I have spent a lot of time on multiple posts and tutorials, but I still do not understand wich "rule" I have to apply to my current data, and why.
My experiment follows a within-subject design, as every subjects (n=17) performed a task in 2 conditions, accross 5 blocks of trials. VD is the mean RTs, fixed effects are condition and block, random effect is subject.
I would like to analyse the interaction between condition and block.
Using aov
I first aggregated my data:
ag<-aggregate(RT~condition+block+subject,data=d, FUN=mean)
But then I don't know if I have to include my within-subject factors into my Error term:
(1)aov<-aov(RT~ condition * block + Error(subject/(condition * block)), data=ag)
OR
(2)aov<-aov(RT~ condition * block + Error(subject), data=ag)
I have seen on several posts that the within factors have to be included in the error term, as in (1), but I do not understand how the dfs are calculated.
Using lmer
Additionally, I would like to attempt using lmer instead of aov.
I suspect that the equivalent of (2) would be:
lmer(RT ~ 1+(1|sujet)+condition*block, ag)
But if it is the (1) which is the correct one, I can not figure out how does it would have to be specified using lmer.
Related
I am trying to analyse a split plot design for a plant growth experiment with these variables:
Biomass (dependent variable)
Transect (sub plot factor with three levels)
Treatment (main plot factor with two levels)
Block (2 blocks in total, serving as replicates of the treatment)
Location (multiple locations within each transect point)
I know what the random effect structure should look like. However, I can’t work out how to write this in R script. Could someone please help me? It’s probably very easy, but I have been looking for hours and hours and can’t find it.
Random effects should be:
Block
Interaction Block and Treatment
Location nested within Transect
(Location nested within Transect), interaction with Treatment
So perhaps something like:
(1|block) + (1|block*treatment) + (1|location:transect) +
(1|(location:transect)*treatment)
OK, I'll take a shot at this.
First: in 'modern' mixed model approaches it is not practical to treat a two-level categorical variable as random. In 'classical' method-of-moment/SSQ ratio approaches it works, although the power is terrible; in modern methods you will end up with 'singular models' (do a web search for "GLMM FAQ" or search here and on CrossValidated for more info). (The exception to this statement is if you go full-Bayesian and put regularizing priors on the random-effects parameters ...) Therefore, I'm going to take block as a fixed effect.
This would be (I think) your maximal model:
~ treatment*block + (treatment|transect/location)
treatment*block (expands to 1 + block + treatment + block:treatment: the baseline biomass (intercept) could differ between blocks, the treatments could differ, the treatment effect could differ between blocks
(treatment|transect/location) (expands to (1+treatment|transect) + (1+treatment|transect:location)); the intercept and treatment effect vary among transects and among locations within transects. (This assumes that transects are uniquely coded between blocks, i.e. you don't have a transect 001 in both blocks, rather they are labeled something like A001 and B001. If not, you need something like (1+treatment|block:(transect/location)) ...
This also assumes you have multiple observations per transect/location/treatment combination. If not (if each treatment is observed only once per location), then the full interaction will be confounded with the residual variation and you instead need something like (1+treatment|transect) + (1|transect:location).
I have 4 random factors and I want to provide its linear model using lme4. But struggled to fit the model.
Assuming A is nested within B (2 levels), which in turn nested within each of xx preceptors (P). All responded to xx Ms (M).
I want to fit my model to get variances for each factor and their interactions.
I have used the following codes to fit the model, but I was unsuccessful.
lme4::lmer(value ~ A +
(1 + A|B) +
(1 + P|A),
(1+ P|M),
data = myData, na.action = na.exclude)
I also read interesting materials here, but Still, I struggle to fit the model. Any help?
At a guess, if the nesting structure is ( P (teachers) / B (occasions) / A (participants) ), meaning that the occasions for one teacher are assumed to be completely independent of the occasions for any other teacher, and that participants in turn are never shared across occasions or teachers, but questions (M) are shared across all teachers and occasions and participants:
value ~ 1 + (1| P / B / A) + (1|M)
Some potential issues:
as you hint in the comments, it may not be practical to fit random effects for factors with small numbers of levels (say, < 5); this is likely to lead to the dreaded "singular model" message (see the GLMM FAQ for more detail).
if all of the questions (M) are answered by every participant, then in principle it's possible to fit a model that takes account of the among-question correlation within participants: the maximal model would be ~ 1 + (M | P / B / A) (which would look for among-question correlations at the level of teacher, occasion within teacher, and participant within occasion within teacher). However, this is very unlikely to work in practice (especially if each participant answers each question only once, in which case the teacher:occasion:participant:question variance will be confounded with the residual variance in a linear model). In this case, you will get an error about "probably unidentifiable": see e.g. this question for more explanation/detail.
I've had a search for similar questions and come up short so apologies if there are related questions that I've missed.
I'm looking at the amount of time spent on feeders (dependent variable) across various conditions with each subject visiting feeders 30 times.
Subjects are exposed to feeders of one type which will have a different combination of being scented/unscented, having visual patterns/being blank, and having these visual or scented patterns presented in one of two spatial arrangements.
So far my model is:
mod<-lmer(timeonfeeder ~ scent_yes_no + visual_yes_no +
pattern_one_or_two + (1|subject), data=data)
How can I incorporate the visit numbers into the model to see if these factors have an effect on the time spent on the feeders over time?
You have a variety of choices (this question might be marginally better for CrossValidated).
as #Dominix suggests, you can allow for a linear increase or decrease in time on feeder over time. It probably makes sense to allow this change to vary across birds:
timeonfeeder ~ time + ... + (time|subject)
you could allow for an arbitrary pattern of change over time (i.e. not just linear):
timeonfeeder ~ factor(time) + ... + (1|subject)
this probably doesn't make sense in your case, because you have a large number of observations, so it would require many parameters (it would be more sensible if you had, say, 3 time points per individual)
you could allow for a more complex pattern of change over time via an additive model, i.e. modeling change over time with a cubic spline. For example:
library(mgcv)
gamm(timeonfeeder ~ s(time) + ... , random = ~1|subject
(1) this assumes the temporal pattern is the same across subjects; (2) because gamm() uses lme rather than lmer under the hood you have to specify the random effect as a separate argument. (You could also use the gamm4 package, which uses lmer under the hood.)
You might want to allow for temporal autocorrelation. For example,
lme(timeonfeeder ~ time + ... ,
random = ~ time|subject,
correlation = corAR1(form= ~time|subject) , ...)
I'm having a huge problem with a nested model I am trying to fit in R.
I have response time experiment with 2 conditions with 46 people each and 32 measures each. I would like measures to be nested within people and people nested within conditions, but I can't get it to work.
The code I thought should make sense was:
nestedmodel <- lmer(responsetime ~ 1 + condition +
(1|condition:person) + (1|person:measure), data=dat)
However, all I get is an error:
Error in checkNlevels(reTrms$flist, n = n, control) :
number of levels of each grouping factor must be < number of observations
Unfortunately, I do not even know where to start looking what the problem is here.
Any ideas? Please, please, please? =)
Cheers!
This might be more appropriate on CrossValidated, but: lme4 is trying to tell you that one or more of your random effects is confounded with the residual variance. As you've described your data, I don't quite see why: you should have 2*46*32=2944 total observations, 2*46=92 combinations of condition and person, and 46*32=1472 combinations of measure and person.
If you do
lf <- lFormula(responsetime ~ 1 + condition +
(1|condition:person) + (1|person:measure), data=dat)
and then
lapply(lf$reTrms$Ztlist,dim)
to look at the transposed random-effect design matrices for each term, what do you get? You should (based on your description of your data) see that these matrices are 1472 by 2944 and 92 by 2944, respectively.
As #MrFlick says, a reproducible example would be nice. Other things you could show us are:
fit the model anyway, using lmerControl(check.nobs.vs.nRE="ignore") to ignore the test, and show us the results (especially the random effects variances and the statement of the numbers of groups)
show us the results of with(dat,table(table(interaction(condition,person))) to give information on the number of replicates per combination (and similarly for measure)
I'm using the following code to try to get at post-hoc comparisons for my cell means:
result.lme3<-lme(Response~Pressure*Treatment*Gender*Group, mydata, ~1|Subject/Pressure/Treatment)
aov.result<-aov(result.lme3, mydata)
TukeyHSD(aov.result, "Pressure:Treatment:Gender:Group")
This gives me a result, but most of the adjusted p-values are incredibly small - so I'm not convinced the result is correct.
Alternatively I'm trying this:
summary(glht(result.lme3,linfct=mcp(????="Tukey")
I don't know how to get the Pressure:Treatment:Gender:Group in the glht code.
Help is appreciated - even if it is just a link to a question I didn't find previously.
I have 504 observations, Pressure has 4 levels and is repeated in each subject, Treatment has 2 levels and is repeated in each subject, Group has 3 levels, and Gender is obvious.
Thanks
I solved a similar problem creating a interaction dummy variable using interaction() function which contains all combinations of the leves of your 4 variables.
I made many tests, the estimates shown for the various levels of this variable show the joint effect of the active levels plus the interaction effect.
For example if:
temperature ~ interaction(infection(y/n), acetaminophen(y/n))
(i put the possible leves in the parenthesis for clarity) the interaction var will have a level like "infection.y:acetaminophen.y" which show the effect on temperature of both infection, acetaminophen and the interaction of the two in comparison with the intercept (where both variables are n).
Instead if the model was:
temperature ~ infection(y/n) * acetaminophen(y/n)
to have the same coefficient for the case when both vars are y, you would have had to add the two simple effect plus the interaction effect. The result is the same but i prefer using interaction since is more clean and elegant.
The in glht you use:
summary(glht(model, linfct= mcp(interaction_var = 'Tukey'))
to achieve your post-hoc, where interaction_var <- interaction(infection, acetaminophen).
TO BE NOTED: i never tested this methodology with nested and mixed models so beware!