Description Usage Arguments Value See Also Examples

Analyzes data using `lmer`

, using model selection to test
significance of random slope terms in the model (likelihood ratio tests).
Does forward and backward selection, starting with subject slope or item
slope first (one-factor design only)

1 | ```
fitstepwise(mcr.data, wsbi, mf, crit = c(0.01, 0.05, seq(0.1, 0.8, 0.1)))
``` |

`mcr.data` |
A dataframe formatted as described in |

`wsbi` |
Whether the design is between-items (TRUE) or within-items (FALSE). |

`mf` |
List of the models to be tested, in decreasing order of complexity. |

`crit` |
alpha level for each likelihood-ratio test of slope variance. |

A single row of a dataframe with number of fields depending on
`crit`

. Stepwise lmer models output six values for each alpha level
(i.e., level of `crit`

. These values are:

The values are given twice, once from each direction (forward or backward).
Thus, if there are two values of crit, there will be 2 (direction) x 6
(value) x 2 (levels of crit) = 24 values in each row of the dataframe. To
assemble a dataframe of results from a file into a three-dimensional array,
see `reassembleStepwiseFile`

.

`fm` |
the model that was selected. 4 means that no model converged. For forward stepping models, 3 = maximal model was selected; 2 = model includes only first slope; 1 = model is random intercept only. For backward stepping models, 1 = maximal model, 2 = model minus one slope, 3 = random intercept model. |

`t` |
t-statistic for the treatment effect |

`chi` |
chi-square statistic for the likelihood ratio test (1 df) |

`pt` |
p-value for the t-statistic (normal distribution) |

`pchi` |
p-value for the chi-square statistic |

`fitlmer`

,
`fitstepwise.bestpath`

, `reassembleStepwiseFile`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
nmc <- 10
pmx <- cbind(randParams(genParamRanges(), nmc, 1001), seed=mkSeeds(nmc, 1001))
x8 <- mkDf(nsubj=24, nitem=24, pmx[8,], wsbi=FALSE)
mf.sfirst <- list(min=Resp ~ Cond + (1 | SubjID) + (1 | ItemID),
srs=Resp ~ Cond + (1 + Cond | SubjID) + (1 | ItemID),
max=Resp ~ Cond + (1 + Cond | SubjID) + (1 + Cond | ItemID))
mf.ifirst <- list(min=Resp ~ Cond + (1 | SubjID) + (1 | ItemID),
irs=Resp ~ Cond + (1 | SubjID) + (1 + Cond | ItemID),
max=Resp ~ Cond + (1 + Cond | SubjID) + (1 + Cond | ItemID))
# forward, subj first
fitstepwise(x8, wsbi=FALSE, mf=mf.sfirst, crit=.05)
# forward, item first
fitstepwise(x8, wsbi=FALSE, mf=mf.ifirst, crit=.05)
``` |

dalejbarr/simgen documentation built on Jan. 28, 2019, 7:43 p.m.

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