Description Usage Arguments Details Value Examples

This function calculates predicted conditional means and their corresponding standard errors for objects of class weightfunct.

1 2 |

`object` |
an object of class weightfunct |

`values` |
a vector or matrix specifying the values of the moderator variables for which predicted values should be calculated; defaults to |

`...` |
other arguments |

`predict(object)`

requires that the user specify a vector or matrix of predictor values. Without specifying values, the function will not work.

For models including `y`

number of moderator variables, users should set `values`

equal to a `k`

x `y`

matrix, where `k`

is the number of rows of data (i.e., "new" studies). In the example code, for example, there are 3 moderator variables and one row of data, so `values`

is a 1 x 3 matrix. The intercept is incldued by default.

Note that `weightfunct`

handles categorical moderators automatically. To include them here, the appropriate contrast (dummy) variables must be explicitly specified. The `contrasts`

function can help to understand the contrast matrix for a given factor.

The function returns a list containing the following components: `unadjusted`

, `adjusted`

, and `values`

. The `values`

section simply prints the `values`

matrix for verification. The `unadjusted`

and `adjusted`

sections print the conditional means for each row of new data, unadjusted and adjusted for publication bias (respectively), and their standard errors.

1 2 3 4 5 6 | ```
## Not run:
test <- weightfunct(effect, v, mods=~x1 + x2 + x3, steps)
values <- matrix(c(0,1,0),ncol=3) # An arbitrary set of 3 dummy-coded moderators
predict(test, values)
## End(Not run)
``` |

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