Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/cluster_fit_model.R

Compute the cluster-mass test for longitudinal linear model.

1 2 3 |

`formula` |
A formula object where the left part is a matrix defined in the global environment. |

`data` |
A data frame for the independant variables. |

`np` |
The number of permutations. Default value is |

`method` |
A character string indicating the method used to handle nuisance variables. Default is |

`test` |
A character string to specify the name of the test. Default is |

`threshold` |
A numerical value that specify the threshold for the |

`aggr_FUN` |
A function used as mass function. It should aggregate the statistics of a cluster into one scalar. Default is the sum of squares fot t statistic and sum for F statistic. |

`multcomp` |
A vector of character defining the methods of multiple comparisons to compute. Default is |

`...` |
Futher arguments, see details. |

The random effects model is only avaible with a F statistic.

Other arguments could be pass in `...`

:

`P`

: A matrix containing the permutation of class `matrix`

or `Pmat`

; which is used for the reproductibility of the results. The first column must be the identity. `P`

overwrites `np`

argument.

`rnd_rotation`

: A matrix of random value to compute a rotation of size *n \times n* that will be used for the `"huh_jhun"`

method.

`p_scale = FALSE`

: if set to `TRUE`

, the several multiple comparisons procedures are compute on the `1 - p`

scale, where `p`

is the p-value. The threshold have to be set between 0 and 1 (eg: `threshold = 0.95`

). The function `aggr_FUN`

should be big when there is evidence against the null (eg: `aggr_FUN = function(p)sum(abs(log(1-p)))`

. Moreover under the probability scale the cluster mass statistics is sensitive to the number permutations.

`H`

, `E`

, `ndh`

: the parameters used for the `"tfce"`

method. Default values are set to `H = 2`

for the height parameter, to `E = 0.5`

for the extend parameter and to `ndh = 500`

for the number terms to approximate the integral.

`alpha = 0.05`

: the type I error rate. Used for the `troendle`

multiple comparisons procedure.

`return_distribution = FALSE`

: return the permutation distribution of the statistics. Warnings : return one high dimentional matrices (number of test times number of permutation) for each test.

`coding_sum`

: a logical defining the coding of the design matrix to `contr.sum`

: set by default to `TRUE`

for ANOVA (when the argument `test`

is `"fisher"`

) to tests main effects and is set to `FALSE`

when `test`

is `"t"`

. If `coding_sum`

is set to `FALSE`

the design matrix is computed with the coding defined in the dataframe and the tests of simple effets are possible with a coding of the dataframe set to `contr.treatment`

.

A list containing : a table of the clusters, or a `multcomp`

object for the other multiple comparison procedures. Use the plot.clusterlm method to have a quick overview of the results.

Maris, E., & Oostenveld, R. (2007). Nonparametric statistical testing of EEG-and MEG-data. Journal of neuroscience methods, 164(1), 177-190.

Smith, S. M., & Nichols, T. E. (2009). Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. Neuroimage, 44(1), 83-98.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | ```
## Cluster-mass for repeated measures ANOVA
## Warning : np argument must be greater (recommendation: np >= 5000)
electrod_O1 <- clusterlm(attentionshifting_signal ~ visibility*emotion*direction
+ Error(id/(visibility*emotion*direction)), data = attentionshifting_design,
np = 50)
## Results
plot(electrod_O1)
## Results with labels on the x axis that represent seconds from time-locked event:
plot(electrod_O1, nbbaselinepts = 200, nbptsperunit = 1024)
## Tables of clusters
electrod_O1
## Not run:
## Change the function of the aggregation
## Sum of squares of F statistics
electrod_O1_sum <- clusterlm(attentionshifting_signal ~ visibility*emotion*direction
+ Error(id/(visibility*emotion*direction)), data = attentionshifting_design,
aggr_FUN = function(x)sum(x^2))
## Length of the cluster
electrod_O1_length <- clusterlm(attentionshifting_signal ~ visibility*emotion*direction
+ Error(id/(visibility*emotion*direction)), data = attentionshifting_design,
aggr_FUN = function(x)length(x))
## All multiple comparisons procedures for repeated measures ANOVA
## Permutation method "Rde_kheradPajouh_renaud"
full_electrod_O1 <- clusterlm(attentionshifting_signal ~ visibility*emotion*direction
+ Error(id/(visibility*emotion*direction)), data = attentionshifting_design,
method = "Rde_kheradPajouh_renaud", multcomp = c("troendle", "tfce",
"clustermass", "bonferroni", "holm", "benjamini_hochberg"))
## End(Not run)
``` |

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