# summary(aov(depvar ~ B * A * U + Error( S/U ), data=data8 ) ) # BEWARE, as above. # summary(aov(depvar ~ A * B * U + Error( S/U ), data=data8 ) ) # BEWARE, as above. See the reference on ?aov, and MASS (the book, see the FAQ). Your over-long lines make it difficult to see, but this appears to be very similar to examples both in MASS and in Pinheiro & Bates.

# First, aov: the problem is that this provides Type I SS (so analysing A*B*U differs from analysing B*A*U), and the drop1() command doesn't like the multi-stratum output from aov(). On Tue, 12 Aug 2008, Brett Magill wrote: R and Analysis of Variance. Author(s) The design was inspired by the S function of the same name described in Chambers et al (1992). In many different types of experiments, with one or more treatments, one of the most widely used statistical methods is analysis of variance or simply ANOVA . - read.csv(file.choose()). Prof Brian Ripley There are worked examples in MASS (at least the last two editions) of getting `the same results with lme as with aov with Error()'. I think you need to understand the underlying theory first, and that is no longer (even for my time) part of a statistical education. In psychological research this usually reflects experimental design where the independent variables are multiple levels of some experimental manipulation (e.g., drug administration, recall instructions, etc.) I learnt it from Bill Venables who has educated in the 1960s -- so his account in MASS comes with at least one satisfied client. In R, the QR algorithm is used. The default ‘contrasts’ in R are not orthogonal contrasts, and aov and its helper functions will work better with such contrasts: see the examples for how to select these. The columns are usually interpreted as values of real-valued observations. A special case of the linear model is the situation where the predictor variables are categorical.

Import your data into R. Prepare your data as specified here: Best practices for preparing your data set for R. Save your data in an external .txt tab or .csv files. The difference between the two is intent of the analysis and the default presentation of the results. OBS: This is a full translation of a portuguese version. summary(stress.aov) Error: PID Df Sum Sq Mean Sq F value Pr(>F) Residuals 49 8344 170.3 Error: PID:music Df Sum Sq Mean Sq F value Pr(>F) music 1 1 0.78 0.003 0.954 Residuals 49 11524 235.19 Error: PID:image Df Sum Sq Mean Sq F value Pr(>F) image 1 61 61.11 0.296 0.589 Residuals 49 10127 206.66 Error: PID:music:image Df Sum Sq Mean Sq F value Pr(>F) music:image 1 564 563.8 2.626 … Here, we’ll use the built-in R data set named PlantGrowth. With lm [Linear Model], the focus is on the effect of the individual columns of the predictor matrix. The underlying least squares arithmetic of aov and lm is identical. Import your data into R as follow: # If .txt tab file, use this my_data - read.delim(file.choose()) # Or, if .csv file, use this my_data . Residual standard error: 0.6234 on 27 degrees of freedom Multiple R-squared: 0.2641, Adjusted R-squared: 0.2096 F-statistic: 4.846 on 2 and 27 DF, p-value: 0.01591 > summary.aov(lm.out) # we can ask for the corresponding ANOVA table Df Sum Sq Mean Sq F value Pr(>F) group 2 3.766 1.8832 4.846 0.0159 Residuals 27 10.492 0.3886