Modeling and Multivariate Methods - SAS

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446 Correlations and Multivariate Techniques Chapter 17 Multivariate Platform Options About Missing Values In most of the analysis options, a missing value in an observation does not cause the entire observation to be deleted. However, the Pairwise Correlations option excludes rows that are missing for either of the variables under consideration. The Simple Statistics > Univariate option calculates its statistics column-by-column, without regard to missing values in other columns. Multivariate Platform Options Correlations Multivariate CI of Correlation Shows or hides the Correlations table, which is a matrix of correlation coefficients that summarizes the strength of the linear relationships between each pair of response (Y) variables. This option is on by default. This correlation matrix is calculated by the method that you select in the launch window. Shows the two-tailed confidence intervals of the correlations. This option is off by default. The default confidence coefficient is 95%. Use the Set α Level option to change the confidence coefficient. Inverse Correlations Shows or hides the inverse correlation matrix (Inverse Corr table). This option is off by default. The diagonal elements of the matrix are a function of how closely the variable is a linear function of the other variables. In the inverse correlation, the diagonal is 1/(1 – R 2 ) for the fit of that variable by all the other variables. If the multiple correlation is zero, the diagonal inverse element is 1. If the multiple correlation is 1, then the inverse element becomes infinite and is reported missing. For statistical details about inverse correlations, see the “Inverse Correlation Matrix” on page 457. Partial Correlations Covariance Matrix Shows or hides the partial correlation table (Partial Corr), which shows the partial correlations of each pair of variables after adjusting for all the other variables. This option is off by default. This table is the negative of the inverse correlation matrix, scaled to unit diagonal. Shows or hides the covariance matrix for the analysis. This option is off by default.
Chapter 17 Correlations and Multivariate Techniques 447 Multivariate Platform Options Pairwise Correlations Simple Statistics Shows or hides the Pairwise Correlations table, which lists the Pearson product-moment correlations for each pair of Y variables. This option is off by default. The correlations are calculated by the pairwise deletion method. The count values differ if any pair has a missing value for either variable. The Pairwise Correlations report also shows significance probabilities and compares the correlations in a bar chart. All results are based on the pairwise method. This menu contains two options that each show or hide simple statistics (mean, standard deviation, and so on) for each column. The univariate and multivariate simple statistics can differ when there are missing values present, or when the Robust method is used. Univariate Simple Statistics Shows statistics that are calculated on each column, regardless of values in other columns. These values match those produced by the Distribution platform. Multivariate Simple Statistics Shows statistics that correspond to the estimation method selected in the launch window. If the REML, ML, or Robust method is selected, the mean vector and covariance matrix are estimated by that selected method. If the Row-wise method is selected, all rows with at least one missing value are excluded from the calculation of means and variances. If the Pairwise method is selected, the mean and variance are calculated for each column. These options are off by default. Nonparametric Correlations Set α Level This menu contains three nonparametric measures: Spearman’s Rho, Kendall’s Tau, and Hoeffding’s D. These options are off by default. For details, see “Nonparametric Correlations” on page 449. You can specify any alpha value for the correlation confidence intervals. The default value of alpha is 0.05. Four alpha values are listed: 0.01, 0.05, 0.10, and 0.50. Select Other to enter any other value. Scatterplot Matrix Shows or hides a scatterplot matrix of each pair of response variables. This option is on by default. For details, see “Scatterplot Matrix” on page 449.
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Version 10 Modeling and Multivariat
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INDIRECT OR CONSEQUENTIAL DAMAGES O
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6 Emphasis Options for Standard Lea
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8 Models with Crossed, Interaction,
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10 8 Analyzing Screening Designs Us
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12 Examining the Residuals . . . .
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14 Response Frequencies . . . . . .
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16 19 Analyzing Principal Component
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18 Interpreting the Profiles . . .
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20 Discriminant Analysis . . . . .
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Contents Book Conventions. . . . .
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24 Learn about JMP Chapter 1 Book C
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26 Learn about JMP Chapter 1 Book C
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28 Learn about JMP Chapter 1 Additi
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30 Learn about JMP Chapter 1 Additi
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Contents Launch the Fit Model Platf
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Contents Example of a Multiple Resp
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158 Fitting Multiple Response Model
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180 Fitting Generalized Linear Mode
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200 Performing Logistic Regression
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Contents Overview of the Screening
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232 Analyzing Screening Designs Cha
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Contents Introduction to the Nonlin
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244 Performing Nonlinear Regression
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Contents Overview of Neural Network
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280 Creating Neural Networks Chapte
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Contents Launching the Platform. .
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296 Modeling Relationships With Gau
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Contents Overview of the Loglinear
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306 Fitting Dispersion Effects with
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318 Recursively Partitioning Data C
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Contents Launch the Platform . . .
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354 Performing Time Series Analysis
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Contents The Categorical Platform .
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386 Performing Categorical Response
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496 Performing Discriminant Analysi
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Contents Overview of the Partial Le
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508 Fitting Partial Least Squares M
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Contents Introduction to Item Respo
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Contents Surface Plots . . . . . .
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538 Plotting Surfaces Chapter 23 La
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544 Plotting Surfaces Chapter 23 Th
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546 Plotting Surfaces Chapter 23 Th
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548 Plotting Surfaces Chapter 23 Pl
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Contents Introduction to Profiling
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556 Visualizing, Optimizing, and Si
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640 References Becker, R.A. and Cle
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646 References Matsumoto, M. and Ni
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648 References Rawlings, J.O. (1988
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650 References Umetrics (1995), Mul
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Contents The Response Models . . .
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654 Statistical Details Appendix A
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690 Index Birth Death Subset.jmp 46
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692 Index Ellipses Transparency 451
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694 Index test for curvature 46 Kru
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696 Index Customizing 267 Nonlinear
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698 Index reliability analysis 453-
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700 Index Stable 363 Stable Inverti
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702 Index
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Modeling and Multivariate Methods - SAS

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