Description Usage Arguments Details Value References Examples

View source: R/samplesize_mixed.R

Compute an approximated sample size for linear mixed models (two-level-designs), based on power-calculation for standard design and adjusted for design effect for 2-level-designs.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
samplesize_mixed(
eff.size,
df.n = NULL,
power = 0.8,
sig.level = 0.05,
k,
n,
icc = 0.05
)
smpsize_lmm(
eff.size,
df.n = NULL,
power = 0.8,
sig.level = 0.05,
k,
n,
icc = 0.05
)
``` |

`eff.size` |
Effect size. |

`df.n` |
Optional argument for the degrees of freedom for numerator. See 'Details'. |

`power` |
Power of test (1 minus Type II error probability). |

`sig.level` |
Significance level (Type I error probability). |

`k` |
Number of cluster groups (level-2-unit) in multilevel-design. |

`n` |
Optional, number of observations per cluster groups (level-2-unit) in multilevel-design. |

`icc` |
Expected intraclass correlation coefficient for multilevel-model. |

The sample size calculation is based on a power-calculation for the
standard design. If `df.n`

is not specified, a power-calculation
for an unpaired two-sample t-test will be computed (using
`pwr.t.test`

of the pwr-package).
If `df.n`

is given, a power-calculation for general linear models
will be computed (using `pwr.f2.test`

of the
pwr-package). The sample size of the standard design
is then adjusted for the design effect of two-level-designs (see
`design_effect`

). Thus, the sample size calculation is appropriate
in particular for two-level-designs (see Snijders 2005). Models that
additionally include repeated measures (three-level-designs) may work
as well, however, the computed sample size may be less accurate.

A list with two values: The number of subjects per cluster, and the total sample size for the linear mixed model.

Cohen J. 1988. Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale,NJ: Lawrence Erlbaum.

Hsieh FY, Lavori PW, Cohen HJ, Feussner JR. 2003. An Overview of Variance Inflation Factors for Sample-Size Calculation. Evaluation and the Health Professions 26: 239-257. doi: 10.1177/0163278703255230

Snijders TAB. 2005. Power and Sample Size in Multilevel Linear Models. In: Everitt BS, Howell DC (Hrsg.). Encyclopedia of Statistics in Behavioral Science. Chichester, UK: John Wiley and Sons, Ltd. doi: 10.1002/0470013192.bsa492

1 2 3 4 5 6 7 8 9 | ```
# Sample size for multilevel model with 30 cluster groups and a small to
# medium effect size (Cohen's d) of 0.3. 27 subjects per cluster and
# hence a total sample size of about 802 observations is needed.
samplesize_mixed(eff.size = .3, k = 30)
# Sample size for multilevel model with 20 cluster groups and a medium
# to large effect size for linear models of 0.2. Five subjects per cluster and
# hence a total sample size of about 107 observations is needed.
samplesize_mixed(eff.size = .2, df.n = 5, k = 20, power = .9)
``` |

```
$`Subjects per Cluster`
[1] 27
$`Total Sample Size`
[1] 802
$`Subjects per Cluster`
[1] 5
$`Total Sample Size`
[1] 107
```

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