plot_fa: Visualization of the results using adaptive...

In PsyChiLin/ERPplot: Graphical exploration of ERP data

Description

the function plot fa performs factor-adjusted multiple testing of ERP curves in the linear model framework (Causeur et al, 2012) and display results the same way as that of plot_tete.

Usage

plot_fa(data, frames = NULL, uV, subject = NULL, channel = NULL,
  test = NULL, mode = c("test", "test_signal"), curve.col = NULL,
  labs = list(x = "Time (ms)", y = "Amplitude (microvolt)"), ggtheme = NULL,
  order = F, scalp = FALSE, coord.mat = NULL, ylim = c(-20, 20),
  significant.col = "darkgoldenrod", significant.alpha = 0.1,
  design = NULL, design0 = NULL, ...)

Arguments

data	a data frame with ERP data
frames	The time point of the ERP data
uV	The corresponding column indices within the input data frame for the ERP amplitudes
subject	The corresponding column index for the subject variables (factor)
channel	The corresponding column index for the channel variables (factor)
test	The corresponding column index for the variables you want to compare. It could be a factor (i.e, Condition) or numeric variable (i.e, Score).
mode	The options ("test","test_signal") of the functions control the kind of plots to be made.
curve.col	The color for the curves. Numbers must match. (e.g. two conditions need two colors)
labs	The labs of the plot
ggtheme	The theme setting for ggplot2
order	Reordering the plots by the similarities between the curves using TSclust package.
scalp	Logical variable. Plot the curve on the scalp location (follow the coord.mat option).
coord.mat	Read users' own coordinate matrix. Default is the full 10/10 system.
ylim	The limits of the plot on Y axis. The setting is same for ggplot2.
significant.col	The color used to indicated the significant time point.
significant.alpha	The alpha of the significant.col that used to indicated the significant time point.
design	Design matrix of the full model for the relationship between the ERP and the experimental variables. Typically the output of the function model.matrix (see the R package Causeur, David, Ching-Fan Sheu, and Mei-Chen Chu. "Significance analysis of ERP data." (2014).)
design0	Design matrix of the null model. Typically a submodel of the full model, obtained by removing columns from design. Default is NULL, corresponding to the model with no covariates. (see the R package Causeur, David, Ching-Fan Sheu, and Mei-Chen Chu. "Significance analysis of ERP data." (2014).)
...	Other parameters in erpfatest.

Value

Plot A "ggplot2" plot will automatically generate.

Test_Rst A same testing results of erpfatest.

Author(s)

Chi-Lin Yu <[e-mail]>, Ching-Fan Sheu <[e-mail]>

See Also

plot_tete plot_coord erpfatest

Examples

data(DirectedForgetting)
time_pt <- seq(-200, 1000, 1)

dta_c <- DirectedForgetting %>%
         filter(Channel %in% c("FZ","CZ","PZ")) %>%
         droplevels()

plot_fa(data = dta_c, 
        frames = time_pt,
        channel = 5, subject = 1, uV = 6:1206, test = 4,
        mode = "test_signal",
        design = (~Subject + Condition), design0 = (~Subject), nbf = 5)

PsyChiLin/ERPplot documentation built on May 20, 2019, 3:34 p.m.