PROC GLM.pdf统计分析软件SAS中关于GLM过程步的详细说明文档Chapter 39 Th

文件名称: PROC GLM.pdf

所属分类: 其它

开发工具:

文件大小: 3mb

下载次数: 0

上传时间: 2019-07-04

提供者: weixin_********

下载 (3mb)

不能下载？报告错误

详细说明：统计分析软件SAS中关于GLM过程步的详细说明文档Chapter 39 The glm Procedure Contents Overview GLM Procedure 2430 PROC GLM Features 2431 PROC GLM Contrasted with Other Sas Procedures 2432 Getting Started: GLM Procedure 2433 PROC GLM for Unbalanced ANOVA 2433 PROC GLM for Quadratic Least Squares Regression 2436 Syntax: GLM Procedure 2442 PROC GLM Statement 2444 ABSORB Statement 2450 BY Statement 2450 CLASS Statement 2451 CONTRAST Statement 2452 ESTIMATE Statement 2454 FREO Statement 2455 ID Statement 2456 LSMEANS Statement 2456 MANOVA Statement 2462 MEANS Statement 2466 MODEL Statement OUTPUT Statement 2475 RANDOM Statement 2478 REPEATED Statement 2479 TEST Statement 2483 WEIGHT Statement 2484 Details: GLM Procedure 2485 Statistical Assumptions for Using PROC GLM 2485 Specification of Effects 2486 Using PROC GLM Interactively 2488 Parameterization of Proc glm models 2489 Hypothesis Testing in PROC GLM 2494 Effect Size Measures for F Tests in GLM (Experimental 2 2505 Specification of ESTIMATE Expressions 2507 Comparing groups 2509 2430t Chapter 39: The GLM Procedure Means versus ls-means 2509 Multiple comparisons 2512 Simple effects 2523 Homogeneity of variance in One-Way models 2524 Weighted means .2525 Construction of Least Squares Means 2526 Multivariate Analysis of Variance 2529 Repeated Measures Analysis of Variance 530 Random-Effects Analysis 2538 Missing values ···· 2541 Computational resources 2542 Computational Method 545 Output Data S 2546 Displayed Output 2548 ODS Table n ODS Graphics 2552 Examples: GLM Procedure 2554 Example 39.1: Randomized Complete Blocks with Means Comparisons and Contrasts 2554 Example 39.2: Regression with Mileage Data Example 39.3: Unbalanced ANOVA for Two-Way Design with Interaction. 2563 Example 39.4: Analysis of Covariance .2569 Example 39.5: Three-Way Analysis of Variance with Contrasts 2576 Example 39.6: Multivariate Analysis of Variance ..2580 Example 39.7: Repeated Measures Analysis of Variance 2588 Example 39.8: Mixed Model analysis of Variance with the random state ment 2593 Example 39.9: Analyzing a Doubly Multivariate Repeated Measures Design 2596 Example 39.10: Testing for Equal Group Variances 2602 Example 39.11: Analysis of a Screening Design 2606 References 2611 Overview: GLM Procedure The glm procedure uses the method of least squares to fit general linear models. among the statis tical methods available in PROC GLM are regression, analysis of variance, analysis of covariance multivariate analysis of variance, and partial correlation PROC GLM analyzes data within the framework of general linear models. PROC GLM handles models relating one or several continuous dependent variables to one or several independent vari- ables. The independent variables can be either classification variables, which divide the observa- tions into discrete groups, or continuous variables. Thus, the GLM procedure can be used for many different analyses, including the following PROC GLM Features+ 2431 Simple regression · multiple regression analysis of variance(ANOVA), especially for unbalanced data e analysis of covariance response surface models eighted regression polynomial regression partial correlation multivariate analysis of variance ( MANOVA) repeated measures analysis of variance PROC GLM Features The following list summarizes the features in PROC GLM PROC GLM enables you to specify any degree of interaction(crossed effects) and nested effects. It also provides for polynomial, continuous-by-class, and continuous-nesting-class erects Through the concept of estimability, the Glm procedure can provide tests of hypotheses for the effects of a linear model regardless of the number of missing cells or the extent of confounding. PROC GLM displays the sum of squares(Ss)associated with each hypothesis tested and, upon request the form of the estimable functions employed in the test. PRoc GLM can produce the general form of all estimable functions The rEpeated statement enables you to specify effects in the model that represent repeated measurements on the same experimental unit for the same response, providing both univariate and multivariate tests of hypotheses The RANdOM statement enables you to specify random effects in the model; expected mean squares are produced for each Type I, Type II, Type Ill, Type IV, and contrast mean square used in the analysis. Upon request, F tests that use appropriate mean squares or linear com- binations of mean squares as error terms are performed The EStimate statement enables you to specify an L vector for estimating a linear function of the parameters Lβ The CONtRast statement enables you to specify a contrast vector or matrix for testing the hypothesis that LB =0. When specified, the contrasts are also incorporated into analyses that use the manova and repeated statements 2432+ Chapter 39: The GLM Procedure The MANova statement enables you to specify both the hypothesis effects and the error effect to use for a multivariate analysis of variance PROC GLM can create an output data set containing the input data set in addition to predicted values. residuals, and other diagnostic measures PROC GLM can be used interactively. After you specify and fit a model, you can execute a variety of statements without recomputing the model parameters or sums of squares For analysis involving multiple dependent variables but not the MANOVa or REPEAtED statements, a missing value in one dependent variable does not eliminate the observation from the analysis for other dependent variables. PROC GLM automatically groups together those variables that have the same pattern of missing values within the data set or within a bY group. This ensures that the analysis for each dependent variable brings into use all possible observations The glm procedure automatically produces graphics as part of its ODs output. For gen eral information about ODS Graphics, see the section"ODS Graphics"on page 2552 and Chapter 21, Statistical Graphics Using ODS PROC GLM Contrasted with Other SAS Procedures As described previously, PROC GLM can be used for many different analyses and has many spe cial features not available in other SAs procedures. However, for some types of analyses, other procedures are available. As discussed in the sections"PROC GLM for Unbalanced ANOVa"on page 2433 and"PROC GLM for Quadratic Least Squares Regression"on page 246, sometimes these other procedures are more efficient than PROC GLM. The following procedures perform some of the same analyses as PRoc glm ANOVA performs analysis of variance for balanced designs. The ANoVa procedure is generally more efficient than PROC GLM for these designs MIXED fits mixed linear models by incorporating covariance structures in the model fitting process. Its RANdOM and REPEatED statements are similar to those in proc glm but offer different functionalities NESTED performs analysis of variance and estimates variance components for nested ran dom models. The NEsted procedure is generally more efficient than PROC GLM for these models NPARIWAY performs nonparametric one-way analysis of rank scores. This can also be done using the rank procedure and PROC GLm REG performs simple linear regression. The REG procedure allows several MODEL statements and gives additional regression diagnostics especially for detection of collinearity RSREG performs quadratic response surface regression, and canonical and ridge analy- sis. The RsrEG procedure is generally recommended for data from a response surface experiment Getting Started GLM Procedure t 2433 TTEST compares the means of two groups of observations. Also, tests for equality of variances for the two groups are available. The TtEst procedure is usually more efficient than PRoc glm for this type of data VARCOMP estimates variance components for a general linear model. Getting Started: GLM Procedure PROC GLM for Unbalanced ANova Analysis of variance, or ANOVA, typically refers to partitioning the variation in a variables values into variation between and within several groups or classes of observations. The Glm procedure can perform simple or complicated ANOVA for balanced or unbalanced data This example discusses the analysis of variance for the unbalanced 2 x2 data shown in Table 39.1 The experimental design is a full factorial, in which each level of one treatment factor occurs at each level of the other treatment factor. Note that there is only one value for the cell with A=A2 and B=B2,. Since one cell contains a different number of values from the other cells in the table this is an unbalanced design Table 39.1 Unbalanced Two-Way data A1 A2 B112,1420,18 B211917 The following statements read the data into a sas data set and then invoke PROC Glm to produce the analysi title ' Analysis of Unbalanced 2-by-2 Factorial data expi input A s BsY datalines A1B112A1B114 A1B211A1B29 A2B120A2B118 A2B217 proc glm data=exp; class a b mode⊥Y=ABA*B Both treatments are listed in the Class statement because they are classification variables. A"B denotes the interaction of the a effect and the b effect. The results are shown in Figure 39. 1 and Figure 39.2 2434+ Chapter 39: The GLM Procedure Figure 39.1 Class Level Information Analysis of Unbalanced 2-by-2 Factorial The Glm procedure Class level information Class evels Values Al A2 B1 B2 Number of observations read Number of observations used Figure 39. 1 displays information about the classes as well as the number of observations in the data set. Figure 39.2 shows the aNoVa table, simple statistics, and tests of effects Figure 39.2 ANOVA Table and Tests of Effects nalysis of Unbal d2-by-2Fact。ria1 The GlM proce Dependent Variable: Y f Source DE Sa quarts Mean Square F Value Pr >F Model 91.71428571 30.57142857 15.290.0253 E卫。 6.00000000 2.00000000 C。 erected tota1 697.71428571 R-Square C。 eff var Root mse Y Mean 0.938596 9.801480 1.414214 14.42857 Source DE ype I ss Mean Square F Value Pr >F 180.0476190580.04761905 40.020.0080 ABA 1 11.26666667 11.26666667 5.630.0982 0.40000000 0.40000000 0.200.6850 s。uxce DE rype工工 I ss Mean Square F va1uePx> ABA 57.6000000067.60000000 33.800.0101 10.0000000010.00000000 5.000.1114 0.40000000 0.40000000 0.200.6850 PROC GLM for Unbalanced ANOVA 2435 The degrees of freedom can be used to check your data. The model degrees of freedom for a 2x 2 factorial design with interaction are(ab-1), where a is the number of levels of a and b is the number of levels of B; in this case,(2X2-1)=3. The Corrected Total degrees of freedom are always one less than the number of observations used in the analysis: in this case, 7-1=6 The overall F test is significant (F= 15.29, P=0.0253), indicating strong evidence that the means for the four different AXB cells are different. You can further analyze this difference by examining the individual tests for each effect Four types of estimable functions of parameters are available for testing hypotheses in PROC GLM For data with no missing cells, the Type IlI and Type iv estimable functions are the same and test the same hypotheses that would be tested if the data were balanced. Type I and Type Ill sums of squares are typically not equal when the data are unbalanced; Type IiI sums of squares are preferred in testing effects in unbalanced cases because they test a function of the underlying parameters that is independent of the number of observations per treatment combination According to a significance level of 5%(a=0.05), the A*B interaction is not significant (F 0.20, P=0.6850). This indicates that the effect of a does not depend on the level of B and (F=33.80, p=0.0101)but no significant B effect(F=5.00,P=0.1\14 Significant A effect vice versa. Therefore, the tests for the individual effects are valid, showing a If you enable Ods Graphics, GLM also displays by default an interaction plot for this analysis The following statements, which are the same as in the previous analysis but with Ods graphics enabled, additionally produce Figure 39.3 ods graphics on; proc glm data=exp; class a B mode1Y=ABA★B run; ods graphics off 2436+ Chapter 39: The GLM Procedure Figure 39. 3 Plot of y by a and B Interaction Plot for y 0 16 14 12 10 + A A2 A B0B1-+一B2 The insignificance of the A "B interaction is reflected in the fact that two lines in Figure 39.3 are nearly parallel. For more information about the graphics that GLM can produce, see the section "Ods Graphics' on page 2552 PROC GLM for Quadratic Least Squares regression In polynomial regression, the values of a dependent variable(also called a response variable)are described or predicted in terms of polynomial terms involving one or more independent or explana tory variables. An example of quadratic regression in PRoc glm follows. These data are taken from Draper and Smith(1966, p. 57). Thirteen specimens of 90/10 Cu-Ni alloys are tested in a corrosion-wheel setup in order to examine corrosion. Each specimen has a certain iron content The wheel is rotated in salt sea water at 30 ft/sec for 60 days. Weight loss is used to quantify the corrosion. The fe variable represents the iron content, and the loss variable denotes the weight loss in milligrams/square decimeter/day in the following DatA step 七it1e′ Regression in PROC GLM"; data iron;

(系统自动生成,下载前可以参看下载内容)