I just noticed that this post never got a reply. Sorry about that. Essentially you need to add design data that creates a factor variable which pools the days after day1 or just by having a numeric variable that is 1 for day 1 an 0 for all other days. Below is an example but if that is not what you intended you'll have to provide more information. --jeff

Here is an examples using the robust data in RMark

- Code: Select all
`data(robust)`

# data from Robust.dbf with MARK

# 5 primary sessions with secondary sessions of length 2,2,4,5,2

#

time.intervals=c(0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0)

rb.proc=process.data(robust,model="Robust",time.intervals=time.intervals)

rb.ddl=make.design.data(rb.proc)

# create variable in p design data which is 1 for day (time) 1 and 0 otherwise

rb.ddl$p$day1=ifelse(rb.ddl$p$time==1,1,0)

# need to add the same variable to c because we are sharing p/c - in this case day1=0 because c is only for occasions past day1

rb.ddl$c$day1=0

S.time=list(formula=~time)

# p varies by day1 for each session and by session for other days but constant across those days within session

p.time.session=list(formula=~-1+session:day1+session,share=TRUE)

mark(rb.proc,rb.ddl,model.parameters=list(S=S.time,p=p.time.session))

Here is beginning of output.

Output summary for Robust model

Name : S(~time)Gamma''(~1)Gamma'(~1)p(~-1 + session:day1 + session)c()f0(~session)

Npar : 21

-2lnL: -18057.08

AICc : -18014.92

Beta

estimate se lcl ucl

S:(Intercept) 2.0771695 0.1325296 1.8174115 2.3369276

S:time2 -0.1977186 0.2091827 -0.6077167 0.2122794

S:time3 -0.9472555 0.1654696 -1.2715759 -0.6229351

S:time4 -0.8406458 0.3081152 -1.4445515 -0.2367400

GammaDoublePrime:(Intercept) -1.9649189 0.0995623 -2.1600611 -1.7697767

GammaPrime:(Intercept) -2.1762911 0.5102500 -3.1763811 -1.1762011

p:session1 0.5996324 0.0795988 0.4436187 0.7556461

p:session2 0.3431816 0.0803888 0.1856196 0.5007436

p:session3 0.5910726 0.0491665 0.4947064 0.6874389

p:session4 0.4682220 0.0471904 0.3757288 0.5607153

p:session5 -0.1260259 0.1436539 -0.4075875 0.1555357

p:session1:day1 0.1835033 0.0948776 -0.0024569 0.3694634

p:session2:day1 0.2275862 0.1031532 0.0254059 0.4297666

p:session3:day1 -0.6412770 0.0893858 -0.8164733 -0.4660808

p:session4:day1 -0.1575436 0.1007473 -0.3550082 0.0399210

p:session5:day1 0.0803370 0.1417940 -0.1975793 0.3582534

f0:(Intercept) 4.7123162 0.1427475 4.4325311 4.9921012

f0:session2 0.0771959 0.1972439 -0.3094020 0.4637939

f0:session3 -1.9772190 0.3179338 -2.6003694 -1.3540687

f0:session4 -3.2746265 0.5575347 -4.3673946 -2.1818584

f0:session5 -0.0291131 0.2471420 -0.5135114 0.4552852