SPSS® 12.0 Command Syntax Reference

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160 Syntax Reference • APPLY with the keyword FIT sets MXITER to 0. If you apply a model that used FIT and want to obtain estimates, you will need to respecify MXITER. The keywords available for APPLY with ARIMA are: SPECIFICATIONS Use only the specifications from the original model. ARIMA should create the initial values. This is the default. INITIAL Use the original model’s final estimates as initial values for estimation. FIT No estimation. Estimates from the original model should be applied directly. Example ARIMA VAR1 /MODEL=(0,1,1)(0,1,1) 12 LOG NOCONSTANT. ARIMA APPLY /MODEL=CONSTANT. ARIMA VAR2 /APPLY INITIAL. ARIMA VAR2 /APPLY FIT. • The first command specifies a model with one degree of differencing, one moving-average term, one degree of seasonal differencing, and one seasonal moving-average term. The length of the period is 12. A base 10 log of the series is taken before estimation and no constant is estimated. This model is assigned the name MOD_1. • The second command applies the same model to the same series, but this time estimates a constant term. Everything else stays the same. This model is assigned the name MOD_2. • The third command uses the same model as the previous command (MOD_2) but applies it to series VAR2. Keyword INITIAL specifies that the final estimates of MOD_2 are to be used as the initial values for estimation. • The last command uses the same model but this time specifies no estimation. Instead, the values from the previous model are applied directly. FORECAST Subcommand The FORECAST subcommand specifies the forecasting method to use. Available methods are: EXACT Unconditional least squares. The forecasts are unconditional least squares forecasts. They are also called finite memory forecasts. This is the default. CLS Conditional least squares using model constraint for initialization. The forecasts are computed by assuming that the unobserved past errors are zero and the unobserved past values of the response series are equal to the mean. AUTOINIT Conditional least squares using the beginning series values for initialization. The beginning series values are used to initialize the recursive conditional least squares forecasting algorithm.
References ARIMA 161 Akaike, H. 1974. A new look at the statistical model identification. IEEE Transaction on Automatic Control AC-19: 716–723. Box, G. E., and G. C. Tiao. 1975. Intervention analysis with applications to economic and environmental problems. Journal of the American Statistical Association 70: 70–79. Cryer, J. D. 1986. Time series analysis. Boston: Duxbury Press. Harvey, A. C. 1981. The econometric analysis of time series. Oxford: Philip Allan. Harvey, A. C. 1981. Time series models. Oxford: Philip Allan. Kohn, R., and C. Ansley. 1985. Regression algorithm. Biometrika 81: 751–761. Kohn, R., and C. Ansley. 1986. Estimation, prediction, and interpolation for ARIMA models with missing data. Journal of the American Statistical Association 81: 751–761. McCleary, R., and R. A. Hay. 1980. Applied time series analysis for the social sciences. Beverly Hills, Calif.: Sage Publications. Melard, G. 1984. A fast algorithm for the exact likelihood of autoregressive-moving average models. Applied Statistics 33(1): 104–119. Schwartz, G. 1978. Estimating the dimensions of a model. Annals of Statistics 6: 461–464.
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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
Page 110 and 111: 110 ALSCAL • Algorithmic options
Page 112 and 113: 112 ALSCAL Table 2 Types of configu
Page 114 and 115: Matrix mode Object by object 114 AL
Page 116 and 117: Matrix mode Object by object Object
Page 118 and 119: 118 Syntax Reference Basic Specific
Page 120 and 121: 120 Syntax Reference Example DATA L
Page 122 and 123: 122 Syntax Reference PLOT Subcomman
Page 124 and 125: 124 Syntax Reference VARNAME_ Strin
Page 127 and 128: ANOVA ANOVA [VARIABLES=] varlist BY
Page 129 and 130: Example Example ANOVA 129 ANOVA VAR
Page 131 and 132: ANOVA 131 Some restrictions apply t
Page 133 and 134: Table 2 Combinations of COVARIATES
Page 135 and 136: Multiple Classification Analysis AN
Page 137 and 138: APPLY DICTIONARY 137 • The applie
Page 139 and 140: APPLY DICTIONARY 139 target file as
Page 141 and 142: APPLY DICTIONARY 141 MRSETS = MERGE
Page 143 and 144: APPLY DICTIONARY 143
Page 145 and 146: Options Basic Specification Subcomm
Page 147 and 148: • Only one method can be specifie
Page 149 and 150: APPLY Subcommand AREG 149 • Itera
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Page 153 and 154: Options Basic Specification ARIMA 1
Page 155 and 156: Limitations Example ARIMA 155 • F
Page 157 and 158: D Order of differencing. Q Moving-a
Page 159: Termination Criteria Subcommands CI
Page 163 and 164: Subcommand Order • VARIABLES must
Page 165 and 166: Example AUTORECODE 165 DATA LIST /
Page 167 and 168: BEGIN DATA—END DATA BEGIN DATA da
Page 169 and 170: BREAK BREAK Overview BREAK controls
Page 171 and 172: CASEPLOT Overview CASEPLOT [VARIABL
Page 173 and 174: Subcommand Order • Subcommands ca
Page 175 and 176: LN and NOLOG Subcommands CASEPLOT 1
Page 177 and 178: CASEPLOT 177 erence line. Figure 3
Page 179 and 180: CASEPLOT 179 • If FORMAT=REFERENC
Page 181 and 182: Options CASESTOVARS 181 Automatic c
Page 183 and 184: The commands: CASESTOVARS 183 SPLIT
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Page 187 and 188: SEPARATOR Subcommand CASESTOVARS 18
Page 189 and 190: CATPCA CATPCA is available in the C
Page 191 and 192: Optional output. You can request op
Page 193 and 194: CATPCA 193 • The NORMALIZATION su
Page 195 and 196: SPORD and SPNOM Keywords The follow
Page 197 and 198: ACTIVE Keyword The ACTIVE keyword h
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Page 201 and 202: CATPCA 201 The keyword TO in a vari
Page 203 and 204: TRIPLOT(varlist(varlist))(n) CATPCA
Page 205 and 206: CATPCA 205 procedures with the succ
Page 207 and 208: CATREG Overview CATREG is available
Page 209 and 210: 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
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Example Example Example DATA LIST F
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COMMENT {COMMENT} text { * } Overvi
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Cumulative distribution functions (
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Cumulative distribution functions (
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Logical functions: RANGE(varname,ra
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String Transformations COMPUTE 253
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Examples COMPUTE 255 • The string
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COMPUTE 257 • Alternatively, you
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Equivalence COMPUTE 259 STRING DEPT
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CONJOINT Overview CONJOINT is avail
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CONJOINT 263 the experimental profi
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CONJOINT 265 • The ADD FILES comm
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CONJOINT 267 subject is asked to as
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CONJOINT 269 • New value labels a
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PLOT Subcommand CONJOINT 271 The PL
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Basic Specification CORRELATIONS 27
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MISSING Subcommand CORRELATIONS 275
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Example CORRELATIONS 277 • CORREL
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CORRESPONDENCE CORRESPONDENCE is av
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Syntax Rules Operations Limitations
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DIMENSION Subcommand CORRESPONDENCE
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CORRESPONDENCE 285 EUCLID Euclidean
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PLOT Subcommand NONE No output othe
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CORRESPONDENCE 289 DIM1...DIMn Nume
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COUNT COUNT varname=varlist(value l
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COXREG COXREG is available in the A
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Basic Specification COXREG 295 •
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VARIABLES Subcommand COXREG 297 VAR
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COXREG 299 • If the categorical v
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COXREG 301 BSTEP Backward stepwise.
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CRITERIA Subcommand COXREG 303 CRIT
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COXREG 305 TABLE Write the survival
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CREATE CREATE new series={CSUM (ser
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Example CREATE NEWVAR1 = DIFF(OLDVA
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LAG Function CREATE 311 LAG creates
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Example CREATE TICKMA = MA(TICKETS,
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T4253H Function CREATE 315 T4253H p
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Overview Options CROSSTABS 317 CROS
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Example CROSSTABS 319 • The varia
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CROSSTABS 321 • VARIABLES names t
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CROSSTABS 323 CORR Display Pearson
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FORMAT Subcommand CROSSTABS 325 By
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CROSSTABS 327 • The split-file gr
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CROSSTABS 329
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Operations CSDESCRIPTIVES 331 • C
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CSDESCRIPTIVES 333 • Plan file an
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CSDESCRIPTIVES 335 • The set of s
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CSDESCRIPTIVES 337
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CSSELECT 339 inclusion probabilitie
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PLAN Subcommand CSSELECT 341 The PL
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Example CSPLAN /PLAN OUTFILE=’c:\
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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
Page 807 and 808:
LOGISTIC REGRESSION 807 DEVIATION(r
Page 809 and 810:
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
Page 819 and 820:
Example Example Example Example LOG
Page 821 and 822:
LOGLINEAR 821 • To enter cell cov
Page 823 and 824:
CONTRAST Subcommand LOGLINEAR 823 C
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Example * Contrasts for a logistic
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Example LOGLINEAR RESPONSE(1,2) BY
Page 829 and 830:
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
Page 837 and 838:
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
Page 845 and 846:
MANOVA: Univariate 845 in the model
Page 847 and 848:
MANOVA: Univariate 847 WITHIN Terms
Page 849 and 850:
MANOVA: Univariate 849 The specifie
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MANOVA: Univariate 851 it generally
Page 853 and 854:
PARAMETERS Keyword MANOVA: Univaria
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ERROR Keyword MANOVA: Univariate 85
Page 857 and 858:
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
Page 873 and 874:
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
Page 903:
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
Page 912 and 913:
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
Page 918 and 919:
918 MATRIX—END MATRIX Example {11
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920 MATRIX—END MATRIX • All of
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922 MATRIX—END MATRIX ** &* &/ &*
Page 924 and 925:
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
Page 954 and 955:
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
Page 978 and 979:
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
Page 992 and 993:
992 MIXED Basic Specification • T
Page 994 and 995:
994 MIXED ARMA1 Autoregressive movi
Page 996 and 997:
996 MIXED Example matrix: σ2 1 σ2
Page 998 and 999:
998 MIXED Variable List The variabl
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1000 MIXED WITH (option) Covariate
Page 1002 and 1003:
1002 MIXED • The following option
Page 1004 and 1005:
1004 MIXED Otherwise, it will be ig
Page 1006 and 1007:
1006 MIXED REGWGT Subcommand The RE
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1008 MIXED Example MIXED SCORE BY S
Page 1010 and 1011:
1010 MIXED Interpretation of Random
Page 1012 and 1013:
1012 MIXED
Page 1014 and 1015:
1014 Syntax Reference Examples MODE
Page 1016 and 1017:
1016 MRSETS These variables are cod
Page 1018 and 1019:
1018 MRSETS DISPLAY Subcommand /DIS
Page 1020 and 1021:
1020 MULT RESPONSE Options did not
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1022 MULT RESPONSE • The group na
Page 1024 and 1025:
1024 MULT RESPONSE TABLES Subcomman
Page 1026 and 1027:
1026 MULT RESPONSE ROW Row percenta
Page 1028 and 1029:
1028 MULT RESPONSE The following ke
Page 1030 and 1031:
1030 MVA Overview Options [/REGRESS
Page 1032 and 1033:
1032 MVA Symbols The symbols displa
Page 1034 and 1035:
1034 MVA Examples MVA VARIABLES=pop
Page 1036 and 1037:
1036 MVA PERCENT=n Omit rows for va
Page 1038 and 1039:
1038 MVA TPATTERN Subcommand The TP
Page 1040 and 1041:
1040 MVA CONVERGENCE=value Converge
Page 1042 and 1043:
1042 MVA ADDTYPE Type of distributi
Page 1044 and 1045:
NLR NLR and CNLR are available in t
Page 1046 and 1047:
1046 NLR Basic Specification The ba
Page 1048 and 1049:
1048 NLR • The two COMPUTE statem
Page 1050 and 1051:
1050 NLR Example MODEL PROGRAM A=1,
Page 1052 and 1053:
1052 NLR • Some new parameters ma
Page 1054 and 1055:
1054 NLR Example MODEL PROGRAM A=.5
Page 1056 and 1057:
1056 NLR sponding to a more accurat
Page 1058 and 1059:
1058 NLR Nonlinear Constraints Nonl
Page 1060 and 1061:
1060 NLR • CNLR generates the boo
Page 1062 and 1063:
1062 N OF CASES Example GET FILE=CI
Page 1064 and 1065:
1064 NOMREG Overview Options NOMREG
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1066 NOMREG mates convergence crite
Page 1068 and 1069:
1068 NOMREG B are factors. The expr
Page 1070 and 1071:
1070 NOMREG • The initial model c
Page 1072 and 1073:
1072 NOMREG • Only one OUTFILE su
Page 1074 and 1075:
1074 NOMREG SCALE Subcommand The SC
Page 1076 and 1077:
NONPAR CORR Overview Options NONPAR
Page 1078 and 1079:
1078 NONPAR CORR Example NONPAR COR
Page 1080 and 1081:
1080 NONPAR CORR Format of the Matr
Page 1082 and 1083:
NPAR TESTS NPAR TESTS [CHISQUARE=va
Page 1084 and 1085:
1084 NPAR TESTS Options Statistical
Page 1086 and 1087:
1086 NPAR TESTS • If more than 0.
Page 1088 and 1089:
1088 NPAR TESTS Operations Example
Page 1090 and 1091:
1090 NPAR TESTS Example • A test
Page 1092 and 1093:
Page 1094 and 1095:
1094 NPAR TESTS MEDIAN Subcommand S
Page 1096 and 1097:
1096 NPAR TESTS MOSES Subcommand Sy
Page 1098 and 1099:
Page 1100 and 1101:
1100 NPAR TESTS Example • Under t
Page 1102 and 1103:
NUMERIC NUMERIC varlist[(format)] [
Page 1104 and 1105:
OLAP CUBES OLAP CUBES {varlist} BY
Page 1106 and 1107:
1106 OLAP CUBES • A Layered Repor
Page 1108 and 1109:
1108 OLAP CUBES The percentage diff
Page 1110 and 1111:
OMS Note: Square brackets used in t
Page 1112 and 1113:
1112 OMS bles from the FREQUENCIES
Page 1114 and 1115:
1114 OMS Example OMS /SELECT TABLES
Page 1116 and 1117:
1116 OMS be followed by an equals s
Page 1118 and 1119:
1118 OMS SVWSOXML XML used by Smart
Page 1120 and 1121:
1120 OMS • The first OMS command
Page 1122 and 1123:
1122 OMS keyword must be followed b
Page 1124 and 1125:
1124 OMS Figure 4 Single two-dimens
Page 1126 and 1127:
1126 OMS Figure 6 Multiple Tables w
Page 1128 and 1129:
1128 OMS Figure 8 Tables with diffe
Page 1130 and 1131:
1130 OMS tics in the columns. To in
Page 1132 and 1133:
1132 OMS Figure 11 Variable names i
Page 1134 and 1135:
1134 OMS Figure 13 OXML for simple
Page 1136 and 1137:
1136 OMS Command and Subtype Identi
Page 1138 and 1139:
1138 OMS Table 2 Command Command Id
Page 1140 and 1141:
Page 1142 and 1143:
Page 1144 and 1145:
Page 1146 and 1147:
Page 1148 and 1149:
Page 1150 and 1151:
Page 1152 and 1153:
Page 1154 and 1155:
OMSINFO OMSINFO. Example OMSINFO. O
Page 1156 and 1157:
13 1156 OMSEND OMSEND Note: Square
Page 1158 and 1159:
OMSLOG OMSLOG FILE = ’filespec’
Page 1160 and 1161:
ONEWAY Overview Options ONEWAY varl
Page 1162 and 1163:
1162 ONEWAY Analysis List The analy
Page 1164 and 1165:
1164 ONEWAY groups. In addition, ho
Page 1166 and 1167:
1166 ONEWAY • Up to 10 RANGE subc
Page 1168 and 1169:
1168 ONEWAY • The dependent varia
Page 1170 and 1171:
ORTHOPLAN Overview Options ORTHOPLA
Page 1172 and 1173:
1172 ORTHOPLAN Example • The FACT
Page 1174 and 1175:
1174 ORTHOPLAN • MINIMUM is follo
Page 1176 and 1177:
1176 Syntax Reference Basic Specifi
Page 1178 and 1179:
1178 Syntax Reference SETS Subcomma
Page 1180 and 1181:
1180 Syntax Reference CONVERGENCE S
Page 1182 and 1183:
1182 Syntax Reference SAVE Subcomma
Page 1185 and 1186:
PACF Overview Options PACF [VARIABL
Page 1187 and 1188:
PACF 1187 • DIFF differences the
Page 1189 and 1190:
PACF 1189 • If SEASONAL is specif
Page 1191 and 1192:
PARTIAL CORR Overview Options PARTI
Page 1193 and 1194:
VARIABLES Subcommand PARTIAL CORR 1
Page 1195 and 1196:
FORMAT Subcommand FORMAT determines
Page 1197 and 1198:
Format of the Matrix Data File Spli
Page 1199 and 1200:
PARTIAL CORR 1199 • REGRESSION co
Page 1201 and 1202:
PERMISSIONS PERMISSIONS FILE=’fil
Page 1203 and 1204:
PLANCARDS Overview Options PLANCARD
Page 1205 and 1206:
Example PLANCARDS 1205 DATA LIST FR
Page 1207 and 1208:
Figure 2 Single-profile format Prof
Page 1209:
Profile # 1 Circle the number in th
Page 1212 and 1213:
1212 PLUM Basic Specification The b
Page 1214 and 1215:
1214 PLUM value. The criterion is n
Page 1216 and 1217:
1216 PLUM CELLINFO Cell Information
Page 1218 and 1219:
1218 PLUM • Covariates can be con
Page 1220 and 1221:
POINT POINT KEY=varname [FILE=file]
Page 1222 and 1223:
1222 POINT Example • DATA LIST re
Page 1224 and 1225:
PPLOT PPLOT [VARIABLES=] varlist [/
Page 1226 and 1227:
1226 PPLOT Operations • Subcomman
Page 1228 and 1229:
1228 PPLOT FRACTION Subcommand FRAC
Page 1230 and 1231:
1230 PPLOT • In low resolution, a
Page 1232 and 1233:
1232 PPLOT • If a natural log tra
Page 1234 and 1235:
PREDICT PREDICT [{start date }] [TH
Page 1236 and 1237:
1236 PREDICT • If there is a gap
Page 1238 and 1239:
PRESERVE PRESERVE Overview PRESERVE
Page 1240 and 1241:
1240 Syntax Reference Basic Specifi
Page 1242 and 1243:
1242 Syntax Reference Example DATA
Page 1244 and 1245:
1244 Syntax Reference CONVERGENCE S
Page 1246 and 1247:
Page 1248 and 1249:
1248 Syntax Reference VARNAME_ Stri
Page 1250 and 1251:
1250 PRINT Syntax Rules • A slash
Page 1252 and 1253:
1252 PRINT Strings You can specify
Page 1254 and 1255:
1254 PRINT Example PRINT OUTFILE=PR
Page 1256 and 1257:
1256 PRINT EJECT Basic Specificatio
Page 1258 and 1259:
PRINT FORMATS PRINT FORMATS varlist
Page 1260 and 1261:
1260 PRINT FORMATS Example COMPUTE
Page 1262 and 1263:
1262 PRINT SPACE Example PRINT / NA
Page 1264 and 1265:
PROBIT Overview Options PROBIT is a
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1266 PROBIT • If individual cases
Page 1268 and 1269:
1268 PROBIT • Keywords BY and WIT
Page 1270 and 1271:
1270 PROBIT mately six significant
Page 1272 and 1273:
1272 PROBIT RMP Relative median pot
Page 1274 and 1275:
1274 PROCEDURE OUTPUT Example PROCE
Page 1276 and 1277:
1276 PROXIMITIES Example Overview O
Page 1278 and 1279:
1278 PROXIMITIES RANGE Standardize
Page 1280 and 1281:
1280 PROXIMITIES CORRELATION( xy ,
Page 1282 and 1283:
1282 PROXIMITIES 0, 1, 1, 0, 0 for
Page 1284 and 1285:
1284 PROXIMITIES K1[(p[,np])] Kulcz
Page 1286 and 1287:
1286 PROXIMITIES SS5[(p[,np])] Soka
Page 1288 and 1289:
1288 PROXIMITIES • When used with
Page 1290 and 1291:
1290 PROXIMITIES analysis. If a var
Page 1292 and 1293:
1292 PROXIMITIES Example Example GE
Page 1294 and 1295:
1294 PROXIMITIES
Page 1296 and 1297:
1296 Syntax Reference Overview Opti
Page 1298 and 1299:
Page 1300 and 1301:
Page 1302 and 1303:
1302 Syntax Reference TORGERSON Tor
Page 1304 and 1305:
1304 Syntax Reference SPLINE Monoto
Page 1306 and 1307:
1306 Syntax Reference number of cas
Page 1308 and 1309:
1308 Syntax Reference DIFFSTRESS Co
Page 1310 and 1311:
1310 Syntax Reference PLOT Subcomma
Page 1312 and 1313:
1312 Syntax Reference cases in the
Page 1314 and 1315:
1314 QUICK CLUSTER distances betwee
Page 1316 and 1317:
1316 QUICK CLUSTER is specified, QU
Page 1318 and 1319:
1318 QUICK CLUSTER NONE No addition
Page 1320 and 1321:
RANK RANK [VARIABLES=] varlist [({A
Page 1322 and 1323:
1322 RANK • String variables cann
Page 1324 and 1325:
1324 RANK TIES Subcommand TIES dete
Page 1326 and 1327:
RATIO STATISTICS RATIO STATISTICS n
Page 1328 and 1329:
1328 RATIO STATISTICS • Keywords
Page 1330 and 1331:
1330 RATIO STATISTICS MEAN Mean. Th
Page 1332 and 1333:
1332 Syntax Reference Syntax Rules
Page 1334 and 1335:
RECODE For numeric variables: RECOD
Page 1336 and 1337:
1336 RECODE • SYSMIS specifies th
Page 1338 and 1339:
1338 RECODE • Values for V1 throu
Page 1340 and 1341:
RECORD TYPE For mixed file types: R
Page 1342 and 1343:
1342 RECORD TYPE Example * Reading
Page 1344 and 1345:
1344 RECORD TYPE Example * A mixed
Page 1346 and 1347:
1346 RECORD TYPE Example * Specifyi
Page 1348 and 1349:
1348 RECORD TYPE • SPREAD can be
Page 1350 and 1351:
1350 REFORMAT Example * Convert an
Page 1352 and 1353:
1352 REGRESSION Overview Options RE
Page 1354 and 1355:
1354 REGRESSION • The DEPENDENT s
Page 1356 and 1357:
1356 REGRESSION • When more than
Page 1358 and 1359:
1358 REGRESSION Equation Statistics
Page 1360 and 1361:
1360 REGRESSION PIN[(value)] Probab
Page 1362 and 1363:
1362 REGRESSION • COMPUTE creates
Page 1364 and 1365:
1364 REGRESSION • If SELECT is sp
Page 1366 and 1367:
1366 REGRESSION MISSING Subcommand
Page 1368 and 1369:
1368 REGRESSION: Residuals (RESIDUA
Page 1370 and 1371:
1370 REGRESSION: Residuals • REGR
Page 1372 and 1373:
1372 REGRESSION: Residuals are RESI
Page 1374 and 1375:
1374 REGRESSION: Residuals Example
Page 1376 and 1377:
RELIABILITY Overview RELIABILITY VA
Page 1378 and 1379:
1378 RELIABILITY • This example a
Page 1380 and 1381:
1380 RELIABILITY ICC Subcommand ICC
Page 1382 and 1383:
1382 RELIABILITY Matrix Output Matr
Page 1384 and 1385:
1384 RELIABILITY Example Example Ex
Page 1386 and 1387:
RENAME VARIABLES RENAME VARIABLES {
Page 1388 and 1389:
REPEATING DATA REPEATING DATA [FILE
Page 1390 and 1391:
1390 REPEATING DATA • The DATA LI
Page 1392 and 1393:
1392 REPEATING DATA Example Figure
Page 1394 and 1395:
1394 REPEATING DATA STARTS Subcomma
Page 1396 and 1397:
1396 REPEATING DATA • In the data
Page 1398 and 1399:
1398 REPEATING DATA CONTINUED Subco
Page 1400 and 1401:
1400 REPEATING DATA in positions 1-
Page 1402 and 1403:
REPORT REPORT [/FORMAT=[{MANUAL }]
Page 1404 and 1405:
1404 REPORT Column Widths. Default
Page 1406 and 1407:
1406 REPORT statistics. The maximum
Page 1408 and 1409:
1408 REPORT Example • The maximum
Page 1410 and 1411:
1410 REPORT headings with the first
Page 1412 and 1413:
1412 REPORT Figure 1 Page layout fo
Page 1414 and 1415:
1414 REPORT Example REPORT FORMAT=A
Page 1416 and 1417:
1416 REPORT Column Format The follo
Page 1418 and 1419:
1418 REPORT BREAK Subcommand BREAK
Page 1420 and 1421:
1420 REPORT (OFFSET) Position of th
Page 1422 and 1423:
1422 REPORT • To use the summary
Page 1424 and 1425:
1424 REPORT means of all three vari
Page 1426 and 1427:
1426 REPORT • The summary title m
Page 1428 and 1429:
1428 REPORT Table 3 Default print f
Page 1430 and 1431:
1430 REPORT • The right-justified
Page 1432 and 1433:
1432 REREAD Subcommand Order Subcom
Page 1434 and 1435:
1434 REREAD FILE Subcommand FILE sp
Page 1436 and 1437:
1436 REREAD Example * Reading both
Page 1438 and 1439:
RMV RMV new variables={LINT (varlis
Page 1440 and 1441:
1440 RMV • NEWVAR1 will have the
Page 1442 and 1443:
ROC Overview Options ROC varlist BY
Page 1444 and 1445:
1444 ROC MISSING Subcommand Cases w
Page 1446 and 1447:
SAMPLE SAMPLE {decimal value} {n FR
Page 1448 and 1449:
SAVE SAVE OUTFILE=’filespec’ [/
Page 1450 and 1451:
1450 SAVE Example GET FILE=HUBEMPL.
Page 1452 and 1453:
1452 SAVE Example GET FILE=PRSNL. C
Page 1454 and 1455:
1454 SAVE PERMISSIONS Subcommand Th
Page 1456 and 1457:
1456 Syntax Reference SAVE MODEL Ov
Page 1458 and 1459:
1458 Syntax Reference TYPE Subcomma
Page 1460 and 1461:
1460 SAVE TRANSLATE ** Required for
Page 1462 and 1463:
1462 SAVE TRANSLATE • User-missin
Page 1464 and 1465:
1464 SAVE TRANSLATE The following t
Page 1466 and 1467:
1466 SAVE TRANSLATE WK1 1-2-3 Relea
Page 1468 and 1469:
1468 SAVE TRANSLATE CELLS Subcomman
Page 1470 and 1471:
1470 SAVE TRANSLATE Example SAVE TR
Page 1472 and 1473:
1472 SCRIPT
Page 1474 and 1475:
1474 Syntax Reference Subcommand Or
Page 1476 and 1477:
1476 Syntax Reference PERIOD Subcom
Page 1478 and 1479:
SELECT IF SELECT IF [(]logical expr
Page 1480 and 1481:
1480 SELECT IF Missing Values • I
Page 1482 and 1483:
1482 SELECT IF Example Example Exam
Page 1484 and 1485:
1484 SET [HEADER={NO** }] {YES } {B
Page 1486 and 1487:
1486 SET Operations Example • Set
Page 1488 and 1489:
1488 SET LABELS Displays variable l
Page 1490 and 1491:
1490 SET JOURNAL Subcommand Alias t
Page 1492 and 1493:
1492 SET SPSSB in the server versio
Page 1494 and 1495:
1494 SET • You can specify any le
Page 1496 and 1497:
1496 SET Example SET SMALL = 0. SET
Page 1498 and 1499:
SHOW SHOW [ALL] [BLANKS] [BLKSIZE]
Page 1500 and 1501:
1500 SHOW EPOCH Range of years for
Page 1502 and 1503:
1502 SHOW WEIGHT Variable used to w
Page 1504 and 1505:
1504 SORT CASES addition, special c
Page 1506 and 1507:
1506 SPCHART Overview Options SPCHA
Page 1508 and 1509:
1508 SPCHART • The default span (
Page 1510 and 1511:
1510 SPCHART Figure 3 S chart Data
Page 1512 and 1513:
1512 SPCHART Figure 4 Individuals c
Page 1514 and 1515:
1514 SPCHART Figure 7 NP chart Data
Page 1516 and 1517:
1516 SPCHART based on the Poisson d
Page 1518 and 1519:
1518 SPCHART • A duplicated subco
Page 1520 and 1521:
1520 SPCHART CAPSIGMA Subcommand Th
Page 1522 and 1523:
1522 SPCHART fixed control limits a
Page 1524 and 1525:
1524 Syntax Reference SPECTRA Overv
Page 1526 and 1527:
1526 Syntax Reference Syntax Rules
Page 1528 and 1529:
1528 Syntax Reference TUKEY Tukey-H
Page 1530 and 1531:
Page 1532 and 1533:
1532 Syntax Reference References
Page 1534 and 1535:
1534 SPLIT FILE • AGGREGATE ignor
Page 1536 and 1537:
STRING STRING varlist (An) [/varlis
Page 1538 and 1539:
SUBTITLE SUBTITLE [’]text[’] Ex
Page 1540 and 1541:
SUMMARIZE SUMMARIZE [TABLES=]{varli
Page 1542 and 1543:
1542 SUMMARIZE Example the FORMAT s
Page 1544 and 1545:
1544 SUMMARIZE GEOMETRIC Geometric
Page 1546 and 1547:
SURVIVAL Overview SURVIVAL is avail
Page 1548 and 1549:
1548 SURVIVAL Operations • SURVIV
Page 1550 and 1551:
1550 SURVIVAL • INTERVALS produce
Page 1552 and 1553:
1552 SURVIVAL HAZARD Plot the hazar
Page 1554 and 1555:
1554 SURVIVAL large samples. It is
Page 1556 and 1557:
1556 SURVIVAL WRITE Subcommand Form
Page 1558 and 1559:
1558 SURVIVAL • Record type 31 is
Page 1560 and 1561:
1560 SYSFILE INFO
Page 1562 and 1563:
1562 Syntax Reference Operations Ex
Page 1564 and 1565:
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
Page 1769 and 1770:
output, 1489, 1494 output page size
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elative risk ratio in Crosstabs pro
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scientific notation controlling dis
Page 1775 and 1776:
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
Page 1793 and 1794:
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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SPSS® 12.0 Command Syntax Reference

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