Linear Mixed Model Line Plotted with R
I have been trying to solve this for 1 day. I'm new to linear mixed models, so I guess this explains my failure. I quickly created some data, for the sole purpose of illustration:
#Data
df <- data.frame(
subject=rep(c("1","2","3","4","5","6"),each=100),
order=rep(1:20),
similarity = rep(c("Similar", "Dissimilar"), each=150,times=2),
relate = rep(c("related", "unrelated"), each=75,times=4),
stack = as.numeric(rep(c("112","155","76","88","90","122","145","102","159","233")), each=60),
target= rep(c("banana","apple","peach","pineapple","coconut","cherry"),times=10)
)
# add RT data
df$RT <- 0.02*df$order +
-6*as.numeric(df$similarity=="Similar")* as.numeric(df$stack) +
6*as.numeric(df$similarity=="Dissimilar")* as.numeric(df$stack) +
4*as.numeric(df$stack)*as.numeric(df$relate=="unrelated") +
-11*as.numeric(df$target=="banana")*as.numeric(df$order>1 & df$order<6)+
df$stack/10*rnorm(600, mean=0, sd=2)
df$RT<--1*df$RT
Here is my model:
##model
model=lmer(RT~similarity*relate*stack
+order + (1|subject)
+ (1|target),data=df,REML=F,control=lmerControl(optimizer = c("bobyqa")))
df$fit<-predict(model) ##add fitted values
Results:
Linear mixed model fit by maximum likelihood t-tests use Satterthwaite approximations to degrees of freedom [
lmerMod]
Formula: RT ~ similarity * relate * stack + order + (1 | subject) + (1 | target)
Data: df
Control: lmerControl(optimizer = c("bobyqa"))
AIC BIC logLik deviance df.resid
5668.6 5721.3 -2822.3 5644.6 588
Scaled residuals:
Min 1Q Median 3Q Max
-3.5247 -0.6163 0.0226 0.5944 4.0280
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 0.0 0.00
target (Intercept) 0.0 0.00
Residual 713.2 26.71
Number of obs: 600, groups: subject, 6; target, 6
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -7.46457 6.74238 600.00000 -1.107 0.269
similaritySimilar -0.86579 9.41010 600.00000 -0.092 0.927
relateunrelated 13.96619 9.43009 600.00000 1.481 0.139
stack -5.92555 0.05030 600.00000 -117.802 <2e-16 ***
order -0.06343 0.19765 600.00000 -0.321 0.748
similaritySimilar:relateunrelated -8.96977 13.33903 600.00000 -0.672 0.502
similaritySimilar:stack 12.00979 0.07024 600.00000 170.974 <2e-16 ***
relateunrelated:stack -4.12125 0.06952 600.00000 -59.283 <2e-16 ***
similaritySimilar:relateunrelated:stack 0.08997 0.09835 600.00000 0.915 0.361
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) smlrtS rltnrl stack order smlrtySmlr:r smlrtySmlr:s rltnr:
simlrtySmlr -0.696
relatenrltd -0.692 0.499
stack -0.895 0.661 0.662
order -0.162 -0.010 -0.026 -0.160
smlrtySmlr:r 0.487 -0.706 -0.707 -0.470 0.033
smlrtySmlr:s 0.655 -0.945 -0.472 -0.702 0.025 0.667
rltnrltd:st 0.662 -0.477 -0.945 -0.709 0.022 0.668 0.505
smlrtySml:: -0.465 0.675 0.668 0.504 -0.035 -0.945 -0.715 -0.707
Obviously, the model might look odd since I didn't spend too much time trying to reproduce the original dataset, which I can't share here. What I wanted to do was just show the model mounted for RT as a stack function under two different conditions of similarity == "Incomparable" and similarity == "Similar". This is probably getting in the way of my lack of understanding of model theory, but should it be pretty easy to do this, or am I missing something? Any advice on how to do this in ggplot? Thanks in advance.
source to share
Several ideas. Try the package first sjPlot
. It includes a function sjp.lmer
that can generate many different summaries for a linear mixed model. For example, to build RT vs stack by similarity, you can use:
library(sjPlot)
sjp.lmer(model, type = "pred", vars = c("stack", "similarity"))
I would also install the package broom
. It provides a function augment
that generates a neat dataframe from your model:
model %>% augment()
and then you can pass the data frame to ggplot
to achieve the desired result; for example, a simple plot of the scatter of set values across a stack in likelihood:
model %>% augment() %>%
ggplot(aes(stack, RT)) + geom_point() + facet_grid(similarity ~ .)
source to share