ev.combo: Calculates the posterior probability of hypotheses for...

Description Usage Arguments Details Value Examples

View source: R/ev.combo.R

Description

The function takes multiple effect sizes and a their standard errors and calculates the posterior probability for each hypothesis (H<: the effect size is less than 0, H0: the effect size is zero, or H>: the effect size is greater than zero).

Usage

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ev.combo(beta, se.beta, beta0 = 0, ci = 99, H0 = c(0, 0), scale = FALSE,
  H.priors = rep(1/3, 3), se.mult = 1, adjust = FALSE, epsilon = 1e-06,
  adj.factor = 1e-04, ...)

Arguments

beta

Effect size.

se.beta

Standard error for the effect.

beta0

A prior value for the effect size. Default is zero.

ci

Is used to calculate the prior standard error if se0 = NULL. The default value of 99 calculates the prior standard error so that the 99 largest (furthest from zero) confidence interval of the data distribution.

H0

A vector of length two that defines the null hypothesis. If the values are identical (e.g. H0 = c(0,0)) a point null is used, otherwise the null is defined as the range between the lower and upper value.

scale

Logical. Whether to scale the effect size by its standard error. Standardising has no effect on the calculations but standardised effect sizes may be easier to compare in a forest plot.

H.priors

Prior hypothesis probabilities; default is an equal probability of 1/3, and they are specified in the following order: H<0, H=0, H>0.

se.mult

Standard error multiplier used to increase or decrease the prior SE and used in conjunction with ci when se0 = NULL.

adjust

Logical. Whether to adjust very small posterior hypothesis probabilities. Adjusting prevents a single study from having too much influence on the results when combining multiple studies. For example, if the probability for a hypothesis from one study is zero, then additional studies cannot alter this probability (multiplying anything by zero is still zero).

epsilon

A small value that a posterior hypothesis probability must fall below before an adjustment is made. Ignored if adjust = FALSE.

adj.factor

A small number added to each posterior hypothesis probability if adjust = TRUE and one of the posterior hypothesis probabilities is less than epsilon. The PPHs are then re-scaled to sum to one.

...

Options to be passed to pph().

Details

This function calls pph() once for each study to be combined, where the posterior probabilities for one study are used as the priors for the next study. One exeption is that values for se0 are ignored as they are calculated automatically.

Value

Object of class EV which contains a matrix of posterior probabilities for each updated step and other calculated values.

Examples

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x <- ev.combo(beta = c(0.0126, 5.0052, 1.2976, 0.0005),
       se.beta = c(0.050, 2.581, 2.054, 0.003) )
x
plot(x)

Example output

$N
[1] 4

$beta
[1] 0.0126 5.0052 1.2976 0.0005

$se.beta
[1] 0.050 2.581 2.054 0.003

$beta0
[1] 0

$ci
[1] 99

$se0
[1] 0.054891628 4.524141183 2.557760089 0.003194112

$post.b
[1] 0.0068863267 3.7761854488 0.7888704577 0.0002656537

$post.se
[1] 0.036963964 2.241836541 1.601521846 0.002186725

$H.priors
[1] 0.3333333 0.3333333 0.3333333

$pph.uniform
             H<        H0        H>
[1,] 0.24634242 0.4218744 0.3317831
[2,] 0.03701135 0.1962903 0.7666984
[3,] 0.18224933 0.4142836 0.4034671
[4,] 0.26183241 0.4202804 0.3178872

$pphs
              H<        H0        H>
[1,] 0.333333333 0.3333333 0.3333333
[2,] 0.246342420 0.4218744 0.3317831
[3,] 0.026327856 0.2391241 0.7345480
[4,] 0.011988711 0.2475211 0.7404902
[5,] 0.009163464 0.3036793 0.6871573

attr(,"class")
[1] "EV"

BayesCombo documentation built on May 29, 2017, 10:21 a.m.