SPSS® 12.0 Command Syntax Reference

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294 COXREG Temporary variables created by COXREG are: SURVIVAL SE HAZARD RESID LML DFBETA PRESID XBETA Example TIME PROGRAM. COMPUTE Z=AGE + T_. Overview Options COXREG SURVIVAL WITH Z /STATUS SURVSTA EVENT(1). COXREG applies Cox proportional hazards regression to analysis of survival times—that is, the length of time before the occurrence of an event. COXREG supports continuous and categorical independent variables (covariates), which can be time-dependent. Unlike SURVIVAL and KM, which compare only distinct subgroups of cases, COXREG provides an easy way of considering differences in subgroups as well as analyzing effects of a set of covariates. Processing of Independent Variables. You can specify which of the independent variables are categorical with the CATEGORICAL subcommand and control treatment of these variables with the CONTRAST subcommand. You can select one of seven methods for entering independent variables into the model using the METHOD subcommand. You can also indicate interaction terms using the keyword BY between variable names on either the VARIABLES subcommand or the METHOD subcommand. Specifying Termination and Model-Building Criteria. You can specify the criteria for termination of iteration and control variable entry and removal with the CRITERIA subcommand. Adding New Variables to Working Data File. You can use the SAVE subcommand to save the cumulative survival, standard error, cumulative hazard, log-minus-log-of-survival function, residuals, XBeta, and, wherever available, partial residuals and DfBeta. Output. You can print optional output using the PRINT subcommand, suppress or request plots with the PLOT subcommand, and, with the OUTFILE subcommand, write SPSS data files containing coefficients from the final model or a survival table. When only time-constant covariates are used, you can use the PATTERN subcommand to specify a pattern of covariate values in addition to the covariate means to use for the plots and the survival table.
Basic Specification COXREG 295 • The minimum specification on COXREG is a dependent variable with the STATUS subcommand. • To analyze the influence of time-constant covariates on the survival times, the minimum specification requires either the WITH keyword followed by at least one covariate (independent variable) on the VARIABLES subcommand or a METHOD subcommand with at least one independent variable. • To analyze the influence of time-dependent covariates on the survival times, the TIME PROGRAM command and transformation language are required to define the functions for the time-dependent covariate(s). Subcommand Order • The VARIABLES subcommand must be specified first; the subcommand keyword is optional. • Remaining subcommands can be named in any order. <strong>Syntax</strong> Rules • Only one dependent variable can be specified for each COXREG command. • Any number of covariates (independent variables) can be specified. The dependent variable cannot appear on the covariate list. • The covariate list is required if any of the METHOD subcommands are used without a variable list or if the METHOD subcommand is not used. • Only one status variable can be specified on the STATUS subcommand. If multiple STATUS subcommands are specified, only the last specification is in effect. • You can use the BY keyword to specify interaction between covariates. Operations • TIME PROGRAM computes the values for time-dependent covariates. • COXREG replaces covariates specified on CATEGORICAL with sets of contrast variables. In stepwise analyses, the set of contrast variables associated with one categorical variable is entered or removed from the model as a block. • Covariates are screened to detect and eliminate redundancies. • COXREG deletes all cases that have negative values for the dependent variable. Limitations • Only one dependent variable is allowed. • Maximum 100 covariates in a single interaction term. • Maximum 35 levels for a BY variable on PATTERN.
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SPSS® 12.0 Command Syntax Referenc
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Universals This part of the SPSS Sy
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Commands 5 before processing. All u
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Commands 7 • Subcommands begin wi
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Commands 9 • Transformation comma
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Files SPSS reads, creates, and writ
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SPSS-Format Data File Files 13 An S
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SPSS Matrix Data Files Files 15 An
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Files 17 4. VARNAME_ variable (or F
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Figure 6 Dictionary of a matrix sys
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Variables To prepare data for proce
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Keyword TO Variables 23 You can est
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Variable Formats Variables 25 SPSS
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Format type Description Ew.d Scient
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Table 4 Numeric formats with punctu
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Variables 31 PIBHEXw (hexadecimal o
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Variables 33 RBw (real binary): The
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Variables 35 is read as two separat
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Transformation Expressions Transfor
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SQRT(arg) Square root. SQRT(SIBS) i
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The following are suffixes for cont
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Transformation Expressions 43 0≤p
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Domain Errors Transformation Expres
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Transformation Expressions 47 origi
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Transformation Expressions 49 • L
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NOT Logical Operator Transformation
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and COMPUTE lagvar=LAG(var1). EXECU
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Date and Time SPSS reads and writes
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Input Data Specification Date and T
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The LIST output from these commands
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Date and Time 61 • A time interva
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Aggregation Functions Date and Time
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Conversion Functions Date and Time
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Date and Time 67 XDATE.HOUR(arg) Re
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Precautions with Date and Time Vari
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72 ACF and DIFF subcommands. With s
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74 ACF SDIFF Subcommand If the seri
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76 ACF SERROR Subcommand SERROR spe
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ADD DOCUMENT ADD DOCUMENT ’text
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ADD FILES ADD FILES FILE={file} {*
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82 ADD FILES • The resulting file
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84 ADD FILES • Input data files a
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86 ADD FILES FIRST and LAST Subcomm
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88 ADD VALUE LABELS • The added v
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AGGREGATE AGGREGATE OUTFILE={file}
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92 AGGREGATE Example Example • If
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94 AGGREGATE Example AGGREGATE OUTF
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96 AGGREGATE FIN(varlist,value1,val
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98 AGGREGATE • User-missing value
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ALSCAL ALSCAL VARIABLES=varlist [/F
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102 ALSCAL Operations • ALSCAL ca
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104 ALSCAL ASYMMETRIC Asymmetric da
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106 ALSCAL • INITIAL is the defau
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108 ALSCAL differences model propos
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110 ALSCAL • Algorithmic options
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112 ALSCAL Table 2 Types of configu
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Matrix mode Object by object 114 AL
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Matrix mode Object by object Object
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118 Syntax Reference Basic Specific
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120 Syntax Reference Example DATA L
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122 Syntax Reference PLOT Subcomman
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124 Syntax Reference VARNAME_ Strin
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ANOVA ANOVA [VARIABLES=] varlist BY
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Example Example ANOVA 129 ANOVA VAR
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ANOVA 131 Some restrictions apply t
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Table 2 Combinations of COVARIATES
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Multiple Classification Analysis AN
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APPLY DICTIONARY 137 • The applie
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APPLY DICTIONARY 139 target file as
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APPLY DICTIONARY 141 MRSETS = MERGE
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APPLY DICTIONARY 143
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Options Basic Specification Subcomm
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• Only one method can be specifie
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APPLY Subcommand AREG 149 • Itera
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AREG 151
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Options Basic Specification ARIMA 1
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Limitations Example ARIMA 155 • F
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D Order of differencing. Q Moving-a
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Termination Criteria Subcommands CI
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References ARIMA 161 Akaike, H. 197
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Subcommand Order • VARIABLES must
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Example AUTORECODE 165 DATA LIST /
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BEGIN DATA—END DATA BEGIN DATA da
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BREAK BREAK Overview BREAK controls
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CASEPLOT Overview CASEPLOT [VARIABL
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Subcommand Order • Subcommands ca
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LN and NOLOG Subcommands CASEPLOT 1
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CASEPLOT 177 erence line. Figure 3
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CASEPLOT 179 • If FORMAT=REFERENC
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Options CASESTOVARS 181 Automatic c
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The commands: CASESTOVARS 183 SPLIT
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create a new file with the followin
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SEPARATOR Subcommand CASESTOVARS 18
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CATPCA CATPCA is available in the C
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Optional output. You can request op
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CATPCA 193 • The NORMALIZATION su
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SPORD and SPNOM Keywords The follow
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ACTIVE Keyword The ACTIVE keyword h
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MAXITER Subcommand CATPCA 199 MAXIT
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CATPCA 201 The keyword TO in a vari
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TRIPLOT(varlist(varlist))(n) CATPCA
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CATPCA 205 procedures with the succ
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CATREG Overview CATREG is available
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Operations Limitations CATREG 209
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ANALYSIS Subcommand LEVEL Keyword C
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GROUPING Keyword DISTR Keyword MISS
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PRINT Subcommand CATREG 215 The PRI
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SAVE Subcommand OUTFILE Subcommand
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CCF Overview Options CCF [VARIABLES
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CCF 221 • LN transforms the data
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CCF 223 • If a natural log transf
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CLEAR TRANSFORMATIONS Overview CLEA
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Example Overview Options CLUSTER V1
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CLUSTER 229 • The variable list i
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Measures for Binary Data CLUSTER 23
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DICE[(p[,np])] Dice (or Czekanowski
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Y( x, y) ad - bc = ----------------
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CLUSTER 237 MEDIAN Median clusterin
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CLUSTER 239 • This example displa
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Matrix Input CLUSTER 241 • The ma
Page 243 and 244: Example Example Example DATA LIST F
Page 245 and 246: COMMENT {COMMENT} text { * } Overvi
Page 247 and 248: Cumulative distribution functions (
Page 249 and 250: Cumulative distribution functions (
Page 251 and 252: Logical functions: RANGE(varname,ra
Page 253 and 254: String Transformations COMPUTE 253
Page 255 and 256: Examples COMPUTE 255 • The string
Page 257 and 258: COMPUTE 257 • Alternatively, you
Page 259 and 260: Equivalence COMPUTE 259 STRING DEPT
Page 261 and 262: CONJOINT Overview CONJOINT is avail
Page 263 and 264: CONJOINT 263 the experimental profi
Page 265 and 266: CONJOINT 265 • The ADD FILES comm
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Page 269 and 270: CONJOINT 269 • New value labels a
Page 271 and 272: PLOT Subcommand CONJOINT 271 The PL
Page 273 and 274: Basic Specification CORRELATIONS 27
Page 275 and 276: MISSING Subcommand CORRELATIONS 275
Page 277 and 278: Example CORRELATIONS 277 • CORREL
Page 279 and 280: CORRESPONDENCE CORRESPONDENCE is av
Page 281 and 282: Syntax Rules Operations Limitations
Page 283 and 284: DIMENSION Subcommand CORRESPONDENCE
Page 285 and 286: CORRESPONDENCE 285 EUCLID Euclidean
Page 287 and 288: PLOT Subcommand NONE No output othe
Page 289 and 290: CORRESPONDENCE 289 DIM1...DIMn Nume
Page 291 and 292: COUNT COUNT varname=varlist(value l
Page 293: COXREG COXREG is available in the A
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Page 299 and 300: COXREG 299 • If the categorical v
Page 301 and 302: COXREG 301 BSTEP Backward stepwise.
Page 303 and 304: CRITERIA Subcommand COXREG 303 CRIT
Page 305 and 306: COXREG 305 TABLE Write the survival
Page 307 and 308: CREATE CREATE new series={CSUM (ser
Page 309 and 310: Example CREATE NEWVAR1 = DIFF(OLDVA
Page 311 and 312: LAG Function CREATE 311 LAG creates
Page 313 and 314: Example CREATE TICKMA = MA(TICKETS,
Page 315 and 316: T4253H Function CREATE 315 T4253H p
Page 317 and 318: Overview Options CROSSTABS 317 CROS
Page 319 and 320: Example CROSSTABS 319 • The varia
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Page 323 and 324: CROSSTABS 323 CORR Display Pearson
Page 325 and 326: FORMAT Subcommand CROSSTABS 325 By
Page 327 and 328: CROSSTABS 327 • The split-file gr
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Page 331 and 332: Operations CSDESCRIPTIVES 331 • C
Page 333 and 334: CSDESCRIPTIVES 333 • Plan file an
Page 335 and 336: CSDESCRIPTIVES 335 • The set of s
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Page 339 and 340: CSSELECT 339 inclusion probabilitie
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CSSELECT 345
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Design Block: Stages 2 and 3 CSPLAN
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Example Overview Options CSPLAN SAM
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CSPLAN 351 • A PLAN subcommand is
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Stratified sample design CSPLAN SAM
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CSPLAN 355 • The variable samplew
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CSPLAN 357 Typically the previous w
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CSPL AN 359 • CLUSTER is required
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SIZE Subcommand CSPLAN 361 The SIZE
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MINSIZE Keyword CSPLAN 363 MINSIZE
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Keyword Default Root Name Descripti
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CSPLAN 367 strata only the last one
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CSPLAN 369
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CSTABULATE 371 • This specificati
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CSTABULATE 373 on the left defines
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CSTABULATE 375 • For example, /SU
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ADJUST= {BONFERRONI} ORIGIN=COLUMN
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Example Think of self as liberal or
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Example CTABLES /TABLE HAPPY. Gener
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Scale Variables CTABLES 383 Scale v
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CTABLES 385 • Each summary functi
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Table 2 Summary functions: scale va
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Table 3 Summary functions: multiple
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Missing Values in Summaries CTABLES
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Example CTABLES 393 CTABLES /TABLE
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CTABLES 395 • The SUBTOTAL keywor
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Totals Example CTABLES /TABLE AGE [
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CTABLES 399 CORNER Corner text. Cor
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Pairwise Comparisons of Proportions
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CTABLES 403 EMPTY Fill characters u
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CURVEFIT CURVEFIT [VARIABLES=] varn
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Operations CURVEFIT 407 • When CU
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CURVEFIT 409 • This command fits
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APPLY Subcommand CURVEFIT 411 APPLY
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Example Overview Options Week and y
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Fixed-Format Data DATA LIST 415 •
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DATA LIST 417 LIST Freefield data w
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DATA LIST 419 • RECORDS can be us
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Example DATA LIST 421 INPUT PROGRAM
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Variable Names DATA LIST 423 • Va
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Example * Mixing column-style and F
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DATA LIST 427 • For freefield dat
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DATA LIST 429 • The width (w) por
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DATE DATE keyword [starting value [
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DATE 433 • The starting value is
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DATE 435 • For DAY_, the starting
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Example 5 DATE Y 1950 Q 2 M. DATE 4
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The following is a partial listing
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Example DEFINE sesvars () age sex e
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Example Example * Macro without arg
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DEFINE—!ENDDEFINE 445 • The mac
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DEFINE—!ENDDEFINE 447 • This ex
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Example * Keyword !CHAREND. DEFINE
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DEFINE—!ENDDEFINE 451 !DEFAULT De
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DEFINE—!ENDDEFINE 453 !HEAD (str)
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Conditional Processing DEFINE—!EN
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DEFINE—!ENDDEFINE 457 • The fir
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DEFINE—!ENDDEFINE 459
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DESCRIPTIVES Overview Options DESCR
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Z Scores DESCRIPTIVES 463 The z-sco
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MAX Maximum observed value. SUM Sum
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DISCRIMINANT DISCRIMINANT GROUPS=va
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Basic Specification DISCRIMINANT 46
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DISCRIMINANT 471 • Variables shou
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Example DISCRIMINANT 473 DISCRIMINA
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DISCRIMINANT 475 • You can set FI
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SAVE Subcommand DISCRIMINANT 477 SA
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STATISTICS Subcommand DISCRIMINANT
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• The default keywords for CLASSI
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Matrix Input DISCRIMINANT 483 recor
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Example Example Example * Use matri
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DISPLAY DISPLAY [SORTED] [{NAMES**
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DISPLAY 489 • If the keyword SORT
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Example Example DOCUMENT 491 • If
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Overview DO IF 493 The DO IF—END
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Flow of Control DO IF 495 • If th
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Example DO IF 497 DO IF (YRHIRED GT
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Nested DO IF Structures DO IF 499 T
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DO REPEAT—END REPEAT DO REPEAT st
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Example DO REPEAT—END REPEAT 503
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Figure 2 Output from the PRINT subc
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ECHO ECHO "text". Example Overview
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Operations Example END CASE 509 •
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END CASE 511 • Data are extracted
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END CASE 513 BEGIN DATA 1 AC 91 2 C
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END CASE 515 • The frequencies ta
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Example Example *Select cases. INPU
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EXAMINE EXAMINE VARIABLES=varlist [
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Example EXAMINE VARIABLES=MIPERGAL
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• The method keywords follow the
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STATISTICS Subcommand EXAMINE 525 S
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EXAMINE 527 PAIRWISE Delete cases w
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EXPORT EXPORT OUTFILE=file [/TYPE={
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EXPORT 531 • Tape density—200,
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DROP and KEEP Subcommands EXPORT 53
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EXSMOOTH EXSMOOTH is available in t
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Subcommand Order Syntax Rules Opera
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Table 1 Models for different types
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Example EXSMOOTH 541 EXSMOOTH VAR2
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INITIAL Subcommand EXSMOOTH 543 •
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References EXSMOOTH 545 • The fir
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Example Overview Options FACTOR VAR
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Operations Example FACTOR 549 • V
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FACTOR 551 • By default, FACTOR u
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FACTOR 553 DET Determinant of the c
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• The factor pattern matrix is ro
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ROTATION Subcommand FACTOR 557 ROTA
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FACTOR 559 • The ROTATION subcomm
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Example Example Example Example FAC
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FACTOR 563 Jöreskog, K. G. 1977. F
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MULTIPUNCH Column binary file. IMAG
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FILE TYPE—END FILE TYPE For mixed
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Syntax Rules FILE TYPE—END FILE T
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Example * A grouped file of student
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FILE TYPE—END FILE TYPE 573 GROUP
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FILE TYPE—END FILE TYPE 575 • T
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FILE TYPE—END FILE TYPE 577 • T
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FILE TYPE—END FILE TYPE 579 WARN
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FILE TYPE—END FILE TYPE 581 BEGIN
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Example FILTER 583 • Both system-
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Example * A command file. FINISH 58
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Syntax Rules FIT 587 • If OBS is
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FLIP FLIP [[VARIABLES=] {ALL }] {va
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FLIP 591 Example Using the untransp
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FORMATS FORMATS varlist(format) [va
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Table 1 Output data formats (Contin
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FREQUENCIES FREQUENCIES [VARIABLES=
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Example FREQUENCIES 599 • FREQUEN
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FREQUENCIES 601 • MISSING and NOM
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Example FREQUENCIES 603 FREQUENCIES
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Example FREQUENCIES VAR=AGE /STATS=
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Options GENLOG 607 mates for unsatu
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GENLOG 609 • To separate the inde
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GENLOG 611 • For each effect, GEN
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GENLOG 613 DESIGN The design matrix
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GENLOG 615 • The saved variables
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GET GET FILE=file [/KEEP={ALL** }]
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GET 619 • Variables can be specif
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GET CAPTURE GET CAPTURE {ODBC }* [/
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GET CAPTURE 623 • You can display
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Overview GET DATA 625 GET DATA read
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READNAMES Subcommand GET DATA 627 O
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QUALIFIER Subcommand GET DATA 629 T
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GET SAS Overview Options GET SAS DA
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GET SAS 633 • Value labels read f
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GET TRANSLATE GET TRANSLATE FILE=fi
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GET TRANSLATE 637 • The first row
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GET TRANSLATE 639 • For string va
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GET TRANSLATE 641 • RANGE cannot
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GLM: Overview GLM is available in t
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GLM: Overview 645 • Display expec
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Model 2: Fixed-effects ANOVA and MA
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GLM: Overview 649 For univariate an
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GLM: Univariate GLM is available in
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Basic Specification GLM: Univariate
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GLM: Univariate 655 • If you spec
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INTERCEPT Subcommand GLM: Univariat
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GLM: Univariate 659 LOF Perform a l
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GLM: Univariate 661 • A coefficie
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Example GLM: Univariate 663 GLM DEP
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Example GLM: Univariate 665 GLM DEP
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GLM: Univariate 667 • Multiple PO
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COMPARE(factor) ADJ(method) GLM: Un
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DESIGN Subcommand GLM: Univariate 6
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Overview Options GLM: Multivariate
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GLM: Multivariate 675 Residual cova
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GLM: Repeated Measures GLM is avail
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Subcommand Order • The list of de
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GLM: Repeated Measures 681 • Leve
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GLM: Repeated Measures 683 • WSFA
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GLM: Repeated Measures 685 • Thes
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GRAPH 687 The following table shows
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GRAPH 689 NGT(x) Number of cases fo
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• The default span (2) and sigma
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GRAPH 693 Figure 3 /BAR=MEAN(SALBEG
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GRAPH 695 Figure 9 /BAR(STACKED)=CO
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LINE Subcommand GRAPH 697 LINE crea
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Figure 19 /LINE=MEAN(THEFT AUTO BUR
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Figure 25 /LINE(AREA)=COUNT BY TIME
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Figure 31 LINE(DIFFERENCE)=VALUE(SA
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GRAPH 705 the variable must be dich
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ERRORBAR Subcommand GRAPH 707 ERROR
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GRAPH 709 MATRIX Scatterplot matrix
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Figure 49 /SCATTERPLOT(XYZ)=JOBCAT
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Figure 52 PARETO(CUM SIMPLE)=SUM(DE
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Elements and Attributes Dependent o
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HILOGLINEAR Overview HILOGLINEAR is
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Operations HILOGLINEAR 719 • HILO
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Example HILOGLINEAR 721 HILOGLINEAR
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Example * An Incomplete Rectangular
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HILOGLINEAR 725 NONE No plots. Spec
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HOMALS Overview Options HOMALS is a
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Example HOMALS 729 Values outside o
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HOMALS 731 The total number of vali
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Example HOMALS 733 HOMALS VARIABLES
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HOMALS 735 LEVEL String variable LE
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Basic Specification IF 737 The basi
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Example Example Example IF (AGE > 2
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Example Example IF 741 tions on eve
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{VERTICAL } {THREE } [/EFFECT={NONE
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Overview Options IGRAPH 745 The int
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CATORDER Subcommand IGRAPH 747 The
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Example IGRAPH /VIEWNAME=’Scatter
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CHARTLOOK Subcommand IGRAPH 751 CHA
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FORMAT Subcommand IGRAPH 753 For ch
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IGRAPH 755 The INTERPOLATE keyword
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IGRAPH 757 START num Indicates the
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Example IGRAPH /X1=VAR(region) TYPE
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ERRORBAR Subcommand IGRAPH 761 Erro
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IGRAPH 763 REGRESSION Fits a straig
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IGRAPH 765 Number of Cases Less Tha
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IMPORT IMPORT FILE=file [/TYPE={COM
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IMPORT 769 • DROP excludes a vari
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INCLUDE INCLUDE FILE=file Example O
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INFO This command is not available
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Types of Information The following
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INPUT PROGRAM—END INPUT PROGRAM I
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Example Example * Select cases with
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KEYED DATA LIST KEYED DATA LIST KEY
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Syntax Rules KEYED DATA LIST 783
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Example * Reading a keyed file: rea
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KM Overview KM is available in the
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KM 789 • Only one status variable
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STRATA Subcommand KM 791 STRATA ide
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TEST Subcommand KM 793 TEST specifi
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SAVE Subcommand KM 795 SAVE saves t
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Example LEAVE 797 • Numeric varia
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Operations Example Example LIST 799
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LIST 801 • If SPLIT FILE is in ef
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Temporary variables created by LOGI
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Limitations Example LOGISTIC REGRES
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LOGISTIC REGRESSION 807 DEVIATION(r
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LOGISTIC REGRESSION 809 Wald statis
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ID Subcommand LOGISTIC REGRESSION 8
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LOGISTIC REGRESSION 813 • POUT re
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LOGISTIC REGRESSION 815 • Assigne
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LOGLINEAR 817 multiway contingency
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Example Example Example Example LOG
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LOGLINEAR 821 • To enter cell cov
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CONTRAST Subcommand LOGLINEAR 823 C
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Example * Contrasts for a logistic
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Example LOGLINEAR RESPONSE(1,2) BY
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Example * Equiprobability model LOG
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Options LOOP—END LOOP 831 Missing
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LOOP—END LOOP 833 • The program
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LOOP—END LOOP 835 time, resulting
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LOOP—END LOOP 837 • The loop is
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MANOVA: Overview MANOVA is availabl
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Overview Example 2 * Analysis of Co
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MANOVA: Univariate MANOVA is availa
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MANOVA: Univariate 845 in the model
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MANOVA: Univariate 847 WITHIN Terms
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MANOVA: Univariate 849 The specifie
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MANOVA: Univariate 851 it generally
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PARAMETERS Keyword MANOVA: Univaria
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ERROR Keyword MANOVA: Univariate 85
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RESIDUALS Subcommand MANOVA: Univar
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MANOVA: Univariate 859 CELLPLOTS Ce
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Split Files and Variable Order MANO
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DESIGN Subcommand MANOVA: Univariat
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MANOVA: Univariate 865 • The numb
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MANOVA: Univariate 867 with similar
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MANOVA: Multivariate MANOVA is avai
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Limitations MANOVA: Multivariate 87
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MANOVA: Multivariate 873 DEVIATION
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MANOVA: Multivariate 875 • You mu
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MANOVA: Multivariate 877 SINGLEDF S
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MANOVA: Multivariate 879 ROTATE Rot
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CONDITIONAL and UNCONDITIONAL Keywo
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MANOVA: Repeated Measures 883 descr
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MANOVA: Repeated Measures 885 • I
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Example MANOVA: Repeated Measures 8
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MANOVA: Repeated Measures 889 • A
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1 MAPS MAPS {/GVAR = VAR(varname)[V
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Operations 893 MAPS • Each MAPS c
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895 MAPS • The total (sum) of sal
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Example 897 MAPS standard deviation
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Example 899 MAPS MAPS /GVAR = VAR(f
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Example 901 MAPS MAPS /GVAR = VAR(c
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Number of Cases Less Than (N of Cas
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906 MATCH FILES Variable Names. You
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908 MATCH FILES • If the files ha
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910 MATCH FILES RENAME Subcommand R
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912 MATCH FILES • FIRST creates a
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914 MATRIX—END MATRIX INV Inverse
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916 MATRIX—END MATRIX • A symme
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918 MATRIX—END MATRIX Example {11
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920 MATRIX—END MATRIX • All of
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922 MATRIX—END MATRIX ** &* &/ &*
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924 MATRIX—END MATRIX • The res
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926 MATRIX—END MATRIX COMPUTE Sta
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928 MATRIX—END MATRIX A = 111 111
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930 MATRIX—END MATRIX DET(M) Dete
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932 MATRIX—END MATRIX MOD(M,S) Re
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934 MATRIX—END MATRIX and the num
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936 MATRIX—END MATRIX • Constan
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938 MATRIX—END MATRIX • The fir
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940 MATRIX—END MATRIX • A logic
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942 MATRIX—END MATRIX • The mat
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944 MATRIX—END MATRIX FORMAT Spec
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946 MATRIX—END MATRIX HOLD Specif
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948 MATRIX—END MATRIX NAMES Speci
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950 MATRIX—END MATRIX • If more
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952 MATRIX—END MATRIX Valid keywo
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954 MATRIX—END MATRIX • The mat
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956 MATRIX—END MATRIX FNAMES Spec
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MATRIX DATA MATRIX DATA VARIABLES=v
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960 MATRIX DATA Table 1 summarizes
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962 MATRIX DATA Example • No othe
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964 MATRIX DATA Example * Matrix da
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966 MATRIX DATA • ROWTYPE_ is spe
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968 MATRIX DATA FULL do not enter t
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970 MATRIX DATA • The split varia
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972 MATRIX DATA Example MATRIX DATA
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974 MATRIX DATA Within-Cells Record
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976 MATRIX DATA • Because ROWTYPE
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978 MCONVERT • If there are multi
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MEANS Overview Options MEANS [TABLE
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982 MEANS • The first table list
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984 MEANS LINEARITY Test of lineari
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986 MISSING VALUES • For date for
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MIXED MIXED is available in the Adv
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990 MIXED Models The following are
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992 MIXED Basic Specification • T
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994 MIXED ARMA1 Autoregressive movi
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996 MIXED Example matrix: σ2 1 σ2
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998 MIXED Variable List The variabl
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1000 MIXED WITH (option) Covariate
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1002 MIXED • The following option
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1004 MIXED Otherwise, it will be ig
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1006 MIXED REGWGT Subcommand The RE
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1008 MIXED Example MIXED SCORE BY S
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1010 MIXED Interpretation of Random
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1012 MIXED
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1014 Syntax Reference Examples MODE
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1016 MRSETS These variables are cod
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1018 MRSETS DISPLAY Subcommand /DIS
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1020 MULT RESPONSE Options did not
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1022 MULT RESPONSE • The group na
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1024 MULT RESPONSE TABLES Subcomman
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1026 MULT RESPONSE ROW Row percenta
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1028 MULT RESPONSE The following ke
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1030 MVA Overview Options [/REGRESS
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1032 MVA Symbols The symbols displa
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1034 MVA Examples MVA VARIABLES=pop
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1036 MVA PERCENT=n Omit rows for va
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1038 MVA TPATTERN Subcommand The TP
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1040 MVA CONVERGENCE=value Converge
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1042 MVA ADDTYPE Type of distributi
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NLR NLR and CNLR are available in t
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1046 NLR Basic Specification The ba
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1048 NLR • The two COMPUTE statem
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1050 NLR Example MODEL PROGRAM A=1,
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1052 NLR • Some new parameters ma
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1054 NLR Example MODEL PROGRAM A=.5
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1056 NLR sponding to a more accurat
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1058 NLR Nonlinear Constraints Nonl
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1060 NLR • CNLR generates the boo
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1062 N OF CASES Example GET FILE=CI
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1064 NOMREG Overview Options NOMREG
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1066 NOMREG mates convergence crite
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1068 NOMREG B are factors. The expr
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1070 NOMREG • The initial model c
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1072 NOMREG • Only one OUTFILE su
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1074 NOMREG SCALE Subcommand The SC
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NONPAR CORR Overview Options NONPAR
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1078 NONPAR CORR Example NONPAR COR
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1080 NONPAR CORR Format of the Matr
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NPAR TESTS NPAR TESTS [CHISQUARE=va
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1084 NPAR TESTS Options Statistical
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1086 NPAR TESTS • If more than 0.
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1088 NPAR TESTS Operations Example
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1090 NPAR TESTS Example • A test
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1094 NPAR TESTS MEDIAN Subcommand S
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1096 NPAR TESTS MOSES Subcommand Sy
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1100 NPAR TESTS Example • Under t
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NUMERIC NUMERIC varlist[(format)] [
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OLAP CUBES OLAP CUBES {varlist} BY
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1106 OLAP CUBES • A Layered Repor
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1108 OLAP CUBES The percentage diff
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OMS Note: Square brackets used in t
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1112 OMS bles from the FREQUENCIES
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1114 OMS Example OMS /SELECT TABLES
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1116 OMS be followed by an equals s
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1118 OMS SVWSOXML XML used by Smart
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1120 OMS • The first OMS command
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1122 OMS keyword must be followed b
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1124 OMS Figure 4 Single two-dimens
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1126 OMS Figure 6 Multiple Tables w
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1128 OMS Figure 8 Tables with diffe
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1130 OMS tics in the columns. To in
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1132 OMS Figure 11 Variable names i
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1134 OMS Figure 13 OXML for simple
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1136 OMS Command and Subtype Identi
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1138 OMS Table 2 Command Command Id
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OMSINFO OMSINFO. Example OMSINFO. O
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13 1156 OMSEND OMSEND Note: Square
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OMSLOG OMSLOG FILE = ’filespec’
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ONEWAY Overview Options ONEWAY varl
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1162 ONEWAY Analysis List The analy
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1164 ONEWAY groups. In addition, ho
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1166 ONEWAY • Up to 10 RANGE subc
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1168 ONEWAY • The dependent varia
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ORTHOPLAN Overview Options ORTHOPLA
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1172 ORTHOPLAN Example • The FACT
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1174 ORTHOPLAN • MINIMUM is follo
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1176 Syntax Reference Basic Specifi
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1178 Syntax Reference SETS Subcomma
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1180 Syntax Reference CONVERGENCE S
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1182 Syntax Reference SAVE Subcomma
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PACF Overview Options PACF [VARIABL
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PACF 1187 • DIFF differences the
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PACF 1189 • If SEASONAL is specif
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PARTIAL CORR Overview Options PARTI
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VARIABLES Subcommand PARTIAL CORR 1
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FORMAT Subcommand FORMAT determines
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Format of the Matrix Data File Spli
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PARTIAL CORR 1199 • REGRESSION co
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PERMISSIONS PERMISSIONS FILE=’fil
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PLANCARDS Overview Options PLANCARD
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Example PLANCARDS 1205 DATA LIST FR
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Figure 2 Single-profile format Prof
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Profile # 1 Circle the number in th
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1212 PLUM Basic Specification The b
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1214 PLUM value. The criterion is n
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1216 PLUM CELLINFO Cell Information
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1218 PLUM • Covariates can be con
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POINT POINT KEY=varname [FILE=file]
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1222 POINT Example • DATA LIST re
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PPLOT PPLOT [VARIABLES=] varlist [/
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1226 PPLOT Operations • Subcomman
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1228 PPLOT FRACTION Subcommand FRAC
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1230 PPLOT • In low resolution, a
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1232 PPLOT • If a natural log tra
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PREDICT PREDICT [{start date }] [TH
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1236 PREDICT • If there is a gap
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PRESERVE PRESERVE Overview PRESERVE
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1240 Syntax Reference Basic Specifi
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1242 Syntax Reference Example DATA
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1244 Syntax Reference CONVERGENCE S
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1248 Syntax Reference VARNAME_ Stri
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1250 PRINT Syntax Rules • A slash
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1252 PRINT Strings You can specify
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1254 PRINT Example PRINT OUTFILE=PR
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1256 PRINT EJECT Basic Specificatio
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PRINT FORMATS PRINT FORMATS varlist
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1260 PRINT FORMATS Example COMPUTE
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1262 PRINT SPACE Example PRINT / NA
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PROBIT Overview Options PROBIT is a
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1266 PROBIT • If individual cases
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1268 PROBIT • Keywords BY and WIT
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1270 PROBIT mately six significant
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1272 PROBIT RMP Relative median pot
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1274 PROCEDURE OUTPUT Example PROCE
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1276 PROXIMITIES Example Overview O
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1278 PROXIMITIES RANGE Standardize
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1280 PROXIMITIES CORRELATION( xy ,
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1282 PROXIMITIES 0, 1, 1, 0, 0 for
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1284 PROXIMITIES K1[(p[,np])] Kulcz
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1286 PROXIMITIES SS5[(p[,np])] Soka
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1288 PROXIMITIES • When used with
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1290 PROXIMITIES analysis. If a var
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1292 PROXIMITIES Example Example GE
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1294 PROXIMITIES
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1296 Syntax Reference Overview Opti
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1302 Syntax Reference TORGERSON Tor
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1304 Syntax Reference SPLINE Monoto
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1306 Syntax Reference number of cas
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1308 Syntax Reference DIFFSTRESS Co
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1310 Syntax Reference PLOT Subcomma
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1312 Syntax Reference cases in the
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1314 QUICK CLUSTER distances betwee
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1316 QUICK CLUSTER is specified, QU
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1318 QUICK CLUSTER NONE No addition
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RANK RANK [VARIABLES=] varlist [({A
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1322 RANK • String variables cann
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1324 RANK TIES Subcommand TIES dete
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RATIO STATISTICS RATIO STATISTICS n
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1328 RATIO STATISTICS • Keywords
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1330 RATIO STATISTICS MEAN Mean. Th
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1332 Syntax Reference Syntax Rules
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RECODE For numeric variables: RECOD
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1336 RECODE • SYSMIS specifies th
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1338 RECODE • Values for V1 throu
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RECORD TYPE For mixed file types: R
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1342 RECORD TYPE Example * Reading
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1344 RECORD TYPE Example * A mixed
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1346 RECORD TYPE Example * Specifyi
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1348 RECORD TYPE • SPREAD can be
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1350 REFORMAT Example * Convert an
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1352 REGRESSION Overview Options RE
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1354 REGRESSION • The DEPENDENT s
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1356 REGRESSION • When more than
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1358 REGRESSION Equation Statistics
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1360 REGRESSION PIN[(value)] Probab
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1362 REGRESSION • COMPUTE creates
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1364 REGRESSION • If SELECT is sp
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1366 REGRESSION MISSING Subcommand
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1368 REGRESSION: Residuals (RESIDUA
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1370 REGRESSION: Residuals • REGR
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1372 REGRESSION: Residuals are RESI
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1374 REGRESSION: Residuals Example
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RELIABILITY Overview RELIABILITY VA
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1378 RELIABILITY • This example a
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1380 RELIABILITY ICC Subcommand ICC
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1382 RELIABILITY Matrix Output Matr
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1384 RELIABILITY Example Example Ex
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RENAME VARIABLES RENAME VARIABLES {
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REPEATING DATA REPEATING DATA [FILE
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1390 REPEATING DATA • The DATA LI
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1392 REPEATING DATA Example Figure
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1394 REPEATING DATA STARTS Subcomma
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1396 REPEATING DATA • In the data
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1398 REPEATING DATA CONTINUED Subco
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1400 REPEATING DATA in positions 1-
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REPORT REPORT [/FORMAT=[{MANUAL }]
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1404 REPORT Column Widths. Default
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1406 REPORT statistics. The maximum
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1408 REPORT Example • The maximum
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1410 REPORT headings with the first
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1412 REPORT Figure 1 Page layout fo
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1414 REPORT Example REPORT FORMAT=A
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1416 REPORT Column Format The follo
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1418 REPORT BREAK Subcommand BREAK
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1420 REPORT (OFFSET) Position of th
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1422 REPORT • To use the summary
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1424 REPORT means of all three vari
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1426 REPORT • The summary title m
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1428 REPORT Table 3 Default print f
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1430 REPORT • The right-justified
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1432 REREAD Subcommand Order Subcom
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1434 REREAD FILE Subcommand FILE sp
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1436 REREAD Example * Reading both
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RMV RMV new variables={LINT (varlis
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1440 RMV • NEWVAR1 will have the
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ROC Overview Options ROC varlist BY
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1444 ROC MISSING Subcommand Cases w
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SAMPLE SAMPLE {decimal value} {n FR
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SAVE SAVE OUTFILE=’filespec’ [/
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1450 SAVE Example GET FILE=HUBEMPL.
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1452 SAVE Example GET FILE=PRSNL. C
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1454 SAVE PERMISSIONS Subcommand Th
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1456 Syntax Reference SAVE MODEL Ov
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1458 Syntax Reference TYPE Subcomma
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1460 SAVE TRANSLATE ** Required for
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1462 SAVE TRANSLATE • User-missin
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1464 SAVE TRANSLATE The following t
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1466 SAVE TRANSLATE WK1 1-2-3 Relea
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1468 SAVE TRANSLATE CELLS Subcomman
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1470 SAVE TRANSLATE Example SAVE TR
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1472 SCRIPT
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1474 Syntax Reference Subcommand Or
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1476 Syntax Reference PERIOD Subcom
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SELECT IF SELECT IF [(]logical expr
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1480 SELECT IF Missing Values • I
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1482 SELECT IF Example Example Exam
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1484 SET [HEADER={NO** }] {YES } {B
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1486 SET Operations Example • Set
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1488 SET LABELS Displays variable l
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1490 SET JOURNAL Subcommand Alias t
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1492 SET SPSSB in the server versio
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1494 SET • You can specify any le
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1496 SET Example SET SMALL = 0. SET
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SHOW SHOW [ALL] [BLANKS] [BLKSIZE]
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1500 SHOW EPOCH Range of years for
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1502 SHOW WEIGHT Variable used to w
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1504 SORT CASES addition, special c
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1506 SPCHART Overview Options SPCHA
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1508 SPCHART • The default span (
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1510 SPCHART Figure 3 S chart Data
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1512 SPCHART Figure 4 Individuals c
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1514 SPCHART Figure 7 NP chart Data
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1516 SPCHART based on the Poisson d
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1518 SPCHART • A duplicated subco
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1520 SPCHART CAPSIGMA Subcommand Th
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1522 SPCHART fixed control limits a
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1524 Syntax Reference SPECTRA Overv
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1526 Syntax Reference Syntax Rules
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1528 Syntax Reference TUKEY Tukey-H
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1532 Syntax Reference References
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1534 SPLIT FILE • AGGREGATE ignor
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STRING STRING varlist (An) [/varlis
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SUBTITLE SUBTITLE [’]text[’] Ex
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SUMMARIZE SUMMARIZE [TABLES=]{varli
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1542 SUMMARIZE Example the FORMAT s
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1544 SUMMARIZE GEOMETRIC Geometric
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SURVIVAL Overview SURVIVAL is avail
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1548 SURVIVAL Operations • SURVIV
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1550 SURVIVAL • INTERVALS produce
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1552 SURVIVAL HAZARD Plot the hazar
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1554 SURVIVAL large samples. It is
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1556 SURVIVAL WRITE Subcommand Form
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1558 SURVIVAL • Record type 31 is
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1560 SYSFILE INFO
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1562 Syntax Reference Operations Ex
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1564 TEMPORARY Example • The XSAV
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TITLE TITLE [’]text[’] Example
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TSET TSET [PRINT={DEFAULT**}] [/NEW
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1570 TSET MXPREDICT Subcommand MXPR
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TSPLOT TSPLOT [VARIABLES=] variable
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1574 TSPLOT Subcommand Order • Su
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1576 TSPLOT Example TSPLOT TICKETS
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1578 TSPLOT Figure 3 FORMAT=REFEREN
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1580 TSPLOT UNIFORM Scale uniformly
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1582 T-TEST degrees of freedom, and
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1584 T-TEST PAIRS Subcommand PAIRS
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2SLS Overview Options 2SLS is avail
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1588 2SLS • In this example, Y1 i
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1590 2SLS • In this example, the
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TWOSTEP CLUSTER Overview TWOSTEP CL
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1594 TWOSTEP CLUSTER Example TWOSTE
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1596 TWOSTEP CLUSTER Example TWOSTE
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1598 TWOSTEP CLUSTER STATE Save the
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1600 TWOSTEP CLUSTER SUMMARY Descri
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1602 UNIANOVA Overview Options ** D
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1604 UNIANOVA Example UNIANOVA YIEL
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1606 UNIANOVA appropriate effect is
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1608 UNIANOVA PRINT Subcommand PRIN
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1610 UNIANOVA Example UNIANOVA DEP
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1612 UNIANOVA • B1 versus B2 at A
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1614 UNIANOVA HELMERT Helmert contr
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1616 UNIANOVA Post Hoc Tests Statis
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1618 UNIANOVA QREGW Ryan-Einot-Gabr
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1620 UNIANOVA SRESID Studentized re
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UPDATE UPDATE FILE={master file} {*
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1624 UPDATE • UPDATE copies all v
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1626 UPDATE BY Subcommand BY specif
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1628 UPDATE • IN has only one spe
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1630 USE • Values on keyword YEAR
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1632 USE USE THRU 5. • This comma
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1634 VALUE LABELS Operations Exampl
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VARCOMP Overview Options VARCOMP is
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1638 VARCOMP • You can specify mu
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1640 VARCOMP The default is 1.0E-8.
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1642 VARCOMP Nesting within an inte
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VARIABLE LABELS VARIABLE LABELS var
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VARIABLE LEVEL VARIABLE LEVEL varli
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VARSTOCASES VARSTOCASES /MAKE new v
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1650 VARSTOCASES The command: VARST
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1652 VARSTOCASES Example VARSTOCASE
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1654 VARSTOCASES COUNT Subcommand W
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1656 VECTOR Syntax Rules • Multip
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1658 VECTOR Example VECTOR X,Y(A5,6
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1660 VECTOR • Aggregating by vari
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1662 VERIFY Operations • VERIFY r
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1664 WEIGHT Example • Each WEIGHT
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1666 WLS without erasing values sav
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1668 WLS DELTA Subcommand DELTA, al
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1670 WLS Example WLS X1 WITH Y1 /SO
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1672 WRITE Basic Specification The
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1674 WRITE Strings • MOHIRED, YRH
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WRITE FORMATS WRITE FORMATS varlist
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1678 WRITE FORMATS Example COMPUTE
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1680 XSAVE Basic Specification The
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1682 XSAVE • Keyword ALL on KEEP
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1684 XSAVE • The only specificati
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Appendix A Commands and Program Sta
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Determining Command Order Commands
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Table A.1 Commands and program stat
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Input Program Commands Commands and
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Appendix B IMPORT/EXPORT Character
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Position Graphic Macintosh Microsof
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Position Graphic Macintosh Microsof
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Position Graphic Macintosh 222 â 1
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1704 Appendix C Figure C.1 File REC
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1706 Appendix C Figure C.4 NESTED.D
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1708 Appendix C • The output from
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1710 Appendix C FILE TYPE GROUPED R
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1712 Appendix C Figure C.8 LIST out
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1714 Appendix C • REPEATING DATA
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1716 Appendix C The loop terminates
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1718 Appendix D Example 1: Automati
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1720 Appendix D • Commands betwee
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1722 Appendix D Figure D.7 Listing
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1724 Appendix D You can modify the
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1726 Appendix D • !TOKENS(1) spec
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1728 Appendix D Figure D.15 Output
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1730 Appendix D Figure D.19 !DATAGE
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Appendix A Trends Options: Special
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Trends Options: Special Considerati
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Trends Options: Special Considerati
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Subject Index active data file cach
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inary shape difference in Distances
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in Logistic Regression procedure, 8
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in Multidimensional Scaling procedu
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scratch files, 1492 data dictionary
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matrices, 479 matrix input, 482 mat
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in Hierarchical Loglinear Analysis
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cell weights, 610, 821 contrasts, 8
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hierarchical loglinear analysis, 71
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in Autoregression procedure, 148- 1
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See also Linear Regression procedur
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linear transformations, 871 naming
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date format variables, 986 defining
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function precision, 1056 infinite s
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in Cox Regression procedure, 302 in
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output, 1489, 1494 output page size
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elative risk ratio in Crosstabs pro
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scientific notation controlling dis
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in ROC Curve procedure, 1445 standa
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fixed format, 414, 415, 416, 423 fr
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date format variables, 1633 in Homo
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Syntax Index !BLANKS (function) DEF
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AGGREGATE (command), 90 BREAK subco
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AR (subcommand) ARIMA command, 158
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REPORT command, 1417 BLANKS (subcom
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CASENUM$ (system variable) in PRINT
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REGRESSION command, 1360 CIN (subco
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SHOW command, 1499 COMPUTE (command
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TWOSTEP CLUSTER command, 1600 COUNT
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WRITE subcommand, 325 CROSSVALID (k
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Symbols D (keyword) CLUSTER command
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NPAR TESTS command, 1100 ONEWAY com
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DISPER (keyword) CLUSTER command, 2
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MIXED command, 999 UNIANOVA command
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EXPERIMENTAL (keyword) ANOVA comman
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MEANS command, 983 PROXSCAL command
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GAC (keyword) OLAP CUBES command, 1
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TEST(SSCP) keyword, 674 TUKEY keywo
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HISTORY (keyword) CATPCA command, 2
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INCLUDE (command), 771 FILE subcomm
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JITTER (keyword) IGRAPH command, 75
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LEAVE (command), 796 LEFT (keyword)
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CWEIGHT subcommand, 821 DESIGN subc
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ROVMAP subcommand, 896 SHOWLABEL su
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RMV command, 1440 MEAN (keyword) AN
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MANOVA command, 858 MEANS command,
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CATREG command, 212 MULTIPUNCH (key
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NMISS (function) AGGREGATE command,
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NOREFERENCE (keyword) TSPLOT comman
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CATREG command, 215 ODBC (keyword)
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RATIO STATISTICS command, 1328 REGR
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PER (keyword) SPECTRA command, 1530
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SUMMARY keyword, 1216 TEST subcomma
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CATREG command, 215 CLUSTER command
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QUANT (keyword) CATPCA command, 200
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with FORMATS command, 1349 REG (key
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CSTABULATE command, 374 CURVEFIT co
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SAMPLEFILE (subcommand) CSSELECT co
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SEED (keyword) CSSELECT command, 34
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MXLOOPS subcommand, 1500 MXMEMORY s
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SP (subcommand) ARIMA command, 156-
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DISCRIMINANT command, 479 FREQUENCI
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Symbols T (function) MATRIX command
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TOLERANCE (subcommand) DISCRIMINANT
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UNCLASSIFIED (keyword) DISCRIMINANT
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REPORT command, 1414, 1418 value la
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VIN (subcommand) DISCRIMINANT comma
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X1MULTIPLIER (keyword) IGRAPH comma
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