TMB1964r: Data of Tulving, Mandler, & Baumal, 1964 (reproduction of...

Description Usage Format References Examples

Description

The data comes from \insertCiteb21;textualsuperb. It is a near exact replication of the original study from \insertCitetmb64superb.

The design is a (7) x 4 with: 7 levels of stimulus duration (within-subject) and 4 between-subject conditions. Additional variables included in the reproduction is the primary language of the participant in which he/she participated (mainly francophones and anglophones; and the gender (mainly male and female).

Usage

1

Format

An object of class data.frame.

References

\insertAllCited

Examples

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library(ggplot2)

data(TMB1964r)

options(superb.feedback = 'none') # shut down 'warnings' and 'design' interpretation messages

# general plot ignoring covariates sex and languages with only defaults
# We illustrate correlation- and difference-adjusted 95% confidence intervals of the mean
superbPlot(TMB1964r,
    WSFactors = "T(7)",      # the within-subject factor (spanning 7 columns)
    BSFactors = "Condition", # the between-subject factor (4 levels)
    variables = c("T1","T2","T3","T4","T5","T6","T7"),
    adjustments = list(purpose="difference", decorrelation="CM"),
    plotStyle = "line"
)

# We add directives for the error bars (thick), for the points (larger) and for the lines (thick)
plt <- superbPlot(TMB1964r,
    WSFactors = "T(7)",
    BSFactors = "Condition",
    variables = c("T1","T2","T3","T4","T5","T6","T7"),
    adjustments = list(purpose="difference", decorrelation="CM"),
    plotStyle = "line", 
    errorbarParams = list(width = 0.5, size=1.25, position = position_dodge(.5) ),
    pointParams = list(size=2.5, position = position_dodge(.5)),
    lineParams = list(size=1.25)
)
plt

# Additional directives to set manually the colors, shapes, thick marks and labels.
plt + 
scale_colour_manual( 
    labels = c("Context 0", "Context 2", "Context 4", "Context 8"), 
    values = c("blue", "black", "purple", "red")) +
scale_shape_manual( 
    labels = c("Context 0", "Context 2", "Context 4", "Context 8"), 
    values = c("circle", "triangle", "square", "plus")) +
theme_bw(base_size = 16) +
labs(x = "Exposure duration (ms)", y = "Mean of correct responses", 
    colour = "Context length\n", shape = "Context length\n" ) + 
scale_x_discrete(labels=c("1" = "16.67", "2" = "33.33",
    "3"="50.00", "4" = "66.67", "5"="83.33", "6"="100.00", "7"="116.67"))



# Exploring three factors simultaneously: T, Condition and Sex (last two between-group)
superbPlot(TMB1964r,
    WSFactors = "T(7)",
    BSFactors = c("Condition","Sex"),
    variables = c("T1","T2","T3","T4","T5","T6","T7"),
    adjustments = list(purpose="difference", decorrelation="CM"),
    plotStyle = "line", 
    errorbarParams = list(size=0.15, position = position_dodge(.5) ),
    pointParams = list(size=2.5, position = position_dodge(.5)),
    lineParams = list(size=0.25)
) + 
scale_colour_manual( 
    labels = c("Context 0", "Context 2", "Context 4", "Context 8"), 
    values = c("blue", "black", "purple", "red")) +
scale_shape_manual( 
    labels = c("Context 0", "Context 2", "Context 4", "Context 8"), 
    values = c("circle", "triangle", "square", "plus")) +
theme_bw(base_size = 16) +
labs(x = "Exposure duration (ms)", y = "Mean of correct responses", 
    colour = "Context length\n", shape = "Context length\n" ) + 
scale_x_discrete(labels=c("1" = "16.67", "2" = "33.33",
    "3"="50.00", "4" = "66.67", "5"="83.33", "6"="100.00", "7"="116.67"))


#only keep 2 sex and 2 languages; the remaining cases are too sparse.
# even then, one cell is near empty. Only CA would work...
mee3 <- TMB1964r[(TMB1964r$Language != "I prefer not to answer")&TMB1964r$Language !="Other",]


# advanced plots are available, such as pointjitter ...
superbPlot(mee3,
    WSFactors = "T(7)",
    BSFactors = c("Condition","Language"),
    variables = c("T1","T2","T3","T4","T5","T6","T7"),
    adjustments = list(purpose="difference", decorrelation="CM"), 
    plotStyle = "pointjitter",
    jitterParams = list(alpha = 0.25) #near transparent jitter points
) + 
scale_fill_manual( name = "Amount of context", 
    labels = c("Context 0", "Context 2", "Context 4", "Context 8"), 
    values = c("blue", "black", "purple", "red")) +
scale_colour_manual( name = "Amount of context", 
    labels = c("Context 0", "Context 2", "Context 4", "Context 8"), 
    values = c("blue", "black", "purple", "red")) +
scale_shape_manual( name = "Amount of context",
    labels = c("Context 0", "Context 2", "Context 4", "Context 8"), 
    values = c("circle", "triangle", "square", "cross")) +
theme_bw(base_size = 16) +
labs(x = "Exposure duration (ms)", y = "Mean of correct responses" )+ 
scale_x_discrete(labels=c("1" = "16.67", "2" = "33.33",
    "3"="50.00", "4" = "66.67", "5"="83.33", "6"="100.00", "7"="116.67"))


# ... and pointjitterviolin : a plot that superimposes the distribution as a violin plot
# 
superbPlot(mee3,
    WSFactors = "T(7)",
    BSFactors = c("Condition","Language"),
    variables = c("T1","T2","T3","T4","T5","T6","T7"),
    adjustments = list(purpose="difference", decorrelation="CM"), 
    plotStyle = "pointjitterviolin",
    jitterParams = list(alpha = 0.4), #near transparent jitter points
    violinParams = list(alpha = 0.2)
) + 
scale_fill_manual( name = "Amount of context", 
    labels = c("Context 0", "Context 2", "Context 4", "Context 8"), 
    values = c("blue", "black", "purple", "red")) +
scale_colour_manual( name = "Amount of context", 
    labels = c("Context 0", "Context 2", "Context 4", "Context 8"), 
    values = c("blue", "black", "purple", "red")) +
scale_shape_manual( name = "Amount of context",
    labels = c("Context 0", "Context 2", "Context 4", "Context 8"), 
    values = c("circle", "triangle", "square", "cross")) +
theme_bw(base_size = 16) +
labs(x = "Exposure duration (ms)", y = "Mean of correct responses" )+ 
scale_x_discrete(labels=c("1" = "16.67", "2" = "33.33",
    "3"="50.00", "4" = "66.67", "5"="83.33", "6"="100.00", "7"="116.67"))

superb documentation built on June 23, 2021, 9:08 a.m.