In my data set the file contains the variable names in the first row, so I shall use TRUE for this argument. In header argument you can set a logical value that will indicate whether the data file contains first variable names as first row. The file argument specify the file name with extension CSV. I have already saved the data file as CSV (comma delimited file) in the working directory. Now let’s import the data set using read.csv() function. You can also clear the plots using graphics.off() and clear everything in console using shell() function. I often recommend to first clear all the objects or values in the global environment using rm(list = ls(all = TRUE)) before importing the data set.
Amphipods were separated by gender to know whether the gender also affected enzyme activity. Here, the example used shows the enzyme activity of mannose-6-phosphate isomerase and MPI genotypes in the amphipod crustacean. Each treatment is applied to one or more plots within the larger block, and the positions of the treatments are assigned at random. Because the larger blocks may differ in some way that may affect the measurement variable, the data are analyzed with a two-way ANOVA, with the block as one of the nominal variables. This often occurs in agriculture, where you may want to test different treatments on small plots within larger blocks of land. Randomized blocksĪnother experimental design that is analyzed by a two-way ANOVA is randomized blocks.
Repeated measures experiments are often done without replication, although they could be done with replication. This usually involves measurements taken at different time points or at different places. In this design, the observation has been made on the same individual more than once. One experimental design that people analyze with a two-way ANOVA is repeated measures. H 1: There is significant interaction When the interaction term is significant, the usual advice is that you should not test the effects of the individual factors. H 0: There is no interaction between genotype and gender The interaction test tells you whether the effects of one factor depend on the other factor. There is no interaction between the two factors. The graphical results are shown in Output 39.3.5 through Output 39.3.7.H 1: At least two means of gender are unequal Additionally, the PLOTS=MEANPLOT(CL) option specifies that confidence limits for the LS-means should also be displayed in the mean plot. The following statements reproduce the previous analysis with ODS Graphics enabled. If you enable ODS Graphics for the previous analysis, GLM also displays three additional plots by default:Īn interaction plot for the effects of disease and drugĪ plot of the adjusted pairwise differences and their significance levels Evidently, the main contribution to the significant drug effect is the difference between the 1/2 pair and the 3/4 pair. The multiple-comparison analysis shows that drugs 1 and 2 have very similar effects, and that drugs 3 and 4 are also insignificantly different from each other. Since the GLM procedure is interactive, you can accomplish this by submitting the following statements after the previous ones that performed the ANOVA. As the previous discussion indicates, Type III sums of squares correspond to differences between LS-means, so you can follow up the Type III tests with a multiple-comparison analysis of the drug LS-means. No matter which sum of squares you prefer to use, this analysis shows a significant difference among the four drugs, while the disease effect and the drug-by-disease interaction are not significant. Finally, the Type IV sum of squares is the same as the Type III sum of squares in this case, since there are data for every drug-by-disease combination. The Type III sum of squares measures the differences between predicted drug means over a balanced drug disease population-that is, between the LS-means for drug. By contrast, the Type II sum of squares for drug measures the differences between arithmetic means for each drug after adjusting for disease. The Type I sum of squares for drug essentially tests for differences between the expected values of the arithmetic mean response for different drugs, unadjusted for the effect of disease. Note the differences among the four types of sums of squares.