Laboratory Topics 4 & 5

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1 PLS205 Lab 3 January 23, 2014 Orthogonal contrasts Class comparisons in SAS Trend analysis in SAS Multiple mean comparisons Laboratory Topics 4 & 5 Orthogonal contrasts Planned, single degree-of-freedom orthogonal contrasts are powerful means of perfectly partitioning the ANOVA model sum of squares to gain greater insight into your data; and this method of analysis is available in SAS via Proc GLM. Whenever you program contrasts, be sure to use the "Order = Data" option in Proc GLM so that the coefficients featured in the subsequent Contrast statements will correspond accurately to the levels of the indicated classification variable. For example: Proc GLM Order = Data; The Contrast statements can come anywhere after the Model statement in Proc GLM. These statements specify the independent F-test to be conducted. Its syntax: Contrast 'ID' ClassVariable Coefficients; where ID, enclosed in single quotes, is the label you assign to the contrast (just a title, it can be anything); ClassVariable is the classification variable whose means are being compared; and Coefficients is the set of orthogonal coefficient values, separated by spaces or tabs. Note that in a nested design, it is imperative that the coefficients be followed by a declaration of the appropriate error term: Contrast 'A vs. B' Trtmt / e = Pot(Trtmt); Before looking at our first example, remember that Proc GLM uses several different methods for determining SS, the details of which will be covered later in the course [For more details, refer to Topic 11.4 in your class notes]. For now, let's reiterate the following rule of thumb: Use the Type I SS (sum of squares) for regressions The Type I SS measures incremental sums of squares for the model as each variable is added. Use the Type III SS for F-tests Type III is the sum of squares for each effect adjusted for every other effect and is used for both balanced and unbalanced designs. CLASS COMPARISONS USING CONTRASTS PLS Lab 3 (Topics 4-5)

2 Example 4.1 ST&D pg. 159 [Lab3ex1.sas] This is a CRD in which 18 mint plants were randomly assigned to 6 different treatments (i.e. all combinations of two temperature [High and Low] and three light [8, 12, and 16 hour days] conditions) and their growth measured. Data MintMean; Input Trtmt $ Cards; L L L ; L L L L L L H H H H H H H H H * L08 means Low Temp and 8 hours of light, H12 means High Temp and 12 hours of light, etc.; Proc GLM Order = Data; * To maintain the order in which we entered data; Class Trtmt; Model Growth = Trtmt; * L08 L12 L16 H08 H12 H16; Contrast 'Temp' Trtmt ; Contrast 'Light linear' Trtmt ; Contrast 'Light quadratic' Trtmt ; Contrast 'Temp * Light linear' Trtmt ; Contrast 'Temp * Light quadratic' Trtmt ; Run; Quit; What questions we are asking here exactly? To answer this, it is helpful to articulate the null hypothesis for each contrast: Contrast Temp H 0 : Mean plant growth under low temperature conditions is the same as under high temperature conditions. Contrast Light Linear H 0 : Mean plant growth under 8 hour days is the same as under 16 hour days (OR: The response of growth to light has no linear component). Contrast Light Quadratic H 0 : Mean plant growth under 12 hour days is the same as the average mean growth under 8 and 16 hour days combined (OR: The growth response to light is perfectly linear; OR: The response of growth to light has no quadratic component). Contrast Temp * Light Linear H 0 : The linear component of the response of growth to light is the same at both temperatures. Contrast Temp * Light Quadratic H 0 : The quadratic component of the response of growth to light is the same at both temperatures. So what would it mean to find significant results and to reject each of these null hypotheses? Reject contrast Temp H 0 = There is a significant response of growth to temperature. Reject contrast Light linear H 0 = The response of growth to light has a significant linear component. Reject contrast Light quadratic H 0 = The response of growth to light has a significant quadratic component. Reject contrast Temp * Light Linear H 0 = The linear component of the response of growth to light depends on temperature. Reject contrast Temp * Light Quadratic H 0 = The quadratic component of the response of growth to light depends on temperature. PLS Lab 3 (Topics 4-5)

3 Results of the GLM procedure Dependent Variable: Growth Sum of Source DF Squares Mean Square F Value Pr > F Model <.0001 Error Corrected Total R-Square Coeff Var Root MSE Growth Mean Source DF Type III SS Mean Square F Value Pr > F Trtmt <.0001 Contrast DF Contrast SS Mean Square F Value Pr > F Temp <.0001 *** Light linear * Light quadratic Temp * Light linear Temp * Light quadratic Things to notice Notice the sum of the contrast degrees of freedom. What does it equal? Why? Notice the sum of the contrast SS. What does it equal? Why? What insight does this analysis give you into your experiment? PLS Lab 3 (Topics 4-5)

4 TREND ANALYSIS USING CONTRASTS Example 4.2 ST&D pg. 387 [Lab3ex2.sas] This experiment was conducted to investigate the relationship between plant spacing and yield in soybeans. The researcher randomly assigned five different plant spacings to 30 field plots, planted the soybeans accordingly, and measured the yield of each plot at the end of the season. Since we are interested in the overall relationship between plant spacing and yield (i.e. characterizing the response of yield to plant spacing), it is appropriate to perform a trend analysis. Title 'Equally spaced treatments in a CRD'; Data SoyRows; Input Sp Yield; Cards; ; Proc GLM Order = Data; Class Sp; Model Yield = Sp; Means Sp; * ; Contrast 'Linear' Sp ; Contrast 'Quadratic' Sp ; Contrast 'Cubic' Sp ; Contrast 'Quartic' Sp ; Run; Quit; What questions we are asking here exactly? As before, it is helpful to articulate the null hypothesis for each contrast: Contrast Linear H 0 : The response of yield to spacing has no linear component. Contrast Quadratic H 0 : The response of yield to spacing has no quadratic component. Contrast Cubic H 0 : The response of yield to spacing has no cubic component. Contrast Quartic H 0 : The response of yield to spacing has no quartic component. Can you see, based on the contrast coefficients, why these are the null hypotheses? PLS Lab 3 (Topics 4-5)

5 Results of the GLM procedure Dependent Variable: Yield Sum of Source DF Squares Mean Square F Value Pr > F Model <.0001 Error Corrected Total R-Square Coeff Var Root MSE Yield Mean Source DF Type III SS Mean Square F Value Pr > F Sp <.0001 Contrast DF Contrast SS Mean Square F Value Pr > F Linear <.0001 *** Quadratic ** Cubic Quartic Interpretation There is a quadratic relationship between row spacing and yield. Why? Because there is a significant quadratic component to the response but no significant cubic or quartic components. Please note that we are only able to carry out trend comparisons in this way because the treatments are equally spaced. Now, exactly the same result can be obtained through a regression approach, as shown in the next example. Example 4.3 Title 'Equally spaced treatments in a CRD'; Data SoyRows; Input Sp Yield; Cards; ; Proc GLM; Model Yield = Run; Quit; Sp Sp*Sp Sp*Sp*Sp Sp*Sp*Sp*Sp; 'Linear' 'Quadratic' 'Cubic' 'Quartic' [Lab3ex3.sas] H 0: There is no linear component H 0: There is no quadratic component H 0: There is no cubic component H 0: There is no quartic component PLS Lab 3 (Topics 4-5)

6 Results of the GLM procedure Dependent Variable: Yield Sum of Source DF Squares Mean Square F Value Pr > F Model <.0001 Error Corrected Total R-Square Coeff Var Root MSE Yield Mean Source DF Type I SS Mean Square F Value Pr > F Sp <.0001 *** Sp*Sp ** Sp*Sp*Sp Sp*Sp*Sp*Sp Source DF Type III SS Mean Square F Value Pr > F Sp Sp*Sp Sp*Sp*Sp Sp*Sp*Sp*Sp Again, since this is a regression analysis, use the Type I SS, not the Type III SS. Notice in this case that the Type I SS results match perfectly those from our earlier analysis by contrasts. For the interested: When you carry out a trend analysis using a regression approach, SAS also provides estimates of the parameters for your model: Standard Parameter Estimate Error t Value Pr > t Intercept Sp Sp*Sp Sp*Sp*Sp Sp*Sp*Sp*Sp In this case, the equation of the trend line that best fits out data would be: Yield = 0.30 * Sp * Sp PLS Lab 3 (Topics 4-5)

7 Multiple Mean Comparisons Orthogonal contrasts are planned, a priori tests that partition the experimental variance cleanly. They are a powerful tool for analyzing data, but they are not appropriate for all experiments. Less restrictive comparisons among treatment means can be performed using Proc GLM by way of the Means statement. Any number of Means statements may be used within a given Proc GLM, provided they appear after the Model statement. The syntax: Means Class-Variables / Options; This statement tells SAS to: 1. Compute the means of the response variable for each level of the specified classification variable(s), all of which were featured in the original Model statement; then 2. Perform multiple comparisons among these means using the stated Options. Some of the available Options are listed below: Fixed Range Tests DUNNETT ('control') T or LSD TUKEY SCHEFFE Dunnett's test [NOTE: If no control is specified, the first treatment is used.] Fisher's least significant difference test Tukey's studentized range test (HSD: Honestly significant difference) Scheffé s test Multiple Range Tests DUNCAN SNK REGWQ Duncan's test Student-Newman-Keuls test Ryan-Einot-Gabriel-Welsch test The default significance level for comparisons among means is α = 0.05, but this can be changed easily using the option Alpha = α, where α is the desired significance level. The important thing to keep in mind is the EER (experimentwise error rate); we want to keep it controlled while keeping the test as sensitive as possible, so our choice of test should reflect that. PLS Lab 3 (Topics 4-5)

8 Example 4.4 (One-Way Multiple Comparison) [Lab3ex4.sas] Here s the clover experiment again, a CRD in which 30 different clover plants were randomly inoculated with six different strains of rhizobium are the resulting level of nitrogen fixation measured. Data Clover; Input Culture $ Nlevel; Cards; 3DOk DOk DOk1 27 3DOk DOk DOk DOk DOk DOk DOk ; Proc GLM; Class Culture; Model Nlevel = Culture; Means Culture / LSD; 3DOk DOk DOk DOk DOk DOk DOk DOk DOk DOk Means Culture / Dunnett ('Comp'); Means Culture / Tukey; Means Culture / Scheffe; Means Culture / Duncan; Means Culture / SNK; Means Culture / REGWQ; Proc Boxplot; Title 'Boxplot Comparing Treatment Means'; Plot NLevel*Culture / cboxes = black; Run; Quit; 3DOk DOk DOk DOk DOk Comp 17.3 Comp 19.4 Comp 19.1 Comp 16.9 Comp 20.8 * The control treatment is 'Comp'; In this experiment, there is no obvious structure to the treatment levels and therefore no way to anticipate the relevant questions to ask. We want to know how the different rhizobial strains performed; and to do this, we must systematically make all pair-wise comparisons among them. In the output on the following pages, keep an eye on the least (or minimum) significant difference(s) used for each test. What is indicated by changes in these values from test to test? Also notice how the comparisons change significance with the different tests. PLS Lab 3 (Topics 4-5)

9 t Tests (LSD) for Nlevel This test controls the Type I comparisonwise error rate, not the experimentwise error rate. Dunnett's t Tests for Nlevel Critical Value of t Least Significant Difference Means with the same letter are not significantly different. t Grouping Mean N Culture A DOk1 B DOk5 C DOk7 C Comp D DOk4 D DOk13 This test controls the Type I experimentwise error for comparisons of all treatments against a control. Critical Value of Dunnett's t Minimum Significant Difference Comparisons significant at the 0.05 level are indicated by ***. Difference Culture Between Simultaneous 95% Comparison Means Confidence Limits 3DOk1 - Comp *** 3DOk5 - Comp *** 3DOk7 - Comp DOk4 - Comp DOk13 - Comp *** Tukey's Studentized Range (HSD) Test for Nlevel This test controls the Type I experimentwise error rate (MEER), but it generally has a higher Type II error rate than REGWQ. Critical Value of Studentized Range Minimum Significant Difference Means with the same letter are not significantly different. Tukey Grouping Mean N Culture A DOk1 B A DOk5 B C DOk7 D C Comp D E DOk4 E DOk13 PLS Lab 3 (Topics 4-5)

10 Scheffe's Test for Nlevel [For group comparisons with Scheffe, see Section ] This test controls the Type I MEER. Critical Value of F Minimum Significant Difference Means with the same letter are not significantly different. Duncan's Multiple Range Test for Nlevel Scheffe Grouping Mean N Culture A DOk1 B A DOk5 B C DOk7 B C D Comp C D DOk4 D DOk13 This test controls the Type I comparisonwise error rate, not the MEER. Number of Means Critical Range Means with the same letter are not significantly different. Duncan Grouping Mean N Culture Student-Newman-Keuls (SNK) Test for Nlevel A DOk1 B DOk5 C DOk7 C Comp D DOk4 D DOk13 This test controls the Type I experimentwise error rate under the complete null hypothesis (EERC) but not under partial null hypotheses (EERP). Number of Means Critical Range Means with the same letter are not significantly different. SNK Grouping Mean N Culture A DOk1 B DOk5 C DOk7 C Comp D DOk4 D DOk13 PLS Lab 3 (Topics 4-5)

11 Ryan-Einot-Gabriel-Welsch (REGWQ) Multiple Range Test for Nlevel This test controls the Type I MEER. Number of Means Critical Range Means with the same letter are not significantly different. REGWQ Grouping Mean N Culture A DOk1 B DOk5 C B DOk7 C D Comp E D DOk4 E DOk13 And to make the relationships among the tests easier to see (i.e. to make sure the dead horse is thoroughly beaten), here is a nice little summary table of all the above results: Significance Groupings Culture LSD Dunnett Tukey Scheffe Duncan SNK REGWQ 3DOk1 A *** A A A A A 3DOk5 B *** AB AB B B B 3DOk7 C BC BC C C BC Comp C CD BCD C C CD 3DOk4 D DE CD D D DE 3DOk13 D *** E D D D E Least Sig't Difference fixed fixed fixed fixed EER Control no yes yes yes no EERC only yes Notice where the non-eer-controlling tests get you into potential Type I trouble, namely by their readiness to declare significant differences between 3DOk5 and 3DOk7 and between Comp and 3DOk4. On the other hand, regarding potential Type II trouble, notice where the relatively insensitive Scheffe's test (insensitive due to its ability to make unlimited pair-wise and group comparisons) failed to pick up a difference detected by other EER-controlling tests (e.g. between 3DOk7 and 3DOk4). Notice, too, how the multiple-range REGWQ was able to detect the difference between 3DOk1 and 3DOk5 when the fixed-range Tukey test was not (both control for EER). Remember, while you should steer clear of tests that do not control for EER, there's no "right" test or "wrong" test. There's only knowing the characteristics of each and choosing the most appropriate one for your experiment (and the culture of your discipline). PLS Lab 3 (Topics 4-5)

12 It is instructive to consider the above table of comparisons with the boxplot below in hand: N l e v e l DOk 1 3 DOk 5 3 DOk 4 3 DOk 7 3 DOk 1 3 Co mp Cu l t u r e Something to think about: Does the boxplot above raise any red flags for you about your data? How would go about investigating such concerns? PLS Lab 3 (Topics 4-5)

Orthogonal contrasts and multiple comparisons

BIOL 933 Lab 4 Fall 2017 Orthogonal contrasts Class comparisons in R Trend analysis in R Multiple mean comparisons Orthogonal contrasts and multiple comparisons Orthogonal contrasts Planned, single degree-of-freedom