Description of Experiment and Data
Singmann and Klauer (2011) were interested in whether or not
conditional reasoning can be explained by a single process or whether
multiple processes are necessary to explain it. To provide evidence for
multiple processes we aimed to establish a double dissociation of two
variables: instruction type and problem type. Instruction type was
manipulated between-subjects, one group of participants received
deductive instructions (i.e., to treat the premises as given and only
draw necessary conclusions) and a second group of participants received
probabilistic instructions (i.e., to reason as in an everyday situation;
we called this “inductive instruction” in the manuscript). Problem type
consisted of two different orthogonally crossed variables that were
manipulated within-subjects, validity of the problem (formally valid or
formally invalid) and plausibility of the problem (inferences which were
consisted with the background knowledge versus problems that were
inconsistent with the background knowledge). The critical comparison
across the two conditions was among problems which were valid and
implausible with problems that were invalid and plausible. For example,
the next problem was invalid and plausible:
If a person is wet, then the person fell into a swimming pool.
A person fell into a swimming pool.
How valid is the conclusion/How likely is it that the person is wet?
For those problems we predicted that under deductive instructions
responses should be lower (as the conclusion does not necessarily follow
from the premises) as under probabilistic instructions. For the valid
but implausible problem, an example is presented next, we predicted the
opposite pattern:
If a person is wet, then the person fell into a swimming pool.
A person is wet.
How valid is the conclusion/How likely is it that the person fell into a
swimming pool?
Our study also included valid and plausible and invalid and
implausible problems.
In contrast to the analysis reported in the manuscript, we initially
do not separate the analysis into affirmation and denial problems, but
first report an analysis on the full set of inferences, MP, MT, AC, and
DA, where MP and MT are valid and AC and DA invalid. We report a
reanalysis of our Experiment 1 only. Note that the factor
plausibility
is not present in the original manuscript,
there it is a results of a combination of other factors.
Data and R Preperation
We begin by loading the packages we will be using throughout.
library("afex") # needed for ANOVA functions.
library("emmeans") # emmeans must now be loaded explicitly for follow-up tests.
library("multcomp") # for advanced control for multiple testing/Type 1 errors.
library("ggplot2") # for customizing plots.
data(sk2011.1)
str(sk2011.1)
## 'data.frame': 640 obs. of 9 variables:
## $ id : Factor w/ 40 levels "8","9","10","12",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ instruction : Factor w/ 2 levels "deductive","probabilistic": 2 2 2 2 2 2 2 2 2 2 ...
## $ plausibility: Factor w/ 2 levels "plausible","implausible": 1 2 2 1 2 1 1 2 1 2 ...
## $ inference : Factor w/ 4 levels "MP","MT","AC",..: 4 2 1 3 4 2 1 3 4 2 ...
## $ validity : Factor w/ 2 levels "valid","invalid": 2 1 1 2 2 1 1 2 2 1 ...
## $ what : Factor w/ 2 levels "affirmation",..: 2 2 1 1 2 2 1 1 2 2 ...
## $ type : Factor w/ 2 levels "original","reversed": 2 2 2 2 1 1 1 1 2 2 ...
## $ response : int 100 60 94 70 100 99 98 49 82 50 ...
## $ content : Factor w/ 4 levels "C1","C2","C3",..: 1 1 1 1 2 2 2 2 3 3 ...
An important feature in the data is that each participant provided
two responses for each cell of the design (the content is different for
each of those, each participant saw all four contents). These two data
points will be aggregated automatically by afex
.
with(sk2011.1, table(inference, id, plausibility))
## , , plausibility = plausible
##
## id
## inference 8 9 10 12 13 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
## MP 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## MT 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## AC 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## DA 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## id
## inference 37 38 39 40 41 42 43 44 46 47 48 49 50
## MP 2 2 2 2 2 2 2 2 2 2 2 2 2
## MT 2 2 2 2 2 2 2 2 2 2 2 2 2
## AC 2 2 2 2 2 2 2 2 2 2 2 2 2
## DA 2 2 2 2 2 2 2 2 2 2 2 2 2
##
## , , plausibility = implausible
##
## id
## inference 8 9 10 12 13 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
## MP 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## MT 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## AC 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## DA 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## id
## inference 37 38 39 40 41 42 43 44 46 47 48 49 50
## MP 2 2 2 2 2 2 2 2 2 2 2 2 2
## MT 2 2 2 2 2 2 2 2 2 2 2 2 2
## AC 2 2 2 2 2 2 2 2 2 2 2 2 2
## DA 2 2 2 2 2 2 2 2 2 2 2 2 2
ANOVA
To get the full ANOVA table for the model, we simply pass it to
aov_ez
using the design as described above. We save the
returned object for further analysis.
a1 <- aov_ez("id", "response", sk2011.1, between = "instruction",
within = c("inference", "plausibility"))
## Warning: More than one observation per design cell, aggregating data using `fun_aggregate = mean`.
## To turn off this warning, pass `fun_aggregate = mean` explicitly.
## Contrasts set to contr.sum for the following variables: instruction
a1 # the default print method prints a data.frame produced by nice
## Anova Table (Type 3 tests)
##
## Response: response
## Effect df MSE F ges p.value
## 1 instruction 1, 38 2027.42 0.31 .003 .583
## 2 inference 2.66, 101.12 959.12 5.81 ** .063 .002
## 3 instruction:inference 2.66, 101.12 959.12 6.00 ** .065 .001
## 4 plausibility 1, 38 468.82 34.23 *** .068 <.001
## 5 instruction:plausibility 1, 38 468.82 10.67 ** .022 .002
## 6 inference:plausibility 2.29, 87.11 318.91 2.87 + .009 .055
## 7 instruction:inference:plausibility 2.29, 87.11 318.91 3.98 * .013 .018
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
##
## Sphericity correction method: GG
The equivalent calls (i.e., producing exactly the same output) of the
other two ANOVA functions aov_car
or aov4
is
shown below.
aov_car(response ~ instruction + Error(id/inference*plausibility), sk2011.1)
aov_4(response ~ instruction + (inference*plausibility|id), sk2011.1)
As mentioned before, the two responses per cell of the design and
participants are aggregated for the analysis as indicated by the warning
message. Furthermore, the degrees of freedom are Greenhouse-Geisser
corrected per default for all effects involving inference
,
as inference
is a within-subject factor with more than two
levels (i.e., MP, MT, AC, & DA). In line with our expectations, the
three-way interaction is significant.
The object printed per default for afex_aov
objects
(produced by nice
) can also be printed nicely using
knitr
:
instruction |
1, 38 |
2027.42 |
0.31 |
.003 |
.583 |
inference |
2.66, 101.12 |
959.12 |
5.81 ** |
.063 |
.002 |
instruction:inference |
2.66, 101.12 |
959.12 |
6.00 ** |
.065 |
.001 |
plausibility |
1, 38 |
468.82 |
34.23 *** |
.068 |
<.001 |
instruction:plausibility |
1, 38 |
468.82 |
10.67 ** |
.022 |
.002 |
inference:plausibility |
2.29, 87.11 |
318.91 |
2.87 + |
.009 |
.055 |
instruction:inference:plausibility |
2.29, 87.11 |
318.91 |
3.98 * |
.013 |
.018 |
Alternatively, the anova
method for
afex_aov
objects returns a data.frame
of class
anova
that can be passed to, for example,
xtable
for nice formatting:
print(xtable::xtable(anova(a1), digits = c(rep(2, 5), 3, 4)), type = "html")
|
num Df
|
den Df
|
MSE
|
F
|
ges
|
Pr(>F)
|
instruction
|
1.00
|
38.00
|
2027.42
|
0.31
|
0.003
|
0.5830
|
inference
|
2.66
|
101.12
|
959.12
|
5.81
|
0.063
|
0.0016
|
instruction:inference
|
2.66
|
101.12
|
959.12
|
6.00
|
0.065
|
0.0013
|
plausibility
|
1.00
|
38.00
|
468.82
|
34.23
|
0.068
|
0.0000
|
instruction:plausibility
|
1.00
|
38.00
|
468.82
|
10.67
|
0.022
|
0.0023
|
inference:plausibility
|
2.29
|
87.11
|
318.91
|
2.87
|
0.009
|
0.0551
|
instruction:inference:plausibility
|
2.29
|
87.11
|
318.91
|
3.98
|
0.013
|
0.0177
|
Post-Hoc Contrasts and Plotting
To further analyze the data we need to pass it to package
emmeans
, a package that offers great functionality for both
plotting and contrasts of all kind. A lot of information on
emmeans
can be obtained in its vignettes and
faq.
emmeans
can work with afex_aov
objects
directly as afex comes with the necessary methods for
the generic functions defined in emmeans
. When using the
default multivariate
option for follow-up tests,
emmeans
uses the ANOVA model estimated via base R’s
lm
method (which in the case of a multivariate response is
an object of class c("mlm", "lm")
). afex
also
supports a univariate model (i.e.,
emmeans_model = "univariate"
, which requires that
include_aov = TRUE
in the ANOVA call) in which case
emmeans
uses the object created by base R’s
aov
function (this was the previous default but is not
recommended as it does not handle unbalanced data well).
Some First Contrasts
Main Effects Only
This object can now be passed to emmeans
, for example to
obtain the marginal means of the four inferences:
m1 <- emmeans(a1, ~ inference)
m1
## inference emmean SE df lower.CL upper.CL
## MP 87.5 1.80 38 83.9 91.2
## MT 76.7 4.06 38 68.5 84.9
## AC 69.4 4.77 38 59.8 79.1
## DA 83.0 3.84 38 75.2 90.7
##
## Results are averaged over the levels of: instruction, plausibility
## Confidence level used: 0.95
This object can now also be used to compare whether or not there are
differences between the levels of the factor:
## contrast estimate SE df t.ratio p.value
## MP - MT 10.83 4.33 38 2.501 0.0759
## MP - AC 18.10 5.02 38 3.607 0.0047
## MP - DA 4.56 4.20 38 1.086 0.7002
## MT - AC 7.27 3.98 38 1.825 0.2778
## MT - DA -6.28 4.70 38 -1.334 0.5473
## AC - DA -13.54 5.30 38 -2.556 0.0672
##
## Results are averaged over the levels of: instruction, plausibility
## P value adjustment: tukey method for comparing a family of 4 estimates
To obtain more powerful p-value adjustments, we can furthermore pass
it to multcomp
(Bretz, Hothorn, & Westfall, 2011):
summary(as.glht(pairs(m1)), test=adjusted("free"))
##
## Simultaneous Tests for General Linear Hypotheses
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## MP - MT == 0 10.831 4.331 2.501 0.05897 .
## MP - AC == 0 18.100 5.018 3.607 0.00463 **
## MP - DA == 0 4.556 4.196 1.086 0.31350
## MT - AC == 0 7.269 3.984 1.825 0.19410
## MT - DA == 0 -6.275 4.703 -1.334 0.31350
## AC - DA == 0 -13.544 5.299 -2.556 0.05897 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- free method)
A Simple interaction
We could now also be interested in the marginal means of the
inferences across the two instruction types. emmeans
offers
two ways to do so. The first splits the contrasts across levels of the
factor using the by
argument.
m2 <- emmeans(a1, "inference", by = "instruction")
## equal: emmeans(a1, ~ inference|instruction)
m2
## instruction = deductive:
## inference emmean SE df lower.CL upper.CL
## MP 97.3 2.54 38 92.1 102.4
## MT 70.4 5.75 38 58.8 82.0
## AC 61.5 6.75 38 47.8 75.1
## DA 81.8 5.43 38 70.8 92.8
##
## instruction = probabilistic:
## inference emmean SE df lower.CL upper.CL
## MP 77.7 2.54 38 72.6 82.9
## MT 83.0 5.75 38 71.3 94.6
## AC 77.3 6.75 38 63.7 91.0
## DA 84.1 5.43 38 73.1 95.1
##
## Results are averaged over the levels of: plausibility
## Confidence level used: 0.95
Consequently, tests are also only performed within each level of the
by
factor:
## instruction = deductive:
## contrast estimate SE df t.ratio p.value
## MP - MT 26.89 6.13 38 4.389 0.0005
## MP - AC 35.80 7.10 38 5.045 0.0001
## MP - DA 15.47 5.93 38 2.608 0.0599
## MT - AC 8.91 5.63 38 1.582 0.4007
## MT - DA -11.41 6.65 38 -1.716 0.3297
## AC - DA -20.32 7.49 38 -2.712 0.0471
##
## instruction = probabilistic:
## contrast estimate SE df t.ratio p.value
## MP - MT -5.22 6.13 38 -0.853 0.8287
## MP - AC 0.40 7.10 38 0.056 0.9999
## MP - DA -6.36 5.93 38 -1.072 0.7084
## MT - AC 5.62 5.63 38 0.998 0.7512
## MT - DA -1.14 6.65 38 -0.171 0.9982
## AC - DA -6.76 7.49 38 -0.902 0.8036
##
## Results are averaged over the levels of: plausibility
## P value adjustment: tukey method for comparing a family of 4 estimates
The second version considers all factor levels together.
Consequently, the number of pairwise comparisons is a lot larger:
m3 <- emmeans(a1, c("inference", "instruction"))
## equal: emmeans(a1, ~inference*instruction)
m3
## inference instruction emmean SE df lower.CL upper.CL
## MP deductive 97.3 2.54 38 92.1 102.4
## MT deductive 70.4 5.75 38 58.8 82.0
## AC deductive 61.5 6.75 38 47.8 75.1
## DA deductive 81.8 5.43 38 70.8 92.8
## MP probabilistic 77.7 2.54 38 72.6 82.9
## MT probabilistic 83.0 5.75 38 71.3 94.6
## AC probabilistic 77.3 6.75 38 63.7 91.0
## DA probabilistic 84.1 5.43 38 73.1 95.1
##
## Results are averaged over the levels of: plausibility
## Confidence level used: 0.95
## contrast estimate SE df t.ratio p.value
## MP deductive - MT deductive 26.89 6.13 38 4.389 0.0020
## MP deductive - AC deductive 35.80 7.10 38 5.045 0.0003
## MP deductive - DA deductive 15.47 5.93 38 2.608 0.1848
## MP deductive - MP probabilistic 19.55 3.59 38 5.439 0.0001
## MP deductive - MT probabilistic 14.32 6.29 38 2.279 0.3310
## MP deductive - AC probabilistic 19.95 7.21 38 2.767 0.1342
## MP deductive - DA probabilistic 13.19 5.99 38 2.201 0.3741
## MT deductive - AC deductive 8.91 5.63 38 1.582 0.7577
## MT deductive - DA deductive -11.41 6.65 38 -1.716 0.6772
## MT deductive - MP probabilistic -7.34 6.29 38 -1.167 0.9363
## MT deductive - MT probabilistic -12.56 8.13 38 -1.545 0.7783
## MT deductive - AC probabilistic -6.94 8.86 38 -0.783 0.9931
## MT deductive - DA probabilistic -13.70 7.91 38 -1.733 0.6666
## AC deductive - DA deductive -20.32 7.49 38 -2.712 0.1501
## AC deductive - MP probabilistic -16.25 7.21 38 -2.254 0.3446
## AC deductive - MT probabilistic -21.48 8.86 38 -2.423 0.2600
## AC deductive - AC probabilistic -15.85 9.54 38 -1.661 0.7111
## AC deductive - DA probabilistic -22.61 8.66 38 -2.611 0.1834
## DA deductive - MP probabilistic 4.08 5.99 38 0.680 0.9971
## DA deductive - MT probabilistic -1.15 7.91 38 -0.145 1.0000
## DA deductive - AC probabilistic 4.47 8.66 38 0.517 0.9995
## DA deductive - DA probabilistic -2.29 7.68 38 -0.298 1.0000
## MP probabilistic - MT probabilistic -5.22 6.13 38 -0.853 0.9885
## MP probabilistic - AC probabilistic 0.40 7.10 38 0.056 1.0000
## MP probabilistic - DA probabilistic -6.36 5.93 38 -1.072 0.9588
## MT probabilistic - AC probabilistic 5.62 5.63 38 0.998 0.9719
## MT probabilistic - DA probabilistic -1.14 6.65 38 -0.171 1.0000
## AC probabilistic - DA probabilistic -6.76 7.49 38 -0.902 0.9840
##
## Results are averaged over the levels of: plausibility
## P value adjustment: tukey method for comparing a family of 8 estimates
Running Custom Contrasts
Objects returned from emmeans
can also be used to test
specific contrasts. For this, we can simply create a list, where each
element corresponds to one contrasts. A contrast is defined as a vector
of constants on the reference grid (i.e., the object returned from
emmeans
, here m3
). For example, we might be
interested in whether there is a difference between the valid and
invalid inferences in each of the two conditions.
c1 <- list(
v_i.ded = c(0.5, 0.5, -0.5, -0.5, 0, 0, 0, 0),
v_i.prob = c(0, 0, 0, 0, 0.5, 0.5, -0.5, -0.5)
)
contrast(m3, c1, adjust = "holm")
## contrast estimate SE df t.ratio p.value
## v_i.ded 12.194 4.12 38 2.960 0.0105
## v_i.prob -0.369 4.12 38 -0.090 0.9291
##
## Results are averaged over the levels of: plausibility
## P value adjustment: holm method for 2 tests
summary(as.glht(contrast(m3, c1)), test = adjusted("free"))
##
## Simultaneous Tests for General Linear Hypotheses
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## v_i.ded == 0 12.1937 4.1190 2.96 0.0105 *
## v_i.prob == 0 -0.3687 4.1190 -0.09 0.9291
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- free method)
The results can be interpreted as in line with expectations.
Responses are larger for valid than invalid problems in the deductive,
but not the probabilistic condition.
Plotting
Since version 0.22
, afex
comes with its own
plotting function based on ggplot2
, afex_plot
,
which works directly with afex_aov
objects.
As said initially, we are interested in the three-way interaction of
instruction with inference, plausibility, and instruction. As we saw
above, this interaction was significant. Consequently, we are interested
in plotting this interaction.
Basic Plots
For afex_plot
, we need to specify the
x
-factor(s), which determine which factor-levels or
combinations of factor-levels are plotted on the x-axis. We can also
define trace
factor(s), which determine which factor levels
are connected by lines. Finally, we can also define panel
factor(s), which determine if the plot is split into subplots.
afex_plot
then plots the estimated marginal means obtained
from emmeans
, confidence intervals, and the raw data in the
background. Note that the raw data in the background is per default
drawn using an alpha blending of .5 (i.e., 50% semi-transparency). Thus,
in case of several points lying directly on top of each other, this
point appears noticeably darker.
afex_plot(a1, x = "inference", trace = "instruction", panel = "plausibility")
## Warning: Panel(s) show a mixed within-between-design.
## Error bars do not allow comparisons across all means.
## Suppress error bars with: error = "none"
![](data:image/png;base64,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)
In the default settings, the error bars show 95%-confidence intervals
based on the standard error of the underlying model (i.e., the
lm
model in the present case). In the present case, in
which each subplot (defined by x
- and
trace
-factor) shows a combination of a within-subjects
factor (i.e., inference
) and a between-subjects (i.e.,
instruction
) factor, this is not optimal. The error bars
only allow to assess differences regarding the between-subjects factor
(i.e., across the lines), but not inferences regarding the
within-subjects factor (i.e., within one line). This is also indicated
by a warning.
An alternative would be within-subject confidence intervals:
afex_plot(a1, x = "inference", trace = "instruction", panel = "plausibility",
error = "within")
## Warning: Panel(s) show a mixed within-between-design.
## Error bars do not allow comparisons across all means.
## Suppress error bars with: error = "none"
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABDgAAAJACAMAAABfbyYrAAACOlBMVEUAAAAAADoAAGYAOjoAOmYAOpAAZpAAZrYZGT8ZGWIZPz8ZP2IZP4EZYoEZYp8aGhozMzM6AAA6OgA6Ojo6OmY6OpA6ZmY6ZpA6ZrY6kLY6kNs/GRk/Pxk/P2I/YoE/Yp8/gZ8/gb1NTU1NTW5NTY5Nbm5Nbo5NbqtNjshiGRliPxliPz9iYoFigZ9igb1in71in9lmAABmADpmOgBmOjpmZgBmZjpmZmZmZpBmkJBmkLZmkNtmtttmtv9uTU1uTW5ubk1ubm5ubo5ujo5ujqtujshuq6tuq+SBPxmBYhmBYj+BYmKBn5+Bn72Bvb2BvdmOTU2Obk2Obm6Ojm6Oq6uOq8iOyOSOyP+QOgCQZgCQZjqQZmaQkDqQkLaQtraQttuQ2/+fYhmfYj+fgT+fgWKfgYGfn5+fvdmf2dmpqamqqqqrbk2rjk2rjm6rq6uryKuryOSryP+r5Mir5P+urq6xsbGzs7O2ZgC2Zjq2kDq2kGa2kJC2tpC2tra2ttu229u22/+2/9u2//+5ubm9gT+9n2K9n4G9vZ+9vb292b292dm+vr7Ijk3Ijm7Iq27Iq6vI5OTI5P/I///KysrU1NTZn2LZvYHZvZ/Z2Z/Z2b3Z2dnbkDrbkGbbtmbbtpDbtrbb25Db27bb29vb2//b/9vb///kq27kq47kyI7kyKvk5Kvk5Mjk5P/k/8jk/+Tk///r6+v/tmb/yI7/25D/27b/29v/5Kv/5Mj/5OT//7b//8j//9v//+T///8U1KJZAAAACXBIWXMAABYlAAAWJQFJUiTwAAAgAElEQVR4nO2d/aMdx3nX99oScpOSG9kOVuSmaY78opSXWFVAwfW9bYKDQqEgBxKrCtQmBYIkLAK+vddx6kRYSaE5ckDWcRJbtYMdJGJKka4sS5Wuzv/Gzsues6+zM7PPzO7sfD8/6OrePTvP7s7s58zO2yZzAAAwJOn7AAAA4QFxAACMgTgAAMZAHAAAYyAOAIAxEAcAwBiIAwBgDMQBADAG4gAAGANxAACMgTgAAMZAHAAAYyAOAIAxDsXxcTBukN1R4U8cb4ERUxFH3wcEXAJxABogjqiAOAANEEdUQByABogjKiAOQAPEERUQB6AB4ogKiAPQAHFEBcQBaIA4ogLiADRAHFEBcQAaII6ogDgADRBHVEAcgAaIIyogDkADxBEVEAegAeKICogD0ABxRAXEAWiAOKIC4rDkZ5+9759pf/gHqx976603Vn/1R5b7h8AYxMHzqR3kPcRhDQpPCYhDkeb48h7isMQ888dXeAqMQRyaIO8hDmtQeEpAHArGl/cQhzZp5v/Zv3l4dfXXv8V+yzL/T//m6urqR/42KxY/efhX/n1u009/L93yqd9nf8mqq3/2zYdX7yvu/9PfS5P81Lf6OCFaxiAOnk/pP29+c3X1vs+99dYP07z5NMta5H0ZiEObNPP/0SrjvifeWmT+N1cF7AulWHh+8rDY8pm3FoXno79Z2f8N+aknejwxGkYkjr/+FZ4nn/vmImuR92UgDm3eWOVfPz/9yiorIyLz3+BfTOwL6oly4fnXqx/70Vtv/hH/sCw8q6u/ke7/m7n9f/ZZlmT6qfHVXUPMbimO1V/5p2+9+RVR6fjBqshn5H0RiEObNPN5m3tapD6zLCGfWf6pUHjkt0q65Yll4flMef+sGV+zOX/IjEgcPOfSWgPLLpGByPsyEIc2b2RfDbylq9DAVVN40j999Pez7bLwiM25/d/8ikzkJw/nG8+CZETi4HmRZTD3A/K+DMShzaJlnBeSReF587/+u3/8a6uVwsNrp7LlLCs88ptluX9aW82QBStcRiQOnk8/+6zIEikO5H0RiEOb2sLzw19bXbSDlVrW/1Q0h/36j0ZbeArEJ46o8x7i0Kau8KRfLfd96m/9k/9Wra4y3vy3rL/uY6rCE2TDWC3RiSPuvIc4tKl9Tm1sIMv4Adur+Tk3zL64OkYuDuR9EYhDm5qW8axwvfGwKDyi0PBm+awk8Z9Zy/oTpf3TUim+yYojC4Nk5OJA3heBOLRZ9sWzjM6eU1mHPRtT+BleKj76LdYxvyq+UH71W2+xnv9ldXX1vr9T6sv/ycP8Uz8UXX9BM3ZxIO8LQBzavLH6EdEaxr9bsr54MXbwj3hhe0P89htfyY8ezA0CykYPLvdfjB78dL/nRsDIxYG8LwJxaMPnG6RZL3rZci3rH/lcVt1kkxc+8jnZQ89nIohPF+YrfDq/v/jUstc/XEYuDuR9EYhDmzCfRb0xBnE0grwvA3Fog8KjBOKICohDGxQeJRBHVEAc2qDwKIE4ogLi0AaFRwnEERUQB6Bh1OIAZSAOQAPEERUQB6AB4ogKiAPQAHFEBcQBaIA4ogLiADRAHFEBcQAaII6o8CkOMG6Q3VEBcQAakN1R4U8c7pIG/VMRRz+HAfwAcQAaII6ogDgADRBHVEAcgAaIIyogDkADxBEVEAegAeKICogD0ABxRAXEAWiAOKIC4gA0QBxRAXEAGiCOqIA4AA0QR1RAHIAGiCMqIA5AA8QRFRAHoAHiiIrBiyNxeESAEIgjKiAOQAPEERUQB6AB4ogKiAPQAHFEBcQBaIA4ogLiADRAHFEBcQAaII6ogDgADSTiQG6HAsQBaIA4ogLiADRAHFEBcQAaII6ogDgADRBHVEAcgAaIIyp8iuOaDUlitRvwTUUcNokgt0MBNQ5AA2ocUQFxABogjqiAOAANEEdUQByABogjKiAOQAPEERUQB6AB4ogKiAPQAHFEBcQBaIA4oiJscUztjiQsAjlJD+KY2STZCf8RQyFocUw3XNxUm5uKjRsOAs6nqtOYnnFwksqIdqpyL47ZzMF9rExzNnVhDnVIBwHnG/TFduDi2NhIkuaz3nBwQTY3T55sVoeLiNPpyZPNN/L0zJkzfiOmm23MQSCOzc0kab72MwfimM1OnlSkancl2kJubjaHdCHHjY2TJ8nL7cDFkXAaNk5TqZBn7ElO09aNk/TiUEc8c+YUeZVDHXE6PdWTOE6eTJLm45ql3//Ud5X6SsxScZDfx5ucxpAOxKE+SUuGLY4NIY6GmzXdSn4fb4qr3JCxqbrJI05FxIZ7dXrm1KlTxFUOdcT5NI1oYY7u4tgU4mi49jMHt/FMXImmVKdT+ipHWt84dSqtczRsdVCt2hAn2Vhu7eINWxxJoqhyTLk4iDP25EmVn7k4iM2hjMi9QV3lUJ9j6o2T/YjjpBBH/XFxb1BXOdRXgkekdtXmKU6zHR3VqhqrHJamClgcG7yYEd/GZ8RFrv+O31Cr245TIuKp+sPh4iCucqhVNWUHZGEOL+Igvo2V115UOKirHFMhjvpURa2K2BzKIj1ScZxRiGMqxUGbscrKa1ulzwrVg4OocFBXOZSPKtwb/YjjjBBHbQmX3iC+q5S5LSMSu0pZ43AiDnWpta3jhCGOuqOayu8nYnGkFfWTp07VXkqZA8QPK6rbeHNDouohtonIrly9qk6Jb2Fzc7gVh3j6p24AUIvDSchNVok8c6Y2Q7NaFW3EFnFYBhy2ONKqepKcOVOvDlXF1h5VV+XGqSRJvy2In1VUEVm5nZK3l02nSdJ4jmc45udI0Ksicrt+m5O7uK071gWbqToae1VcNI4qu2Ot6zhBiGNeZ45TUhwND6i2KJ5rN05JcRA/qzRH5N6Y0n/rNY+XkN6wMIdbcczcfP+7kFELqu5YJ3Kci0prQ0TbOs7AxbEYS1hVR3pLpcWful6X3am1G8U3Nf2gIGWKToY9JUlTqtb3Z6BzVTxrg6Ee4+ZmCFhjQNumo1DEUWcOdhvT112b826aicPn7BEHBWkmxFGbrP03e6DiGBJualU6Ic13DEYcXB2FLVwc5CP71BcyveGI47WgqhzYp9ksDnsgjhAhq2AOWBylSseMff+7qnI0H80IxOEm0VGKY+yzY7PG9xE/qoj/F8yRFn/68q986p0xU5EHVOCmctAmjr5mxypnJjlCPVW1hxYQ34xyANi8/B2UK1jcG55rAGlIxcxGFwGdiKNtlZO+Zsf6F0dLPd2NOPxPq1dhuXRAYOLImUOKw6c5WKvKGZ9VjhkXB/1ADvUqJ32JQznBwA1qcThpqGwNSR1Qje3UwdDEkVNHrpg1/SRmtpmKY9Pzw4oTNbaJo5dJbv7F0dI0GI04xt2rsvzL0g4t4qAVyWzKByh5flhx0qyiuBy2M7vIHlUGIw4nfaMaIWkDthzOdLppVeUIUByyYzbR+IIiFgmbyJ9sbHj1xtSzOLLBKsZJ0onDozraKxyjF8fmppU5ghQHL2EzMXC6aUmUhsS61ExmG0IcHp9VZm4GqwxUHOK4fKpDo8Lhs7/f//ivqRTHuLtj839lpZuX8C6X2UwkrIkjYbLyKI6pZ3FMp7bj6gnHcfisdHjvcBVzGpq2+a5yyEeVWGoc86xau+nmMtcLhI84S5onltIzmxLYsY6WBzyKZvZOA8Dq1eG9b9QFCnEshmP5PJjNMzHVOObSHJ5uYVmOF0/gjntvlmTdsdSpqgaAWfbj0I4crXte8d7F4YRh9arM0ufvjR57VW59dTI58LWr8pcXJpPJ0xcqoaxSVtyZXr/8ZUBRoN323iyZzeRMPuopwG3iIChJXYecV66l/75RF7T3APs8mrROu8Ea7fp6VLky4Tz2OvvlzjH+y4EXy6GsklZ1HPrtu1s2+it7iOt+dsD7JLeZZQcw/VyV0tXz3sXhhF56VRpLIa9wWJmDRBw31yePX5jffW3yeFrnuHua/XLrNP+lEMomaWX7v9+uu7m8izWuMZ1IxIQc0+NsS1NxHmKZk2Gsx1HIX/99o25o7wGmj9lc6mbq17w0QyKO81IS5yfPMos8wioeab3j2VIom6RbxOG30mE9caRFJLPGsxDj6n22ccxY35HN6Fgns2OXF8Z/36gj+uiObb5HhDcs+gkpxJHWMYQjrk8OsseWg/yXK5OjpVAWSas7DkWXrM+uOy6O7hlbEMdsNmusmbiZyacQB2tm5z3OQ1nIJ7sUWY+DTarNtN2oTpyibBp1MDFJ9aiSSmPDyhwU4rhzTLZn3FxPax7nFxYpPavQv3TaVYtk4wY544y6qXKRZJ1AmtpVOqGou9qOHHG1Hoc4d+6N5kUd7VFPVfW+iIKTc1R++/IBYN2H7XSscTzy+uIXbhEZQ3DNhiRp3caKllXadVzkNEds3NQtYH3IvDiy30mCNidz7ty5JEn/MU6yIg7zo7p4Mb2+1Qshz1qVL46wuQ7dcHOOqlJjGZGojeOg/PnI67nqB2/rcCmO3IfIzKEUR33BpgjYkGzeGteW4ugsksbdLmbiMD5Nd+IQV+Ga0uhOuGhzHbpFdHOOilJiG5FEHNcnky9fZb0q9eLISpJN0pqLyZHV5Vt6y1ysqqMI2PSg0rnXRtE/Z9uQ0/1RRdHbI5qIfbdj+l6YuodeFevmYcpxHAf+xXpf4iAzh1IcbpbjUmXdpniVS1t3mbFIWoact4Srxak4luqwOTRLnLx0Wh3Re6+KdUSikaPvPzOZfOkCa9aoaePIQtklrQuROtS9ZW4Kb3OKp+Q7oAwTbBXIIMWh7iZOXHRMK3Hy0mklvYmj50luvDuWuFdFHxpzKCscM88V5s1MHB3fHWv2SGN3Hd2KYzZzM6RFdTSpNDa8Vjlm1rdxC8on0wGscn6eDd0gHsdhApU6VBG6p28A62Hni44QvXRa65HG8ir6eD0Cebe0ivRuYgMcxrDQuXKVmR5XOZeuuLnOmjeIR44a4bxgeRaHs5Ay0eZxI+ZJenmvCv2IFiW+20Zdoa5f9ieOm+uTr8/n769zf9w9PXmMcK6KIV7LlRccnFCjGwYvDr+VDv+9Ko5Q9apYPv7RPKq8JmbHClWkGiGcHWuK368kD/gUx3zpDuNEvb3JzV8ORyOO/npVfvm7k8mjX3exHocxYzMH/dko3TB8cXh7XvHfq+IKVa9Kv+LQCuUu6SLjUodfcdg/rPh8d6yfHI5FHFazY8YojnGZw5k4arfNrNcc8yCO3DF5yOLpdDTmUHXHWs7bDFscjac7InVQL6zaWqUYzApgZQpzVT33r3jDSefvYBtH9UKRp6gQ5WiKFVuPizA5eb+pSpLlK6Cci2NW+vIfTRbncTNsZLiNo1qhyFNUnvFIihVfj4sorWVNQ/XQO7D1OBZUJ46MUB2+xWG9in7I4mgZKjuKYiXW4yJJKv+AEtBCPhmzmvaG0T2veJ8dG2XjaNtVHkGpmooh5wRVjuI91txaZv0KKMfiEN6oHNYI8jiPb3Fk9cuoGkc1JgRZFashlUS5lmznKkf5q1k5XdLuqdexOJg1Nuu6OJyqw/M8Ff+zY2Ns49C5yjalakDimGbi6FblqNboB7mQz7ylJsQXx6wel8PnFd8T3PqYVh+dODTnIJuXqgGJY6Z5kkrqbqxhrsfRNv97s6FO7cwcnqfG+p9Wb7/ETLji0MW4VA1IHATUfx8PVRwqyt2xedyow+fSK05RDwDrfz2OllDuklZjWKjGJI6manyA4pgp1/FzYg6viza5RD2t3irJGMRhWKr8i8NVRNVUNvLD8SIORVsPvTocNTj0gFocJNk9SnGYFaqRiEM5rjw8cTR0x+YgbyR11VSpDuoiUYjDGoNCNQpxtMxwbREHxeQF4uzWmXFGa45sPU66FPWCOkhVWRLsLlos4tAuVKqVtt3gIGJraVDOCRmiOGaZOKi739UvcnXyQkb14fQhDou7Phpx6BaqltvYQbaSi0OjKLSJo69Jbo3odkwb3gZtue1/IIfF677bab4kLTOlm4lIHFrmaBk46aAkWQ/VbECrIKiGElo+2DvvjtXE6D5Qi8N/E8fM5sXx7bSs9mZjjpjEodMQFLo4NEuBcsi53f0yFHGYqKMtt/0/tfIXMpCn2y4O4/s+LnFolCnld5CTryDCRxXtIjBqcehfBrU43LzxU3k8fH6B17kqGMehR2uRahcH/btjicRh8M2hHNptd5YDEgdRU7h3ccykONwPOdeZKK0mNnFolKnWb2MHh0SSiEGFU/1Jkjf09Jzd3ZvCraf72eNqkdPKFMfcxYE4dLHvqvQ/B0k/BcLRsXZnODBxdG/tadvoAH/iyP/S35vcNEO5S9oQdYlyUI1vPZ6u+5s2bym7Y8chDs1Kx5DE4Whd9VzxqJ3vCHFoYz+q0gndIto0ireJI+Du2DwaV2ZI4nDEQhx1JSV9HLNqjY1THMoSFZY47HrhRzyOo0D71Rm/OJYdrnXXgovD8UI+Oz9/5XuX578wDyJD2e7ohOYSFZI4LIf9jbw7No99i1bbxlBQjtSY8TVtnYrj7fvT4Pe8evvQrrPmYXgou91c0XgtwxGHrTZiEkebOqIRR/1GIQ4Lc+iKY+c4j87Ekaw8bxyGh7LayyENlzMUcdhrY/zjOAooL9TYxaEeGjpjq+hv2Awd0RXHVpKsPPQnh+55declpg/TMDyUzU5Oqb+eYYijizacnKMHcVj3ZimuVQ/i8Pg22kXjRvOxsBd+WRyRpji2E1bPuH2IKSN1yH7jOPMhiqOuuSiMafXdtOHkHN2Lo8shN1yvlivhQhweX2QtS8lm80u9plNe47A4IE1xCFkIcaQS2X3ZNM58mOKoFqgAxGE+bKNrRB18iKOjOWr6FPoQB3vNA326FRbFRCEO+YpxZ42jO8d5w4YQx+1Dds8qH782RNjFzf168eLFJEn/8XcAphFFeegU8dy5JDl3jvgcK+KgTZ5fqE6HXHPZ2q59urFLxDrO/THnHHW6ZZalJI2WJOk/tR+7aFsU9MQhXdFRHBb7+KDwVUS9OkY7ZhG7VjZ4RNvXwypxvlhx9zG7NdVL9bW3HFWpYkrxhq1W8sVkU9Q46qsc2SsgjSNAHBVzDPhRhUIbOuv+2hCAOEwfTNON5OYQE2Ab+z9dzHdUPKrMsjcFO+pVYZ2xa6Ns4xDkLvSAlw6k0Uag4iCaJlS6hOprnwQpjkoxUbVxuH6T2yUuCy4OJpHR9Kpk5K91yzwOJ9G1PkSjjTl/R7lVg5iSMMRRuYyqwfeJE3PMVMMmOmZxw5iN9mE7xpE0xZE+niS7zzJx7GyNZxxHnuXlHqQ4CLXhaD2iYSxWrEHxSipnO7oRh+osOmVy41AvD+P9miJssyO6P0keYD/XTKOIUFZ7eWNxydsmgLkI3foBQm0wHJzFcEeOVtCrX86SzBzEV1918buLgzzVerTnqnywV17HZOUpy1B2u3kju+oOZo62Rm7ZTKyNNga6AhjdRchd0fovaDmXNFn+jyp065FZ76YoJn2KY77zzueZOx540qZhlIey3M8fWTFp2t7H0oFNtU+HDHUhH8rLoKxfFsWRVHdyh+Vc58aHlA6pqol0PY4GWnLA/9KBPWhjuCuA0V4IeWGLiZZCVH4dtDiIU1UDcRRR5YH3pQN7sMZ8wCuAEV+LpPI1UbnejQsvOMsWNxOl+xfHjcNJ8uBZy1B2u6lw8dTQw5d8U4tWH9oY8Apg5FejRhxaEZetYQ4OyejDstLUdhw9imPnBOuDlS2ka3ahrPZS4eSpoQh1+g1BGw7ES/QiA17Ih/56tGV0S2fsIMShU1D6E8fO8UTOqF+5P7FcyScIcVTM4cMhdSN2+tEGxFHarnUdDDOLsjVCr6D0J45LSfKJy6zCkepjMOtxOGqoLGSFF4lUa8h9aWPQK4DRX5OZOkdN5gIYRO0sjkU83YLSnzi2+PSUVB97+PPKMOaqeB6O5c4h5Ra53rTRhQDF0ZaTNgulaH3Mbls5jn5B6U0c6ZPK/nk2S2Uos2P7GFWx/AylQ4pDoIPURqjiyNxQl4PGKyz4EscimO6HexNH6oo1/q9YzgfiKHy8s0QWn3bxHOSLgMSxvMj52bGVjLNemqUlEzuIo1hSDA5I+6O6mIhjO0kGtB6Hq0EVHa6yrUPyT6yBaiNQcZQSLWTYzHrKeZZGQ3ZaiyPJJ0zVqmKJyaOKaOIY43ocBWiusr5E5IagrTEPShyqRAv51DGiA3FkyZrWio0+roN24+jK8x8c4iM4RrkeRx7XbXBlh9g93gyOAMRRd41rEl3mBlGWlOJaiKPcz2d8AIY7tKMpDjnyiy/mM871OJY4vYEbJBK8NsYkjnkumzpGrI1rLI5CHcjmoHocObrNzCGbRsfyJrcGfN3C4/LGkMWhuryNW0SukLagadRjmsSROyarwBY7qdGeq/Lhy0f4hPrbv7XP8rXTEEdTvDF4g0YcypZIv+KY0+eMrTjyh2MZ2Go3FZgdW0F5lZ0MOIM4BC29ZF4aRwvH46Y2qEqy0smjtVd7RMVGuyINcVRQFqX+FiseOgMUh86tpvrEdLrBXh9A7Y5FejXpNomj2zE4KNIQRwWIw4ru4mgbmONbHLPpxsYGe4sEsTsKNqhsa2kd7RaxBtfi2HnnyJIvRDqOo6/FikNgQOIwuc0Un5xycUwXSdJ1slT+kHdJTS9M18gOirSuON5erFXMGMLIUWe0XeXeB7kPkwE9qtCIg1c4RJVjkSqJOxTiEBHI2jYaIy6hWrdJPY4jcnEMZHbMMBmQOExQ5bZ459os/2Gnt3FN8jSuclCktUeOJitPWXbDLkJ1290brVd5QLNjBsQAumNtbjLlowqnGqPjvdwmDtrahjIi3bpN9RFuH7JdLzAfqmsCnmi8yvaL3NhGDIkBDADzIY55d3eoBpXkU3bYqpJBt25TozjshpkXQnVNwBP+b2OIQwvlNzV5opk3aszR0R2tNY5FXy1VuehtABjEMbKIDvAhjobvRTfiaN3R9tbWGTlKqY0+R45utUxQufva+mTy2LfFL7demEwmT1+ohLI7Qu9AHFY4F8eM/gXQ827X3tId7eKg1Uaf4tgWS3E0cTPVBuMo++XOMf7/Ay+WQ9kfpVcgDiu8iKNoDorbq/MICfObvE0c1NroUxysW+WTjaO+UlU8dmF+98dcFndPTx6/ML+V/nu1FKrjofoC4rDCtThmgxTH3MIdanHQa8MJmiuAfeMIG8hx7wOSB4sNHlekJM6zKsfN9Uden3OZPFsKRXbQboE4hvi2er6QnzQH5a1F1N1pcru3ioPgiJyj3x3bPAAsrWPkHHFlclD+PFoK1f1ovRC9OGgmL5CLIxmuOOZG7lB8KhRt0IjjzjFex5Cclxa5Xn5WgTiGE1EJzeQF2uwWFQ7D95zoQNp3oecOdXfs1MU0SnooZsfeXH/86vvPyF6VRfWD/TWLIbgWBkky/ogqLqZY7FYRB/FhyduSOFXia691kA3b5b7nbC6+fyjEkdYtXlj0qtw5JrtTZFsHxDHEiAouXrQzh2NxJEGI45qOO2o38p0unkvFEYY5aMQxmXz56vzua5O0slEjjqwkWSTdB5E/qlDNeiLO7kwctKm6oeVIazbJHdiyQdMwnlVMxPHuKynfr/79uhzAMT8/OQhxBBGxGbJZT7TZvWxfI03WGapjrZ1Wz/42E+IIwhz6C/m8nM2s31cez3F9knNFTRtHFqr70XoB4higOBbvVaNN1SHNniv9cfmxkM5RVxy3Dy+dv6s0bWVRt+D/Qa9KABEboZsuSTytPqSbKqPBHY3Le9m/dNI/muJgr29LHvree+/9/ERSeQPkYqwXdwXGcQQQ0QFuF/KZ9XNTdY5W5w7VghvBFAX9uSrZLDe2GNhacet56Qr+EyNHA4jogEBXAFNC4qmKO1QLbgRTFPRXAFu8L/ZSZcLbzXU2Gfbua9lclccwV2XgER0woMWKyaCq4BTdIf/T/hgzZLRHji6n1adVjvLiHNfl7Fhex5BTZYOdHeufYEqLihGKg3K1t4U75M+GltNgioLFQj51q/rceiG1xZcuZL8EvR6Hf4IpLSpG+Kgym00p21SSEvWfoYvnFCJxaIWy2CcOgiktKsYnjtQbU1JzFNzR9AHKcA7RnFZ/PNcgul3pVtEMZbFPHARTWlT4WOXcc5cKvThaR8AGUxQ0G0cvLWfEspmy++s/1RLKZqcoCKa0qAh06UBFvBlfqtiNOZq30kZzhqY4mC12Pcf+985hy/cxQRyNBFNaVPSwdKBrHL1ERy2OukXVB4juyNFtPuB8Rfy7ZhfKaq8YgDg0qFk60DF9vLhvdOKYf7AYc77r+abPtISy2y0CII52ehhy3oM4+CQ38oAuMJgd++432GqjX6iZHqsZynbH0QNxtONfHK7e26cUR8MLoIYHxXocuqHcJR04EEcrYq7KNIwJYC00Z3dqjc1AzAFxDACIQwffvSrOUIljc3NzfOJ497tHjvzhWftQ1nuOHYhDB/+9KgSzY+tozO6pFEcI5tAWx40Tsm105Umb0V9ziKMZiEMD/70qdJPciiiy201rrAt0xfFBtv5XzUI+uqGs9ooBiKOdXhbygTgaMRgAtvLke++99+6JvZYjziGORiCOdvoQh6PbuDm7XXUAO8BgyLls3ahZyEczlM1OoAcG+Ca3XlYA60McoXQcYZJbBf8Zp47Yw+EM701uDCcVM8W5eh8AxryxGYg5MK2+xGy2uem7QqycSeW9HFneLD7EQX4lZrONjcazdfP9r27k7aFLxe4EIY4SmxyfEafK4YL+n3ktv2aDHMexwWmIJ4xOffmnqmHlM/9vVbE8Qe01RwtLB473UWUmxOFzZLMQh+Jrr4+pXeb7hTiOYybEoXonA23AxUuXGtLtYci5W3GkslgsULw15sbRzU3fVY7pVFXl8N7Mbj1BI8RxHBsbqiqHk9reVL6trT6g8jvECbblS3scx6jqpJgAABgGSURBVOFkD69m7HwnWXnKIlAg4pgKb3i0vrLG4WqeleJwBioOJ92xm8IbTV8TLi69ssah/A5xg1tx7HzjyJG9SfLgkSN/jw0AOyL4gtkDSxDiGFgbh3dxLKaE9vMmt2bcjONorXC4qHI0tnHI7xCfVQ7r8qXdOFqDYRNpGOJg0wV8O7+xV8X+NvZPkOM4NtM6R+PXhJtLr3jk6iG3ySqY0YuDqcN3xBC80Eqg4ziaa5fO7uIBDRS2VxWm1QMa3IvDM86+/4cojs5PphAHsGN04nDGgMRhD8QBaIA4dIlRHDcOJ8mDlmv5oCSNGohDl6jEsXOCNYXKVTnW7EJZ7QUCAeLQJSZx7BznfShbSbJyf5Ibfm4UymYnEAoQhy4xieNSknziMqtwpPrYwisgQRWIQ5eYxLHF57Wl+tgz8kluwBaIQ5eIxJE+qewXy/nsH/u0emAJxKFLROJIXbHG/2WtG9biuAZGTEUcfR/QYEmSvo+AABNxbItR5qhxgBpQ49AlohqHeFQRTRyjX3MUWAFx6BKROPgKYB8c4iM4ZEuHRSibnUAoQBy6xCQOOfIrrWmwibJWTyooSeMG4tAlJnHMt5k5ZNOo3fgvlKRxA3HoEpU45h++fIS/NPb2b+37hWUou91AGEAcusQlDoJQ7pIG/QNx6AJxGIZylzToH4hDF6U4QlkMzkQcOz9/5XuX55YPKihJIwfioCCI5WUZ+uJ4+36+zOjtQ7uwHgeo4kEcgdxTXZj5fpGbLdrrcRyX6xOjVwXU4l4cwXwb29PDKyAt0RUHW4njoT85dM+rOy9hHAeowf0rICGOAaEpjm0+iOP2IbmaD0aOgjJexBHGTWVND6+AtEV7yLmYUM/EgbkqoAb3746FOAaE9iQ3MWoUs2NBA+5fAdmDOfyG6+EVkNZoT6sXyoA4QANjFEcP8UKpVkEcgAb37471f1f5DxeMObQfVcQSYGjjAA2McABYKDdxH+ivcs6n1KfiwHocoA6IIyo0xcFW4dh9loljZwvjOEAN4xNHOM8NPaC9HgcbN3p/kjyAN7mBWiCOqNCeqyLXAGPL+TxlGcpuNxAGoxNHSE2V/tGf5LbzzueZOx540qZhlIey3A8EASa5RYWmOD60tUU+VPckwHDxMcmNPElgi/aQ8090VgfEMWowOzYqjAaAdQzVNQEwZDA7NiogDkADJrlFhf7s2LXOobomAIYMxBEVmuJg02Mte2GXoTruDwbNGCe5gUY056r8/JUTSZLc+4DkQUxyAyUgjqjQbuMogNmxoMwYZ8eCRiAOQMPoRo4CFXghE6AB4ogKInG8/8xkcuBrV8Uvt16YTCZPX6iEsksahAHEERU04rgy4Tz2OvvlzjH+y4EXy6GskgaBAHFEBYk4bq4f+Hpa0Tg2OZj+cvf05PEL81vpv1dLoSwPEQQBxBEVJOI4PznKftxcf+T17F9W73i2FMrqAEEgQBxRQdk4eucYU8YVXu9gP4+WQnVIGgweiCMqKMVxnT+dnJc1jevlZxWUpFEDcUQFoTh+uc6Ucfe0FMfN9UwcH5dcAyOmIo6+Dwi4hEwc5yeTA/9pzp5XZHeKbOuAOCIB4ogKKnHcfeGL6xPWt1IjjqwkWSYNggCPKlFB2cbxPntWgTgiBeKICtIh51cmj1+taePIQnVJGgwdiCMqSMXBKxnoVYkTiCMqKMSRq2Sk4sA4jjiBOKKCaOToweVPjByNE4gjKojmqky+fHV+9zU+sS2tfzyGuSrxAXFEBU0bxy/F7FjWHcs1gtmx8QFxRAVR4+itr6amyJbgwHocMQJxRAVWAAM04N2xw0W5UqvdVYU4AA0eXgE5hTlsUC/ybLn+M8RRAQtpW+H+FZBTiMMKiMMHWIPfEh/igDksUL9Xwra4QxwlIA5LnL8CcgpxWKEhDovLCnEUwWt/bHH9JrfpFOawo90bEEdnIA5bII6hoiEO88sKcZSANyxx/ArI6RTmsKaxQNu/WRPiKAFxWIKXTkcFxFEBhdMKvHQ6KiAOQAOGnEcFxAFogDiiAuIANEAcUQFxABogjqiAOAANEEdUQByABogjKiAOQAPEERUQB6AB4ogKiAPQAHFEBcQBaIA4ogLiADRAHFEBcQAaII6ogDgADRBHVEAcgAaIIyogDkADxBEVEAegAeKICogD0ABxkBDKQkUQB6AB4tBFtYpZMEucQRyABohDj5b3qoWyGjPEAWiAOPRQiiOct9VBHIAGiEOLlveqQRw1oa6BEVMRR98HNEwuSuo3nmPUbxsYqHEAGlDj0KOtwhFIlQPiADRAHHooxDEL6G11EAegwYM4QrihNFD0qYTzzimIA9DgXhxh3FEdCOltdRAHoMGHOEK4oyIB4gA0OBdHIF/FkQBxABpciyOYSnwcQByABogjKiAOQINjcQTUbhgFEAegAeKICogD0OBWHLOQuipjAOIANGDkaFRAHIAGiCMqIA5AA8QRFRAHoAHiiAqIA9AAcUQFxAFogDiiAuIANEAcUQFxABogjqiAOAANWMgnKiAOQAPW44gKiAPQgPU4ogLiADR4EQfMMRQgDkAD1uOICogD0IBp9VEBcQAaII6ogDgADViPIyogDkADBoBFBcQBaIA4ogLiADRAHFEBcQAaII6ogDgADRBHVEAcgAaIIyogDkADxBEVLsRx64XJZPL0hUoogqTBYIE4osKBOO4cmzAOvFgO1T1pMFwgjqigF8fd05PHL8xvpf9eLYXqnDQYMBBHVNCL4+b6I6/Peb3j2VKozkn7YXPTd8QzZ3xHnE7JkwxUHP5ze37qlO+IDk6SXhxXJgflz6OlUJ2T9sEmx2fEMxyfEacc4kSDFIf/3E61wfAZ0clJ0ovjvKxpXC8/qwRRkiAOWyAOPSCOeu6eluK4uZ6J4+OSawHwxxJ/Ec9I/EU8J6FNtSIO2uSd4D+3r52S+Ivo5iTJxXHnmOxOkW0dEEcLEEd/QBzW+BBHVpK6Ju0FPKpYgkcVPfCoUg/EYQrE0R8Qhy0+2jiyUF2T9gS6Y60IUhzojrUFvSqAhkDFAezAOA5AA8QRFRg5CmiAOKLCyVyVxzBXJT4gjqhwMDv25jpmx0YIxBEVWI8D0ABxRAVWAAM0QBxRAXEAGiCOqIA4AA0QR1RAHIAGiCMqfIoDjBtkd1RAHIAGZHdUeBMHBeXDRcQwI+rRw3HFcPGdRIQ4EHEwQBzhRIQ4EHEwQBzhRIQ4EHEwQBzhRIQ4EHEwQBzhRIQ4EHEwQBzhRBy4OAAAQwTiAAAYA3EAAIyBOAAAxkAcAABjIA4AgDEQBwDAGIgDAGDMwMRx/cA/+Nr/lf+/+9//+e+8yF/YIvnSt72EvHt6kuOgi5hzvjZ8/gVWf/HC+mRyoLriM100wRe/djX/x6OKfdzTQ24ju4kYmjhyeXdFvHJhWZTcZGslpJ+SlIZavqT71jNZOEd38qIk5V9jUTiEPught5HdRAxOHI9m766+e/rRdVGU5One/d9OviFrQjIWYd1w59iB3128+i69Wx779lXxngk3RUm+bm9+9y+emSzPMHcIvdBDbiO7iRicOB7/D/JM7xz7h8cKRYk5s/y2ODchxf+dlqQ0avbSXfYuvOy8rjiqA2QliQc7WD2Efught5HdRAxPHD+WFr5+4D+Xi9LyergNyXBcks5Pji7O5vqy+KT57OQ7KHflbmbfsvlD6IcechvZTcTwxPF/ZE3y/ON/6Usc5ZAMtyUprTe+uCg153Ol568cfMnOi1dOhiscQj/0kNvIbiKGJ47/d5qf9J1jB+/4elQph5zPXZek6+xMrovTSbPTeUtDviTJCmv+EHqih9xGdhOFGJ44rl7hF/b65NlSUfqrH09cXPKakAynJUkUHhlsGdMd+ZIkSk/hEHqih9xGdhOFGKA4rnNFnn/k9awoLTvLvuwnJMNpSZIZe56H7ackFQ6hJ3rIbWQ3UYgBioPnYVqPnJeK0qNP/w9PIRlOS9Ki+shi+C9JB8uH0BM95Daym+g0ByiOVIvPsnrkvPLU6yskw2XY3JijZ3t66C0eQk/0kNvIbqLgQxTHlclRVo/0KY5iSIbLsLmBfawamW9mvz552kVrZaWZvXQI/dBDbiO7ibJ7iOK4uf74X6b1SJ/iKIZkuAx7fjkoh4UrZrOTNodKx37pEPqhh9xGdhNl9xDFcff0gf+4ftSrOIohGQ7Dps/xzy7+d7QwlPCXjm7j8lDC8iH0Qw+5jewmyu4hiiOtSX5RFCJv4iiGZDgMm2uhOi+bvCePft3P5IX3n+HBK4fQCz3kNrKbKLsHKY7r4knMpzgKIRkOw+bqp9dFY9XNxXRJJ12Q1emS1UPogx5yG9lNlN2DFIesT/kURyHkfO4ybJqri6zL5iDdff+rEy8LNHyJTcusPYQe6CG3kd1E2T0wcQAAQgDiAAAYA3EAAIyBOAAAxkAcAABjIA4AgDEQBwDAGIgDAGAMxAEAMAbiAAAYA3EAAIyBOAAAxkAcAABjIA4wn+8cT+55tX7Ly3uTZGXN7+GA4QNxAJU4thLGmt/DAcMH4gAKcXywl3lj5XnfBwSGDsQBFGwnyZ7LfR8EGCAQB1CwjccUUAvEARRAHKAeiAMs2jjSH7svv/v5JEkefI79WbSMCnfcOLE3Se59Ujy33D6UrO2cSD92trylnMR88YGnir8+iSegoIE4QEEcJ6Qs9s0L4rgk/yt6ZlNxPHU8/Y3tVNxSTmK+/IBsLCl+HgQKxAHy4liylhfHpcWfeQ9LKo4j3AXz8pZyEvxpR7Kf/Vr6PAgUiAMUxfFQ+vjxzl4uhUUbB+uVZX+/kX5g92UujmSFP4qUt1SSYB9lv7+9l4cofx4ECsQBCuLgvmD3N7+tpTguZX9nlZA1YYM1/nt5SyWJ7ez3S9kjT+HzIFAgDpAXh3x+4G2c80wcufFhqQ72c3GIP1S2VJK4VHgkqXweBArEAYq9KtkfcuJIPbFn+ck9/A/ig5UtlSS2CmNSK58HgQJxAB1x5Eg35MVR3FJOYvn7vPbzIFAgDtAqDjFjpU4clS0t4qh8HgQKxAFMHlUEdY8q88KO7Y8qIGggDtAqjsrk2YU4KltqxCEbR7kzmufvg7CAOECrOFjXqewBSZ818o2jlS2VJBbdsdv8k+XPg0CBOEC7OLJhWztv7+UViKU4ylsqScgBYNn28udBoEAcoF0cuYHi2XDQrGGztKU2ifoh56hwhAzEATTEsbzf97E/58RR2lJNYvGBfXWfB4ECcQAdcYjJ8CsPfp//khdHcUuNOMQHdj1X+3kQKBAHAMAYiAMAYAzEAQAwBuIAABgDcQAAjIE4AADGQBwAAGMgDgCAMRAHAMAYiAMAYAzEAQAwBuIAABgDcQAAjIE4AADGQBwAAGMgDgCAMRAHAMAYiAMAYAzEAQAwBuIAABgDcQAAjIE4AADGQBwAAGMgDgCAMRAHAMAYiAMAYAzEAQAwBuIAABgDcQAAjIE4AADGQBwAAGMgDgCAMRAHAMAYiAMAYAzEAQAwBuIAABgDcQAAjIE4AADGQBwAAGMgDgCAMRAHAMAYiAMAYAzEAQAwBuIAABgDcQAAjIE4AADGQBwAAGMgDgCAMRAHAMAYiAMAYAzEAQAwBuIAABgDcQAAjIE4AADGQBwAAGMgDgCAMRAHAMAYiAMAYAzEQcbO8eSeV7U++eFl07T5HvoBAHAMxEGG7n2985Lp7S/3CEocO8ZyHDCjOhkaIA4yNO9r89s/28O7OLaSNYtNnHcOs+2Xkj2kB2TMdrK76Z4vbuJHevvQyvN1Bz2QkxkUEIdv7MXhHXtxpIfMtvd+r5GIYygnMyggDt/EJI5OUBRNbXFwhDgqUJzM6IA4fNO3OBL9LO9VHInBgTYCcbgC4iBD3t/pj92X3/18Wu4ffE5uuXFiL/+VFdSthLM/LaXJ2s6J9M9n+R5ZEvJ/fJd7n5rn91gK5MOX0q0r+36x3KkYsBmRVtunePr7Lks78GN58Gx10wd7xfFkN9w7h+UxX+JR1njtfis9coa4UQtptR1oy0fSFP/Xy2lyD50Vv+3f3pvsep7FSLIY7CPpAa88JCO+wy7TvU9ermyqPKqIRJ4rnUz+JOMG4iAjJ44T4v5M9vEN23vlr7teLYrjqePp/+55tUYcl+Qeey7XieNtmeDKU9lOxYAKtMTxgUh/19/l4sgOf62yqSSOS9mBFu61bdk0wAVSSKv9QNUfSW/9PxDXgR9m8jf2squZxZB/3HV4+dv8JXmZ2EUubiqLQ55n+WTyJxk3EAcZS3EsWZvzMrjy0CuvfDctpnuK4jjC3TCvimN7kcD+GnFsF9OvBKyQKKj5eHpku87Od74jkkt/23eZ/cZuquKmojjS8/zk5XSTvAjsSNi9dvsQ/xD/TCGt+uMsH2/+7yXYdUiTuyEuS/rb7rPzX7DDSH9+eILHYB/5ZPqRw/IjTLU7L/GDL24qi2OLObt6MoWTjBqIg4y8OFj99529ifyOkrfJVrZd3knpl91zcseiONgmlkJas8jvIX+yrendy4p8fcAKhuLInv63+Q2WdSbwn8VNRXGID4q7LH+viQcevmchrfrjLB9v/u+V4xTJpMH2cys8L66yOMQtcbyibiA+spX/pbipJA5Zh6qeTP4kowbiICMnDnFbsO8+/qhR+H7Ni2Mt+0tRHNkdwerFaxVxXBJPMCLS/pqAKhptkSNrlWCtMGnq8ug/2JumXdhUFEfhZsrfa+JZZauSVvththxopgohpe3FRVqr/LHYFJqJI7+pJI70I7uey30+OxkYIwPiIGMpDlmepQbSG33l73+//DF278mSWxHHpXrVyJ9LEaWi2FMTUIXG/bi8OdjNzvwm4a0xuU1FcRS6JPLi4M8q8p9cWu1H2XakCxnw45C/LQ5D/lFWbbJD3Xn3lW/cnwhx5DeVH1X446BoRS0+d9U/ZMUHxEFGvlcl+0P24JGy619dzn+M/bnSlyL/t1W8r4riyNlB/LeuU0ZBa9Pool5QJ478Jk1xzLfSDfLG1BSHHg3ikEnz/5TE8fb9MnyrOHjnCXsEvAxx1AJxkNEkjvkHh2VxZS0TGuIo3/9FcSz3q5hESxw657Em/ifEUfXB3Ewc28l+8TvxbWdc40irESsPfuEP3zuuIY70HL7L+m73QBy1QBxkNIoj/fqS3aWs1HUVR9caRztZQwa/YYpP9YVNmm0c6ebd/3NvrnuCimIrhRSHoo1DtG1otXFkXJJXPDsZtHFkQBxkKMSRcoN/f4n7vNdHlXayW0p0nWS9FIUbMetVEd+/ok1mS9xwl0pf0ilb9/xL+Z98WgTHWRRB1puSCWRPdpzyI9lTzPZe2auy3FQWR6ZE/rPYRbQ8yaiBOMhQiyPl9mF54zeKo9wPk35mj07jKK04WJfC2fnOn++VdmDjIljX8P7yJvnbd0RVig9xYH3CYstvzxf313YiD7mQVmeWIzFEV5Q49/I4jpXfzgZrsKuZepYNNt1f2lTTq8IO9MZxeYEXJ5M/yaiBOMhoEEfh9yZxLEZ6FLtjt7P+1lJ37O5SdyytOOY3RCPm7j8ojBzdV7NJbPnk8fzIUVm3YMcmxbE81UJaXdlO7r1/8QS4rMXUjxzNHeDu74jKSHFT/chRfuHzJ5M/yZiBOMhoqnGkxW4X64398KVEjmjgRTXXyLklhoLx6RG5AWA76Tcz/6jcY6dxABixOOY71bkq2bCG4iY29+Pep7LOltw0DjYLZ8+iRr+1qGEU0uoIn22SXronS48/N9hDYTaBRUxI2Sc2sV6V9ACzNpHcppq5KnsXSRdOBnNVOBAHGS3dsYsvty3RH5gTx2IM+V+rG3K+2COreVSHnFOLIwxoWkqAFRAHGc3dsdkkNzEpbVtUdXPiyCZf7fov5Ulucpac2CMTx/zPK5PcIA7gF4iDjObG0Z2XH0jYOEQxDZ7f97sv58XBq/xpvXi5b7FKL/ZYiENOus9Pqy/9Lwogjh6BOECoQBw9AnGAUIE4egTiAAAYA3EAAIyBOAAAxkAcAABjIA4AgDEQBwDAGIgDAGAMxAEAMAbiAAAYA3EAAIyBOAAAxkAcAABjIA4AgDEQBwDAGIgDAGAMxAEAMAbiAAAYA3EAAIyBOAAAxkAcAABjIA4AgDEQBwDAGIgDAGAMxAEAMAbiAAAYA3EAAIyBOAAAxkAcAABjIA4AgDEQBwDAGIgDAGAMxAEAMAbiAAAY8/8BvKp1s3qmoZoAAAAASUVORK5CYII=)
However, those only allow inferences regarding the within-subject
factors and not regarding the between-subjecta factor. So the same
warning is emitted again.
A further alternative is to suppress the error bars altogether. This
is the approach used in our original paper and probably a good idea in
general when figures show both between- and within-subjects factors
within the same panel. The presence of the raw data in the background
still provides a visual depiction of the variability of the data.
afex_plot(a1, x = "inference", trace = "instruction", panel = "plausibility",
error = "none")
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABDgAAAJACAMAAABfbyYrAAACN1BMVEUAAAAAADoAAGYAOjoAOmYAOpAAZpAAZrYZGT8ZGWIZPz8ZP2IZP4EZYoEZYp8aGhozMzM6AAA6OgA6Ojo6OmY6OpA6ZmY6ZpA6ZrY6kLY6kNs/GRk/Pxk/P2I/YoE/Yp8/gZ8/gb1NTU1NTW5NTY5Nbm5Nbo5NbqtNjshiGRliPxliPz9iYoFigZ9igb1in71in9lmAABmADpmOgBmOjpmZgBmZjpmZmZmZpBmkJBmkLZmkNtmtttmtv9uTU1uTW5ubk1ubm5ubo5ujo5ujqtujshuq6tuq+SBPxmBYhmBYj+BYmKBn5+Bn72Bvb2BvdmOTU2Obk2Obm6Ojm6Oq8iOyOSOyP+QOgCQZgCQZjqQZmaQkDqQkLaQtraQttuQ2/+fYhmfYj+fgT+fgWKfgYGfn5+fvdmf2dmpqamqqqqrbk2rjk2rjm6rq6uryKuryOSr5Mir5P+tra2urq6xsbGzs7O2ZgC2Zjq2kDq2kGa2kJC2tpC2tra2ttu229u22/+2/9u2//+5ubm9gT+9n2K9n4G9vZ+9vb292b292dm+vr7Ijk3Ijm7Iq27Iq6vI5OTI5P/I///KysrU1NTZn2LZvYHZvZ/Z2Z/Z2b3Z2dnbkDrbkGbbtmbbtpDbtrbb25Db27bb29vb2//b/9vb///kq27kq47kyI7kyKvk5Kvk5Mjk5P/k/8jk/+Tk///r6+v/tmb/yI7/25D/27b/29v/5Kv/5Mj/5OT//7b//8j//9v//+T///+zLiXLAAAACXBIWXMAABYlAAAWJQFJUiTwAAAgAElEQVR4nO2djZ/cxnnfcRJZKnbq80lyRVNxHC9lUU5fbIZu6Sok26RS6aZNS7mJGLqNVKetS7I6u/LxTpYjh9HZaeNl0kq3si0xkiu5ZOOmKXlHijR5t39cMS/A4nWAGTwzGGB+38+HvNvD7jwAZvDdeQWiOQAAaBL1vQMAgOEBcQAAtIE4AADaQBwAAG0gDgCANhAHAEAbiAMAoA3EAQDQBuIAAGgDcQAAtIE4AADaQBwAAG0gDgCANq7E8XEwUpDPgdCTON4BY6Qkjr53CNgB4gCUQByBAHEASiCOQIA4ACUQRyBAHIASiCMQIA5ACcQRCBAHoATiCASIA1ACcQQCxAEogTgCAeIAlEAcgQBxAEogjkCAOAAlEEcgQByAEogjECAOQAnEEQgQB6AE4ggEiANQAnEEAsTRmR9/7qF/1frN31v+2DvvvLX8iz8w/LzvDF4cPIOaCT7TIY7OBF+GskAcdWmOLNMhjs7ol4GRlaEsgxdHS4LPdIijM8GXoSwQRx0jy3SIw4C4DPzpv398efmXv85eJWXgT/728vLyR/4uKx0/fPwX/lNm049+K97yqd9mf0lqrX/6tceXH8p//ke/FSf5qa/3cUB0DF4cPIPi/97+2vLyQ59/553vx5nyaZanyPQcEIcBcRn4Z8uMh77wTloGvrYsYN8r+TL0w8fFls+8k5ahj/5q6fNvyXd9occD685YxPE3v8wz4/NfS/MUmZ4D4jDgrWX+LfSjLy+zoiLKwFv8+4l9T32hWIb+3fLHfvDO23/A3yzL0PLyr8Sf/9XM53/8OZZk/K5hV2HHIo7lX/iX77z9ZVHp+N6yyGBkegaIw4C4DPCu97hkfWZRUD6z+FOuDMkvl3jLFxZl6DPFzye9+S179X1lLOLgWRbXGlg+iZxDpueAOAx4K/mG4B1euX6uijIU/+mjv51sl2VIbM58/u0vy0R++Hi2D21wjEUcPBOSnOV+QKbngDgMSDvIeVlJy9Db//U//vNfWi6VIV5JlR1oSRmSXzCLz8eV1gRZvobJWMTBM+jHnxN5IcWBTM8AcRhQWYa+/0vLaXdYoYP9T0Sv2C//YJxlKEtg4gg30yEOA6rKUPwN89Cn/s6/+G/lWivj7f/Ahu0+pipDw+sfqyIscQSc6RCHAZXN1dp+soTvsU/VN3cHOCRXwZjFgUzPAHEYUNFBnpSxtx4XZUiUHd47nxQo/jPpYP9C4fNx4RRfaPkJhoNjzOJApmeAOAxYDMmz/E6aq2zcnk0t/AwvHB/9OhufXxbfK7/49XfYBIBFrXX5ob9XGNL/4eP8Xd8XI4CDZdTiQKYvgDgMeGv5I6JTjH/FJEPyYgrhH/Ay95Z49Stfzk4izMwFSiYRLj6fTiL8dL/H1pExiwOZngHiMIAvO4hLgBhsy3Swf+TzSa2TrWH4yOflQD1fkCDenVu28Ons58W7FoP/w2TM4kCmZ4A4DBhgk9QVgxdHLcj0HBCHAShDtUAcgQBxGIAyVAvEEQgQhwEoQ7VAHIEAcRiAMlQLxBEIEAegZLziADkgDkAJxBEIEAegBOIIBIgDUAJxBALEASiBOAIB4gCUQByBAHEASiCOQOhNHGCkIJ8DAeIAlCCfA6EncTiKA9xSEkc/uwFsA3EASiCOQIA4ACUQRyBAHIASiCMQIA5ACcQRCBAHoATiCASIA1ACcQQCnTjun3lK/nb3pclk8swbFS8ycY3jAJ+BOAKBThxXJlIc989MGIdfLr3IxjWOA3wG4ggEKnHsXZlIcexdnBx5Y343/v9G4UUurmGceqbkKXrGIA7Qvjhm5Cn6FnAQEInj7nOTRBx3Tj3x5pxXNZ4vvMjFNYmytla/bbpm4cJaX1dsVOyMKVPFMUxXLRygKqCRqUjEoTrts5mFC1mV5mxKH1B5DDZEpbpyzKARx85k8uxfSnFcT3+eLrzIxdUPssap30p+Xa1z6gNSx5ty6raurq66DaiUSh0E4lCfdgvimHHqNk7JxaGMZ+MA1VeOGUTiePKbsTyEI67IysUOa57kXuTi6gdZW4ui2sOfrp0nN8f6+vnzCnGcpzbHdHr+fO3VOl29QF7lUAacTy8YxCMRh+K0z+IKAP2FfP68ShzU5pjN1tcdi+P8eT/FwZDi2LsoXXHn1JEbuRdJQMFNXb4Vcb5Vs/X8+fOb2mkquXSec6k+IG28TRGv5ihWL1y4sEp7hOqAm3FA/TRL4tBOQX3atzY3N7f0d0vFlghYnSqLRxxw69KlCxcuXapJdItBGo8X1ZiaKycOaZIouTjun5EjKKx7I/fCpjg22cn5Fu11pSzB31JmhRHK63izF3Hox7Msji0uDuILWSEO7g1ic2xd4HgjDrNwzsWRFCjt9CNJ5cY1fmpoq/Kr4nxX9yyIgKTxLoh4F6p3hhc22sbKeUnlxumFC+cvVO+Liu5NFeVezXivDG1dXnXeRTzagFMhjuqsnPG2GO0BKguyadtoHOKYiuuYtpdDVaBEPNpeDtUVMxXioDVHgzjYsWuHsyuOmRQH6YWlCCi9QWuOJCsrN/IjJDaHMp95r5FBos77OJK42umvqsShPDWGzOLv3PhLt/KsqvPCDHkQldfquugYX1MND5MGZBWO8wZVju7iUH4/ztQjEkbMRMDKRGcWAq6vxupYXa3MypkNcai/5WZmIiYXh71RFUWVIxEHbVtFiKNqy1oiDgtVjspNiThozdFU4TCochCMqqi/HunFoQg4m9kwxyqnclNapbJQ5ajeNjMcNqIXh7V5HKtxnYP9q9hF2aqo6R8wpX60cm1NVDdpx7gUo6N2SrAi4NoaL93aB0ggjtW4zlF7XVmpcqiGYy2wHtc5FOPN9GZUDcfODNt+9OKwOHOUF6cqczBtrlsYb689pUnblzages6VjQmM9RMKDK9Qkpmj9FPdGnCpDYZyfhu9OBSzFWem3c304ti7OHkyu1blSeq1KhXNFeaN9XXiTjPVt5slcTTsjoUka1PtUxwBY6VKpYo3NexuphdHXMvILIjNvcjGNY5TbY4ZeWe7Kg+nU/fmoC9NqkJq2iiAODphp0naIqL25yyIw8H9OIrmYN6ILDRVlKfUbfXWQmmyUUYhjmFhXAYGegewQqUjFkf8BztVjtqtFlZNGu+MN2mOUByjXlbPCsCUok06EHGUzRG/ttEJUL/JeQ9HOOKomR5sD+Wyetc9p64xO8ChiqPQXJnxGR4uc5gN4ziMZ6npq0ywp2X1ipUFdlCeWZf9Df0QmjiylY6ZEIdDc8ym66vr7qocaY3SYSE2qlKNUhwua7LOm0aGNyoYsDiy5shMKi3+tALzxuqq014O5998PYlDuZjRBsrKnPsWout87n3mqGZcklTS8qUQhxWRTKdsXqXDKofzzti52WjzyMRhpYXolThMJzIMWxzzjBTqSpsNkfAKR+S0ymHhTlRq+hLH3LU4VBeyDXGo03TcHp2bzrkeuDjk5T+b8vtUtjp+CpHMpuw+htGaa3G47eEwMQedONypo7nCQXriW4jDYbGaGc65Hro45PXeYR6nkUjW19lm5U3QabFxBxsl/YlDDMc6bq04CzW3MtO/y84E2VRhsNGU+ODXiC6sNiKZCXFE687XFLiKx4KtG5iDcAKY47EVdygWTzpfqxJsU4XBB2LX2UJPG+e7SiDTqahMO5sDltyKir6DX7WNoLOdeE2SJdz3Rzb0qTjboXCbKgxxDTu6iHlxzjTDnQz/Jvei9L57n3bKedU5tXF3AdetA2U812ug4rp6mE0VhktzyHh14rAikllySzkL/fu1GynKE+2apLnzuwtYoKFO4Xo4drq2ZtLJPxJxiEoHcZrqaHU9/7YEQl+gGrr3Se7v0jmfi6fP9WmgxytxzKQ4wmyqsL48t+bQGG8nEslsRr6IT1mCWeOXoDyRrEnKLkqiv8Td90c2eMOtOfhEhmBrHKzj0JdJQzV0bNLYWP6rrnC0nRiTw8ay+tx4loXryqs+Duc7E7Q4xJCD8zlDnTK4SiAswWqRWLlvQEOFw6S33cr9OBanxErlwPW1qhrFcV77EY/41jfHOMQhcTl+R/7ELSEOsc43eZ0JGEUWzKEqwWbzRizdyCc5GfSnfS6SVW2kjqZiZmP0TIWcr6P9uVGJg9wcqmk69I+/yH/Z5ASS3jfA8YyzfsRRcZTibFg57U074/i2KxZGzxRMxW2+u08RHrY4qh+eYEpjW5QsUiZcKVVxyZSHf+2SzjjT/ByBOGpOuzxux40K13d66+H2CWYBxyYOyuvKce+3st9kMfxrdb5IaW90P2ZPHEnVy/GF5XiJkPsDNAw4OnHQXUjKC9lxJ13FxBG7E89myqOvp7s4FIGTbiDtNM2Z9fD4HMdHCHEk0M24qj2phpdVm4DV29bFMycVn3ZUE2nAqjjcm8P12sI+VseaBRyjOKiumcYKh8NBDuGNCxrr+LuLxOQcWmyq8G1ue4jdP7HPUsFqDKj9uVGKg7S6Xr/FYQbLCoeWOSTGAjE6h3bFMZu5NQevcFDdr6FVQNcFi4UK6rkqTVDVOQgSoWA9pXNSrUXSlziaTrvDNhi7oqzdr8EXzI5urOIgHZcdJU0CiYwuUBdPcnPae+O4b7QHII48/XQODoyGURo/xeE0b12PqjhnaKMqN63DSpf9KENGDPBW/DHdpn0CS+Kg2dMizvJ2k+MkVE9sMfQ/Nt4axxzNlSYiRa2iOG2kJc4eOu2m0uF8VMU5GFWpAs0VFaU5ZRXbdE+fu6fVO8nbIMRhtPhn3OKAOVSo3DAzXE7mThwuMnc6Hbs5TPN57OIYU3OFvPA21CmMloXaF0dmp/C1QEHooyp1Rz+W0jUlflJtU2PEk3uOFsnrbCyZm8X94thBjapQJ1h/9CMpXBSTvxZIZSjOjKfiKDQbRpK5GVzPNoM46o9+DIWL32+FKrEW3Z5+3QEspayzsanDsThC7xxVe3MEZUs8VZsmrTbXmqfiqOipHJc5HC+ODfnZsZyG8z34wjUlWqkyb3kuerwDmIpqnQ0+dzO4Xxwb9KhK4zSWoZctucSte5Wj7eQMw4lBlsVRMzg69Nxd4Px+HIH3cbQ434MuW9N1GnO0ntNlurzbrjhm8p7ctXcktYH7G/k5NUfQ4mhVzIf8tURznwazdWtaWBaHeNB77R1JbeD+69+pOTDlvJkhm4MAB9pwPo8ji6XDG/fNOCCOVgRsDifacDMcW7fNyhE67nFwjXENJyxxBFvpcKSNPuZxZKE/Sud9lT1gdMoCE0eY5nCmDUfDsfXbyQ80AHGYnbLQxBFgc8VYGxRrn5zM48hCqw73gxzO16pAHG0JyxzmtQ2SRZP0w7FNi9xJzdGDOFxXcAwLSIDi0Cha7h/jSZxgl0YKyfC+HXGod4xOHVwabp8eD3E0xHUUpxKtyZP1m+l2qFU8A7pcQjTzgnqYr2Nw2KoEXTdUZutOAzbclKWWIMXRsrmiLqP02Ustjm7fvDTD+0MYPVOed+c9HOw5Lg7jQRx6tDhZDd9u5AWKuD3dcSiFaF7QEEbPGsXhtKHCHwDlLF4UGZojVHG0KFnqC5m+PJGKo/MILNG8oF5Hz9qdAOV5d903OuMrktxE7FRAghVHi+ZKkzfsmIMipe4TN0wt5o84WptDdaiuR1Vm7sTRsYwELI7GkjVUcVDM92rZDVnCI3FQdII7F4dcBO0gYscyErI4mguWuuVLX6B80YY5XomjpTmaKxzuzOHicQwkxSNocZhr13V5ag2lNvybAKZPW3XY35NW2HqOS+EZwflTYnTwgYvD0Bzuv4naQVrbMDo638QxsMVJdp4AlRs3KZ2PmdHwb+jiGFjBUkLcSBmHOEaVw4aoB1zNbmUbvDhGU66o+zbMKlQeimM0OWxKOlej+mGfZjPOyMWxd3GS8MSb8/n9M4vfc3G7xiFkDOWKvkt0POIIXR2R0hyGd8+3LY47p/wXxwjKFf0RDH8eR5bh53AHGiscJlUOa02VO6cOvxz/2Jk8VR2XKg4JAy9XNnZ/XOIYfBZ3QD2pPLbGmkmVw5Y44orHafbzivhRjksUh4oBFysrEzdGMQEsR6DmEKWj/tHisTfW1gyqHLbEcX1y5Mac+YPXOyriEsUho7JcDeB+HN20QX6A/opDpQ7X+Wx3gleGpLJRW6VgD6pZXzcY/rUkjvtnJs+Ln09847nJ5MmSPXwqUIJysXK+wEk/XldtkB+gfXF02N+ak+U6n+3PDZUkpUP5+FB+pyL9tC2J44qocKR9o4sGy8clN/0jPsu511scd/G14/GC0Sng5ibxAZbEQZr6TX6SOny68oQ1nHfyErC5eenS5iZ1qmXSg73EqXlXXAY2DQ7RjjiSCsd8ZzI58sb85y9N0haLx+IomGNry605dON11QYvMmalph7PxVFljobzTl4CNsV1bNscUcEbNebY4uLQP0Q74pA9HItfrhQHV/xrqjBydVn3S6p14hF0ic7MHkivxPpzVTrnSOm8qc/7bBYRl4Ap1fPDVWSLR/Lc4co3Nt83vhor4kiGVBbsJCZJ41LEoafCHO6Ca8QjGUkJUxx15qgLGL+btgzIpfM2C1aheKi8IcXhx6jKnVPF+V6lv3gqjty4rLfiIBqAHaA4aCqBhdOnrHBEwxNHqXioOkdNz6gVcVwvzfoajjhypUp5Qi3kvEttzMXXzbBGVYhaj8UzqLjvSmTBHDObd+qpLB71M7y8Ekc66ytusySdpMNoqjBaXpc9ramnnO41uOFYursZtM3kyIo4LNZkNcuH8Rm1IY77Z9IxFNkpWu708Fgc7aaRum3FpBDPfwxqAliOVidylkzXJr9JJG16Aj5D1NX8WBviyLRL7pyaPHtjfvc5vxe5lWhx+h33fwj8nzY9GHG0O5mJOFzsT0cc76oNcWTbJdfF/K/SxHOPCxSjMQdcd5wyXBfhMdw6UIX6dEY5/Je2Y8PZFsf87ldibTx7o/genwsURxaWmq2u53jMe7gJsdHhDUkcahkUtw1CHO7C4Q5gdSirfs7F0UOFOQBxJDYoKqLFRzwiqQ853S2IoxaFOGbGndEddsVNqBSaUTrv8zkqfkE0nWvfxFE6ADdAHLV40zHWz14EIo5SNrc9114Ujbny+80m4xEH+Zd/vnest0LSU3Ci4X3/xbEwR12ztP5j2Z+90c8O6Ihj9yevfffa/Kc0cUlSyWCh2RDVQB2ncR9cBkwITxw1Z7rpJPSVQWn3TD/x24vj3YfjPXzg9XtH912miEuQRg5r4si/cquQ/qo6pp04AxTHTJmdWusOSferTbz+Ckhbceye5fvIxBEtvUgQt3sSOez0VFZniiuF9NpCMmR44mjIx9YFq4/M6rGAtBXHRhQtPfaHRx94ffcVpo/ucTunkKeXiZxWFTJEbQxXHKzwVOWffrXLfrYl6fdaQFqKYzti9Yx7R5kyYocc6h63cwo53M/HKlClkE65OkhtDFEcOTkU882gwWY745I97PmLpaU4hCyEOGKJ7L/WOW7XBPL0Lo4FJAoZqDaGJI7sKc6UnHyGdZiuY7EBO+9dG23FsXuWd2wIcdw7StBW8XW5NSnaCpGbey8V5gxTHOUtYluXiX5W87D/AtJOHNIV3orDe1opRP55wNoYkjiUdG9q5pIiSWjRVe9DAYE4HKNQiHnLxh/8F0fL80uWEVT5me6PHyWkdVMlOulzH8cQqVKIH4WiA6MRxzx1B1H7t2vOeqWN1p2jV7ksuDiYRLwbVRk24/GG1+Iw66Puv8mS+Zw/BaSlOOLmSbT/MhPH7oaX8zjGwAi84bE4DE8ueaboJldszBLuShfaTgDbZvv8cBQ9wn6eJIjbPYm+sTzFfZiQiMPK6JjhqZ1Np7T5YiAOw0+2xehst16rcmslqUovnTAJVIxLkEa/uJviPiQIxEE8sN7tcuPPnpnZUHptiqpBYtpdEJid7PaL3Hbf+yJzxyNPd+4Y5XEpEukT/2aNeMHIxMGePLPGn1nlrMmSrWHk323pW8W2OGgZhThgjhLdxeHVXD7mjbU18bA7S33XxTSTl7lgFtuwhicb4jDDo8LtFf6Ig+JaE95YSx+T6aDJkkZwow1X4rh9LIoe9fJ+HG7x6mvRJ/xpqpBcbbNZ8Tmv1nqwZbLl5O12mZsW5Nb34zjHxmBlDylGVSCOGvwRBwWz5AHR2d2xpA6RaKk9ZHukzbI4ds9GckX90sMRxZ18hi2OLoufxk3/w7GkF9pUUhHD4s2bXLVS5vafHXs1ij5xjVU4Yn34eD8O4An9TwBzIQ6rPaWZhD2e19P6fhxseUqsjwO8vYK1KqAa++Ko/26kv8im01pz2OspTdO13UrpROtFbofmySoVrI4FtVgXR22luo/LzF6TxXNtaCyr54tj5e18KMRxE4yRkjioA2xtbVVviC8z6lgtkPUDOwnTp0qHjji2owj34wBKbNc4yv14vX8xO5jc4SE6TRXRxYH7cYB67IsjKpjDh2uM2B2et1I4rTtHl168dZTP4MD9OEA9lsURe6MoDj8gVMcQtNFaHHLmF7+ZD+7HEQgUa58siEOaw7vLi8Ydw9CGxv04mDlk16iPT3ID5JAsmqS+m33krzhIJnewz1eN/XpH67UqH756nC+ov/drBykeOw1xeA/J2icb4vCzsSLopg6ujXRJnddgdSyohmbRJHE+W5qwSYrxLkbSG4MwB8QBqqFZ+xSgOAzVIT4zgzga4jqKAwwhWjRJm8/RMMQxN3CHfLuc4e6/OVqLY/e94wu+hHkcY4do0aQdcQzAHHqVo+Sds+lQzNFWHO+m9ypmYObo2KFabT3eh4u3oK06Fm8bzu0a9OZxQByhYFqCIY48LdyRfcdwbvTSeuZotHSCYhg2jUuYFvAHuzfyGcxllaFJHQNpeRVpvciN4n6B2bikqQFfGNetAzk0tz+te4jKMLWh+bR6yrikqQFfGJ84SPalpqd0KB29FUAcgBJ/Ho9ABNmuZNXh14PnjdBYHUsblzQ14AujFAfVvkh3ZH4QJdwDLcWxLW7FQRiXNDXgC2NrqsR7MiV+HuUCsmR7oO08jo0o+iTJQ2OTuIRpAX8YmTiYN6aU5ijcj3i4tLwD2FePs4kcDz4ieRTzOEAl9p+r4lQpM/G0etKYo/CGxnAsJoCBZhzc5dyhOUSFw445ajcP4nYcEAcgZVzisNNuUouj+iEu3oHVsYASF3c5d1vlsBFR1U6BONRxHcUBboE4OlL33DjfgDgAJeNa5NbDmrPaJ056ho443n8t5o+I4tIkAzzDvjim3ozV2qD+UbWe0f5GPq8mK+sPUszngDjGie2bFYtpFeM1R3x464MwR1tx3Du2GFPZR7BsBeIYJ2MbVXEM88b6IMzRUhzs8W3RY9/94IOfnIsongAJcYyUkXWO8pCOww3DjO3XqiSr3NjNwLrfmwPiGCfjE0cfE0dcBjSk/R3A0ufFXm1Y8Hb/zITzxJvs1d2X4l+feaMUV39XwQAY16jKnHBZvUa8IZij9czRxbL6uMqhnDl651RGHNIih18uxjXbXeA5oxSH+4kj7gKaYnAjn6a7+uxMnkp/37s4OfLG/G78/41CXO09Ba7x8dmxri8s19exHG/23xwWxHFlcjr9/c6ppN7xfCGu7o72gvN+OJ8KjFHxtT6qYoXGuyM7jCfGm30qCJW0XFZ/NtMhuq0eVtm7mGmXXJe1j+sZmYi4mvvZBz1826jiuS5LnoqD/jQozrsVcaiTnPn/MCZGy87Rq4sVsWyl7KHqd3Hun3niG89NJk9ye1yRNY2dYltlGOJYX3crDtUC7h5GE7x7BOTcymmYzdbWqlPl54C8AqDM55n7x7gZhWspDmaLfS+w39471rCqPukbZXWMvYtSHHdOJeL4uOSm92xd4mw5i7fJqYu3teVsT5J4BhFL4rCwW9Qpfovj7Lyr85ltdZvTZgfYduboNp9wviT+V07j2Jmw/tCfv8RGUu6fkc0W2dcBcajiqQuU0XXcYW+2jCLaFoeF06AWh4V4qnzeUn55WMGuOOa30jnn+9T3O78uWyVXJk9ViSMpUMo0vIBN/mXzf13FmykfN9xPh4tvz461MTq6vsZZdxVQmc/qQmADwwPUWB37/lfZ3Ua/1HZ5LOvWgDg0UBeoXoYh9SMOcB6HUhw2+kYV+Zw8rd51Tut/zN79OJgrKvo4krhkcezBFhy5XG2k6jRzPoHBkAGKY74eq2Nd5Q3yURWP8tk0nl1xDHtUxfWtEVoMC3pvjiFOABM1S2VA0nhe5bMLcbz/nePHf/9yQ4JpJYO7YtDzOJg63Mbz3guNDHMCWI02rF3H/uSz8QG2Fsftc7JvdOlp9aL6K8IVsUBOD37mKNBlmOIA2rQVx62VtjfyuXNq8uyN+d3n+CK3WB9PYq1KQEAcgaAxAWzp6Q8++OD9cytNN/K5PsksiJXTwbA6NhAgjkDQmHIuezeab+Rz9yuxKZ6VVQzcjyMoII5AoF/k1jJu1wSAl0AcgWBhWX27uF0TAF4CcQQCxAEogTgCofU9R3O3DkRTBVQDcQRCS3HEskhvULyBu5yDOiCOQGg9j+NYdIBXM3a/HS2dIIjbPQngIRBHILQcVfnq8eMrUfTo8eP/gE0AOy74UocGCwrUOIE4AqF152gFXbpIUaDGCcQRCBAHoATiCAR7y+ob4jqKA9wCcQQCxAEogTgCAeIAlEAcgaApjtvHoujRpnv5tIpLkAbwD4gjENqKY/cc6wqVd+XoPv8LBWqkQByB0FIcu2f5GMpGFC09HGWmn5vH7ZwC8BGIIxDa34/jE9dYhSPWx4b6EZAt43ZOAfgIxBEIrRe5sXVtsT4OYJEbUABxdMabGxkraX0jn0Pidj6HsKweKBji0+r9YlRPq49dcZL/z3o3IA5QyxCfVu8XTh/lY46OOLbFLHOIA9QCcXRk5vbJscboNFVEFwfuOQrqGeBDp/3C9TOnTTjdTOsAABaPSURBVNG4A9ito3wGh+zp6Bq3cwrARyCObsxcP3fUlPZ3AGPENQ22ULZ7SwXiGClDfOi0T0wVj7L3irYzR7eZOWTXKMH8L4hjpIxQHC6jyQrHAMzReq3Kh68e5w+NvfdrB39KEZcgDeAfQ3xafWNMp8EGUqnC6lhAyegmgA3hIu4DiANQMkpxwBxldMSx+5PXvnttTtFQGUGBApWMTRzDaDf0QHtxvPswv83ovaP7SO7HcROMkZI4+t6hbmxJ+t4P/2h9P46z8v7EGFUBCkZW4xhKV6V72oqD3YnjsT88+sDru69gHgeoZVziGM4gh3NaimObT+K4d1TezQczR0E14xIHqKX1lHOxoJ6JA2tVQC1YVh8IrRe5iVmjWB0LlGB1bCC0XlYvlAFxACUQRyBAHIASrI4NhNZNFXELMPRxACUQRyC0v8s5X1IfiwP34wD1jHB1LKiipTjYXTj2X2bi2N3APA5QC8QRCK3vx8HmjT4cRY/gSW5AwfiW1YNKWq9VkfcAY7fzOUERlyAN4B+YABYI7Re57b73ReaOR57u3DHK41IkArwD4giEluL4kMQW2bjE6QE/gDgCofWU80/QqgMFapxAHIGgNQGMMi5pasAXII5AgDgAJRBHILRfHUswBpuNS5oa8AWIIxBaioMtjz1BGpcyMeANEEcgtFyr8pPXzkVR9OAjkkexyA1UAnEEQus+jhxYHQuqgTgCAeIAlEAcgYAHMgFKII5AgDgAJRBHIEAcgBKIIxAgDkAJxBEIEAegBOIIBIgDUAJxBALEASiBOAIB4gCUQByBYEMcP/vKZHL4mTf47/fPTDhPvFmISxAH+AfEEQgWxPFnQhWHn2cv7pyCOEIC4ggEenHsTA7/7nx+96Jwxc7kqeq4neMAH4E4AoFcHHsXJ6fZz7iNwqocV8SrctyucYCX4Gn1XqJ8oITRGSUXx/0zh1/mv3Bl7F2Ur0pxu8YBXuLgodPUKY4f9bNozM6ovVEVLo77Z574xnOTyZMle0Ac48S+OKYwhy5N4jA5o9bEIWoeSd/oosHyccnNIbC11fceDI2SOIjT39rc3CROcvRsSeq3GiRqTRzXJ0dusL7RyZE35j9/aZK2WAYkDtXpBtW4EAeyRI9mcRicUVvi2BGqEPpg7ZbC4MoQmip4TKk+th86PWXQpjl+VAXZtJBbEsfOKTGNI30tBbKISxPHJnjAsQFOxIEs0aNZHPpn1I44rk8Kgyl3ThVmgEEc48Ty0+qnU1Q5TKgtxcaF3IY49v6s6I1BigNNFQPciAOZQsXM1BwWxLF3cfLkm+mvosUyxKYKxGGAZXGgFugL9OLYu5KRhOwUTaaTZuJ2juMClFBdMOU8EOjFcT1bubhzavLsjfnd57DILRAgjkCwMOV8ksAqG9flUtni1FEUqHECcQQCuTh2JjlxzO+ym3M8e6P4NhSocQJxBALuAAYogTgCAeIAlEAcgQBxAEogjkCAOAAlEEcgQByAEogjECAOQAnEEQgQB6AE4ggEiANQAnEEAsQBKIE4AgHiAJRAHIEAcQBKII5AgDgAJRBHIEAcgBKIIxAgDkAJxBEIEAegBOIIBIgDUAJxdGYYt6uEOAAlEEcbVPeyHcitmCEOQAnE0UzDM6CH8fAHiANQAnE0oxTHbCCPjYE4ACUQRyPqZ8NwcQzAHBAHoATiaEQpjsE8HBfiAJRAHM00VTgGYQ6IA1ACcTTTWOEYQmMF4gCUQBxtUIypDOXhuBAHoMS+OPy/pjqQPlXb+6OEOAAl1sUxgGsqCCAOQAnEEQgQB6DEtjgGUY0Pgd7EcROMkZI4iNPfYhCnCQxAjQNQYrnGMZCewwCAOAAlEEcgQByAErviGMxg5fiBOAAlmAAWCBAHoATiCASIA1ACcQQCxAEogTgCAeIAlEAcgQBxAEogjkCAOAAlEEcgQByAEogjECAOQAnEEQgQB6AE4ggEiANQgjuABQLEAShxcCMf6hSBCRAHoAR3AAsEiANQ4uIOYMRJAhMgDkAJbh0YCBAHoMTJjXxo0wQmQByAEtwBLBAgDkCJmzuAkSYKTIA4ACW4dWAgQByAEswcDQSIA1ACcQQCxAEogTgCAeIAlEAcgQBxAEogjkCAOAAlEEcgQByAEogjECAOQAnEEQjWxXH3pclk8swbpbjUcYAXQByBYFsc989MGIdfLsYljgP8AOIIBMvi2Ls4OfLG/G78/41CXNo4wBMgjkCwLI47p554c87rHc8X4tLGscT6utt4q6tu482nU+IEhykO1/l84YLbeBYO0LI4rk+ekj9PF+LSxrHCOsddvFWOu3ixNhikSQ5RHK7z+QLHXTwrB2hZHFdkTWOn2FZBgSoDcfQDxGGAXXHsXZTiuHMqEcfHJTe955LEVbxViat4NzcllGmWxEGZuB1c5/MFiat4dg7Qrjjun5HDKbKvA+JQAHH0A8RhgnNxJAWKNI4d0FTRB02VZtBUaQTi0AHi6AeIwwDnfRxJXNI4tsBwrC5DFAeGYw3AqAqgZJjiANpgHgegBOIIBMwcBZRAHIFgf63Kk1irEhAQRyDYXh175xRWx4YExBEIuB8HoATiCATcAQxQAnEEAsQBKIE4AgHiAJRAHIEAcQBKII5A6E0cYKQgnwMB4gCUIJ8DoR9xEFDc9dEFHP0BtmP0p2EUBwhx+BNw9AfYjtGfhlEcIMThT8DRH2A7Rn8aRnGAEIc/AUd/gO0Y/WkYxQFCHP4EHP0BtmP0p2EUBwhx+BNw9AfYjtGfhlEc4IDEAQDwBYgDAKANxAEA0AbiAABoA3EAALSBOAAA2kAcAABtIA4AgDYei2Pn8D/5nf8rf9/77//6H73Mn9ci+c1vuoi4d3GS4SkLIfl94bMPr/qrl05NJofLd3smCyb4jd+5kf3jacVnbIN8thLMcj77LI5MHl4XT1xYFCgr2VuK6KJAxYEWD+i++1wSzM6lnBao7BMscnvgHuQzPfbz2WtxfDZ5dPXexc+eEgVKHvne/7bxLVkRkZFGtcH9M4f/cfrYu/iCefKbN8QzJqyUKPmkvfneXz03WRxeZg96APlMj/189locR/6zPOj7Z/7pmVyBYvosPizOSkTxu8UCFcdMHrjLnoOXHNR1O5WApEDxWE+V96APkM/02M9nv8Xx59LHO4f/S7FALU6N1YgMqwXqyuR0eig7i1IU57eNr6LMWbuTfNNm96APkM/02M9nv8Xxf2SN8sqRv3ZUoIoRGTYLVFx/fDktPFcyhejn5N+zjOxZk9Fye9AHyGd67Oez3+L4fxf58d8/89R9R1XYYsT53G6B2mGHsSOOJc5W210N2QIl663ZPegF5DM99vPZb3HcuM5P8c7k+UKB+vmfTyyc/IqIDIsFSpQhGWoR0RrZAiUKUW4PegH5TI/9fPZcHDvclleeeDMpUItBs2edRGRYLFAyg6/woL0UqNwe9ALy2Vo8jp189lwcPC/j+uS8UKA++8z/cBORYbFApdVIFsF5gXqquAe9gHymx34+ey6O2JDPs/rkvNT2dRSRYS9qZuLR8/20ffN70AvIZ3rs57Pv4rg+Oc3qkw4LVD4iw17UzAQ/Vp3M9rbvTJ6x0F1Z6m0v7EEfIJ9tBLSdz76L486pI38d1ycdFqh8RIa9qFcWk3NYsHx22+h0KI3vF/agD5DP9NjPZ9/FsXfx8DdOnXZZoPIRGdaixk3559PfTudmFP6lneu4OKOwuAd9gHymx34++y6OuEb5G6IouSpQ+YgMa1EzPVVXZNf35LO/62QNw8+e47FLe9ADyGd67Oez9+LYEY0yhwUqF5FhLWqmmrojOq3upKsmLYxCzsurJst74B7kMz3289l7cciqlcMClYs4n9uLGudumoXJWqS9n31l4uI+Db/JFmdW7oFzkM9WQlrOZ4/FAQDwFYgDAKANxAEA0AbiAABoA3EAALSBOAAA2kAcAABtIA4AgDYQBwBAG4gDAKANxAEA0AbiAABoA3EAALSBOEDK7tnogdert7y6EkVLJx3vD/AXiAOk1ItjI2JAHCAB4gApteK4tcK8sfSi6x0C3gJxgGa2o+jAtb53AvgExAGa2UYzBeSBOEAzEAcoAHGAFNnHEf/Yf+39L0ZR9OgL7M+iZ1S44/a5lSh68GnRbrl3NDq5ey5+2+XilmIS8/QNJ/Ivn0YLaJhAHCAlI45zUhYH5zlxXJW/ipHZWBwnzsav2IfyW4pJzBdvkJ0l+feDoQFxgJSFOBaczIrjavpnPsISi+M4d8G8uKWYBG/tSA6xl4X3g6EBcYCUrDgei5sf761wKaR9HGxUlv39dvyG/de4OKIl3hQpbiklwd7KXr+7wkMU3w+GBsQBUjLi4L5g1ze/rKU4riZ/Z5WQk8IGoqVR3FJKYjt5fTVp8uTeD4YGxAFSFuKQ7QfexzlPxJGZHxbr4BAXh/hDaUspiau5Jknp/WBoQBwgJTuqkvwhI47YEwcW7zzA/yDeWNpSSmIjNye19H4wNCAOkNIsjgzxhqw48luKSSxezyvfD4YGxAFSGsQhVqxUiaO0pUEcpfeDoQFxgJT2TRVBVVNlnvtgc1MFDBOIA6Q0iKO0eDYVR2lLhThk5yh3Rv36fTAQIA6Q0iAONnQqR0Ditka2c7S0pZREOhy7zd9ZfD8YGhAHSGkSRzJta/fdFV6BWIijuKWUhJwAlmwvvh8MDYgDpDSJIzNRPJkOmnRsFrZUJlE95RwVjkECcYCURnEsrveD7M8ZcRS2lJNI33Cw6v1gaEAcIKVZHGIx/NKjf8RfZMWR31IhDvGGfS9Uvh8MDYgDAKANxAEA0AbiAABoA3EAALSBOAAA2kAcAABtIA4AgDYQBwBAG4gDAKANxAEA0AbiAABoA3EAALSBOAAA2kAcAABtIA4AgDYQBwBAG4gDAKANxAEA0AbiAABoA3EAALSBOAAA2kAcAABtIA4AgDYQBwBAG4gDAKANxAEA0AbiAABoA3EAALSBOAAA2kAcAABtIA4AgDYQBwBAG4gDAKANxAEA0AbiAABoA3EAALSBOAAA2kAcAABtIA4AgDYQBwBAG4gDAKANxAEA0AbiAABoA3EAALSBOAAA2kAcAABtIA4AgDYQBwBAG4gDAKANxAEA0AbiAABoA3EAALSBOAAA2kAcAABtIA4AgDYQBwBAG4gDAKANxAEA0AbioGb3bPTA663e+eE13bT5J9oHAMAWEAc1ba/r3Vd0L3/5iUGJY1dbjv4ypmPpDMRBTcvrWv/yTz7hXBwb0UmDTZz3jrHtV6MD1PukxXa0v+6az2/iO3rv6NKLVfvsx7H4AsTRE+bicI65OOJd9uFiIxGHJ8fiCxBHT4Qkji5QFNDW4uAIcZQgOJYxAXH0RN/iiNrnfJ/iiDT2sxaIgx6Igxp5fcc/9l97/4txwX/0Bbnl9rkV/pKV1I2IcyguptHJ3XPxny/zTyRJyN/4Rx48Mc9+YiGQD1+Jty4d/OniQ/mA9Yi0mt7F0z94TdqB78ujl8ubbq2I/UmuuPeOyX2+yqOc5NX7jXjPGeJKzaXVsKMN74gT/F+vxqk9dlm8OrS9Eu17kYWIkhDsLfH+Lj0mA77HztKDT18rbSo1VUQiLxSOJXuMoQJxUJMRxzlxfUYH+YbtFfly3+t5cZw4G//2wOsV4rgqP3HgWpU43pUJLp1IPpQPqKCVOG6J9Pf9fS6OZPdPljYVxHE12dHcxbYt+wa4QHJpNe6n+i3xpf974jTwvYz+1go7mUkI+cd9xxav5q/Is8TOcX5TURzyMIvHkj3GUIE4qFmIYwErlLfY99prr30nLqcH8uI4zt0wL4tjO03gUIU4tvPplwKWiBRUvD3es32X57vfFsnFrw5eY6/YVZXflBdHfJyfvBZvkichuR7vHeVv4u/JpVW5m8Xdzf69ADsNcWq3xVmJX+2/PP8p24v454fneAj2lk/Gbzkm38JMu/sK3/f8pqI4Npiyy8eSO8ZAgTioyYqDVYDfW4nkl5S8TDaS7fJKir/tXpAfzIuDbWIpxDWL7CfkT7Y1vnpZma8OWEJTHEnzf5tfYcloAv+Z35QXh3ijuMyyF5to8PBP5tKq3M3i7mb/XtpNkUoc6xC3woviJIs93BC7K+oG4i0b2Rf5TQVxyCpU+ViyxxgoEAc1GXGIy4J9+fGmRu77NSuOk8lf8uJILglWMT5ZEsdV0YIRkQ5VBFRRa4sMSa8E64WJU5d7f2slTju3KS+O3NWUvdhEW2WjlFbzbqr3M1GFcNJ2eo5Olv6Y7wpNxJHdVBBH/JZ9L2TenxxL0MaQQBzULMQhC7TUQHyhL/3DPyq+jV17suiWxHG1WjXy50JEsSgOVARU0eKCXFwd7GJnfpPw3pjMprw4cmMSWXHwtor8L5NW81427GgqA74b8lW6F/KPsmaT7Onu+6999eFIiCO7qdhU4a1B0Yuab3ZVtrGCAuKgJjuqkvwhaXjE7Pu317JvY38ujaXI3zby11VeHBk7iF+rBmUUNHaNpvWCKnFkN7UUx3wj3iCvzJbiaEWNOGTK/JeCON59WEZvFAcfPGEtwGsQRwGIg5o6ccxvHZPllfVMtBBH8frPi2PxuZJJWomjzXHkahxlH8z1xLEdHRKvaa877RpHXI1YevRLv//B2RbiiA/hO2zs9gDEUQDioKZWHPH3lxwuZcWuqzi61jiaSToy+BWTb9bnNrXs44g37/+fK5nxCSLyvRRSHIo+DtG30aqPI+GqPOHo41gAcVCjEEfMbf4FJq7zXpsqzSTXlBg6SYYpcldiMqoivoBFn8yGuOKuFr6lYzYe+Dfyl2xa3XczL4JkNCURyIFkN+VbklbM9oocVVlsKoojMSL/mR8hWhxjoEAc1KjFEXPvmLzwa8VRHIeJ33OgTecorTjYmMLl+e5frEg7sIkRbGj4UHGTfPVtUZXicxzYmLDY8uvz9ALbjuQu59LqymImhhiJEodenMex9OvJZA12MmPNssmmhwqbKkZV2H7ePivPb3os2WMMFIiDmhpx5F7XiSOd6ZEfjt1OxlsLw7H7C8OxtOKY3xadmPt/Lzdz9GDFJrHlk2ezM0dl3YLtmxTH4lBzaXVkO3rw4bQBuKjEVM8czezf/m+Lykh+U/XMUX7es8eSPcYwgTioqatxxOVuHxuN/fCVSM5o4GU108m5IaaC8fURmQlgu/E3M3+r/MRu7QQwYnHMd8trVZJ5DflNbPHHgyeSwZbMOg62CudAWqXfSGsYubS6wVebxGfu6ULr5zZrEyYLWMSClINiExtVifcv6RPJbKpYq7KSJp07FqxV6XsHRkfDcGz67bYhBgQz4kjnkP+Nqinn6SeSmkd5yjm1OAYBSUcJ0AXioKZ+ODZZ5CYWpW2Lum5GHMnqq31/XFzkJlfJiU8k4pj/RWmRG8QBHAFxUFPfObr76iMRm4golsHz637/taw4eJU/rhgvPpuv0otPpOKQi+6zy+oLv4UAxNELEAcYNhBHL0AcYNhAHL0AcQAAtIE4AADaQBwAAG0gDgCANhAHAEAbiAMAoA3EAQDQBuIAAGgDcQAAtIE4AADaQBwAAG0gDgCANhAHAEAbiAMAoA3EAQDQBuIAAGgDcQAAtIE4AADaQBwAAG0gDgCANhAHAEAbiAMAoA3EAQDQBuIAAGgDcQAAtIE4AADaQBwAAG0gDgCANhAHAEAbiAMAoA3EAQDQBuIAAGgDcQAAtPn/ikroGXzQNT8AAAAASUVORK5CYII=)
Customizing Plots
afex_plot
allows to customize the plot in a number of
different ways. For example, we can easily change the aesthetic mapping
associated with the trace
factor. So instead of using
lineytpe and shape of the symbols, we can use color. Furthermore, we can
change the graphical element used for plotting the data points in the
background. For example, instead of plotting the raw data, we can
replace this with a boxplot. Finally, we can also make both the points
showing the means and the lines connecting the means larger.
p1 <- afex_plot(a1, x = "inference", trace = "instruction",
panel = "plausibility", error = "none",
mapping = c("color", "fill"),
data_geom = geom_boxplot, data_arg = list(width = 0.4),
point_arg = list(size = 1.5), line_arg = list(size = 1))
p1
![](data:image/png;base64,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)
Note that afex_plot
returns a ggplot2
plot
object which can be used for further customization. For example, one can
easily change the theme
to something that does not have a
grey background:
![](data:image/png;base64,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)
We can also set the theme globally for the remainder of the
R
session.
The full set of customizations provided by afex_plot
is
beyond the scope of this vignette. The examples on the help page at
?afex_plot
provide a good overview.
Replicate Analysis from Singmann and Klauer (2011)
However, the plots shown so far are not particularly helpful with
respect to the research question. Next, we fit a new ANOVA model in
which we separate the data in affirmation and denial inferences. This
was also done in the original manuscript. We then lot the data a second
time.
a2 <- aov_ez("id", "response", sk2011.1, between = "instruction",
within = c("validity", "plausibility", "what"))
## Warning: More than one observation per design cell, aggregating data using `fun_aggregate = mean`.
## To turn off this warning, pass `fun_aggregate = mean` explicitly.
## Contrasts set to contr.sum for the following variables: instruction
## Anova Table (Type 3 tests)
##
## Response: response
## Effect df MSE F ges p.value
## 1 instruction 1, 38 2027.42 0.31 .003 .583
## 2 validity 1, 38 678.65 4.12 * .013 .049
## 3 instruction:validity 1, 38 678.65 4.65 * .014 .037
## 4 plausibility 1, 38 468.82 34.23 *** .068 <.001
## 5 instruction:plausibility 1, 38 468.82 10.67 ** .022 .002
## 6 what 1, 38 660.52 0.22 <.001 .640
## 7 instruction:what 1, 38 660.52 2.60 .008 .115
## 8 validity:plausibility 1, 38 371.87 0.14 <.001 .715
## 9 instruction:validity:plausibility 1, 38 371.87 4.78 * .008 .035
## 10 validity:what 1, 38 1213.14 9.80 ** .051 .003
## 11 instruction:validity:what 1, 38 1213.14 8.60 ** .045 .006
## 12 plausibility:what 1, 38 204.54 9.97 ** .009 .003
## 13 instruction:plausibility:what 1, 38 204.54 5.23 * .005 .028
## 14 validity:plausibility:what 1, 38 154.62 0.03 <.001 .855
## 15 instruction:validity:plausibility:what 1, 38 154.62 0.42 <.001 .521
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
Then we plot the data from this ANOVA. Because each panel would again
show a mixed-design, we suppress the error bars.
afex_plot(a2, x = c("plausibility", "validity"),
trace = "instruction", panel = "what",
error = "none")
![](data:image/png;base64,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)
We see the critical and predicted cross-over interaction in the left
of those two graphs. For implausible but valid problems deductive
responses are larger than probabilistic responses. The opposite is true
for plausible but invalid problems. We now tests these differences at
each of the four x-axis ticks in each plot using custom contrasts
(diff_1
to diff_4
). Furthermore, we test for a
validity effect and plausibility effect in both conditions.
(m4 <- emmeans(a2, ~instruction+plausibility+validity|what))
## what = affirmation:
## instruction plausibility validity emmean SE df lower.CL upper.CL
## deductive plausible valid 99.5 1.16 38 97.1 101.8
## probabilistic plausible valid 95.3 1.16 38 93.0 97.6
## deductive implausible valid 95.1 5.01 38 85.0 105.2
## probabilistic implausible valid 60.2 5.01 38 50.0 70.3
## deductive plausible invalid 67.0 6.95 38 52.9 81.0
## probabilistic plausible invalid 90.5 6.95 38 76.5 104.6
## deductive implausible invalid 56.0 7.97 38 39.9 72.2
## probabilistic implausible invalid 64.1 7.97 38 48.0 80.3
##
## what = denial:
## instruction plausibility validity emmean SE df lower.CL upper.CL
## deductive plausible valid 70.5 6.18 38 58.0 83.1
## probabilistic plausible valid 93.0 6.18 38 80.5 105.5
## deductive implausible valid 70.2 6.36 38 57.4 83.1
## probabilistic implausible valid 73.0 6.36 38 60.1 85.8
## deductive plausible invalid 86.5 5.32 38 75.8 97.3
## probabilistic plausible invalid 87.5 5.32 38 76.7 98.2
## deductive implausible invalid 77.1 6.62 38 63.7 90.5
## probabilistic implausible invalid 80.8 6.62 38 67.4 94.1
##
## Confidence level used: 0.95
c2 <- list(
diff_1 = c(1, -1, 0, 0, 0, 0, 0, 0),
diff_2 = c(0, 0, 1, -1, 0, 0, 0, 0),
diff_3 = c(0, 0, 0, 0, 1, -1, 0, 0),
diff_4 = c(0, 0, 0, 0, 0, 0, 1, -1),
val_ded = c(0.5, 0, 0.5, 0, -0.5, 0, -0.5, 0),
val_prob = c(0, 0.5, 0, 0.5, 0, -0.5, 0, -0.5),
plau_ded = c(0.5, 0, -0.5, 0, -0.5, 0, 0.5, 0),
plau_prob = c(0, 0.5, 0, -0.5, 0, 0.5, 0, -0.5)
)
contrast(m4, c2, adjust = "holm")
## what = affirmation:
## contrast estimate SE df t.ratio p.value
## diff_1 4.175 1.64 38 2.543 0.0759
## diff_2 34.925 7.08 38 4.931 0.0001
## diff_3 -23.600 9.83 38 -2.401 0.0854
## diff_4 -8.100 11.28 38 -0.718 0.9538
## val_ded 35.800 7.10 38 5.045 0.0001
## val_prob 0.400 7.10 38 0.056 0.9553
## plau_ded -3.275 3.07 38 -1.068 0.8761
## plau_prob 30.775 4.99 38 6.164 <.0001
##
## what = denial:
## contrast estimate SE df t.ratio p.value
## diff_1 -22.425 8.74 38 -2.565 0.1007
## diff_2 -2.700 8.99 38 -0.300 1.0000
## diff_3 -0.925 7.52 38 -0.123 1.0000
## diff_4 -3.650 9.36 38 -0.390 1.0000
## val_ded -11.412 6.65 38 -1.716 0.5658
## val_prob -1.137 6.65 38 -0.171 1.0000
## plau_ded -4.562 4.11 38 -1.109 1.0000
## plau_prob 13.363 2.96 38 4.519 0.0005
##
## P value adjustment: holm method for 8 tests
We can also pass these tests to multcomp
which gives us
more powerful Type 1 error corrections.
summary(as.glht(contrast(m4, c2)), test = adjusted("free"))
## Warning in tmp$pfunction("adjusted", ...): Completion with error > abseps
## Warning in tmp$pfunction("adjusted", ...): Completion with error > abseps
## Warning in tmp$pfunction("adjusted", ...): Completion with error > abseps
## Warning in tmp$pfunction("adjusted", ...): Completion with error > abseps
## Warning in tmp$pfunction("adjusted", ...): Completion with error > abseps
## Warning in tmp$pfunction("adjusted", ...): Completion with error > abseps
## Warning in tmp$pfunction("adjusted", ...): Completion with error > abseps
## $`what = affirmation`
##
## Simultaneous Tests for General Linear Hypotheses
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## diff_1 == 0 4.175 1.641 2.543 0.0649 .
## diff_2 == 0 34.925 7.082 4.931 8.65e-05 ***
## diff_3 == 0 -23.600 9.830 -2.401 0.0708 .
## diff_4 == 0 -8.100 11.275 -0.718 0.6882
## val_ded == 0 35.800 7.096 5.045 5.94e-05 ***
## val_prob == 0 0.400 7.096 0.056 0.9553
## plau_ded == 0 -3.275 3.065 -1.068 0.6037
## plau_prob == 0 30.775 4.992 6.164 6.08e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- free method)
##
##
## $`what = denial`
##
## Simultaneous Tests for General Linear Hypotheses
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## diff_1 == 0 -22.425 8.742 -2.565 0.081289 .
## diff_2 == 0 -2.700 8.987 -0.300 0.984917
## diff_3 == 0 -0.925 7.522 -0.123 0.984917
## diff_4 == 0 -3.650 9.358 -0.390 0.984917
## val_ded == 0 -11.412 6.651 -1.716 0.378872
## val_prob == 0 -1.137 6.651 -0.171 0.984917
## plau_ded == 0 -4.562 4.115 -1.109 0.725899
## plau_prob == 0 13.363 2.957 4.519 0.000387 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- free method)
Unfortunately, in the present case this function throws several
warnings. Nevertheless, the p-values from both methods are very similar
and agree on whether or not they are below or above .05. Because of the
warnings it seems advisable to use the one provided by
emmeans
directly and not use the ones from
multcomp
.
The pattern for the affirmation problems is in line with the
expectations: We find the predicted differences between the instruction
types for valid and implausible (diff_2
) and invalid and
plausible (diff_3
) and the predicted non-differences for
the other two problems (diff_1
and diff_4
).
Furthermore, we find a validity effect in the deductive but not in the
probabilistic condition. Likewise, we find a plausibility effect in the
probabilistic but not in the deductive condition.