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Version 2.1.1  (2005-06-20), ISBN 3-900051-07-0

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> ### * <HEADER>
> ###
> attach(NULL, name = "CheckExEnv")
> assign(".CheckExEnv", as.environment(2), pos = length(search())) # base
> ## add some hooks to label plot pages for base and grid graphics
> setHook("plot.new", ".newplot.hook")
> setHook("persp", ".newplot.hook")
> setHook("grid.newpage", ".gridplot.hook")
> 
> assign("cleanEx",
+        function(env = .GlobalEnv) {
+ 	   rm(list = ls(envir = env, all.names = TRUE), envir = env)
+            RNGkind("default", "default")
+ 	   set.seed(1)
+    	   options(warn = 1)
+ 	   delayedAssign("T", stop("T used instead of TRUE"),
+ 		  assign.env = .CheckExEnv)
+ 	   delayedAssign("F", stop("F used instead of FALSE"),
+ 		  assign.env = .CheckExEnv)
+ 	   sch <- search()
+ 	   newitems <- sch[! sch %in% .oldSearch]
+ 	   for(item in rev(newitems))
+                eval(substitute(detach(item), list(item=item)))
+ 	   missitems <- .oldSearch[! .oldSearch %in% sch]
+ 	   if(length(missitems))
+ 	       warning("items ", paste(missitems, collapse=", "),
+ 		       " have been removed from the search path")
+        },
+        env = .CheckExEnv)
> assign("..nameEx", "__{must remake R-ex/*.R}__", env = .CheckExEnv) # for now
> assign("ptime", proc.time(), env = .CheckExEnv)
> grDevices::postscript("depmix-Examples.ps")
> assign("par.postscript", graphics::par(no.readonly = TRUE), env = .CheckExEnv)
> options(contrasts = c(unordered = "contr.treatment", ordered = "contr.poly"))
> options(warn = 1)    
> library('depmix')
Loading required package: MASS
> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "depmix-internal"
> 
> ### * depmix-internal
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: depmix-internal
> ### Title: Fitting Dependent Mixture Models
> ### Aliases: checkSetRecode recode fblo fbuo ppar recitt pp np pa2conr
> ###   paridx fresp bdiag cl2st cl2stob kmstart poststart tr2stin
> ### Keywords: models
> 
> ### ** Examples
> 
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "dmm"
> 
> ### * dmm
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: dmm
> ### Title: Dependent Mixture Model Specifiction
> ### Aliases: dmm summary.dmm lcm lca
> ### Keywords: models
> 
> ### ** Examples
> 
> 
> # create a 2 state model with one continuous and one binary response
> # with start values provided in st
> st <- c(1,0.9,0.1,0.2,0.8,2,1,0.7,0.3,5,2,0.2,0.8,0.5,0.5)
> mod <- dmm(nsta=2,itemt=c(1,2), stval=st)
> summary(mod)
 Model:  2 -state model  
 Number of parameters:  15  
 Free parameters:       9  
 Number of states:      2 
 Number of items:       2 
 Item types:            1 2 

 Parameter values, transition matrix 

       State1 State2
State1    0.9    0.1
State2    0.2    0.8


 Parameter values, observation parameters 

       Item1,mean Item1,stddev Item2,p 1 Item2,p 2
State1          2            1       0.7       0.3
State2          5            2       0.2       0.8


 Parameter values, initial state probabilies 

    State1 State2
val    0.5    0.5

> 
> # 2 class latent class model with equal conditional probabilities in each class
> 
> stv=c(1,rep(c(0.9,0.1),5),rep(c(0.1,0.9),5),0.5,0.5)
> 
> # here the conditional probs of the first item are set equal to those in
> # the subsequent items
> conpat=c(1,rep(c(2,3),5),rep(c(4,5),5),1,1)
> 
> lc=lca(ncl=2,itemtypes=rep(2,5),conpat=conpat,stv=stv)
> summary(lc)
 Model:  2 class model  
 Number of parameters:  27  
 Free parameters:       4  
 Number of states:      2 
 Number of items:       5 
 Item types:            2 2 2 2 2 

 Parameter values, observation parameters 

       Item1,p 1 Item1,p 2 Item2,p 1 Item2,p 2 Item3,p 1 Item3,p 2 Item4,p 1
Class1       0.9       0.1       0.9       0.1       0.9       0.1       0.9
Class2       0.1       0.9       0.1       0.9       0.1       0.9       0.1
       Item4,p 2 Item5,p 1 Item5,p 2
Class1       0.1       0.9       0.1
Class2       0.9       0.1       0.9


 Parameter values, unconditional (class) probabilities 

    Class1 Class2
val    0.5    0.5

> 
> 
> 
> 
> cleanEx(); ..nameEx <- "fitdmm"
> 
> ### * fitdmm
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: fitdmm
> ### Title: Fitting Dependent Mixture Models
> ### Aliases: depmix fitdmm loglike posterior computeSes bootstrap
> ###   summary.fit oneliner
> ### Keywords: models
> 
> ### ** Examples
> 
> 
> # COMBINED RT AND CORRECT/INCORRECT SCORES from a 'switching' experiment
> 
> data(speed)
> mod <- dmm(nsta=2,itemt=c(1,2)) # gaussian and binary items
> fit1 <- fitdmm(dat=speed,dmm=mod)
Initial loglikelihood:  -297.4306  
iteration = 0
Step:
[1] 0 0 0 0 0 0 0 0 0
Parameter:
[1]  0.4074968 -0.6030893 -0.5559689  0.7830820 -0.1846599  6.4032844  0.2318094
[8]  5.5372838  0.2189520
Function Value
[1] 297.4306
Gradient:
[1]  17.838774   4.395048   1.009810   6.808036   9.848922  30.788829 -43.417653
[8]  32.399634  54.905265

iteration = 16
Parameter:
[1]  0.3877964 -0.6050630 -0.5429171  0.7942191 -0.2057752  6.3914953  0.2396807
[8]  5.5200651  0.2019121
Function Value
[1] 296.1200
Gradient:
[1] -0.55687642  0.99484837  3.34099578  0.08263532  3.08080738 -0.57541695
[7] -0.09220585 -0.81976170 -0.25060547

Successive iterates within tolerance.
Current iterate is probably solution.

Final loglikelihood:  -296.1200 
Computing posteriors 
Computing standard errors 
This took  16 iterations,  5.2  seconds 
> summary(fit1)
Model:  2 -state model  fitted at  Wed Jul 13 13:09:46 2005  
Optimization information, method is  nlm 
 Iterations:  16
 Inform:  2  (look up the respective manuals for more information.)

 Loglikelihood of fitted model:  -296.12
 AIC:  610.24
 BIC:  647.001
 Number of observations (used in BIC):  439
 Fitted model 
 Model:  2 -state model  
 Number of parameters:  15  
 Free parameters:       9  
 Number of states:      2 
 Number of items:       2 
 Item types:            1 2 

 Parameter values, transition matrix 

       State1 State2
State1  0.916  0.084
se      0.018  0.018
t      50.854  4.671
State2  0.104  0.896
se      0.024  0.024
t       4.263 36.678


 Parameter values, observation parameters 

       Item1,mean Item1,stddev Item2,p 1 Item2,p 2
State1      6.391        0.240     0.098     0.902
se          0.016        0.012     0.019     0.019
t         399.429       20.735     5.060    46.543
State2      5.520        0.202     0.469     0.531
se          0.017        0.014     0.037     0.037
t         324.383       14.475    12.638    14.313


 Parameter values, initial state probabilies 

    State1 State2
val  1.000  0.000
se   0.578  0.578
t    1.731  0.000

> 
> # add some constraints using conpat
> conpat=rep(1,15)
> conpat[1]=0
> conpat[14:15]=0
> conpat[8:9]=0
> # use starting values from the previous model fit, except for the guessing 
> # parameters which should really be 0.5
> stv=c(1,.896,.104,.084,.916,5.52,.20,.5,.5,6.39,.24,.098,.90,0,1)
> mod=dmm(nstates=2,itemt=c("n",2),stval=stv,conpat=conpat)
> 
> fit2 <- fitdmm(dat=speed,dmm=mod)
Initial loglikelihood:  -296.4092  
iteration = 0
Step:
[1] 0 0 0 0 0 0 0
Parameter:
[1]  0.8120000 -0.3876615  0.4159457  5.5200000  0.2000000  6.3900000  0.2400000
Function Value
[1] 296.4092
Gradient:
[1]  -1.6611909   0.5624142   1.9924059   2.3831597 -10.4053555  -4.8517480
[7]  -0.9033751

iteration = 12
Parameter:
[1]  0.8148616 -0.3892347  0.4142363  5.5203960  0.2021620  6.3916302  0.2397466
Function Value
[1] 296.3901
Gradient:
[1]  1.241281e-04  6.447644e-05  1.740612e-04 -3.993284e-05 -9.087583e-05
[6]  2.185769e-04 -9.794727e-05

Successive iterates within tolerance.
Current iterate is probably solution.

Final loglikelihood:  -296.3901 
Computing posteriors 
Computing standard errors 
This took  12 iterations,  2.36  seconds 
> summary(fit2)
Model:  2 -state model  fitted at  Wed Jul 13 13:09:50 2005  
Optimization information, method is  nlm 
 Iterations:  12
 Inform:  2  (look up the respective manuals for more information.)

 Loglikelihood of fitted model:  -296.39
 AIC:  606.78
 BIC:  635.372
 Number of observations (used in BIC):  439
 Fitted model 
 Model:  2 -state model  
 Number of parameters:  15  
 Free parameters:       7  
 Number of states:      2 
 Number of items:       2 
 Item types:            normal 2 

 Parameter values, transition matrix 

       State1 State2
State1  0.899  0.101
se      0.024  0.024
t      37.670  4.251
State2  0.084  0.916
se      0.018  0.018
t       4.667 51.076


 Parameter values, observation parameters 

       Item1,mean Item1,stddev Item2,p 1 Item2,p 2
State1      5.520        0.202     0.500     0.500
se          0.017        0.014     0.000     0.000
t         324.123       14.481        NA        NA
State2      6.392        0.240     0.098     0.902
se          0.016        0.012     0.019     0.019
t         399.261       20.703     5.058    46.411


 Parameter values, initial state probabilies 

    State1 State2
val      0      1
se       0      0
t       NA     NA

> 
> # add covariates to the model to incorporate the fact the accuracy pay off changes per trial
> # 2-state model with covariates + other constraints
> conpat=rep(1,15)
> conpat[1]=0
> conpat[8:9]=0
> conpat[14:15]=0
> conpat[2]=2
> conpat[5]=2
> stv=c(1,0.9,0.1,0.1,0.9,5.5,0.2,0.5,0.5,6.4,0.25,0.9,0.1,0,1)
> tdfix=rep(0,15)
> tdfix[2:5]=1
> stcov=rep(0,15)
> stcov[2:5]=c(-0.4,0.4,0.15,-0.15)
> 
> mod<-dmm(nstates=2,itemt=c("n",2),stval=stv,conpat=conpat,tdfix=tdfix,tdst=stcov,modname="twoboth+cov")
> 
> fit3 <- fitdmm(dat=speed,dmm=mod,tdcov=1,der=0,ses=0,vfa=80,accu="best")
Initial loglikelihood:  -308.5948  
iteration = 0
Step:
[1] 0 0 0 0 0 0 0 0
Parameter:
[1]  0.28501007  1.08501007  0.28318948 -0.01681052  5.51867043  0.19911769
[7]  6.39294268  0.23771005
Function Value
[1] 308.3823
Gradient:
[1] -60.1917191 -53.9865153  23.1694641 -92.0159912   1.9907909  -0.3677483
[7]   8.0979350 -16.5006371

iteration = 30
Parameter:
[1]  0.448863468  0.897250757 -0.002860367  0.217008470  5.516357184
[6]  0.197571620  6.389501160  0.240874382
Function Value
[1] 284.8562
Gradient:
[1]  0.64226109  0.63952118 -0.65173268  1.91749987 -0.02252730 -0.01339433
[7] -0.01770095 -0.04406268

Successive iterates within tolerance.
Current iterate is probably solution.

Final loglikelihood:  -284.8562 
Computing posteriors 
This took  30 iterations,  4.82  seconds 
> summary(fit3)
Model:  twoboth+cov  fitted at  Wed Jul 13 13:09:55 2005  
Optimization information, method is  nlm 
 Iterations:  30
 Inform:  2  (look up the respective manuals for more information.)

 Loglikelihood of fitted model:  -284.856
 AIC:  585.712
 BIC:  618.388
 Number of observations (used in BIC):  439
 Fitted model 
 Model:  twoboth+cov  
 Number of parameters:  30  
 Free parameters:       8  
 Number of states:      2 
 Number of items:       2 
 Item types:            normal 2 

 Parameter values, transition matrix 

       State1 State2
State1  0.890  0.110
be     -0.224  0.224
State2  0.110  0.890
be     -0.110  0.110


 Parameter values, observation parameters 

       Item1,mean Item1,stddev Item2,p 1 Item2,p 2
State1      5.516        0.198       0.5       0.5
State2      6.390        0.241       0.1       0.9


 Parameter values, initial state probabilies 

    State1 State2
val      0      1

> 
> # split the data into three time series
> data(speed)
> r1=markovdata(dat=speed[1:168,],item=itemtypes(speed))
> r2=markovdata(dat=speed[169:302,],item=itemtypes(speed))
> r3=markovdata(dat=speed[303:439,],item=itemtypes(speed))
> 
> # define 2-state model with constraints
> conpat=rep(1,15)
> conpat[1]=0
> conpat[8:9]=0
> conpat[14:15]=0
> stv=c(1,0.9,0.1,0.1,0.9,5.5,0.2,0.5,0.5,6.4,0.25,0.9,0.1,0.5,0.5)
> mod<-dmm(nstates=2,itemt=c("n",2),stval=stv,conpat=conpat)
> 
> # define 3-group model with equal transition parameters, and no 
> # equalities between the obser parameters
> mgr <-mgdmm(dmm=mod,ng=3,trans=TRUE,obser=FALSE)
> 
> fitmg <- fitdmm(dat=list(r1,r2,r3),dmm=mgr)
Warning in fitdmm(dat = list(r1, r2, r3), dmm = mgr) : 
	 No automatic boosting of starting values for other than single component 1-group models.
Initial loglikelihood:  -743.5488  
iteration = 0
Step:
 [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Parameter:
 [1] 0.1532026 0.9583426 1.0209866 0.9365141 0.1365141 5.5000000 0.2000000
 [8] 6.4000000 0.2500000 5.5000000 0.2000000 6.4000000 0.2500000 5.5000000
[15] 0.2000000 6.4000000 0.2500000
Function Value
[1] 743.5488
Gradient:
 [1]  583.021983  462.223663  -72.639266  482.612681 -223.658416 -115.285567
 [7]  -87.991400    9.367967  -52.028775  -41.194328  105.587298  -14.198741
[13]   35.195885   68.106929  -33.575720   40.380799   98.678073

iteration = 31
Parameter:
 [1] -1.12753226 -0.01383641  1.12281943 -0.08249354  0.70259128  5.61586897
 [7]  0.25903497  6.42474384  0.25415960  5.52577190  0.14899358  6.40779600
[13]  0.23795073  5.42239968  0.16687589  6.35547365  0.21439509
Function Value
[1] 280.0264
Gradient:
 [1]  1.104459e-04  6.955622e-05  1.387325e-04 -8.760627e-05  1.539341e-04
 [6]  8.336745e-06 -6.451362e-05  1.200573e-05 -3.745991e-04 -7.356309e-05
[11]  4.670044e-04 -1.418657e-05  1.377008e-04 -1.529984e-04  5.111825e-05
[16]  6.762901e-05 -2.060450e-04

Successive iterates within tolerance.
Current iterate is probably solution.

Final loglikelihood:  -280.0264 
Computing posteriors 
Computing standard errors 
Warning in computeSes(dat, mod) : Near-singular hessian, ses may be bad.

This took  31 iterations,  5.88  seconds 
> summary(fitmg)
Model:  3 group model  fitted at  Wed Jul 13 13:10:05 2005  
Optimization information, method is  nlm 
 Iterations:  31
 Inform:  2  (look up the respective manuals for more information.)

 Loglikelihood of fitted model:  -280.026
 AIC:  594.053
 BIC:  663.489
 Number of observations (used in BIC):  439
 Fitted model 
 Model:  3 group model 
 Nr of groups:      3 
 Nr of parameters:  45 
 Free parameters:   17 
 Model for group:  1 
 Model:  2 -state model  
 Number of parameters:  15  
 Free parameters:       7  
 Number of states:      2 
 Number of items:       2 
 Item types:            normal 2 

 Parameter values, transition matrix 

       State1 State2
State1  0.903  0.097
se      0.023  0.023
t      38.656  4.152
State2  0.084  0.916
se      0.018  0.018
t       4.601 50.377


 Parameter values, observation parameters 

       Item1,mean Item1,stddev Item2,p 1 Item2,p 2
State1      5.616        0.259     0.500     0.500
se          0.033        0.025     0.000     0.000
t         168.197       10.238        NA        NA
State2      6.425        0.254     0.058     0.942
se          0.029        0.020     0.027     0.027
t         220.433       12.544     2.123    34.539


 Parameter values, initial state probabilies 

    State1 State2
val    0.5    0.5
se     0.0    0.0
t       NA     NA

 Model for group:  2 
 Model:  2 -state model  
 Number of parameters:  15  
 Free parameters:       7  
 Number of states:      2 
 Number of items:       2 
 Item types:            normal 2 

 Parameter values, transition matrix 

       State1 State2
State1  0.903  0.097
se      0.023  0.023
t      38.656  4.152
State2  0.084  0.916
se      0.018  0.018
t       4.601 50.377


 Parameter values, observation parameters 

       Item1,mean Item1,stddev Item2,p 1 Item2,p 2
State1      5.526        0.149     0.500     0.500
se          0.021        0.015     0.000     0.000
t         269.320       10.080        NA        NA
State2      6.408        0.238     0.100     0.900
se          0.027        0.019     0.034     0.034
t         238.862       12.502     2.981    26.689


 Parameter values, initial state probabilies 

    State1 State2
val    0.5    0.5
se     0.0    0.0
t       NA     NA

 Model for group:  3 
 Model:  2 -state model  
 Number of parameters:  15  
 Free parameters:       7  
 Number of states:      2 
 Number of items:       2 
 Item types:            normal 2 

 Parameter values, transition matrix 

       State1 State2
State1  0.903  0.097
se      0.023  0.023
t      38.656  4.152
State2  0.084  0.916
se      0.018  0.018
t       4.601 50.377


 Parameter values, observation parameters 

       Item1,mean Item1,stddev Item2,p 1 Item2,p 2
State1      5.422        0.167     0.500     0.500
se          0.027        0.027     0.000     0.000
t         200.763        6.209        NA        NA
State2      6.355        0.214     0.107     0.893
se          0.025        0.018     0.034     0.034
t         259.249       12.221     3.164    26.282


 Parameter values, initial state probabilies 

    State1 State2
val    0.5    0.5
se     0.0    0.0
t       NA     NA

> 
> # LEARNING DATA AND MODELS (with absorbing states)
> 
> data(discrimination)
> 
> # all or none model with error prob in the learned state
> fixed = c(0,0,0,1,1,1,1,0,0,0,0)
> stv = c(1,1,0,0.03,0.97,0.1,0.9,0.5,0.5,0,1)
> allor <- dmm(nstates=2,itemtypes=2,fixed=fixed,stval=stv,modname="All-or-none")
> 
> # Concept identification model: learning only after an error
> st=c(1,1,0,0,0,0.5,0.5,0.5,0.25,0.25,0.05,0.95,0,1,1,0,0.25,0.375,0.375)
> # fix some parameters
> fx=rep(0,19)
> fx[8:12]=1
> fx[17:19]=1
> # add a couple of constraints
> conr1 <- rep(0,19)
> conr1[9]=1
> conr1[10]=-1
> conr2 <- rep(0,19)
> conr2[18]=1
> conr2[19]=-1
> conr3 <- rep(0,19)
> conr3[8]=1
> conr3[17]=-2
> conr=c(conr1,conr2,conr3)
> cim <- dmm(nstates=3,itemtypes=2,fixed=fx,conrows=conr,stval=st,modname="CIM")
> 
> # define a mixture of the above models ...
> mix <- mixdmm(dmm=list(allor,cim),modname="MixAllCim")
> 
> # ... and fit it on the combined data discrimination
> fitmix <- fitdmm(discrimination,mix)
Warning in fitdmm(discrimination, mix) : No automatic boosting of starting values for other than single component 1-group models.
Initial loglikelihood:  -1678.963  
iteration = 0
Step:
[1] 0 0 0 0 0
Parameter:
[1] -0.40215729  0.49784271 -0.67326675 -0.02781207 -0.56737159
Function Value
[1] 1678.963
Gradient:
[1] -221.7038 -354.8272 -588.4017  258.5217 -223.8521

iteration = 26
Parameter:
[1] -0.37097952  0.56157750 -0.61673263 -0.02784283 -0.60113677
Function Value
[1] 1656.162
Gradient:
[1]  0.0003716630 -0.0006824727 -0.0001190787  0.0001874036 -0.0001628039

Relative gradient close to zero.
Current iterate is probably solution.

Final loglikelihood:  -1656.162 
Computing posteriors 
Computing standard errors 
This took  26 iterations,  60.34  seconds 
> summary(fitmix)
Model:  MixAllCim  fitted at  Wed Jul 13 13:11:29 2005  
Optimization information, method is  nlm 
 Iterations:  26
 Inform:  1  (look up the respective manuals for more information.)

 Loglikelihood of fitted model:  -1656.162
 AIC:  3322.324
 BIC:  3352.582
 Number of observations (used in BIC):  3139
 Fitted model 
 Model:  MixAllCim 
 Nr of components:  2 
 npars:             30 
 freepars:          5 

 Mixture proportions 
       Comp  1    Comp  2
val 0.51063408 0.48936592
se  0.09852686 0.09852686
t   5.18268903 4.96682748

 Component:  1 
 Model:  All-or-none  
 Number of parameters:  11  
 Free parameters:       2  
 Number of states:      2 
 Number of items:       1 
 Item types:            2 

 Parameter values, transition matrix 

       State1 State2
State1  1.000  0.000
se      0.000  0.000
t          NA     NA
State2  0.076  0.924
se      0.012  0.012
t       6.574 79.809


 Parameter values, observation parameters 

       Item1,p 1 Item1,p 2
State1     0.073     0.927
se         0.020     0.020
t          3.581    45.464
State2     0.500     0.500
se         0.000     0.000
t             NA        NA


 Parameter values, initial state probabilies 

    State1 State2
val      0      1
se       0      0
t       NA     NA


 Component:  2 
 Model:  CIM  
 Number of parameters:  19  
 Free parameters:       2  
 Number of states:      3 
 Number of items:       1 
 Item types:            2 

 Parameter values, transition matrix 

       State1 State2 State3
State1  1.000  0.000  0.000
se      0.000  0.000  0.000
t          NA     NA     NA
State2  0.000  0.500  0.500
se      0.000  0.000  0.000
t          NA     NA     NA
State3  0.539  0.230  0.230
se      0.098  0.049  0.049
t       5.500  4.698  4.698


 Parameter values, observation parameters 

       Item1,p 1 Item1,p 2
State1     0.034     0.966
se         0.014     0.014
t          2.474    70.882
State2     0.000     1.000
se         0.000     0.000
t             NA        NA
State3     1.000     0.000
se         0.000     0.000
t             NA        NA


 Parameter values, initial state probabilies 

    State1 State2 State3
val  0.270  0.365  0.365
se   0.049  0.025  0.025
t    5.500 14.896 14.896

> 
> 
> 
> 
> cleanEx(); ..nameEx <- "generate"
> 
> ### * generate
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: generate
> ### Title: Generate data from a dependent mixture model
> ### Aliases: generate
> ### Keywords: datagen
> 
> ### ** Examples
> 
> 
> # create a 2 state model with one continuous and one binary response
> # with start values provided in st
> st <- c(1,0.9,0.1,0.2,0.8,2,1,0.7,0.3,5,2,0.2,0.8,0.5,0.5)
> mod <- dmm(nsta=2,itemt=c(1,2), stval=st)
> 
> # generate two series of lengths 100 and 50 respectively using above model
> gen<-generate(c(100,50),mod)
> 
> summary(gen)
Data set:                   2 -item data 
 nr of items:               2 
 item type(s):              1 2 
 item name(s):              1 2 
 length(s) of series:       100 50
 nr of independent series:  2 
 data:                      4.347533 1  ... 
> plot(gen)
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "markovdata"
> 
> ### * markovdata
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: markovdata
> ### Title: Specifying Markov data objects
> ### Aliases: markovdata summary.md plot.md print.md plot.ts2 dname ntimes
> ###   itemtypes ncov inames nitems ind
> ### Keywords: data
> 
> ### ** Examples
> 
> 
> x=rnorm(100,10,2)
> y=ifelse(runif(100)<0.5,0,1)
> z=matrix(c(x,y),100,2)
> md=markovdata(z,itemtypes=c("cont","cat"))
> summary(md)
Data set:                   2 -item data 
 nr of items:               2 
 item type(s):              continuous categorical 
 item name(s):              continuous categorical 
 length(s) of series:       100
 data:                      8.747092 0  ... 
> md[1:10,]
      continuous categorical
 [1,]   8.747092           0
 [2,]  10.367287           0
 [3,]   8.328743           1
 [4,]  13.190562           0
 [5,]  10.659016           0
 [6,]   8.359063           1
 [7,]  10.974858           1
 [8,]  11.476649           0
 [9,]  11.151563           0
[10,]   9.389223           1
> 
> data(speed)
> summary(speed)
Data set:                   speed 
 nr of items:               3 
 item type(s):              continuous categorical covariate 
 nr of covariates:          1 
 item name(s):              rt corr Pacc 
 length(s) of series:       168 134 137
 nr of independent series:  3 
 data:                      6.45677 1 0  ... 
> plot(speed,nind=2)
> 
> # split the data into three data sets 
> # (to perform multi group analysis) 
> r1=markovdata(dat=speed[1:168,],item=itemtypes(speed))
> r2=markovdata(dat=speed[169:302,],item=itemtypes(speed))
> r3=markovdata(dat=speed[303:439,],item=itemtypes(speed))
> summary(r2)
Data set:                   3 -item data 
 nr of items:               3 
 item type(s):              continuous categorical covariate 
 nr of covariates:          1 
 item name(s):              rt corr Pacc 
 length(s) of series:       134
 data:                      6.621406 1 0  ... 
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "mgdmm"
> 
> ### * mgdmm
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: mgdmm
> ### Title: Multi group model specification
> ### Aliases: mgdmm summary.mgd
> ### Keywords: models
> 
> ### ** Examples
> 
> 
> # create a 2 state model with one continuous and one binary response
> # with start values provided in st
> st <- c(1,0.9,0.1,0.2,0.8,2,1,0.7,0.3,5,2,0.2,0.8,0.5,0.5)
> mod <- dmm(nsta=2,itemt=c(1,2), stval=st)
> 
> # define 3-group model with equal transition parameters, and no 
> # equalities between the obser parameters
> mgr <- mgdmm(dmm=mod,ng=3,trans=TRUE,obser=FALSE)
> summary(mgr)
 Model:  3 group model 
 Nr of groups:      3 
 Nr of parameters:  45 
 Free parameters:   23 
 Model for group:  1 
 Model:  2 -state model  
 Number of parameters:  15  
 Free parameters:       9  
 Number of states:      2 
 Number of items:       2 
 Item types:            1 2 

 Parameter values, transition matrix 

       State1 State2
State1    0.9    0.1
State2    0.2    0.8


 Parameter values, observation parameters 

       Item1,mean Item1,stddev Item2,p 1 Item2,p 2
State1          2            1       0.7       0.3
State2          5            2       0.2       0.8


 Parameter values, initial state probabilies 

    State1 State2
val    0.5    0.5

 Model for group:  2 
 Model:  2 -state model  
 Number of parameters:  15  
 Free parameters:       9  
 Number of states:      2 
 Number of items:       2 
 Item types:            1 2 

 Parameter values, transition matrix 

       State1 State2
State1    0.9    0.1
State2    0.2    0.8


 Parameter values, observation parameters 

       Item1,mean Item1,stddev Item2,p 1 Item2,p 2
State1          2            1       0.7       0.3
State2          5            2       0.2       0.8


 Parameter values, initial state probabilies 

    State1 State2
val    0.5    0.5

 Model for group:  3 
 Model:  2 -state model  
 Number of parameters:  15  
 Free parameters:       9  
 Number of states:      2 
 Number of items:       2 
 Item types:            1 2 

 Parameter values, transition matrix 

       State1 State2
State1    0.9    0.1
State2    0.2    0.8


 Parameter values, observation parameters 

       Item1,mean Item1,stddev Item2,p 1 Item2,p 2
State1          2            1       0.7       0.3
State2          5            2       0.2       0.8


 Parameter values, initial state probabilies 

    State1 State2
val    0.5    0.5

> 
> 
> 
> 
> cleanEx(); ..nameEx <- "mixdmm"
> 
> ### * mixdmm
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: mixdmm
> ### Title: Mixture of dmm's specification
> ### Aliases: mixdmm summary.mixdmm
> ### Keywords: models
> 
> ### ** Examples
> 
> 
> # define component 1
> # all or none model with error prob in the learned state
> fixed = c(0,0,0,1,1,1,1,0,0,0,0)
> stv = c(1,1,0,0.07,0.93,0.9,0.1,0.5,0.5,0,1)
> allor <- dmm(nstates=2,itemtypes=2,fixed=fixed,stval=stv,modname="All-or-none")
> 
> # define component 2
> # Concept identification model: learning only after an error
> st=c(1,1,0,0,0,0.5,0.5,0.5,0.25,0.25,0.8,0.2,1,0,0,1,0.25,0.375,0.375)
> # fix some parameters
> fx=rep(0,19)
> fx[8:12]=1
> fx[17:19]=1
> # add a couple of constraints
> conr1 <- rep(0,19)
> conr1[9]=1
> conr1[10]=-1
> conr2 <- rep(0,19)
> conr2[18]=1
> conr2[19]=-1
> conr3 <- rep(0,19)
> conr3[8]=1
> conr3[17]=-2
> conr=c(conr1,conr2,conr3)
> cim <- dmm(nstates=3,itemtypes=2,fixed=fx,conrows=conr,stval=st,modname="CIM")
> 
> # define a mixture of the above component models
> mix <- mixdmm(dmm=list(allor,cim),modname="MixAllCim")
> summary(mix)
 Model:  MixAllCim 
 Nr of components:  2 
 npars:             30 
 freepars:          5 

 Mixture proportions 
[1] 0.5 0.5

 Component:  1 
 Model:  All-or-none  
 Number of parameters:  11  
 Free parameters:       2  
 Number of states:      2 
 Number of items:       1 
 Item types:            2 

 Parameter values, transition matrix 

       State1 State2
State1   1.00   0.00
State2   0.07   0.93


 Parameter values, observation parameters 

       Item1,p 1 Item1,p 2
State1       0.9       0.1
State2       0.5       0.5


 Parameter values, initial state probabilies 

    State1 State2
val      0      1


 Component:  2 
 Model:  CIM  
 Number of parameters:  19  
 Free parameters:       2  
 Number of states:      3 
 Number of items:       1 
 Item types:            2 

 Parameter values, transition matrix 

       State1 State2 State3
State1    1.0   0.00   0.00
State2    0.0   0.50   0.50
State3    0.5   0.25   0.25


 Parameter values, observation parameters 

       Item1,p 1 Item1,p 2
State1       0.8       0.2
State2       1.0       0.0
State3       0.0       1.0


 Parameter values, initial state probabilies 

    State1 State2 State3
val   0.25  0.375  0.375

> 
> 
> 
> 
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  111.36 0.34 112.05 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')