roundCols = c("effect" = 2, "w.random" = 1),
                        hgap = c(2, 7))                          
drawMeta(matrix,
         plotCol = 1,
         plotHead = "Relative risk (log scale)")
         
### confidence interval plot customisations
drawMeta(matrix,
         plotCol = 2,
         clip = log(c(0.05,6)),
         refLine = log(2),
         boxSize = 0.75,
         plotWidth = unit(2,"inches"), 
         plotHead = "Relative risk (log scale)")
         
### illustrative example
### testing 'add' argument
add <- list(test1 = c(1:4), test2 = c(5:8))          
Data <- meta2DF(meta1, title = "Thrombolytic Therapy", 
                rowOrder = "effect", decreasing = TRUE, add = add)
matrix <- metaDF2Matrix(Data,  
                        order = c("study", "event.e", "event.c", "effect", 
                                  "ci", "w.fixed", "w.random"),     
                        roundCols = c("effect" = 2, "w.fixed" = 1, 
                                       "w.random" = 1),
                         hgap = c(2, 8, 11), 
                         newCols = list(ci = makeCIDesc("e.lower",
                                                        "e.upper", 2, 
                                                        c("[", "]"))))
drawMeta(matrix, 
         plotCol = 5,
         clip = log(c(0.05,6)),                   
         xlab = c("Favours treatment","Favours control"),
         plotHead = "Relative risk (log scale)", 
         plotPar = metaPar(box = list(fill = "royalblue", col = "royalblue"),
                           lines = list(col = "darkblue"),
                           diamond = list(fill = "royalblue",
                                          col = "royalblue"))) 
### customising graphical objects using grid.edit()
### use grid.ls() to find name of component to edit
grid.edit("[.]X51[.]", global = TRUE, grep = TRUE,
          gp = gpar(col = "red"))