m-way Plot with Error Bars and Raw Data
Plots results from factorial experiments. Estimated marginal means and error bars are plotted in the foreground, raw data is plotted in the background. Error bars can be based on different standard errors (e.g., model-based, within-subjects, between-subjects). Functions described here return a ggplot2 plot object, thus allowing further customization of the plot.
afex_plot is the user friendly function that does data preparation
and plotting. It also allows to only return the prepared data (return
= "data").
interaction_plot does the plotting when a trace factor is
present. oneway_plot does the plotting when a trace factor is
absent.
afex_plot(object, ...)
## S3 method for class 'afex_aov'
afex_plot(
object,
x,
trace,
panel,
mapping,
error = "model",
error_ci = TRUE,
error_level = 0.95,
error_arg = list(width = 0),
data_plot = TRUE,
data_geom,
data_alpha = 0.5,
data_arg = list(color = "darkgrey"),
point_arg = list(),
line_arg = list(),
emmeans_arg = list(),
dodge = 0.5,
return = "plot",
factor_levels = list(),
legend_title,
...
)
## S3 method for class 'mixed'
afex_plot(
object,
x,
trace,
panel,
mapping,
id,
error = "model",
error_ci = TRUE,
error_level = 0.95,
error_arg = list(width = 0),
data_plot = TRUE,
data_geom,
data_alpha = 0.5,
data_arg = list(color = "darkgrey"),
point_arg = list(),
line_arg = list(),
emmeans_arg = list(),
dodge = 0.5,
return = "plot",
factor_levels = list(),
legend_title,
...
)
## S3 method for class 'merMod'
afex_plot(
object,
x,
trace,
panel,
mapping,
id,
error = "model",
error_ci = TRUE,
error_level = 0.95,
error_arg = list(width = 0),
data_plot = TRUE,
data_geom,
data_alpha = 0.5,
data_arg = list(color = "darkgrey"),
point_arg = list(),
line_arg = list(),
emmeans_arg = list(),
dodge = 0.5,
return = "plot",
factor_levels = list(),
legend_title,
...
)
## Default S3 method:
afex_plot(
object,
x,
trace,
panel,
mapping,
id,
dv,
data,
within_vars,
between_vars,
error = "model",
error_ci = TRUE,
error_level = 0.95,
error_arg = list(width = 0),
data_plot = TRUE,
data_geom,
data_alpha = 0.5,
data_arg = list(color = "darkgrey"),
point_arg = list(),
line_arg = list(),
emmeans_arg = list(),
dodge = 0.5,
return = "plot",
factor_levels = list(),
legend_title,
...
)
interaction_plot(
means,
data,
mapping = c("shape", "lineytpe"),
error_plot = TRUE,
error_arg = list(width = 0),
data_plot = TRUE,
data_geom = ggplot2::geom_point,
data_alpha = 0.5,
data_arg = list(color = "darkgrey"),
point_arg = list(),
line_arg = list(),
dodge = 0.5,
legend_title,
col_x = "x",
col_y = "y",
col_trace = "trace",
col_panel = "panel",
col_lower = "lower",
col_upper = "upper"
)
oneway_plot(
means,
data,
mapping = "",
error_plot = TRUE,
error_arg = list(width = 0),
data_plot = TRUE,
data_geom = ggbeeswarm::geom_beeswarm,
data_alpha = 0.5,
data_arg = list(color = "darkgrey"),
point_arg = list(),
legend_title,
col_x = "x",
col_y = "y",
col_panel = "panel",
col_lower = "lower",
col_upper = "upper"
)object |
|
... |
currently ignored. |
x |
A |
trace |
An optional |
panel |
An optional |
mapping |
A |
error |
A scalar |
error_ci |
Logical. Should error bars plot confidence intervals
(= |
error_level |
Numeric value between 0 and 1 determing the width of the confidence interval. Default is .95 corresponding to a 95% confidence interval. |
error_arg |
A |
data_plot |
|
data_geom |
Geom |
data_alpha |
numeric |
data_arg |
A |
point_arg, line_arg |
A |
emmeans_arg |
A |
dodge |
Numerical amount of dodging of factor-levels on x-axis. Default
is |
return |
A scalar |
factor_levels |
A |
legend_title |
A scalar |
id |
An optional |
dv |
An optional scalar |
data |
For the |
within_vars, between_vars |
For the |
means |
|
error_plot |
|
col_y, col_x, col_trace, col_panel |
A scalar |
col_lower, col_upper |
A scalar |
afex_plot obtains the estimated marginal means via
emmeans and aggregates the raw data to the same
level. It then calculates the desired confidence interval or standard error
(see below) and passes the prepared data to one of the two plotting
functions: interaction_plot when trace is specified and
oneway_plot otherwise.
Error bars provide a grahical representation of the
variability of the estimated means and should be routinely added to results
figures. However, there exist several possibilities which particular
measure of variability to use. Because of this, any figure depicting error
bars should be accompanied by a note detailing which measure the error bars
shows. The present functions allow plotting of different types of
confidence intervals (if error_ci = TRUE, the default) or standard
errors (if error_ci = FALSE).
A further complication is that readers routinely misinterpret confidence intervals. The most common error is to assume that non-overlapping error bars indicate a significant difference (e.g., Belia et al., 2005). This is often too strong an assumption. (see e.g., Cumming & Finch, 2005; Knol et al., 2011; Schenker & Gentleman, 2005). For example, in a fully between-subjects design in which the error bars depict 95% confidence intervals and groups are of approximately equal size and have equal variance, even error bars that overlap by as much as 50% still correspond to p < .05. Error bars that are just touching roughly correspond to p = .01.
In the case of designs involving repeated-measures factors the usual confidence intervals or standard errors (i.e., model-based confidence intervals or intervals based on the standard error of the mean) cannot be used to gauge significant differences as this requires knowledge about the correlation between measures. One popular alternative in the psychological literature are intervals based on within-subjects standard errors/confidence intervals (e.g., Cousineau & O'Brien, 2014). These attempt to control for the correlation across individuals and thereby allow judging differences between repeated-measures condition. As a downside, when using within-subjects intervals no comparisons across between-subjects conditions or with respect to a fixed-value are possible anymore.
In the case of a mixed-design, no single type of error bar is possible that
allows comparison across all conditions. Likewise, for mixed models
involving multiple crossed random effects, no single set of error
bars (or even data aggregation) adequately represent the true varibility in
the data and adequately allows for "inference by eye". Therefore, special
care is necessary in such cases. One possiblity is to avoid error bars
altogether and plot only the raw data in the background (with error =
"none"). The raw data in the background still provides a visual impression
of the variability in the data and the precision of the mean estimate, but
does not as easily suggest an incorrect inferences. Another possibility is
to use the model-based standard error and note in the figure caption that
it does not permit comparisons across repeated-measures factors.
The following "rules of eye" (Cumming and Finch, 2005) hold, when permitted by design (i.e., within-subjects bars for within-subjects comparisons; other variants for between-subjects comparisons), and groups are approximately equal in size and variance. Note that for more complex designs ususally analyzed with mixed models, such as designs involving complicated dependencies across data points, these rules of thumbs may be highly misleading.
p < .05 when the overlap of the 95% confidence intervals (CIs) is no more than about half the average margin of error, that is, when proportion overlap is about .50 or less.
p < .01 when the two CIs do not overlap, that is, when proportion overlap is about 0 or there is a positive gap.
p < .05 when the gap between standard error (SE) bars is at least about the size of the average SE, that is, when the proportion gap is about 1 or greater.
p < .01 when the proportion gap between SE bars is about 2 or more.
The following lists the
implemented approaches to calculate confidence intervals (CIs) and standard
errors (SEs). CIs are based on the SEs using the t-distribution with
degrees of freedom based on the cell or group size. For ANOVA models,
afex_plot attempts to warn in case the chosen approach is misleading
given the design (e.g., model-based error bars for purely
within-subjects plots). For mixed models, no such warnings are
produced, but users should be aware that all options beside "model"
are not actually appropriate and have only heuristic value. But then again,
"model" based error bars do not permit comparisons for factors
varying within one of the random-effects grouping factors (i.e., factors
for which random-slopes should be estimated).
"model"Uses model-based CIs and SEs. For ANOVAs, the
variant based on the lm or mlm model (i.e.,
emmeans_arg = list(model = "multivariate")) seems generally
preferrable.
"mean"Calculates the standard error of the mean for each cell ignoring any repeated-measures factors.
"within" or "CMO"
Calculates within-subjects SEs using the Cosineau-Morey-O'Brien (Cousineau & O'Brien, 2014) method. This method is based on a double normalization of the data. SEs and CIs are then calculated independently for each cell (i.e., if the desired output contains between-subjects factors, SEs are calculated for each cell including the between-subjects factors).
"between"First aggregates the data per participant and then calculates the SEs for each between-subjects condition. Results in one SE and t-quantile for all conditions in purely within-subjects designs.
"none" or NULL
Suppresses calculation of SEs and plots no error bars.
For mixed models, the within-subjects/repeated-measures factors are
relative to the chosen id effects grouping factor. They are
automatically detected based on the random-slopes of the random-effects
grouping factor in id. All other factors are treated as
independent-samples or between-subjects factors.
Returns a ggplot2 plot (i.e., object of class c("gg",
"ggplot")) unless return = "data".
Only the DV/response variable can be called y, but no
factor/variable used for plotting.
Belia, S., Fidler, F., Williams, J., & Cumming, G. (2005). Researchers Misunderstand Confidence Intervals and Standard Error Bars. Psychological Methods, 10(4), 389-396. https://doi.org/10.1037/1082-989X.10.4.389
Cousineau, D., & O'Brien, F. (2014). Error bars in within-subject designs: a comment on Baguley (2012). Behavior Research Methods, 46(4), 1149-1151. https://doi.org/10.3758/s13428-013-0441-z
Cumming, G., & Finch, S. (2005). Inference by Eye: Confidence Intervals and How to Read Pictures of Data. American Psychologist, 60(2), 170-180. https://doi.org/10.1037/0003-066X.60.2.170
Knol, M. J., Pestman, W. R., & Grobbee, D. E. (2011). The (mis)use of overlap of confidence intervals to assess effect modification. European Journal of Epidemiology, 26(4), 253-254. https://doi.org/10.1007/s10654-011-9563-8
Schenker, N., & Gentleman, J. F. (2001). On Judging the Significance of Differences by Examining the Overlap Between Confidence Intervals. The American Statistician, 55(3), 182-186. https://doi.org/10.1198/000313001317097960
# note: use library("ggplot") to avoid "ggplot2::" in the following
##################################################################
## 2-factor Within-Subject Design ##
##################################################################
data(md_12.1)
aw <- aov_ez("id", "rt", md_12.1, within = c("angle", "noise"))
##---------------------------------------------------------------
## Basic Interaction Plots -
##---------------------------------------------------------------
## all examples require emmeans and ggplot2:
if (requireNamespace("emmeans") && requireNamespace("ggplot2")) {
afex_plot(aw, x = "angle", trace = "noise")
# or: afex_plot(aw, x = ~angle, trace = ~noise)
afex_plot(aw, x = "noise", trace = "angle")
### For within-subject designs, using within-subject CIs is better:
afex_plot(aw, x = "angle", trace = "noise", error = "within")
(p1 <- afex_plot(aw, x = "noise", trace = "angle", error = "within"))
## use different themes for nicer graphs:
p1 + ggplot2::theme_bw()
}
## Not run:
p1 + ggplot2::theme_light()
p1 + ggplot2::theme_minimal()
p1 + jtools::theme_apa()
p1 + ggpubr::theme_pubr()
### set theme globally for R session:
ggplot2::theme_set(ggplot2::theme_bw())
### There are several ways to deal with overlapping points in the background besides alpha
# 1. using the default data geom and ggplot2::position_jitterdodge
afex_plot(aw, x = "noise", trace = "angle", error = "within", dodge = 0.3,
data_arg = list(
position =
ggplot2::position_jitterdodge(
jitter.width = 0,
jitter.height = 5,
dodge.width = 0.3 ## needs to be same as dodge
),
color = "darkgrey"))
# 2. using ggbeeswarm::geom_beeswarm
afex_plot(aw, x = "noise", trace = "angle", error = "within", dodge = 0.5,
data_geom = ggbeeswarm::geom_beeswarm,
data_arg = list(
dodge.width = 0.5, ## needs to be same as dodge
cex = 0.8,
color = "darkgrey"))
# 3. do not display points, but use a violinplot: ggplot2::geom_violin
afex_plot(aw, x = "noise", trace = "angle", error = "within",
data_geom = ggplot2::geom_violin,
data_arg = list(width = 0.5))
# 4. violinplots with color: ggplot2::geom_violin
afex_plot(aw, x = "noise", trace = "angle", error = "within",
mapping = c("linetype", "shape", "fill"),
data_geom = ggplot2::geom_violin,
data_arg = list(width = 0.5))
# 5. do not display points, but use a boxplot: ggplot2::geom_boxplot
afex_plot(aw, x = "noise", trace = "angle", error = "within",
data_geom = ggplot2::geom_boxplot,
data_arg = list(width = 0.3))
# 6. combine points with boxplot: ggpol::geom_boxjitter
afex_plot(aw, x = "noise", trace = "angle", error = "within",
data_geom = ggpol::geom_boxjitter,
data_arg = list(width = 0.3))
## hides error bars!
# 7. nicer variant of ggpol::geom_boxjitter
afex_plot(aw, x = "noise", trace = "angle", error = "within",
mapping = c("shape", "fill"),
data_geom = ggpol::geom_boxjitter,
data_arg = list(
width = 0.3,
jitter.width = 0,
jitter.height = 10,
outlier.intersect = TRUE),
point_arg = list(size = 2.5),
error_arg = list(size = 1.5, width = 0))
# 8. nicer variant of ggpol::geom_boxjitter without lines
afex_plot(aw, x = "noise", trace = "angle", error = "within", dodge = 0.7,
mapping = c("shape", "fill"),
data_geom = ggpol::geom_boxjitter,
data_arg = list(
width = 0.5,
jitter.width = 0,
jitter.height = 10,
outlier.intersect = TRUE),
point_arg = list(size = 2.5),
line_arg = list(linetype = 0),
error_arg = list(size = 1.5, width = 0))
## End(Not run)
##---------------------------------------------------------------
## One-Way Plots -
##---------------------------------------------------------------
## Not run:
afex_plot(aw, x = "angle", error = "within") ## default
## with color we need larger points
afex_plot(aw, x = "angle", mapping = "color", error = "within",
point_arg = list(size = 2.5),
error_arg = list(size = 1.5, width = 0.05))
afex_plot(aw, x = "angle", error = "within", data_geom = ggpol::geom_boxjitter)
## nicer
afex_plot(aw, x = "angle", error = "within", data_geom = ggpol::geom_boxjitter,
mapping = "fill", data_alpha = 0.7,
data_arg = list(
width = 0.6,
jitter.width = 0.07,
jitter.height = 10,
outlier.intersect = TRUE
),
point_arg = list(size = 2.5),
error_arg = list(size = 1.5, width = 0.05))
## we can add a line connecting the means using geom_point(aes(group = 1)):
afex_plot(aw, x = "angle", error = "within") +
ggplot2::geom_line(ggplot2::aes(group = 1))
## One-way plots also supports panels:
afex_plot(aw, x = "angle", panel = "noise", error = "within")
## And panels with lines:
afex_plot(aw, x = "angle", panel = "noise", error = "within") +
ggplot2::geom_line(ggplot2::aes(group = 1))
## For more complicated plots it is easier to attach ggplot2:
library("ggplot2")
## We can hide geoms by plotting them in transparent color and add them
## afterward to use a mapping not directly supported.
## For example, the next plot adds a line to a one-way plot with panels, but
## with all geoms in the foreground having a color conditional on the panel.
afex_plot(aw, x = "angle", panel = "noise", error = "within",
point_arg = list(color = "transparent"),
error_arg = list(color = "transparent")) +
geom_point(aes(color = panel)) +
geom_linerange(aes(color = panel, ymin = lower, ymax = upper)) +
geom_line(aes(group = 1, color = panel)) +
guides(color = guide_legend(title = "NOISE"))
## Note that we need to use guides explicitly, otherwise the legend title would
## be "panel". legend_title does not work in this case.
##---------------------------------------------------------------
## Other Basic Options -
##---------------------------------------------------------------
## relabel factor levels via factor_levels (with message)
afex_plot(aw, x = "noise", trace = "angle",
factor_levels = list(angle = c("0°", "4°", "8°"),
noise = c("Absent", "Present")))
## factor_levels allows named vectors which enable reordering the factor levels
### and renaming subsets of levels:
afex_plot(aw, x = "noise", trace = "angle",
factor_levels = list(
angle = c(X8 = "8°", X4 = "4°", X0 = "0°"),
noise = c(present = "Present")
)
)
## Change title of legend
afex_plot(aw, x = "noise", trace = "angle",
legend_title = "Noise Condition")
## for plots with few factor levels, smaller dodge might be better:
afex_plot(aw, x = "angle", trace = "noise", dodge = 0.25)
#################################################################
## 4-factor Mixed Design ##
#################################################################
data(obk.long, package = "afex")
a1 <- aov_car(value ~ treatment * gender + Error(id/(phase*hour)),
data = obk.long, observed = "gender")
## too difficult to see anything
afex_plot(a1, ~phase*hour, ~treatment) +
ggplot2::theme_light()
## better
afex_plot(a1, ~hour, ~treatment, ~phase) +
ggplot2::theme_light()
## even better and different model-based standard errors
afex_plot(a1, ~hour, ~treatment, ~phase,
dodge = 0.65,
data_arg = list(
position =
ggplot2::position_jitterdodge(
jitter.width = 0,
jitter.height = 0.2,
dodge.width = 0.65 ## needs to be same as dodge
),
color = "darkgrey"),
emmeans_arg = list(model = "multivariate")) +
ggplot2::theme_classic()
# with color instead of linetype to separate trace factor
afex_plot(a1, ~hour, ~treatment, ~phase,
mapping = c("shape", "color"),
dodge = 0.65,
data_arg = list(
position =
ggplot2::position_jitterdodge(
jitter.width = 0,
jitter.height = 0.2,
dodge.width = 0.65 ## needs to be same as dodge
)),
emmeans_arg = list(model = "multivariate")) +
ggplot2::theme_light()
# only color to separate trace factor
afex_plot(a1, ~hour, ~treatment, ~phase,
mapping = "color",
dodge = 0.65,
data_arg = list(
position =
ggplot2::position_jitterdodge(
jitter.width = 0,
jitter.height = 0.2,
dodge.width = 0.65 ## needs to be same as dodge
)),
emmeans_arg = list(model = "multivariate")) +
ggplot2::theme_classic()
## plot involving all 4 factors:
afex_plot(a1, ~hour, ~treatment, ~gender+phase,
dodge = 0.65,
data_arg = list(
position =
ggplot2::position_jitterdodge(
jitter.width = 0,
jitter.height = 0.2,
dodge.width = 0.65 ## needs to be same as dodge
),
color = "darkgrey"),
emmeans_arg = list(model = "multivariate")) +
ggplot2::theme_bw()
##---------------------------------------------------------------
## Different Standard Errors Available -
##---------------------------------------------------------------
## purely within-design
cbind(
afex_plot(a1, ~phase, ~hour,
error = "model", return = "data")$means[,c("phase", "hour", "y", "SE")],
multivariate = afex_plot(a1, ~phase, ~hour,
emmeans_arg = list(model = "multivariate"),
error = "model", return = "data")$means$error,
mean = afex_plot(a1, ~phase, ~hour,
error = "mean", return = "data")$means$error,
within = afex_plot(a1, ~phase, ~hour,
error = "within", return = "data")$means$error,
between = afex_plot(a1, ~phase, ~hour,
error = "between", return = "data")$means$error)
## mixed design
cbind(
afex_plot(a1, ~phase, ~treatment,
error = "model", return = "data")$means[,c("phase", "treatment", "y", "SE")],
multivariate = afex_plot(a1, ~phase, ~treatment,
emmeans_arg = list(model = "multivariate"),
error = "model", return = "data")$means$error,
mean = afex_plot(a1, ~phase, ~treatment,
error = "mean", return = "data")$means$error,
within = afex_plot(a1, ~phase, ~treatment,
error = "within", return = "data")$means$error,
between = afex_plot(a1, ~phase, ~treatment,
error = "between", return = "data")$means$error)
## End(Not run)
##################################################################
## Mixed Models ##
##################################################################
data("Machines", package = "MEMSS")
m1 <- mixed(score ~ Machine + (Machine|Worker), data=Machines)
if (requireNamespace("emmeans") && requireNamespace("ggplot2")) {
pairs(emmeans::emmeans(m1, "Machine"))
# contrast estimate SE df t.ratio p.value
# A - B -7.966667 2.420850 5 -3.291 0.0481
# A - C -13.916667 1.540100 5 -9.036 0.0007
# B - C -5.950000 2.446475 5 -2.432 0.1253
## Default (i.e., model-based) error bars suggest no difference between Machines.
## This contrasts with pairwise comparisons above.
afex_plot(m1, "Machine")
## Impression from within-subject error bars is more in line with pattern of differences.
afex_plot(m1, "Machine", error = "within")
}
## Not run:
data("fhch2010") # load
fhch <- droplevels(fhch2010[ fhch2010$correct,]) # remove errors
### following model should take less than a minute to fit:
mrt <- mixed(log_rt ~ task*stimulus*frequency + (stimulus*frequency||id)+
(task||item), fhch, method = "S", expand_re = TRUE)
## way too many points in background:
afex_plot(mrt, "stimulus", "frequency", "task")
## better to restrict plot of data to one random-effects grouping variable
afex_plot(mrt, "stimulus", "frequency", "task", id = "id")
## when plotting data from a single random effect, different error bars are possible:
afex_plot(mrt, "stimulus", "frequency", "task", id = "id", error = "within")
afex_plot(mrt, "stimulus", "frequency", "task", id = "id", error = "mean")
## compare visual impression with:
pairs(emmeans::emmeans(mrt, c("stimulus", "frequency"), by = "task"))
## same logic also possible for other random-effects grouping factor
afex_plot(mrt, "stimulus", "frequency", "task", id = "item")
## within-item error bars are misleading here. task is sole within-items factor.
afex_plot(mrt, "stimulus", "frequency", "task", id = "item", error = "within")
## CIs based on stanard error of mean look small, but not unreasonable given results.
afex_plot(mrt, "stimulus", "frequency", "task", id = "item", error = "mean")
### compare distribution of individual data for different random effects:
## requires package cowplot
p_id <- afex_plot(mrt, "stimulus", "frequency", "task", id = "id",
error = "within", dodge = 0.7,
data_geom = ggplot2::geom_violin,
mapping = c("shape", "fill"),
data_arg = list(width = 0.7)) +
ggplot2::scale_shape_manual(values = c(4, 17)) +
ggplot2::labs(title = "ID")
p_item <- afex_plot(mrt, "stimulus", "frequency", "task", id = "item",
error = "within", dodge = 0.7,
data_geom = ggplot2::geom_violin,
mapping = c("shape", "fill"),
data_arg = list(width = 0.7)) +
ggplot2::scale_shape_manual(values = c(4, 17)) +
ggplot2::labs(title = "Item")
### see: https://cran.r-project.org/package=cowplot/vignettes/shared_legends.html
p_comb <- cowplot::plot_grid(
p_id + ggplot2::theme_light() + ggplot2::theme(legend.position="none"),
p_item + ggplot2::theme_light() + ggplot2::theme(legend.position="none")
)
legend <- cowplot::get_legend(p_id + ggplot2::theme(legend.position="bottom"))
cowplot::plot_grid(p_comb, legend,
ncol = 1,
rel_heights = c(1, 0.1))
##----------------------------------------------------------------
## Support for lme4::lmer -
##----------------------------------------------------------------
Oats <- nlme::Oats
## afex_plot does currently not support implicit nesting: (1|Block/Variety)
## Instead, we need to create the factor explicitly
Oats$VarBlock <- Oats$Variety:Oats$Block
Oats.lmer <- lmer(yield ~ Variety * factor(nitro) + (1|VarBlock) + (1|Block),
data = Oats)
afex_plot(Oats.lmer, "nitro", "Variety")
afex_plot(Oats.lmer, "nitro", panel = "Variety")
##################################################################
## Default Method works for Models Supported by emmeans ##
##################################################################
## lm
warp.lm <- lm(breaks ~ wool * tension, data = warpbreaks)
afex_plot(warp.lm, "tension")
afex_plot(warp.lm, "tension", "wool")
## poisson glm
ins <- data.frame(
n = c(500, 1200, 100, 400, 500, 300),
size = factor(rep(1:3,2), labels = c("S","M","L")),
age = factor(rep(1:2, each = 3)),
claims = c(42, 37, 1, 101, 73, 14))
ins.glm <- glm(claims ~ size + age + offset(log(n)),
data = ins, family = "poisson")
afex_plot(ins.glm, "size", "age")
## binomial glm adapted from ?predict.glm
ldose <- factor(rep(0:5, 2))
numdead <- c(1, 4, 9, 13, 18, 20, 0, 2, 6, 10, 12, 16)
sex <- factor(rep(c("M", "F"), c(6, 6)))
SF <- numdead/20 ## dv should be a vector, no matrix
budworm.lg <- glm(SF ~ sex*ldose, family = binomial,
weights = rep(20, length(numdead)))
afex_plot(budworm.lg, "ldose")
afex_plot(budworm.lg, "ldose", "sex") ## data point is hidden behind mean!
afex_plot(budworm.lg, "ldose", "sex",
data_arg = list(size = 4, color = "red"))
## nlme mixed model
data(Oats, package = "nlme")
Oats$nitro <- factor(Oats$nitro)
oats.1 <- nlme::lme(yield ~ nitro * Variety,
random = ~ 1 | Block / Variety,
data = Oats)
afex_plot(oats.1, "nitro", "Variety", data = Oats)
afex_plot(oats.1, "nitro", "Variety", data = Oats, id = "Block")
afex_plot(oats.1, "nitro", data = Oats)
afex_plot(oats.1, "nitro", data = Oats, id = c("Block", "Variety"))
afex_plot(oats.1, "nitro", data = Oats, id = "Block")
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.