Description Usage Arguments Value Author(s) See Also Examples

The function first calculates averaged ERP values within a predetermined number of equally-spaced intervals then tests for significance of the relationship between averaged ERPs and covariates in a linear model framework.

1 2 | ```
erpavetest(dta, design, design0 = NULL, nintervals = 10,
method = c("none","BH","holm","hochberg","hommel","bonferroni","BY","fdr"),alpha = 0.05)
``` |

`dta` |
Data frame containing the ERP curves: each column corresponds to a time frame and each row to a curve. |

`design` |
Design matrix of the nonnull model for the relationship between the ERP and the experimental variables. Typically the output of the function model.matrix |

`design0` |
Design matrix of the null model. Typically a submodel of the nonnull model, obtained by removing columns from design. Default is NULL, corresponding to the model with no covariates. |

`nintervals` |
Number of intervals in the partition of the whole interval of observation. Default is 10. |

`method` |
FDR- or FWER- controlling multiple testing procedures as available in the function p.adjust. Default is "none", for no multiplicity correction. |

`alpha` |
The FDR or FWER control level. Default is 0.05 |

`pval` |
p-values of the tests. |

`correctedpval` |
Corrected p-values, for the multiplicity of tests. Depends on the multiple testing method (see function p.adjust). |

`significant` |
Indices of the time points for which the test is positive. |

`segments` |
Factor giving the membership of timepoints to each interval in the partition. |

`breaks` |
Breakpoints of the partition. |

`test` |
Pointwise F-statistics if p>1, where p is the difference between the numbers of parameters in the nonnull and null models. Otherwise, if p=1, the function returns pointwise t-statistics (signed square-roots of F-statistics). |

`df1` |
Residual degrees of freedom for the nonnull model. |

`df0` |
Residual degrees of freedom for the null model. |

`signal` |
Estimated signal: a pxT matrix, where T the number of frames. |

`r2` |
R-squared values for each of the T linear models. |

David Causeur, IRMAR, UMR 6625 CNRS, Agrocampus Ouest, Rennes, France.

`erptest`

, `erpfatest`

, `gbtest`

, `p.adjust`

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 | ```
data(impulsivity)
# Paired t-tests for the comparison of the ERP curves in the two conditions,
# within experimental group High, at channel CPZ
erpdta.high = impulsivity[impulsivity$Group=="High",5:505]
# ERP curves for subjects in group 'High'
covariates.high = impulsivity[impulsivity$Group=="High",1:4]
# Experimental covariates for subjects in group 'High'
design = model.matrix(~C(Subject,sum)+Condition,data=covariates.high)
# Design matrix to compare ERP curves in the two conditions
design0 = model.matrix(~C(Subject,sum),data=covariates.high)
# Design matrix for the null model (no condition effect)
tests = erpavetest(erpdta.high,design,design0)
time_pt = seq(0,1000,2) # sequence of time points (1 time point every 2ms in [0,1000])
nbs = 20 # Number of B-splines for the plot of the effect curve
effect=which(colnames(design)=="ConditionSuccess")
erpplot(erpdta.high,design=design,frames=time_pt,effect=effect,xlab="Time (ms)",
ylab=expression(Effect~curve~(mu~V)),bty="l",ylim=c(-3,3),nbs=nbs,
cex.axis=1.25,cex.lab=1.25,interval="simultaneous")
# with interval="simultaneous", both the pointwise and the simultaneous confidence bands
# are plotted
abline(v=time_pt[tests$breaks],lty=2,col="darkgray")
# Add a grid to show breakpoints
points(time_pt[tests$significant],rep(0,length(tests$significant)),pch=16,col="blue")
# Identifies significant time points by blue dots
title("Success-Failure effect curve with 95 percent C.I.",cex.main=1.25)
mtext(paste("12 subjects - Group 'High' - ",nbs," B-splines",sep=""),cex=1.25)
``` |

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