| Table of Contents | Function Reference | Function Finder | R Project | SINGLE FACTOR REPEATED MEASURES ANOVA Preliminaries Model Formulae If you have not yet read the Model Formulae tutorial, now would definitely be the time to do it. Some Explanations The naming of experimental designs has led to a great deal of confusion on the part of students as well as tutorial writers. The design I will discuss in this tutorial is the single factor within subjects design, also called the single factor repeated measures design. This is a design with one response variable, and each "subject" or "experimental unit" is measured multiple times on that response variable. For example, pre-post designs, in which a group of subjects is measured both before and after some treatment, fit into this category. This design is also referred to in some sources as a "treatment × subjects" design (read "treatment by subjects"). You can also think of the design as having treatments nested within subjects. Or you could also think of it as a block design in which subjects correspond to blocks. I've even heard it called split plot, in which subjects are plots. You see my point. There is some difference of opinion concerning the expressions "repeated measures design" and "within subjects design." Some people reserve the use of "repeated measures" for cases in which subjects ("units") are measured on the same variable over time, such as in pre-post designs, and use "within subjects" for cases in which subjects are measured in multiple experimental conditions, e.g., reaction time to a red light, reaction time to a green light, reaction time to a white light, etc. I have a tendency not to obey this particular convention and use "repeated measures" and "within subjects" interchangeably. In any event, the analysis is the same. In my field (psychology), we are used to referring to our experimental units as "subjects," because they are almost always either human beings or animals. I will retain that terminology in this tutorial. This is why, in the first study discussed below, grocery items are referred to as subjects--because the groceries correspond to what would ordinarily be human subjects in a typical psychology experiment. For a change, it is people in other fields who will have to adjust to strange terminology! Single Factor Designs With Repeated Measures Several (seventeen!) years ago a friend and I decided to compare prices in local grocery stores. We made a list of ten representative grocery items and then went to four local stores and recorded the price on each item in each store. The most convenient and logical way to table the data--and the first way that comes to mind--is as follows (numbers are prices in dollars). ```subject storeA storeB storeC storeD lettuce 1.17 1.78 1.29 1.29 potatoes 1.77 1.98 1.99 1.99 milk 1.49 1.69 1.79 1.59 eggs 0.65 0.99 0.69 1.09 bread 1.58 1.70 1.89 1.89 cereal 3.13 3.15 2.99 3.09 ground.beef 2.09 1.88 2.09 2.49 tomato.soup 0.62 0.65 0.65 0.69 laundry.detergent 5.89 5.99 5.99 6.99 aspirin 4.46 4.84 4.99 5.15``` (IMPORTANT NOTES: For those of you who think this is a treatment-by-blocks design, it is! When I get to the tutorial on blocking, I will draw the parallel between the two designs. For now, just pretend groceries are "subjects." Also, please excuse for the moment the absence of nicities in this table, like stubs and spanners, because shortly you will be happy I did the table in this bare bones way!) First, I remember when a can of tomato soup was 12¢, but that's another story. We should notice laundry detergent costs a lot more than soup. This isn't interesting to us. In other words, there is a great deal of variability--perhaps 99% or more of the total variability--attributable entirely to individual differences between subjects, in which we are not the least bit interested. If we treated this as a straightforward one-way randomized ANOVA, all of this variability would go into the error term, and any variability due to "store" would be totally swamped. The advantage of the repeated measures analysis is that it allows us to parcel out variability due to subjects. Second, we are now faced with getting these data into R. Try this. ```> groceries = read.table(header=T, row.names=1, text=" + subject storeA storeB storeC storeD #### + lettuce 1.17 1.78 1.29 1.29 # + potatoes 1.77 1.98 1.99 1.99 # + milk 1.49 1.69 1.79 1.59 # + eggs 0.65 0.99 0.69 1.09 # + bread 1.58 1.70 1.89 1.89 ### you can copy and paste this + cereal 3.13 3.15 2.99 3.09 # part from the table above + ground.beef 2.09 1.88 2.09 2.49 # + tomato.soup 0.62 0.65 0.65 0.69 # + laundry.detergent 5.89 5.99 5.99 6.99 # + aspirin 4.46 4.84 4.99 5.15 #### + ") > ### apparently you are not allowed to insert comments in this fashion (so > ### much for what comes after a # aways being ignored!), so copy and paste > ### the table part from above > > str(groceries) 'data.frame': 10 obs. of 4 variables: \$ storeA: num 1.17 1.77 1.49 0.65 1.58 3.13 2.09 0.62 5.89 4.46 \$ storeB: num 1.78 1.98 1.69 0.99 1.7 3.15 1.88 0.65 5.99 4.84 \$ storeC: num 1.29 1.99 1.79 0.69 1.89 2.99 2.09 0.65 5.99 4.99 \$ storeD: num 1.29 1.99 1.59 1.09 1.89 3.09 2.49 0.69 6.99 5.15 > > summary(groceries) storeA storeB storeC storeD Min. :0.620 Min. :0.650 Min. :0.650 Min. :0.690 1st Qu.:1.250 1st Qu.:1.692 1st Qu.:1.415 1st Qu.:1.365 Median :1.675 Median :1.830 Median :1.940 Median :1.940 Mean :2.285 Mean :2.465 Mean :2.436 Mean :2.626 3rd Qu.:2.870 3rd Qu.:2.857 3rd Qu.:2.765 3rd Qu.:2.940 Max. :5.890 Max. :5.990 Max. :5.990 Max. :6.990``` If that didn't work, try copying and pasting this script into your R Console. ```### start copying with this line groceries = data.frame( c(1.17,1.77,1.49,0.65,1.58,3.13,2.09,0.62,5.89,4.46), c(1.78,1.98,1.69,0.99,1.70,3.15,1.88,0.65,5.99,4.84), c(1.29,1.99,1.79,0.69,1.89,2.99,2.09,0.65,5.99,4.99), c(1.29,1.99,1.59,1.09,1.89,3.09,2.49,0.69,6.99,5.15)) rownames(groceries) = c("lettuce","potatoes","milk","eggs","bread","cereal", "ground.beef","tomato.soup","laundry.detergent","aspirin") colnames(groceries) = c("storeA","storeB","storeC","storeD") ### end copying with this line and paste``` And if that didn't do it, well, fire up scan() and start typing! This is a wide format data frame. It's not going to work for us. Why not? Because our response variable--our ONE response variable--is the price of the items, and this is listed in four columns. We need each variable in ONE column of the data frame in order to use the aov() function. This sort of problem occurs so often, you'd think some nice R coder would have written a utility to rearrange such tables. `> stack(groceries) # Funny! :)` I have not shown the output, but this is ALMOST what we want. It would have worked beautifully if this were a between subjects design. Here it is but a start. We need a subject identifier. Fortunately, we put the grocery names into the row names of the short data frame. ```> gr2 = stack(groceries) # I'm tired of typing "groceries"! > gr2\$subject = rep(rownames(groceries), 4) # create the "subject" variable > gr2\$subject = factor(gr2\$subject) # "I declare thee a factor." > colnames(gr2) = c("price", "store", "subject") # rename the columns > gr2 # take a look price store subject 1 1.17 storeA lettuce 2 1.77 storeA potatoes 3 1.49 storeA milk 4 0.65 storeA eggs 5 1.58 storeA bread 6 3.13 storeA cereal 7 2.09 storeA ground.beef 8 0.62 storeA tomato.soup 9 5.89 storeA laundry.detergent 10 4.46 storeA aspirin 11 1.78 storeB lettuce 12 1.98 storeB potatoes 13 1.69 storeB milk 14 0.99 storeB eggs 15 1.70 storeB bread 16 3.15 storeB cereal 17 1.88 storeB ground.beef 18 0.65 storeB tomato.soup 19 5.99 storeB laundry.detergent 20 4.84 storeB aspirin 21 1.29 storeC lettuce 22 1.99 storeC potatoes 23 1.79 storeC milk 24 0.69 storeC eggs 25 1.89 storeC bread 26 2.99 storeC cereal 27 2.09 storeC ground.beef 28 0.65 storeC tomato.soup 29 5.99 storeC laundry.detergent 30 4.99 storeC aspirin 31 1.29 storeD lettuce 32 1.99 storeD potatoes 33 1.59 storeD milk 34 1.09 storeD eggs 35 1.89 storeD bread 36 3.09 storeD cereal 37 2.49 storeD ground.beef 38 0.69 storeD tomato.soup 39 6.99 storeD laundry.detergent 40 5.15 storeD aspirin``` NOW we have a proper long format data frame for this analysis. This is what we need for a repeated measures analysis using the aov() function. We need a long format data frame WITH a subjects identifier, which must be a factor. If you numbered your subjects, and your subjects identifier is now a column of numbers, BE SURE to declare it to be a factor. You don't want R seeing a numeric variable here! At which store should we shop? ```> with(gr2, tapply(price, store, sum)) storeA storeB storeC storeD 22.85 24.65 24.36 26.26``` For the items in the sample, it looks like storeA had the lowest prices. But as this is only a random(ish) sample of items we might have wanted to buy, we have to ask, will this difference hold up in general? Once the data frame is set up correctly (all values of the response variable in ONE column, and another column holding the subject variable--i.e., a variable that identifies which values of the response go with which subjects), the trick to doing a repeated measures ANOVA is in getting the model formula right. We need to supply an Error term, which must reflect that we have "treatments nested within subjects." That is to say, in principle at least, we can see the "store" effect within each and every "subject" (grocery item). The ANOVA is properly done this way. ```> aov.out = aov(price ~ store + Error(subject/store), data=gr2) > summary(aov.out) Error: subject Df Sum Sq Mean Sq F value Pr(>F) Residuals 9 115.193 12.799 Error: subject:store Df Sum Sq Mean Sq F value Pr(>F) store 3 0.58586 0.19529 4.3442 0.01273 * Residuals 27 1.21374 0.04495 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1``` "Store" and "subject" are our sources of variability. The treatment we are interested in is "store" (that's what we want to see the effect of), and this treatment effect is visible within each subject (i.e., nested within each subject). So the proper Error term is "subject/store", which is read as "store within subject." Notice that once all that "subject" variability is parceled out, we do have a significant "store" main effect. (Note: Error(subject) will work just as well for the error term. I'll discuss this further in a later tutorial.) Post Hoc Tests The Tukey HSD test will not run on this model object, so I propose pairwise t-tests with adjusted p-values to see which stores are significantly different. (Note: The output will be a table of p-values for each possible pairwise comparison.) ```> ### no p-adjustment > with(gr2, pairwise.t.test(x=price, g=store, p.adjust.method="none", paired=T)) Pairwise comparisons using paired t tests data: price and store storeA storeB storeC storeB 0.033 - - storeC 0.035 0.695 - storeD 0.012 0.245 0.110 P value adjustment method: none > ### Bonferroni adjustment > with(gr2, pairwise.t.test(x=price, g=store, p.adjust.method="bonf", paired=T)) Pairwise comparisons using paired t tests data: price and store storeA storeB storeC storeB 0.20 - - storeC 0.21 1.00 - storeD 0.07 1.00 0.66 P value adjustment method: bonferroni``` The syntax for this function is discussed in the Multiple Comparisons tutorial. The new element here is the paired=T option, which is set because these t-tests are on paired scores. The method of contrasts described in the Multiple Comparisons tutorial will also work. See that tutorial for details. ```> cons = cbind(c(-1,1/3,1/3,1/3), c(0,-1/2,-1/2,1), c(0,1,-1,0)) # define the contrasts > t(cons) %*% cons # test for orthogonality [,1] [,2] [,3] [1,] 1.333333 0.0 0 [2,] 0.000000 1.5 0 [3,] 0.000000 0.0 2 > contrasts(gr2\$store) = cons # assign contrasts to treatment > aov.out = aov(price ~ store + Error(subject/store), data=gr2) # do the ANOVA > summary(aov.out, split=list( + store=list("A vs BCD"=1,"BC vs D"=2,"B vs C"=3) + )) # visualize the results Error: subject Df Sum Sq Mean Sq F value Pr(>F) Residuals 9 115.2 12.8 Error: subject:store Df Sum Sq Mean Sq F value Pr(>F) store 3 0.5859 0.1953 4.344 0.01273 * store: A vs BCD 1 0.3763 0.3763 8.371 0.00745 ** store: BC vs D 1 0.2053 0.2053 4.568 0.04179 * store: B vs C 1 0.0042 0.0042 0.094 0.76207 Residuals 27 1.2137 0.0450 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1``` Notice that the result for B vs C is not quite the same as the t-test on that comparison above with no p-value adjustment. That's because in the paired pairwise t-tests, the overall pooled variance (MSE) is not used. Treatment by Subjects I want to point out, in preparation for a later tutorial, that this can also be treated as a treatment-by-subjects design. That is, it can essentially be treated as a two-factor design without replication and without an interaction. ```> aov.tbys = aov(price ~ store + subject, data=gr2) > summary(aov.tbys) Df Sum Sq Mean Sq F value Pr(>F) store 3 0.59 0.195 4.344 0.0127 * subject 9 115.19 12.799 284.722 <2e-16 *** Residuals 27 1.21 0.045 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1``` The advantage of running the ANOVA this way is that the Tukey test will work, AND it will give the right answers for repeated measures data. ```> TukeyHSD(aov.tbys, which="store") Tukey multiple comparisons of means 95% family-wise confidence level Fit: aov(formula = price ~ store + subject, data = gr2) \$store diff lwr upr p adj storeB-storeA 0.180 -0.07947858 0.4394786 0.2524605 storeC-storeA 0.151 -0.10847858 0.4104786 0.3995675 storeD-storeA 0.341 0.08152142 0.6004786 0.0065761 storeC-storeB -0.029 -0.28847858 0.2304786 0.9898432 storeD-storeB 0.161 -0.09847858 0.4204786 0.3442902 storeD-storeC 0.190 -0.06947858 0.4494786 0.2115536``` The Friedman Rank Sum Test There is a nonparametric version of the oneway ANOVA with repeated measures. It is executed this way. ```> friedman.test(price ~ store | subject, data=gr2) Friedman rank sum test data: price and store and subject Friedman chi-squared = 13.3723, df = 3, p-value = 0.003897``` The formula reads "price as a function of store given subject". Here, "subject" is being treated as a blocking variable. (No, aov() will not take the formula in this format.) An advantage of using this test is that it will work with the short format data frame, provided we are careful to declare the relevant columns to be a matrix. Here, all of the columns in the data frame contain relevant data, so declaring the matrix is easy. ```> friedman.test(as.matrix(groceries)) Friedman rank sum test data: as.matrix(groceries) Friedman chi-squared = 13.3723, df = 3, p-value = 0.003897``` For another interesting example of the Friedman test, run example(friedman.test). And note: In spite of what the output says, it's not 18 players, it's 22 (n=22). The Multivariate Approach to One-way Repeated Measures Designs (Note: You might want to read about contrast matrices in the Multiple Comparisons tutorial before reading this section, if you're unsure what a contrast matrix is. And while I'm at it, a bit of a rant: Just try to find an easy, well explained example of this! Most people seem to be a lot more interested in showing off how smart they are than they are concerned with explaining things clearly. The advantage you have with me is, I'm not that smart to begin with!) A few decades or so ago, there was a lot of talk of an alternative approach to repeated measures analysis using multivariate ANOVA. The traditional repeated measures ANOVA makes some pretty stiff assumptions and is not at all robust to their violation. The multivariate approach relaxes those assumptions somewhat, but also costs degrees of freedom (so you can't have everything). You could go to alternative packages, such as nlme or lme4, to implement this approach, but it's actually fairly easy to do using the base packages (for the oneway design). AND, extra added bonus, it allows you to keep your data in the more sensible short format. In fact, it insists upon it! We need a response matrix, which we can get from the short format data frame. ```> groceries storeA storeB storeC storeD lettuce 1.17 1.78 1.29 1.29 potatoes 1.77 1.98 1.99 1.99 milk 1.49 1.69 1.79 1.59 eggs 0.65 0.99 0.69 1.09 bread 1.58 1.70 1.89 1.89 cereal 3.13 3.15 2.99 3.09 ground.beef 2.09 1.88 2.09 2.49 tomato.soup 0.62 0.65 0.65 0.69 laundry.detergent 5.89 5.99 5.99 6.99 aspirin 4.46 4.84 4.99 5.15 > gromat = as.matrix(groceries[,1:4]) > gromat storeA storeB storeC storeD lettuce 1.17 1.78 1.29 1.29 potatoes 1.77 1.98 1.99 1.99 milk 1.49 1.69 1.79 1.59 eggs 0.65 0.99 0.69 1.09 bread 1.58 1.70 1.89 1.89 cereal 3.13 3.15 2.99 3.09 ground.beef 2.09 1.88 2.09 2.49 tomato.soup 0.62 0.65 0.65 0.69 laundry.detergent 5.89 5.99 5.99 6.99 aspirin 4.46 4.84 4.99 5.15``` Notice that it looks exactly the same in this case, but that won't always be true. We extracted all rows (the blank index) and columns 1 through 4 (which just happens in this case to be all columns) of the groceries data frame and declared the result as a matrix. Allow me to begin by demonstating this method by doing a Related Measures t-Test. We'll compare storeA to storeD. As the related measures t-test is essentially a Single Sample t-Test on difference scores, I'll begin by getting difference scores and putting them into a single-column matrix called D. Then I'll run the command that does the test and compare the result to the t-test. ```> D = gromat[,4] - gromat[,1] # the difference scores > D = matrix(D) # into a matrix > D [,1] [1,] 0.12 [2,] 0.22 [3,] 0.10 [4,] 0.44 [5,] 0.31 [6,] -0.04 [7,] 0.40 [8,] 0.07 [9,] 1.10 [10,] 0.69 > result = lm(D ~ 1) # regress D against the intercept (D tilde one) > summary(result)\$coef # summary(result) is sufficient Estimate Std. Error t value Pr(>|t|) (Intercept) 0.341 0.1081301 3.153609 0.01166957 > > t.test(D, mu=0) # for comparison One Sample t-test data: D t = 3.1536, df = 9, p-value = 0.01167 ... some output omitted ...``` Now I point out that there are six such pairwise comparisons that are possible, and we picked the most favorable one by looking at the data first. A fairer result might be had if we applied a Bonferroni correction to the p-value. ```> 0.01167 * 6 [1] 0.07002``` It remains to generalize this method to larger D matrices, i.e., D matrices with more columns. For comparison, I'll recall the result of the one-way ANOVA we did above: F(3,27) = 4.344, p = .0127. I'll also point out that we have a moderate violation of the sphericity assumption in these data (the effects are not quite additive), and so I calculated a Greenhouse-Geisser correction to the p-value, resulting in p = .0309, still low enough to reject the null at an alpha of .05, but getting close. What we need are k-1 columns of difference scores in our D matrix, where k is the number of levels of the repeated measures factor, or 4-1=3 in this case. R provides us with a convenient way of getting those using a function called contr.sum(). This function creates a contrast matrix of "sum to zero" contrasts, or "effects" contrasts. We have k=4 levels of the repeated measures factor, so we need this matrix: ```> contr.sum(4) [,1] [,2] [,3] 1 1 0 0 2 0 1 0 3 0 0 1 4 -1 -1 -1``` Using that contrast matrix, we will calculate the following differences: A-D, B-D, and C-D. It's done in one step via matrix multiplication. ```> D = gromat %*% contr.sum(4) # percent asterisk percent is matrix multiplication > D [,1] [,2] [,3] lettuce -0.12 0.49 0.00 potatoes -0.22 -0.01 0.00 milk -0.10 0.10 0.20 eggs -0.44 -0.10 -0.40 bread -0.31 -0.19 0.00 cereal 0.04 0.06 -0.10 ground.beef -0.40 -0.61 -0.40 tomato.soup -0.07 -0.04 -0.04 laundry.detergent -1.10 -1.00 -1.00 aspirin -0.69 -0.31 -0.16``` It remains to run the multivariate test. ```> result = lm(D ~ 1) > anova(result) Analysis of Variance Table Df Pillai approx F num Df den Df Pr(>F) (Intercept) 1 0.64746 4.2853 3 7 0.05156 . Residuals 9 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1``` Our test statistic is called Pillai's trace. An approximate F has also been calculated. Notice it is similar to the one from the repeated measures ANOVA but is on 3 and 7 degrees of freedom. Hence, p > .05, and the null--all treatment means equal, or more correctly, all levels of the treatment sampled from populations with equal means--is not rejected at an alpha level of .05. The most commonly used test statistic here is probably Wilks' lambda, and you can have that, too, by setting an option. ```> anova(result, test="Wilks") Analysis of Variance Table Df Wilks approx F num Df den Df Pr(>F) (Intercept) 1 0.35254 4.2853 3 7 0.05156 . Residuals 9 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1``` Other statistics available are Hotelling-Lawley trace (test="Hotelling") and Roy's largest root (test="Roy"). People argue over which of these is best in a multivariate ANOVA, but notice here it makes no difference. That p-value just ain't gonna budge, no matter which one you use. "Why would I want to do that?" you ask. After all, we lost our significant effect. Notice that the Greenhouse-Geisser corrected p-value and the MANOVA p are reasonably close but just happen to be on opposite sides of .05. It sometimes happens in the MANOVA approach that the degrees of freedom are reduced sufficiently to cost us a significant effect due to reduction in the power of the test. That's especially true when n (the number of subjects) is not much greater than k (the number of levels of the treatment), as is the case here. These issues were discussed by Vasey and Thayer (1987). As a second example of the multivariate approach, I'd like to duplicate the analysis in their paper (just about). In this example, we have sufficient subjects to make the two approaches about equal in power. The data appear in their paper in Table 1, p. 484. The data are "measures of mean electromyographic (EMG) amplitude [in microvolts] from the left brow region, collected over four 90-s trials from 22 subjects. These data were drawn from a study of affective facial expressions conducted by" Thayer (p. 484). Get ready to type. Or try a copy and paste. ```### start copying here EMG = read.table(header=T, text=" LB1 LB2 LB3 LB4 143 368 345 772 142 155 161 178 109 167 356 956 123 135 137 187 276 216 232 307 235 386 398 425 208 175 207 293 267 358 698 771 183 193 631 403 245 268 572 1383 324 507 556 504 148 378 342 796 130 142 150 173 119 171 333 1062 102 94 93 69 279 204 229 299 244 365 392 406 196 168 199 287 279 358 822 671 167 183 731 203 345 238 572 1652 524 507 520 504 ") EMG = as.matrix(EMG) ### end copying here and paste into the R Console``` We can get their column means easily enough. The standard deviations are a little harder (but not much). ```> colMeans(EMG) # apply(EMG, 2, FUN=mean) is the equivalent LB1 LB2 LB3 LB4 217.6364 260.7273 394.3636 559.1364 > apply(EMG, 2, FUN=sd) LB1 LB2 LB3 LB4 99.6463 121.5241 213.5919 413.4777``` To duplicate their specific comparisons, we need a little bit different contrast matrix this time. ```> cons = cbind(c(-1,1,0,0),c(-1,0,1,0),c(-1,0,0,1)) > cons [,1] [,2] [,3] [1,] -1 -1 -1 [2,] 1 0 0 [3,] 0 1 0 [4,] 0 0 1``` This will result in the comparison of the first column to the second, the first column to the third, and the first column to the fourth. (It's the same old "sums" matrix but reversed and turned upside down.) Now we'll create the D matrix and do the multivariate analysis. ```> Demg = EMG %*% cons > results = lm(Demg ~ 1) > anova(results) Analysis of Variance Table Df Pillai approx F num Df den Df Pr(>F) (Intercept) 1 0.54745 7.6614 3 19 0.001486 ** Residuals 21 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1``` We have duplicated the F-value, degrees of freedom, and p-value in their Table 2. Their multivariate statistic is something called TSQ. (Beats me! Anybody?) We have a Pillai's trace. Their specific comparisons are in Table 3 and consist of F-tests. Ours will be t-tests, but note the p-values are the same, and F = t2 in all cases. ```> summary(results) Response Y1 : Call: lm(formula = Y1 ~ 1) ### test on column 1 of Demg Residuals: Min 1Q Median 3Q Max -150.09 -57.84 -28.59 44.91 186.91 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 43.09 19.65 2.193 0.0397 * --- Residual standard error: 92.18 on 21 degrees of freedom Response Y2 : Call: lm(formula = Y2 ~ 1) ### test on column 2 of Demg Residuals: Min 1Q Median 3Q Max -226.73 -170.98 1.77 66.52 387.27 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 176.73 40.66 4.346 0.000284 *** --- Residual standard error: 190.7 on 21 degrees of freedom Response Y3 : Call: lm(formula = Y3 ~ 1) ### test on column 3 of Demg Residuals: Min 1Q Median 3Q Max -374.5 -303.8 -170.5 256.2 965.5 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 341.50 86.26 3.959 0.000717 *** --- Residual standard error: 404.6 on 21 degrees of freedom``` So the contrast matrix doesn't have to be exactly the one generated by contr.sum(). In fact, it can be any contrast matrix in which the contrast vectors are linearly independent. The columns of D don't even have to be simple difference scores. R has a function contr.helmert() that generates Helmert contrasts, and those will work, as Helmert contrasts are linearly independent. ("Linearly independent" means that any one contrast vector cannot be derivable by a linear combination of the others. It's not the same thing as orthogonality.) Vasey, M. W. and Thayer, J. F. (1987). The continuing problem of false positives in repeated measures ANOVA in psychophysiology: A multivariate solution. Psychophysiology, 24(4), 479-486. Unbalanced Designs Don't do it! If, for example, StoreC didn't have any lettuce and didn't have a price on the shelf for it (if that value is missing), then all of the data for that "subject" (lettuce) should be removed from the analysis. There are ways around this, but not if you're going to use repeated measures ANOVA. revised 2016 February 9 | Table of Contents | Function Reference | Function Finder | R Project |