SPSS多層面分析案例

3248

收藏 2014-06-03

For the analysis of the Rat Pup data, we first read in the raw data from the tab-delimited file rat_pup.dat (assumed to be located in the C:\temp folder) using the following syntax. This SPSS syntax was pasted after reading in the data using the SPSS menu system
* Read in Rat Pup data .
GET DATA
/TYPE = TXT
/FILE = 'C:\temp\rat_pup.dat'
/DELCASE = LINE
/DELIMITERS = "\t"
/ARRANGEMENT = DELIMITED
/FIRSTCASE = 2
/IMPORTCASE = ALL
/VARIABLES =
pup_id F2.1
weight F4.2
sex A6
litter F1.0
litsize F2.1
treatment A7
.
CACHE.
EXECUTE.
Because the MIXED command in SPSS sets the fixed-effect parameter associated with
the highest-valued level of a fixed factor to 0 by default, to prevent overparameterization
of models (similar to Proc Mixed in SAS; see Subsection 3.4.1), the highest-valued levels
of fixed factors can be thought of as “reference categories” for the factors. As a result, we
recode TREATMENT into a new variable named TREAT, so that the control group (TREAT
= 3) will be the reference category.
* Recode TREATMENT variable .
RECODE
treatment
('High'=1) ('Low'=2) ('Control'=3) INTO treat .
EXECUTE .
VARIABLE LABEL treat “Treatment”.
VALUE LABELS treat 1 "High" 2 "Low" 3 "Control".

MIXED
weight BY treat sex WITH litsize
/CRITERIA = CIN(95) MXITER(100) MXSTEP(5) SCORING(1)
SINGULAR(0.000000000001) HCONVERGE(0, ABSOLUTE) LCONVERGE(0,
ABSOLUTE) PCONVERGE(0.000001, ABSOLUTE)
/FIXED = treat sex litsize treat*sex | SSTYPE(3)
/METHOD = REML
/PRINT = SOLUTION
/RANDOM INTERCEPT | SUBJECT(litter) COVTYPE(VC)
/SAVE = PRED RESID .

The first variable listed after invoking the MIXED command is the dependent variable, WEIGHT. The BY keyword indicates that the TREAT and SEX variables are to be considered as categorical factors (they can be either fixed or random). Note that we do not need to include LITTER as a factor, because this variable is identified as a SUBJECT variable later in the code. The WITH keyword identifies continuous covariates, and in this case, we specify LITSIZE as a continuous covariate.
The CRITERIA subcommand specifies default settings for the convergence criteria obtained by specifying the model using the menu system.
In the FIXED subcommand, we include terms that have fixed effects associated with them in the model: TREAT, SEX, LITSIZE and the TREAT × SEX interaction. The SSTYPE(3) option after the vertical bar indicates that the default Type III analysis is to be used when calculating F-statistics. We also use the METHOD = REML subcommand, which requests that the REML estimation method (the default) be used. The SOLUTION keyword in the PRINT subcommand specifies that the estimates of the fixed-effect parameters, covariance parameters, and their associated standard errors are to be included in the output.
The RANDOM subcommand specifies that there is a random effect in the model associated with the INTERCEPT for each level of the SUBJECT variable (i.e., LITTER). The information about the “subject” variable is specified after the vertical bar (|). Note that because we included LITTER as a subject variable, we did not need to list it after the BY keyword (including LITTER after BY does not affect the analysis if LITTER is also indicated as a SUBJECT variable). The COVTYPE(VC) option indicates that the default Variance Components covariance structure for the random effects (the D matrix) is to be used. We did not need to specify a COVTYPE here because only a single variance associated with the random effects is being estimated.
Conditional predicted values and residuals are saved in the working data set by specifying PRED and RESID in the SAVE subcommand. The keyword PRED saves litter-specific predicted values that incorporate both the estimated fixed effects and the EBLUPs of the random litter effects for each observation. The keyword RESID saves the conditional residuals that represent the difference between the actual value of WEIGHT and the predicted value for each rat pup, based on the estimated fixed effects and the EBLUP of the random effect for each observation. The set of population-averaged predicted values, based only on the estimated fixed-effect parameters, can be obtained by adding the FIXPRED keyword to the /SAVE subcommand, as shown later in this chapter (see Section 3.9 for more details): /SAVE = PRED RESID FIXPRED

PLease read the following attachment for detail:

SPSS01.pdf
大小:(1.43 MB)

马上下载

扫码加我拉你入群

请注明：姓名-公司-职位

以便审核进群资格，未注明则拒绝

全部回复

songlinjl

2014-6-3 20:35:44

这个好些。

扫码加我拉你入群

请注明：姓名-公司-职位

以便审核进群资格，未注明则拒绝

ReneeBK

2014-6-4 02:25:02

Three-level Models for Clustered Data using SPSS

GET DATA  /TYPE = TXT
/FILE = "C:\temp\classroom.csv"
/DELCASE = LINE
/DELIMITERS = ","
/ARRANGEMENT = DELIMITED
/FIRSTCASE = 2
/IMPORTCASE = ALL
/VARIABLES =
sex F1.0
minority F1.0
mathkind F3.2
mathgain F4.2
ses F5.2
yearstea F5.2
mathknow F5.2
housepov F5.2
mathprep F4.2
classid F3.2
schoolid F1.0
childid F2.1
.
CACHE.
EXECUTE.

* Model 4.1 (more efficient syntax) .
MIXED
  mathgain BY classid schoolid
  /CRITERIA = CIN(95) MXITER(100) MXSTEP(5) SCORING(1) SINGULAR(0.000000000001) HCONVERGE(0, ABSOLUTE) LCONVERGE(0, ABSOLUTE)
  PCONVERGE(0.000001, ABSOLUTE)
  /FIXED = | SSTYPE(3)
  /METHOD = REML
  /PRINT = SOLUTION
  /RANDOM classid(schoolid)  | COVTYPE(VC)
  /RANDOM schoolid | COVTYPE(VC) .

* Model 4.1 (less efficient syntax) .
MIXED
  mathgain
  /CRITERIA = CIN(95) MXITER(100) MXSTEP(5) SCORING(1) SINGULAR(0.000000000001) HCONVERGE(0, ABSOLUTE) LCONVERGE(0, ABSOLUTE)
  PCONVERGE(0.000001, ABSOLUTE)
  /FIXED = | SSTYPE(3)
  /METHOD = REML
  /PRINT = SOLUTION
  /RANDOM INTERCEPT  | SUBJECT(classid*schoolid) COVTYPE(VC)
  /RANDOM INTERCEPT  | SUBJECT(schoolid) COVTYPE(VC) .

* Model 4.1A .
MIXED
  mathgain BY classid schoolid
  /CRITERIA = CIN(95) MXITER(100) MXSTEP(5) SCORING(1) SINGULAR(0.000000000001) HCONVERGE(0, ABSOLUTE) LCONVERGE(0, ABSOLUTE)
  PCONVERGE(0.000001, ABSOLUTE)
  /FIXED = | SSTYPE(3)
  /METHOD = REML
  /PRINT = SOLUTION
  /RANDOM schoolid | COVTYPE(VC) .

* Model 4.2 .
MIXED
  mathgain WITH mathkind sex minority ses BY classid schoolid
  /CRITERIA = CIN(95) MXITER(100) MXSTEP(5) SCORING(1) SINGULAR(0.000000000001) HCONVERGE(0, ABSOLUTE) LCONVERGE(0, ABSOLUTE)
  PCONVERGE(0.000001, ABSOLUTE)
  /FIXED = mathkind sex minority ses | SSTYPE(3)
  /METHOD = REML
  /PRINT = SOLUTION
  /RANDOM classid(schoolid) | COVTYPE(VC)
  /RANDOM schoolid | COVTYPE(VC) .

* Model 4.1 (ML Estimation) .
MIXED
  mathgain BY classid schoolid
  /CRITERIA = CIN(95) MXITER(100) MXSTEP(5) SCORING(1) SINGULAR(0.000000000001) HCONVERGE(0, ABSOLUTE) LCONVERGE(0, ABSOLUTE)
  PCONVERGE(0.000001, ABSOLUTE)
  /FIXED = | SSTYPE(3)
  /METHOD = ML
  /PRINT = SOLUTION
  /RANDOM classid(schoolid)  | COVTYPE(VC)
  /RANDOM schoolid | COVTYPE(VC) .

* Model 4.2 (ML Estimation) .
MIXED
  mathgain WITH mathkind sex minority ses BY classid schoolid
  /CRITERIA = CIN(95) MXITER(100) MXSTEP(5) SCORING(1) SINGULAR(0.000000000001) HCONVERGE(0, ABSOLUTE) LCONVERGE(0, ABSOLUTE)
  PCONVERGE(0.000001, ABSOLUTE)
  /FIXED = mathkind sex minority ses | SSTYPE(3)
  /METHOD = ML
  /PRINT = SOLUTION
  /RANDOM classid(schoolid) | COVTYPE(VC)
  /RANDOM schoolid | COVTYPE(VC) .

* Model 4.3 .
MIXED
  mathgain WITH mathkind sex minority ses yearstea mathprep mathknow BY classid schoolid
  /CRITERIA = CIN(95) MXITER(100) MXSTEP(5) SCORING(1) SINGULAR(0.000000000001) HCONVERGE(0, ABSOLUTE) LCONVERGE(0, ABSOLUTE)
  PCONVERGE(0.000001, ABSOLUTE)
  /FIXED = mathkind sex minority ses yearstea mathprep mathknow | SSTYPE(3)
  /METHOD = REML
  /PRINT = SOLUTION
  /RANDOM classid(schoolid) | COVTYPE(VC)
  /RANDOM schoolid | COVTYPE(VC) .

* Model 4.4 .
MIXED
  mathgain WITH mathkind sex minority ses housepov BY classid schoolid
  /CRITERIA = CIN(95) MXITER(100) MXSTEP(5) SCORING(1) SINGULAR(0.000000000001) HCONVERGE(0, ABSOLUTE) LCONVERGE(0, ABSOLUTE)
  PCONVERGE(0.000001, ABSOLUTE)
  /FIXED = mathkind sex minority ses housepov | SSTYPE(3)
  /METHOD = REML
  /PRINT = SOLUTION
  /RANDOM classid(schoolid) | COVTYPE(VC)
  /RANDOM schoolid | COVTYPE(VC) .

* Generate diagnostic plots using residual files saved by HLM.

PPLOT
  /VARIABLES=ebintrcp
  /NOLOG
  /NOSTANDARDIZE
  /TYPE=Q-Q
  /FRACTION=BLOM
  /TIES=MEAN
  /DIST=NORMAL.

PPLOT
  /VARIABLES=eb00
  /NOLOG
  /NOSTANDARDIZE
  /TYPE=Q-Q
  /FRACTION=BLOM
  /TIES=MEAN
  /DIST=NORMAL.

PPLOT
  /VARIABLES=l1resid
  /NOLOG
  /NOSTANDARDIZE
  /TYPE=Q-Q
  /FRACTION=BLOM
  /TIES=MEAN
  /DIST=NORMAL.

GRAPH
  /SCATTERPLOT(BIVAR)=fitval WITH l1resid
  /MISSING=LISTWISE .

Refrence
http://www-personal.umich.edu/~bwest/chapter4.html

扫码加我拉你入群

请注明：姓名-公司-职位

以便审核进群资格，未注明则拒绝

ReneeBK

2014-6-4 02:27:35

Models for Repeated Measures Data using SPSS

GET DATA  /TYPE = TXT
/FILE = 'C:\temp\ratbrain.dat'
/DELCASE = LINE
/DELIMITERS = "\t"
/ARRANGEMENT = DELIMITED
/FIRSTCASE = 2
/IMPORTCASE = ALL
/VARIABLES =
animal A7
Carb.BST F6.2
Carb.LS F6.2
Carb.VDB F6.2
Basal.BST F6.2
Basal.LS F6.2
Basal.VDB F6.2
.
CACHE.
EXECUTE.

VARSTOCASES
/MAKE activate FROM  Basal.BST Basal.LS Basal.VDB Carb.BST Carb.LS Carb.VDB
/INDEX = treatment(2) region(3)
/KEEP =  animal
/NULL = KEEP.

VALUE LABELS treatment 1 'Basal' 2 'Carbachol'
/ region 1 'BST' 2 'LS' 3 'VDB' .

MEANS
  TABLES=activate  BY treatment  BY region
  /CELLS MEAN COUNT STDDEV MIN MAX  .

GRAPH
  /LINE(MULTIPLE)MEAN(activate) BY region BY animal
  /PANEL COLVAR=treatment COLOP=CROSS .

IF (treatment = 1) treat = 0 .
IF (treatment = 2) treat = 1 .
EXECUTE .

* Model 5.1 .
MIXED
  activate  BY region WITH treat
  /CRITERIA = CIN(95) MXITER(100) MXSTEP(5) SCORING(1) SINGULAR(0.000000000001) HCONVERGE(0, ABSOLUTE) LCONVERGE(0, ABSOLUTE)
  PCONVERGE(0.000001, ABSOLUTE)
  /FIXED = region treat region*treat  | SSTYPE(3)
  /METHOD = REML
  /PRINT = SOLUTION
  /RANDOM INTERCEPT  | SUBJECT(animal) COVTYPE(VC) .

* Model 5.2 .
MIXED
  activate  BY region WITH treat
  /CRITERIA = CIN(95) MXITER(100) MXSTEP(5) SCORING(1) SINGULAR(0.000000000001) HCONVERGE(0, ABSOLUTE) LCONVERGE(0, ABSOLUTE)
  PCONVERGE(0.000001, ABSOLUTE)
  /FIXED = region treat region*treat  | SSTYPE(3)
  /METHOD = REML
  /PRINT = SOLUTION G
  /RANDOM INTERCEPT treat  | SUBJECT(animal) COVTYPE(UN) .

* Model 5.3 .
MIXED
  activate  BY region WITH treat
  /CRITERIA = CIN(95) MXITER(100) MXSTEP(5) SCORING(1) SINGULAR(0.000000000001) HCONVERGE(0, ABSOLUTE) LCONVERGE(0, ABSOLUTE)
  PCONVERGE(0.000001, ABSOLUTE)
  /FIXED = region treat region*treat  | SSTYPE(3)
  /METHOD = REML
  /PRINT = SOLUTION
  /RANDOM INTERCEPT treat | SUBJECT(animal) COVTYPE(UN)
  /REPEATED = treatment | SUBJECT(animal*region) COVTYPE(DIAG)  .

* Model 5.2 (w/ interaction means) .
MIXED
  activate  BY region WITH treat
  /CRITERIA = CIN(95) MXITER(100) MXSTEP(5) SCORING(1) SINGULAR(0.000000000001) HCONVERGE(0, ABSOLUTE) LCONVERGE(0, ABSOLUTE)
  PCONVERGE(0.000001, ABSOLUTE)
  /FIXED = region treat region*treat  | SSTYPE(3)
  /METHOD = REML
  /PRINT = SOLUTION G
  /RANDOM INTERCEPT treat  | SUBJECT(animal) COVTYPE(UN)
  /EMMEANS = TABLES(region) WITH(treat=1) COMPARE ADJ(BON)
  /EMMEANS = TABLES(region) WITH(treat=0) COMPARE ADJ(BON) .

* Model 5.2 (Diagnostics) .
MIXED
  activate  BY region  WITH treat
  /CRITERIA = CIN(95) MXITER(100) MXSTEP(5) SCORING(1) SINGULAR(0.000000000001) HCONVERGE(0, ABSOLUTE) LCONVERGE(0, ABSOLUTE)
  PCONVERGE(0.000001, ABSOLUTE)
  /FIXED = region treat region*treat  | SSTYPE(3)
  /METHOD = REML
  /PRINT = SOLUTION G
  /RANDOM INTERCEPT treat  | SUBJECT(animal) COVTYPE(UN)
  /SAVE = PRED RESID .

PPLOT
  /VARIABLES=RESID_1
  /NOLOG
  /NOSTANDARDIZE
  /TYPE=Q-Q
  /FRACTION=BLOM
  /TIES=MEAN
  /DIST=NORMAL.

NPAR TESTS
  /K-S(NORMAL)= RESID_1
  /MISSING ANALYSIS.

GRAPH
  /SCATTERPLOT(BIVAR)=PRED_1 WITH RESID_1 BY treatment
  /MISSING=LISTWISE .

* Marginal model with heterogeneous compound symmetry R(i) matrix .
MIXED
  activate  BY region treatment
  /CRITERIA = CIN(95) MXITER(100) MXSTEP(5) SCORING(1) SINGULAR(0.000000000001) HCONVERGE(0, ABSOLUTE) LCONVERGE(0, ABSOLUTE)
  PCONVERGE(0.000001, ABSOLUTE)
  /FIXED = region treatment region*treatment  | SSTYPE(3)
  /METHOD = REML
  /PRINT = SOLUTION R
  /REPEATED Region Treatment  | SUBJECT(animal) COVTYPE(CSH)  .
Reference

http://www-personal.umich.edu/~bwest/chapter5_spss_final.sps

扫码加我拉你入群

请注明：姓名-公司-职位

以便审核进群资格，未注明则拒绝

ReneeBK

2014-6-4 02:30:42

Random Coefficient Models for Longitudinal Data using SPSS

GET DATA  /TYPE = TXT
/FILE = 'C:\temp\autism.csv'
/DELCASE = LINE
/DELIMITERS = ","
/ARRANGEMENT = DELIMITED
/FIRSTCASE = 2
/IMPORTCASE = ALL
/VARIABLES =
age F2.1
vsae F3.2
sicdegp F1.0
childid F2.1
.
CACHE.
EXECUTE.

COMPUTE age_2 = age - 2 .
EXECUTE .

COMPUTE age_2sq = age_2*age_2 .
EXECUTE .

* Model 6.1 .
MIXED
  vsae  WITH age_2 age_2sq BY sicdegp
  /CRITERIA = CIN(95) MXITER(100) MXSTEP(5) SCORING(1) SINGULAR(0.000000000001) HCONVERGE(0, ABSOLUTE) LCONVERGE(0, ABSOLUTE)
  PCONVERGE(0.000001, ABSOLUTE)
  /FIXED = age_2 age_2sq sicdegp age_2*sicdegp age_2sq*sicdegp | SSTYPE(3)
  /METHOD = REML
  /PRINT = G SOLUTION
  /RANDOM INTERCEPT age_2 age_2sq | SUBJECT(CHILDID) COVTYPE(UN) .

* Model 6.2 .
MIXED
  vsae  WITH age_2 age_2sq BY sicdegp
  /CRITERIA = CIN(95) MXITER(100) MXSTEP(5) SCORING(1) SINGULAR(0.000000000001) HCONVERGE(0, ABSOLUTE) LCONVERGE(0, ABSOLUTE)
  PCONVERGE(0.000001, ABSOLUTE)
  /FIXED = age_2 age_2sq sicdegp age_2*sicdegp age_2sq*sicdegp | SSTYPE(3)
  /METHOD = REML
  /PRINT = G SOLUTION
  /RANDOM age_2 age_2sq | SUBJECT(CHILDID) COVTYPE(UN) .

* Model 6.2 (Hypothesis 6.1) .
MIXED
  vsae  WITH age_2 age_2sq BY sicdegp
  /CRITERIA = CIN(95) MXITER(100) MXSTEP(5) SCORING(1) SINGULAR(0.000000000001) HCONVERGE(0, ABSOLUTE) LCONVERGE(0, ABSOLUTE)
  PCONVERGE(0.000001, ABSOLUTE)
  /FIXED = age_2 age_2sq sicdegp age_2*sicdegp age_2sq*sicdegp | SSTYPE(3)
  /METHOD = REML
  /PRINT = G SOLUTION
  /RANDOM age_2 | SUBJECT(CHILDID) COVTYPE(UN) .

* Model 6.2 (ML) .
MIXED
  vsae  WITH age_2 age_2sq BY sicdegp
  /CRITERIA = CIN(95) MXITER(100) MXSTEP(5) SCORING(1) SINGULAR(0.000000000001) HCONVERGE(0, ABSOLUTE) LCONVERGE(0, ABSOLUTE)
  PCONVERGE(0.000001, ABSOLUTE)
  /FIXED = age_2 age_2sq sicdegp age_2*sicdegp age_2sq*sicdegp | SSTYPE(3)
  /METHOD = ML
  /PRINT = G SOLUTION
  /RANDOM age_2 age_2sq | SUBJECT(CHILDID) COVTYPE(UN) .

* Model 6.3 (ML) .
MIXED
  vsae  WITH age_2 age_2sq BY sicdegp
  /CRITERIA = CIN(95) MXITER(100) MXSTEP(5) SCORING(1) SINGULAR(0.000000000001) HCONVERGE(0, ABSOLUTE) LCONVERGE(0, ABSOLUTE)
  PCONVERGE(0.000001, ABSOLUTE)
  /FIXED = age_2 age_2sq sicdegp age_2*sicdegp | SSTYPE(3)
  /METHOD = ML
  /PRINT = G SOLUTION
  /RANDOM age_2 age_2sq | SUBJECT(CHILDID) COVTYPE(UN) .

* Model 6.3 .
MIXED
  vsae  WITH age_2 age_2sq BY sicdegp
  /CRITERIA = CIN(95) MXITER(100) MXSTEP(5) SCORING(1) SINGULAR(0.000000000001) HCONVERGE(0, ABSOLUTE) LCONVERGE(0, ABSOLUTE)
  PCONVERGE(0.000001, ABSOLUTE)
  /FIXED = age_2 age_2sq sicdegp age_2*sicdegp | SSTYPE(3)
  /METHOD = REML
  /PRINT = G SOLUTION
  /SAVE = PRED RESID
  /RANDOM age_2 age_2sq | SUBJECT(CHILDID) COVTYPE(UN) .
Reference

http://www-personal.umich.edu/~bwest/chapter6_spss_final.sps

扫码加我拉你入群

请注明：姓名-公司-职位

以便审核进群资格，未注明则拒绝

ReneeBK

2014-6-4 02:36:54

Models for Clustered Longitudinal Data using SPSS

GET DATA  /TYPE = TXT
/FILE = 'C:\temp\veneer.dat'
/DELCASE = LINE
/DELIMITERS = "\t"
/ARRANGEMENT = DELIMITED
/FIRSTCASE = 2
/IMPORTCASE = ALL
/VARIABLES =
patient F1.0
tooth F2.1
age F2.1
base_gcf F2.1
cda F8.2
time F1.0
gcf F2.1
.
CACHE.
EXECUTE.

* Model 7.1 .
MIXED
  gcf  WITH time base_gcf cda age
  /CRITERIA = CIN(95) MXITER(100) MXSTEP(5) SCORING(1) SINGULAR(0.000000000001) HCONVERGE(0, ABSOLUTE) LCONVERGE(0, ABSOLUTE)
  PCONVERGE(0.000001, ABSOLUTE)
  /FIXED = time base_gcf cda age time*base_gcf time*cda time*age | SSTYPE(3)
  /METHOD = REML
  /PRINT = G SOLUTION TESTCOV
  /SAVE = PRED RESID
  /RANDOM INTERCEPT time  | SUBJECT(Patient) COVTYPE(UN)
  /RANDOM INTERCEPT | SUBJECT(tooth*Patient) .

* Model 7.1A .
MIXED
  gcf  WITH time base_gcf cda age
  /CRITERIA = CIN(95) MXITER(100) MXSTEP(5) SCORING(1) SINGULAR(0.000000000001) HCONVERGE(0, ABSOLUTE) LCONVERGE(0, ABSOLUTE)
  PCONVERGE(0.000001, ABSOLUTE)
  /FIXED = time base_gcf cda age time*base_gcf time*cda time*age | SSTYPE(3)
  /METHOD = REML
  /PRINT = G SOLUTION TESTCOV
  /SAVE = PRED RESID
  /RANDOM INTERCEPT time  | SUBJECT(Patient) COVTYPE(UN) .

SORT CASES BY
  patient (A) tooth (A) time(A) .

* Model 7.2A .
MIXED
  gcf  WITH time base_gcf cda age
  /CRITERIA = CIN(95) MXITER(100) MXSTEP(5) SCORING(1) SINGULAR(0.000000000001) HCONVERGE(0, ABSOLUTE) LCONVERGE(0, ABSOLUTE)
  PCONVERGE(0.000001, ABSOLUTE)
  /FIXED = time base_gcf cda age time*base_gcf time*cda time*age | SSTYPE(3)
  /METHOD = REML
  /PRINT = G R SOLUTION TESTCOV
  /SAVE = PRED RESID
  /RANDOM INTERCEPT time  | SUBJECT(Patient) COVTYPE(UN)
  /RANDOM INTERCEPT | SUBJECT(tooth*Patient)
  /REPEATED time | SUBJECT(tooth*Patient) COVTYPE(UN) .

* Model 7.2B .
MIXED
  gcf  WITH time base_gcf cda age
  /CRITERIA = CIN(95) MXITER(100) MXSTEP(5) SCORING(1) SINGULAR(0.000000000001) HCONVERGE(0, ABSOLUTE) LCONVERGE(0, ABSOLUTE)
  PCONVERGE(0.000001, ABSOLUTE)
  /FIXED = time base_gcf cda age time*base_gcf time*cda time*age | SSTYPE(3)
  /METHOD = REML
  /PRINT = G R SOLUTION TESTCOV
  /SAVE = PRED RESID
  /RANDOM INTERCEPT time  | SUBJECT(Patient) COVTYPE(UN)
  /RANDOM INTERCEPT | SUBJECT(tooth*Patient)
  /REPEATED time | SUBJECT(tooth*Patient) COVTYPE(CS) .

* Model 7.2C .
MIXED
  gcf  WITH time base_gcf cda age
  /CRITERIA = CIN(95) MXITER(100) MXSTEP(5) SCORING(1) SINGULAR(0.000000000001) HCONVERGE(0, ABSOLUTE) LCONVERGE(0, ABSOLUTE)
  PCONVERGE(0.000001, ABSOLUTE)
  /FIXED = time base_gcf cda age time*base_gcf time*cda time*age | SSTYPE(3)
  /METHOD = REML
  /PRINT = G R SOLUTION TESTCOV
  /SAVE = PRED RESID
  /RANDOM INTERCEPT time  | SUBJECT(Patient) COVTYPE(UN)
  /RANDOM INTERCEPT | SUBJECT(tooth*Patient)
  /REPEATED time | SUBJECT(tooth*Patient) COVTYPE(DIAG) .

* Model 7.1 (ML) .
MIXED
  gcf  WITH time base_gcf cda age
  /CRITERIA = CIN(95) MXITER(100) MXSTEP(5) SCORING(1) SINGULAR(0.000000000001) HCONVERGE(0, ABSOLUTE) LCONVERGE(0, ABSOLUTE)
  PCONVERGE(0.000001, ABSOLUTE)
  /FIXED = time base_gcf cda age time*base_gcf time*cda time*age | SSTYPE(3)
  /METHOD = ML
  /PRINT = G SOLUTION TESTCOV
  /SAVE = PRED RESID
  /RANDOM INTERCEPT time  | SUBJECT(Patient) COVTYPE(UN)
  /RANDOM INTERCEPT | SUBJECT(tooth*Patient) .

* Model 7.3 (ML) .
MIXED
  gcf  WITH time base_gcf cda age
  /CRITERIA = CIN(95) MXITER(100) MXSTEP(5) SCORING(1) SINGULAR(0.000000000001) HCONVERGE(0, ABSOLUTE) LCONVERGE(0, ABSOLUTE)
  PCONVERGE(0.000001, ABSOLUTE)
  /FIXED = time base_gcf cda age | SSTYPE(3)
  /METHOD = ML
  /PRINT = G SOLUTION TESTCOV
  /SAVE = PRED RESID
  /RANDOM INTERCEPT time  | SUBJECT(Patient) COVTYPE(UN)
  /RANDOM INTERCEPT | SUBJECT(tooth*Patient) .

* Model 7.3 .
MIXED
  gcf  WITH time base_gcf cda age
  /CRITERIA = CIN(95) MXITER(100) MXSTEP(5) SCORING(1) SINGULAR(0.000000000001) HCONVERGE(0, ABSOLUTE) LCONVERGE(0, ABSOLUTE)
  PCONVERGE(0.000001, ABSOLUTE)
  /FIXED = time base_gcf cda age | SSTYPE(3)
  /METHOD = REML
  /PRINT = G SOLUTION TESTCOV
  /SAVE = PRED RESID
  /RANDOM INTERCEPT time  | SUBJECT(Patient) COVTYPE(UN)
  /RANDOM INTERCEPT | SUBJECT(tooth* Patient) .
Reference

http://www-personal.umich.edu/~bwest/chapter7.html

扫码加我拉你入群

请注明：姓名-公司-职位

以便审核进群资格，未注明则拒绝

扫码加我 拉你入群

扫码加我 拉你入群

Three-level Models for Clustered Data using SPSS

扫码加我 拉你入群

Models for Repeated Measures Data using SPSS

扫码加我 拉你入群

Random Coefficient Models for Longitudinal Data using SPSS

扫码加我 拉你入群

Models for Clustered Longitudinal Data using SPSS

扫码加我 拉你入群

分享

扫码加好友，拉您进群

扫码加我拉你入群

扫码加我拉你入群

扫码加我拉你入群

扫码加我拉你入群

扫码加我拉你入群

扫码加我拉你入群