ez (version 4.4-0)

ezPlot: Plot data from a factorial experiment

Description

This function provides easy visualization of any given user-requested effect from factorial experiments, including purely within-Ss designs (a.k.a. “repeated measures”), purely between-Ss designs, and mixed within-and-between-Ss designs. By default, Fisher's Least Significant Difference is computed to provide error bars that facilitate visual post-hoc multiple comparisons (see Warning section below).

Usage

ezPlot( data , dv , wid , within = NULL , within_full = NULL , within_covariates = NULL , between = NULL , between_full = NULL , between_covariates = NULL , x , do_lines = TRUE , do_bars = TRUE , bar_width = NULL , bar_size = NULL , split = NULL , row = NULL , col = NULL , to_numeric = NULL , x_lab = NULL , y_lab = NULL , split_lab = NULL , levels = NULL , diff = NULL , reverse_diff = FALSE , type = 2 , dv_levs = NULL , dv_labs = NULL , y_free = FALSE , print_code = FALSE
)

Arguments

data

Data frame containing the data to be analyzed. OR, if multiple values are specified in dv, a list with as many element as values specified in dv, each element specifying a data frame for each dv in sequence.

dv

.() object specifying the column in data that contains the dependent variable. Values in this column should be of the numeric class. Multiple values will yield a plot with dv mapped to row.

wid

.() object specifying the column in data that contains the variable specifying the case/Ss identifier. Values in this column will be converted to factor class if necessary.

within

Names of columns in data that contain predictor variables that are manipulated (or observed) within-Ss. If a single value, may be specified by name alone; if multiple values, must be specified as a .() list.

within_full

Same as within, but intended to specify the full within-Ss design in cases where the data have not already been collapsed to means per condition specified by within and when within only specifies a subset of the full design.

within_covariates

Names of columns in data that contain predictor variables that are manipulated (or observed) within-Ss and are to serve as covariates in the analysis. If a single value, may be specified by name alone; if multiple values, must be specified as a .() list.

between

Names of columns in data that contain predictor variables that are manipulated (or observed) between-Ss. If a single value, may be specified by name alone; if multiple values, must be specified as a .() list.

between_full

Same as between, but must specify the full set of between-Ss variables if between specifies only a subset of the design.

between_covariates

Names of columns in data that contain predictor variables that are manipulated (or observed) between-Ss and are to serve as covariates in the analysis. If a single value, may be specified by name alone; if multiple values, must be specified as a .() list.

x

.() object specifying the variable to plot on the x-axis.

do_lines

Logical. If TRUE, lines will be plotted connecting groups of points.

do_bars

Logical. If TRUE, error bars will be plotted.

bar_width

Optional numeric value specifying custom widths for the error bar hat.

bar_size

Optional numeric value or vector specifying custom size of the error bars.

split

Optional .() object specifying a variable by which to split the data into different shapes/colors (and line types, if do_lines==TRUE).

row

Optional .() object specifying a variable by which to split the data into rows.

col

Optional .() object specifying a variable by which to split the data into columns.

to_numeric

Optional .() object specifying any variables that need to be converted to the numeric class before plotting.

x_lab

Optional character string specifying the x-axis label.

y_lab

Optional character string specifying the y-axis label.

split_lab

Optional character string specifying the key label.

levels

Optional named list where each item name matches a factored column in data that needs either reordering of levels, renaming of levels, or both. Each item should be a list containing named elements new_order or new_names or both.

diff

Optional .() object specifying a 2-level within-Ss varbiable to collapse to a difference score.

reverse_diff

Logical. If TRUE, triggers reversal of the difference collapse requested by diff.

type

Numeric value (either 1, 2 or 3) specifying the Sums of Squares “type” to employ when data are unbalanced (eg. when group sizes differ). See ezANOVA for details.

dv_levs

Optional character vector specifying the factor ordering of multiple values specified in dv.

dv_labs

Optional character vector specifying new factor labels for each of the multiple values specified in dv.

y_free

Logical. If TRUE, then rows will permit different y-axis scales.

print_code

Logical. If TRUE, the code for creating the ggplot2 plot object is printed and the data to be plotted is returned instead of the plot itself.

Value

If print_code is FALSE, printable/modifiable ggplot2 object is returned. If print_code is TRUE, the code for creating the ggplot2 plot object is printed and the data to be plotted is returned instead of the plot itself.

Warnings

Prior to running (though after obtaining running ANCOVA regressions as described in the details section), dv is collapsed to a mean for each cell defined by the combination of wid and any variables supplied to within and/or between and/or diff. Users are warned that while convenient when used properly, this automatic collapsing can lead to inconsistencies if the pre-collapsed data are unbalanced (with respect to cells in the full design) and only the partial design is supplied to ezANOVA. When this is the case, use within_full to specify the full design to ensure proper automatic collapsing. The default error bars are Fisher's Least Significant Difference for the plotted effect, facilitating visual post-hoc multiple comparisons. To obtain accurate FLSDs when only a subset of the full between-Ss design is supplied to between, the full design must be supplied to between_full. Also note that in the context of mixed within-and-between-Ss designs, the computed FLSD bars can only be used for within-Ss comparisons.

Details

ANCOVA is implemented by first regressing the DV against each covariate (after collapsing the data to the means of that covariate's levels per subject) and subtracting from the raw data the fitted values from this regression (then adding back the mean to maintain scale). These regressions are computed across Ss in the case of between-Ss covariates and computed within each Ss in the case of within-Ss covariates.

Fisher's Least Significant Difference is computed as sqrt(2)*qt(.975,DFd)*sqrt(MSd/N), where N is taken as the mean N per group in cases of unbalanced designs.

See Also

ezANOVA, ezStats

Examples

#Read in the ANT data (see ?ANT).
data(ANT)
head(ANT)
ezPrecis(ANT)


## Not run: 
# #Run an ANOVA on the mean correct RT data.
# mean_rt_anova = ezANOVA(
#     data = ANT[ANT$error==0,]
#     , dv = .(rt)
#     , wid = .(subnum)
#     , within = .(cue,flank)
#     , between = .(group)
# )
# 
# #Show the ANOVA and assumption tests.
# print(mean_rt_anova)
# ## End(Not run)

#Plot the main effect of group.
group_plot = ezPlot(
    data = ANT[ANT$error==0,]
    , dv = .(rt)
    , wid = .(subnum)
    , between = .(group)
    , x = .(group)
    , do_lines = FALSE
    , x_lab = 'Group'
    , y_lab = 'RT (ms)'
)

#Show the plot.
print(group_plot)

#tweak the plot
# group_plot = group_plot +
# theme(
#     panel.grid.major = element_blank()
#     , panel.grid.minor = element_blank()
# )
# print(group_plot)



#use the "print_code" argument to print the
# code for creating the plot and return the
# data to plot. This is useful when you want
# to learn how to create plots from scratch
# (which can in turn be useful when you can't
# get a combination of ezPlot and tweaking to
# achieve what you want)
group_plot_data = ezPlot(
    data = ANT[ANT$error==0,]
    , dv = .(rt)
    , wid = .(subnum)
    , between = .(group)
    , x = .(group)
    , do_lines = FALSE
    , x_lab = 'Group'
    , y_lab = 'RT (ms)'
    , print_code = TRUE
)


#Re-plot the main effect of group, using the levels
##argument to re-arrange/rename levels of group
group_plot = ezPlot(
    data = ANT[ANT$error==0,]
    , dv = .(rt)
    , wid = .(subnum)
    , between = .(group)
    , x = .(group)
    , do_lines = FALSE
    , x_lab = 'Group'
    , y_lab = 'RT (ms)'
    , levels = list(
        group = list(
            new_order = c('Treatment','Control')
            , new_names = c('Treatment\nGroup','Control\nGroup')
        )
    )
)

#Show the plot.
print(group_plot)


#Plot the cue*flank interaction.
cue_by_flank_plot = ezPlot(
    data = ANT[ANT$error==0,]
    , dv = .(rt)
    , wid = .(subnum)
    , within = .(cue,flank)
    , x = .(flank)
    , split = .(cue)
    , x_lab = 'Flanker'
    , y_lab = 'RT (ms)'
    , split_lab = 'Cue'
)

#Show the plot.
print(cue_by_flank_plot)


#Plot the cue*flank interaction by collapsing the cue effect to
##the difference between None and Double
cue_by_flank_plot2 = ezPlot(
    data = ANT[ ANT$error==0 & (ANT$cue %in% c('None','Double')) ,]
    , dv = .(rt)
    , wid = .(subnum)
    , within = .(flank)
    , diff = .(cue)
    , reverse_diff = TRUE
    , x = .(flank)
    , x_lab = 'Flanker'
    , y_lab = 'RT Effect (None - Double, ms)'
)

#Show the plot.
print(cue_by_flank_plot2)



#Plot the group*cue*flank interaction.
group_by_cue_by_flank_plot = ezPlot(
    data = ANT[ANT$error==0,]
    , dv = .(rt)
    , wid = .(subnum)
    , within = .(cue,flank)
    , between = .(group)
    , x = .(flank)
    , split = .(cue)
    , col = .(group)
    , x_lab = 'Flanker'
    , y_lab = 'RT (ms)'
    , split_lab = 'Cue'
)

#Show the plot.
print(group_by_cue_by_flank_plot)


#Plot the group*cue*flank interaction in both error rate and mean RT.
group_by_cue_by_flank_plot_both = ezPlot(
    data = list(
        ANT
        , ANT[ANT$error==0,]
    )
    , dv = .(error,rt)
    , wid = .(subnum)
    , within = .(cue,flank)
    , between = .(group)
    , x = .(flank)
    , split = .(cue)
    , col = .(group)
    , x_lab = 'Flanker'
    , split_lab = 'Cue'
    , dv_labs = c('ER (%)', 'RT (ms)')
    , y_free = TRUE
)

#Show the plot.
print(group_by_cue_by_flank_plot_both)