Run a repeated measures anova as a mixed effects model using fitlme

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bsriv
bsriv 2023-10-2
评论： bsriv 2023-10-12
Hi, I am using a repeated measures anova to run a model in which I am looking at the effects of 3 factors, 2 levels each (instruction, picture, time) on the DV which is activation with 22 subjects. Instruction is either "look" or "negative", picture is either "alcohol" or "food", and time is either "pre" or post"
So I start with a table with notations described as above:
t =
22×9 table
subject look_alc_pre look_alc_post negative_alc_pre negative_alc_post look_food_pre look_food_post negative_food_pre negative_food_post
_______ ____________ _____________ ________________ _________________ _____________ ______________ _________________ __________________
1 12.133 -0.58848 1.7663 -1.1068 1.1367 -12.784 -7.5119 -7.8238
2 11.246 -1.3831 11.598 -11.673 6.3135 -10.428 -0.54937 2.0609
3 -3.5529 8.2517 -12.154 18.181 -12.304 5.343 -12.638 15.727
4 -29.929 -17.964 -18.838 -23.523 -27.619 -20.193 -25.449 -18.107
5 -8.6783 5.1554 -10.748 4.1162 -12.658 -0.76942 -7.6787 11.932
6 42.469 60.953 32.816 71.571 14.558 45.931 20.959 38.89
7 11.843 5.0678 6.8864 9.7936 -1.2638 -3.7865 -3.7561 6.1002
8 42.138 -1.6591 33.319 2.8538 20.252 3.1166 21.965 6.3843
9 21.812 7.4502 16.274 -4.9925 23.128 -11.01 12.453 -3.9199
10 -0.46039 12.549 12.595 8.6604 8.4653 -6.2358 -1.1716 21.91
11 32.655 -6.289 23.065 -6.1028 16.403 -4.4612 15.165 -13.452
12 14.303 -8.3735 25.575 -0.040629 16.255 4.1393 19.625 9.6402
13 -5.7241 7.7997 6.1318 14.951 -7.9766 -3.5728 -14.098 4.4836
14 16.11 -4.0946 13.814 0.54614 0.24486 -0.64993 6.3281 -5.7614
15 -13.502 -4.5322 -4.9492 -7.0545 -4.4366 -6.1812 -17.884 -6.3731
16 10.407 -2.5284 -3.9457 7.8309 -0.78716 -6.3991 2.2326 -0.62589
17 13.739 3.0792 5.6006 -2.0368 6.5132 1.4708 3.9475 3.1808
18 15.826 -4.8494 7.9326 -16.506 2.8565 3.8328 7.6348 -4.6485
19 -8.8854 -0.71365 2.9053 -6.4694 2.485 3.3196 0.74676 4.0554
20 0.8475 -2.5931 -1.7847 -6.2106 3.6092 0.33887 -0.42818 -2.1065
21 2.3565 9.5125 -8.8134 3.2384 8.0217 8.9698 17.694 5.6276
22 -6.819 7.9659 -19.151 8.3581 -4.2582 15.056 -11.426 -6.0232
Then the following code successfully runs the model:
WithinStructure=table([1 1 2 2 1 1 2 2]',[1 1 1 1 2 2 2 2]',[1 2 1 2 1 2 1 2]','VariableNames',{'instruction','picture','time'});
WithinStructure.instruction = categorical(WithinStructure.instruction);
WithinStructure.picture = categorical(WithinStructure.picture);
WithinStructure.time = categorical(WithinStructure.time);
rm=fitrm(t,'look_alc_pre-negative_food_post~1','WithinDesign',WithinStructure);
ranovatbl=ranova(rm,'WithinModel','instruction*picture*time');
Howevever I will need to re-run this as a mixed effects model (including fitlme) as I will be including more sujects in the "pre" time. But I am confused as to how to set up the model (just using the original data above):
lmeModel = fitlme(t,'look_alc_pre-negative_food_post ~ 1+instruction*picture*time+(1|subject)');
But I keep getting the following error
Error using classreg.regr.LinearMixedFormula>parseStr
Unable to understand the character vector or string scalar 'look_alc_pre-negative_food_post ~ 1+instruction*picture*time+(1|subject)'.
Error in classreg.regr.LinearMixedFormula (line 166)
[f.str,f.tree] = parseStr(modelSpec);
Error in LinearMixedModel.fit (line 2275)
model.Formula = classreg.regr.LinearMixedFormula(formula,ds.Properties.VariableNames);
Error in fitlme (line 233)
lme = LinearMixedModel.fit(ds,formula,varargin{:});
Not sure what I am doing incorrectly! I have attached the data. THank you!!
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the cyclist
the cyclist 2023-10-11
编辑：the cyclist 2023-10-11
Ran in:
Here is a fairly massive re-write of your analysis. Almost all of this code is restructuring the data into the tidy format. I used some fairly sophisticated table juju here. But the bottom line is to convert the information currently encoded in column names into variables. I display the top few rows of the restructured table, which makes it obvious what I did.
The model fit is trivial, once you format the data properly.
% Load the data. (I re-saved this MAT file with the variable named "tbl", because "table" is a keyword, and it was causing a conflict.)
load("exampledata.mat","tbl")
% Stack the variables into the "tidy" format, so that variable values are not encoded into column names.
tbl = stack(tbl,["look_alc_pre","look_alc_post","negative_alc_pre","negative_alc_post","look_food_pre","look_food_post","negative_food_pre","negative_food_post"],"IndexVariableName","index","NewDataVariableName","value");
% Use the dummy encoding that was created for the repeated measures analysis,
% and replicate it for each subject (but make them categorical)
WithinStructure=table(categorical([1 1 2 2 1 1 2 2])',categorical([1 1 1 1 2 2 2 2])',categorical([1 2 1 2 1 2 1 2])','VariableNames',{'instruction','picture','time'});
WithinStructure = repmat(WithinStructure,[22,1]);
% Concatenate the table with the dummy encoding
tbl = [tbl,WithinStructure];
% Removing the index variable, because that information is encoded in the dummy encoding
tbl = removevars(tbl,"index");
% Display what the first few rows of the table look like now
head(tbl)
subject value instruction picture time _______ ________ ___________ _______ ____ 1 12.133 1 1 1 1 -0.58848 1 1 2 1 1.7663 2 1 1 1 -1.1068 2 1 2 1 1.1367 1 2 1 1 -12.784 1 2 2 1 -7.5119 2 2 1 1 -7.8238 2 2 2
% Fit a model -- It might not be the one you want
lmeModel = fitlme(tbl,'value ~ 1 + instruction*picture*time + (1|subject)')
lmeModel =
Linear mixed-effects model fit by ML Model information: Number of observations 176 Fixed effects coefficients 8 Random effects coefficients 22 Covariance parameters 2 Formula: value ~ 1 + instruction*picture + instruction*time + picture*time + instruction:picture:time + (1 | subject) Model fit statistics: AIC BIC LogLikelihood Deviance 1375.7 1407.4 -677.84 1355.7 Fixed effects coefficients (95% CIs): Name Estimate SE tStat DF pValue Lower Upper {'(Intercept)' } 7.7425 3.0555 2.5339 168 0.012194 1.7103 13.775 {'instruction_2' } -2.2927 2.9754 -0.77054 168 0.44206 -8.1667 3.5814 {'picture_2' } -5.0634 2.9754 -1.7017 168 0.090653 -10.937 0.81063 {'time_2' } -4.4599 2.9754 -1.4989 168 0.13577 -10.334 1.4141 {'instruction_2:picture_2' } 0.80274 4.2079 0.19077 168 0.84894 -7.5044 9.1099 {'instruction_2:time_2' } 1.9367 4.2079 0.46026 168 0.64593 -6.3705 10.244 {'picture_2:time_2' } 2.0103 4.2079 0.47774 168 0.63346 -6.2969 10.317 {'instruction_2:picture_2:time_2'} 2.1034 5.9509 0.35346 168 0.72418 -9.6447 13.852 Random effects covariance parameters (95% CIs): Group: subject (22 Levels) Name1 Name2 Type Estimate Lower Upper {'(Intercept)'} {'(Intercept)'} {'std'} 10.393 7.4791 14.442 Group: Error Name Estimate Lower Upper {'Res Std'} 9.8684 8.8256 11.034

更多回答（1 个）

the cyclist
the cyclist 2023-10-2
编辑：the cyclist 2023-10-2
Please see my comment about helping us help you.
That being said, I don't think you can specify the response variable as
look_alc_pre-negative_food_post
because that is not valid Wilkinson notation. I'm surprised that that worked in fitrm. I'm frankly not sure what is going on there. Maybe there is an interpretation of that that I don't understand.
If you intended to specify a difference (with a minus sign), that won't work. You'd need to define a new variable for the difference. (I have not tried to figure out if that is just a typo.)
  2 个评论
bsriv
bsriv 2023-10-2
Hi it looks like this is standard for fitrm for ModelSpec https://www.mathworks.com/help/stats/fitrm.html#bt9ic32-modelspec
But for fitlme you're right the notation is different, but i am still unsure
the cyclist
the cyclist 2023-10-2
Oh, that's interesting! I've never used fitrm, and made assumptions. Sorry for the misinformation there.

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