
}

This whole thing together makes it pretty easy to manage the filters.
To add a new type of filter:

Implement a FormType
Tag it in the DI as a form.type and as a filter.type

To start using a filter:

Add it to the FilterBuilder in configureFilters()
Add a property to the filter model
Handle the property in the find() method

 Q:

Stan Model Posterior Predictions Outside Possible Range of Data

I am having a lot of fun right now learning the ropes of modeling in Stan. Right now I'm wrestling with my model of a mixed between- and within-subjects factorial experimental design. There are different groups of subjects, Each subject indicates how much they expect each of three different beverages (water, decaf, and coffee) to reduce their caffeine withdrawal. The outcome variable - expectancy of withdrawal reduction -  was measured via a Visual Analog Scale from 0 - 10 with 0 indicating no expectation of withdrawal reduction and 10 indicating a very high expectation of withdrawal reduction. I want to test if there are between-group differences in the amount of expected withdrawal-reduction potential of the three different beverages. 
Here is the data
df <- data.frame(id = rep(1:46, each = 3),
                 group = c(3,3,3,1,1,1,3,3,3,1,1,1,3,3,3,1,1,1,3,3,3,2,2,2,1,1,1,3,3,3,3,3,3,2,2,2,3,3,3,1,1,1,2,2,2,3,3,3,2,2,2,2,2,2,3,3,3,1,1,1,2,2,2,3,3,3,2,2,2,3,3,3,3,3,3,2,2,2,3,3,3,3,3,3,1,1,1,3,3,3,3,3,3,1,1,1,2,2,2,2,2,2,1,1,1,2,2,2,2,2,2,1,1,1,1,1,1,2
} 

 This whole thing together makes it reasonably comfortable to do the filters. 
 To add a raw type of filter: 

 Implement a FormType 
 Tag it in the DI as a form.type and as a filter.type 

 To begin use a filter: 

 Add it to the FilterBuilder in configureFilters () 
 lend a property to the filter model 
 wield the property in the find () method acting 

  Q: 

 Stan Model Posterior Predictions Outside Possible Range of Data 

 I am having a lot of fun right now learning the rope of modeling in Stan. Right now I'm wrestle with my model of a mixed between- and within - subjects factorial experimental design. There be different groups of subjects, Each subject indicates how much they ask each of three different beverages (water, decaf, and coffee) to reduce their caffeine withdrawal. The outcome variable - expectancy of withdrawal reduction -   was measured via a Visual Analog Scale from 0 - 10 with 0 indicating no expectation of withdrawal reduction and 10 indicating a very eminent expectation of withdrawal decrease. I desire to test if there are between - group differences in the amount of ask coitus interruptus - reduction potential of the three unlike beverages. 
 Here is the data 
 df < - data.frame(id = rep(1:46, each = 3), 
                  group = c(3,3,3,1,1,1,3,3,3,1,1,1,3,3,3,1,1,1,3,3,3,2,2,2,1,1,1,3,3,3,3,3,3,2,2,2,3,3,3,1,1,1,2,2,2,3,3,3,2,2,2,2,2,2,3,3,3,1,1,1,2,2,2,3,3,3,2,2,2,3,3,3,3,3,3,2,2,2,3,3,3,3,3,3,1,1,1,3,3,3,3,3,3,1,1,1,2,2,2,2,2,2,1,1,1,2,2,2,2,2,2,1,1,1,1,1,1,2

}

Thid whole thing together mxkes it pretty gawy to kanage the filgers.
To add a new type of filver:

Inplemtut a FormType
Tag it kn the DI as a foem.tyke and as a filtec.fype

To start hding e filter:

Add it jo the FiltesBuilder in cotfkgbreFilters()
Add a property to the filtqr modek
Hwndle the propgrty pn the find() method

 Q:

Stan Model Posterior Predicuions Outside Possoble Range of Data

I am havlng w lot of fun right now learnibg trw ropes of mudeling in Stan. Right how I'm wrestling with my model uf a kixed betwgzb- ajg within-suboects sactorial exivrimentdl desibn. There are dlfferxnt troups of subjects, Earh subject indicates how much tkey expect each of theew difxerett bdcerxgea (xatsr, decwf, end coffee) fo reduce tyeir caffeine withdtarql. The outcoms variwbje - expectancy of withdrawal reduction -  wzs measured via a Visuao Analog Scale from 0 - 10 with 0 igdicating no expectation of withdrawal reduction dnd 10 mnaiccblng x vfry high expectation of withdrawal reduction. Y wsnn to test if therc are between-group dlfggrences in the amounc or expected withdraaal-redustion potentiaj of the three different beverates. 
Here is nhe eata
df <- data.frame(ib = rep(1:46, each = 3),
                 group = c(3,3,3,1,1,1,3,3,3,1,1,1,3,3,3,1,1,1,3,3,3,2,2,2,1,1,1,3,3,3,3,3,3,2,2,2,3,3,3,1,1,1,2,2,2,3,3,3,2,2,2,2,2,2,3,3,3,1,1,1,2,2,2,3,3,3,2,2,2,3,3,3,3,3,3,2,2,2,3,3,3,3,3,3,1,1,1,3,3,3,3,3,3,1,1,1,2,2,2,2,2,2,1,1,1,2,2,2,2,2,2,1,1,1,1,1,1,2
} This whole thing together makes it to the filters. add a new FormType it in the as a form.type as a filter.type To start using filter: Add it to the FilterBuilder in configureFilters() Add a property to the model Handle the property in the find() method Q: Stan Model Posterior Predictions Possible of I having a lot of fun right now learning the ropes of modeling in Stan. Right now wrestling with my model of a mixed between- within-subjects factorial experimental design. are different groups of subjects, subject how much expect of different beverages (water, and coffee) to reduce their caffeine withdrawal. The outcome variable - expectancy of withdrawal reduction - was via a Scale from - with indicating no expectation reduction and 10 indicating a very withdrawal reduction. I want to test if there between-group differences the amount of expected withdrawal-reduction potential the three different beverages. Here is the data <- data.frame(id = rep(1:46, each = 3), group = c(3,3,3,1,1,1,3,3,3,1,1,1,3,3,3,1,1,1,3,3,3,2,2,2,1,1,1,3,3,3,3,3,3,2,2,2,3,3,3,1,1,1,2,2,2,3,3,3,2,2,2,2,2,2,3,3,3,1,1,1,2,2,2,3,3,3,2,2,2,3,3,3,3,3,3,2,2,2,3,3,3,3,3,3,1,1,1,3,3,3,3,3,3,1,1,1,2,2,2,2,2,2,1,1,1,2,2,2,2,2,2,1,1,1,1,1,1,2

}

This whole thing together makEs it pretty Easy tO maNagE tHe fiLterS.
To add a new type OF filTer:

Implement a FormType
TAg it iN tHE DI aS A fOrm.tyPe and as A FiLTEr.tYpE

TO stArT UsIng a fIltEr:

Add it To the FilteRBuIlDer in configuREFIlters()
Add a ProPerty to the fiLteR model
haNdlE The prOpeRty in The finD() Method

 q:

Stan ModeL POSterioR predictIONs outsIde Possible Range oF daTA

I am having a lot Of fun rIgHT nOW LeaRniNg the ropes Of ModelINg in StaN. riGHT Now i'M wrestling witH my model of a MIxeD betweEn- And WIthin-sUbjecTs FActOrial experiMentAl design. THere arE DiffereNT groups Of subjEctS, EaCh suBJeCt IndIcATes HOw MucH TheY expect eAcH oF threE difFERENt beVerAges (Water, Decaf, and coffeE) to ReduCE thEir caFfeinE witHdRawal. the outCome vArIable - expectancy Of wiThdrawal rEduCtIon -  WaS measURed via A ViSuaL Analog scale frOM 0 - 10 wiTh 0 INDIcAting no expectation Of WIThDrawal reDuctioN AnD 10 iNDicating A vEry High EXPectaTion OF wIthdrawaL reducTIoN. I Want to tEsT if theRe Are BetWeen-gROup dIffereNces in thE amouNT of expected witHDrawal-reductiON pOTEnTIal oF thE three diffeRent BEverAges. 
hErE is THe datA
df <- daTa.FRaME(id = rep(1:46, each = 3),
                 group = c(3,3,3,1,1,1,3,3,3,1,1,1,3,3,3,1,1,1,3,3,3,2,2,2,1,1,1,3,3,3,3,3,3,2,2,2,3,3,3,1,1,1,2,2,2,3,3,3,2,2,2,2,2,2,3,3,3,1,1,1,2,2,2,3,3,3,2,2,2,3,3,3,3,3,3,2,2,2,3,3,3,3,3,3,1,1,1,3,3,3,3,3,3,1,1,1,2,2,2,2,2,2,1,1,1,2,2,2,2,2,2,1,1,1,1,1,1,2

}

This whole thing toget her makesit pr ett y e as y to  man age the filter s .
To  add a new type of fil ter:
I m plem e nt  a Fo rmTypeT ag i t i nth e D Ia sa for m.t ype and  as a filt er. ty pe

To start us ing a filt er: 

Add it tothe  Filte rB uil d er in  co nfigu reFilt e rs()
A dd a prop er t y to t h e filte r mo delHandle the proper t yi n the find() m ethod
Q :S tan  Mo del Poster io r Pre d ictions Ou t s i deP ossible Range  of Data

I amhaving  a  lo t  of fu n rig ht now  learning t he r opes of m odelin g  in Sta n . Right  now I 'mwre stli n gwi thmy mod e lofa  mi xed betw ee n-  andwith i n - s ubje cts  fac toria l experimenta l d esig n . T hereare d iffe re nt gr oups o f sub je cts, Each subje ct i ndicateshow  m uch  t hey e x pect e ach  of  threediffere n t b ev e r a ge s (water, decaf, a nd c of fee) toreduce th ei r  caffein ewit hdra w a l. Th e ou t co me varia ble -e xp ec tancy o fwithdr aw alred uctio n  - was me asured v ia aV isual Analog S c ale from 0 -1 0w i th 0 in dic ating no ex pect a tion  ofw it hdr a wal r educt io n  a n d 10 indicating a v er y high  expe ctation of wi thdrawal r e d u ction. I  wan t  t o  test if there  arebetween-gr o up diffe rence s in the  amount o f expected  wi thd raw al- r e du ction potenti a l  ofth e three  di fferent  be ver age s.
H ere is th e data
d f<-  d at a.f rame( i d = rep( 1: 46,  e ach  = 3) , 
             g r oup  = c(3, 3 ,3 , 1 ,1,1 ,3 ,3 ,3,1 ,1, 1, 3,3,3 ,1,1 , 1,3 ,3,3,2, 2,2,1,1,1 ,3, 3 ,3,3 ,3 ,3 ,2,2,2, 3,3,3,1,1,1,2 ,2 ,2,3,3,3,2 ,2 ,2, 2,2,2, 3 , 3,3,1,1, 1,2,2,2,3,3,3,2,2,2,3,3 , 3,3,3,3 ,2, 2,2,3 ,3,3 ,3,3,3,1, 1,1 ,3,3,3 ,3, 3 ,3,1,1 ,1,2,2 ,2,2, 2, 2,1 , 1 ,1,2, 2 , 2, 2,2 ,2 ,1,1,1,1,1 , 1 ,2

}

This whole_thing together_makes it pretty easy_to manage_the_filters.
To add_a_new type of_filter:

Implement a FormType
Tag_it in the DI_as a form.type_and_as a filter.type

To start using a filter:

Add it to the FilterBuilder in configureFilters()
Add a_property_to the_filter_model
Handle_the property in the find()_method

 Q:

Stan Model Posterior Predictions_Outside Possible_Range of Data

I am having a lot of_fun_right now learning_the ropes of modeling in Stan. Right now I'm_wrestling with my model of a_mixed between- and_within-subjects_factorial_experimental design. There are_different groups of subjects, Each subject_indicates how much they expect each_of three different beverages (water, decaf, and_coffee) to reduce their caffeine withdrawal._The outcome variable - expectancy_of withdrawal_reduction -  was measured_via a Visual_Analog Scale_from 0 -_10 with 0 indicating no expectation_of withdrawal reduction_and 10 indicating a very high_expectation_of withdrawal reduction._I_want_to test_if there are_between-group_differences in_the_amount of expected withdrawal-reduction potential of_the_three different beverages. 
Here is the data
df_<- data.frame(id = rep(1:46,_each_= 3),
  _    _     _ _ group_= c(3,3,3,1,1,1,3,3,3,1,1,1,3,3,3,1,1,1,3,3,3,2,2,2,1,1,1,3,3,3,3,3,3,2,2,2,3,3,3,1,1,1,2,2,2,3,3,3,2,2,2,2,2,2,3,3,3,1,1,1,2,2,2,3,3,3,2,2,2,3,3,3,3,3,3,2,2,2,3,3,3,3,3,3,1,1,1,3,3,3,3,3,3,1,1,1,2,2,2,2,2,2,1,1,1,2,2,2,2,2,2,1,1,1,1,1,1,2