#--------------------------------------------------------------------------------------------------- ## Rmark Code December 2018 # CJS Survival analyses #--------------------------------------------------------------------------------------------------- require(RMark) ugs_data <- import.chdata("ch_1964-68_14-17.txt", field.types=c("f","f","f","f", "n")) ugs_data = cbind(ugs_data[1:3], ugs_data[5:6]) ugs_process <- process.data(ugs_data, model="CJS", groups=c("iniage", "sex", "grp", "freq"), age.var=1, initial.age = c(2,0,1), begin.time=1964, time.intervals=c(1,1,1,1,46,1,1,1)) ugs_ddl <- make.design.data(ugs_process) ugs_ddl = add.design.data(ugs_process, ugs_ddl, parameter="Phi", type="age", bins=c(0,1,56),name="agegroup",right=FALSE,replace=TRUE) ugs_ddl = add.design.data(ugs_process, ugs_ddl, parameter="p", type="age", bins=c(0,1.5,56),name="agegroup",right=FALSE,replace=TRUE) #--------------------------------------------------------------------------------------------------- # Fix appropriate parameters to 0 ##### #--------------------------------------------------------------------------------------------------- #_1. historical UGS have p = 0 in current years; 2014 is also p = 0 for contemporary squirrels #### p.indices1 <- as.numeric(row.names(ugs_ddl$p[(ugs_ddl$p$grp=="h" & ugs_ddl$p$time=="2014" | ugs_ddl$p$grp=="c" & ugs_ddl$p$time=="2014" | ugs_ddl$p$grp=="h" & ugs_ddl$p$time=="2015" | ugs_ddl$p$grp=="h" & ugs_ddl$p$time=="2016" | ugs_ddl$p$grp=="h" & ugs_ddl$p$time=="2017"),])) p.values1 <- rep(0, length(p.indices1)) #----------------------------------------------------- #_2. Fix hist p = 1 for historical UGS adults/yearlings/pups #### p.indices2_f<-as.numeric(row.names(ugs_ddl$p[(ugs_ddl$p$grp=="h" & ugs_ddl$p$time=="1965" & ugs_ddl$p$sex == "f" | ugs_ddl$p$grp=="h" & ugs_ddl$p$time=="1966" & ugs_ddl$p$sex == "f"| ugs_ddl$p$grp=="h" & ugs_ddl$p$time=="1967" & ugs_ddl$p$sex == "f"| ugs_ddl$p$grp=="h" & ugs_ddl$p$time=="1968" & ugs_ddl$p$sex == "f"),])) p.indices2_m <-as.numeric(row.names(ugs_ddl$p[(ugs_ddl$p$grp=="h" & ugs_ddl$p$time=="1965" & ugs_ddl$p$sex == "m" & ugs_ddl$p$iniage == "p" | ugs_ddl$p$grp=="h" & ugs_ddl$p$time=="1966" & ugs_ddl$p$sex == "m" & ugs_ddl$p$iniage == "p"| ugs_ddl$p$grp=="h" & ugs_ddl$p$time=="1966" & ugs_ddl$p$sex == "m" & ugs_ddl$p$iniage == "y" & ugs_ddl$p$cohort== "1965" | ugs_ddl$p$grp=="h" & ugs_ddl$p$time=="1967" & ugs_ddl$p$sex == "m" & ugs_ddl$p$Age<=3 | ugs_ddl$p$grp=="h" & ugs_ddl$p$time=="1968" & ugs_ddl$p$sex == "m" & ugs_ddl$p$Age<=4),])) p.values2_f <- rep(1.0, length(p.indices2_f)) p.values2_m <- rep(1.0, length(p.indices2_m)) #----------------------------------------------------- #_3. Fix hist p = 0 for historical male UGS for 1965 #### p.indices_new<-as.numeric(row.names(ugs_ddl$p[(ugs_ddl$p$grp=="h" & ugs_ddl$p$sex=="m" & ugs_ddl$p$cohort=="1964" & ugs_ddl$p$iniage=="a"| ugs_ddl$p$grp=="h" & ugs_ddl$p$sex=="m" & ugs_ddl$p$cohort=="1964" & ugs_ddl$p$iniage=="y"| ugs_ddl$p$grp=="h" & ugs_ddl$p$sex=="m" & ugs_ddl$p$cohort=="1965" & ugs_ddl$p$iniage=="a" ),])) p.values_new <- rep(0, length(p.indices_new)) #----------------------------------------------------- #_4.Contemporary UGS have p = 0 in historical years #### p.indices3 <- as.numeric(row.names(ugs_ddl$p[(ugs_ddl$p$grp=="c" & ugs_ddl$p$cohort=="1964" | ugs_ddl$p$grp=="c" & ugs_ddl$p$cohort=="1965" | ugs_ddl$p$grp=="c" & ugs_ddl$p$cohort=="1966" | ugs_ddl$p$grp=="c" & ugs_ddl$p$cohort=="1967" | ugs_ddl$p$grp=="c" & ugs_ddl$p$cohort=="1968"),])) p.values3 <- rep(0, length(p.indices3)) #----------------------------------------------------- #_5. Fix 2016 p to 1#### p.indices4 <- as.numeric(row.names(ugs_ddl$p[( ugs_ddl$p$grp=="c" & ugs_ddl$p$time=="2016" & ugs_ddl$p$Age< 7 & ugs_ddl$p$iniage=="a" | ugs_ddl$p$grp=="c" & ugs_ddl$p$time=="2016" & ugs_ddl$p$Age< 7 & ugs_ddl$p$iniage=="y" | ugs_ddl$p$grp=="c" & ugs_ddl$p$time=="2016" & ugs_ddl$p$Age< 7 & ugs_ddl$p$iniage=="p"),])) p.values4 <- rep(1.00, length(p.indices4)) #----------------------------------------------------- #_ 6.Estimate for females #### p.indices5 <- as.numeric(row.names(ugs_ddl$p[(ugs_ddl$p$grp=="c" & ugs_ddl$p$sex=="f" & ugs_ddl$p$time== 2015 & ugs_ddl$p$Cohort>5| ugs_ddl$p$grp=="c" & ugs_ddl$p$sex=="f" & ugs_ddl$p$time== 2016 & ugs_ddl$p$Cohort>5| ugs_ddl$p$grp=="c" & ugs_ddl$p$sex=="f" & ugs_ddl$p$time== 2017 & ugs_ddl$p$Cohort>5),])) p.values5 <- rep(1.00, length(p.indices5)) #----------------------------------------------------- ##_7. fix adult and yearling males to 1 #### p.indices6 <- as.numeric(row.names(ugs_ddl$p[(ugs_ddl$p$grp=="c" & ugs_ddl$p$sex=="m" & ugs_ddl$p$time== 2015 & ugs_ddl$p$Cohort>5 & ugs_ddl$p$Age> 1 | ugs_ddl$p$grp=="c" & ugs_ddl$p$sex=="m" & ugs_ddl$p$time== 2016 & ugs_ddl$p$Cohort>5 & ugs_ddl$p$Age> 1| ugs_ddl$p$grp=="c" & ugs_ddl$p$sex=="m" & ugs_ddl$p$time== 2017 & ugs_ddl$p$Cohort>5 & ugs_ddl$p$Age> 1),])) p.values6 <- rep(1.00, length(p.indices6)) #----------------------------------------------------- ##_ Combine all the data sets for p together #### pind = c(p.indices1,p.indices2_f,p.indices2_m, p.indices3,p.indices4,p.indices5,p.indices_new, p.indices6) pval = c(p.values1, p.values2_f, p.values2_m, p.values3, p.values4, p.values5, p.values_new, p.values6) length (unique(pind)) # length(ugs_ddl$p$group) # #--------------------------------------------------------------------------------------------------- #_ 1. Fix hist phi to 0 in current years #### Phi.indices1 <- as.numeric(row.names(ugs_ddl$Phi[(ugs_ddl$Phi$grp=="h" & ugs_ddl$Phi$time=="1968" | ugs_ddl$Phi$grp=="h" & ugs_ddl$Phi$time=="2014" | ugs_ddl$Phi$grp=="h" & ugs_ddl$Phi$time=="2015" | ugs_ddl$Phi$grp=="h" & ugs_ddl$Phi$time=="2016" ),] )) Phi.values1 <- rep(0, length(Phi.indices1)) #----------------------------------------------------- #_ 2. Fix contemporary phi to 0 in historical years #### Phi.indices2 <- as.numeric(row.names(ugs_ddl$p[(ugs_ddl$Phi$grp=="c" & ugs_ddl$Phi$cohort=="1964" | ugs_ddl$Phi$grp=="c" & ugs_ddl$Phi$cohort=="1965" | ugs_ddl$Phi$grp=="c" & ugs_ddl$Phi$cohort=="1966" | ugs_ddl$Phi$grp=="c" & ugs_ddl$Phi$cohort=="1967"| ugs_ddl$Phi$grp=="c" & ugs_ddl$Phi$cohort=="1968"),])) #1968= 1968-69 ("2014") Phi.values2 <- rep(0, length(Phi.indices2)) #----------------------------------------------------- #_ 3. Fix hist phi = 0 for historical adult/yearling male UGS for 1964 #### Phi.indices_new <- as.numeric(row.names(ugs_ddl$Phi[(ugs_ddl$Phi$grp=="h" & ugs_ddl$Phi$sex=="m" & ugs_ddl$Phi$cohort=="1964" & ugs_ddl$p$iniage=="a" | ugs_ddl$Phi$grp=="h" & ugs_ddl$Phi$sex=="m" & ugs_ddl$Phi$cohort=="1965" & ugs_ddl$p$iniage=="a" | ugs_ddl$Phi$grp=="h" & ugs_ddl$Phi$sex=="m" & ugs_ddl$Phi$cohort=="1964" & ugs_ddl$p$iniage=="y"),] )) Phi.values_new <- rep(0, length(Phi.indices_new)) #----------------------------------------------------- Phiind = c(Phi.indices1,Phi.indices2, Phi.indices_new) Phival = c(Phi.values1, Phi.values2, Phi.values_new) #--------------------------------------------------------------------------------------------------- # Create variables: phenological ##### #--------------------------------------------------------------------------------------------------- ## _Emergence Date #### ugs_ddl$Phi$em= NA ugs_ddl$Phi$em[ugs_ddl$Phi$time=="1964"]= 1.47 # emergence in 65 affect 64-65 survival ugs_ddl$Phi$em[ugs_ddl$Phi$time=="1965"]= -0.71 ugs_ddl$Phi$em[ugs_ddl$Phi$time=="1966"]= -1.51 ugs_ddl$Phi$em[ugs_ddl$Phi$time=="1967"]= -0.94 ugs_ddl$Phi$em[ugs_ddl$Phi$time=="1968"]= -0.36 ugs_ddl$Phi$em[ugs_ddl$Phi$time=="2014"]= -0.82 ugs_ddl$Phi$em[ugs_ddl$Phi$time=="2015"]= 0.44 ugs_ddl$Phi$em[ugs_ddl$Phi$time=="2016"]= -0.25 #Emergence with 1-year lag: ugs_ddl$Phi$em_lag= NA ugs_ddl$Phi$em_lag[ugs_ddl$Phi$time=="1964"]= 1.24 # emergence in 64 affecting 64-65 survival ugs_ddl$Phi$em_lag[ugs_ddl$Phi$time=="1965"]= 1.47 # ugs_ddl$Phi$em_lag[ugs_ddl$Phi$time=="1966"]=-0.71 ugs_ddl$Phi$em_lag[ugs_ddl$Phi$time=="1967"]=-1.51 ugs_ddl$Phi$em_lag[ugs_ddl$Phi$time=="1968"]=-0.94 ugs_ddl$Phi$em_lag[ugs_ddl$Phi$time=="2014"]= 0.21 ugs_ddl$Phi$em_lag[ugs_ddl$Phi$time=="2015"]=-0.82 ugs_ddl$Phi$em_lag[ugs_ddl$Phi$time=="2016"]= 0.44 #----------------------------------------------------- #season length with 1-year lag: ugs_ddl$Phi$sl_lag= NA ugs_ddl$Phi$sl_lag[ugs_ddl$Phi$time=="1964"]=-0.77 # season length in 64 affecting 64-65 survival ugs_ddl$Phi$sl_lag[ugs_ddl$Phi$time=="1965"]=-0.58 # ugs_ddl$Phi$sl_lag[ugs_ddl$Phi$time=="1966"]=-0.10 ugs_ddl$Phi$sl_lag[ugs_ddl$Phi$time=="1967"]= 0.56 ugs_ddl$Phi$sl_lag[ugs_ddl$Phi$time=="1968"]= 1.13 ugs_ddl$Phi$sl_lag[ugs_ddl$Phi$time=="2014"]=-1.34 ugs_ddl$Phi$sl_lag[ugs_ddl$Phi$time=="2015"]= 0.37 ugs_ddl$Phi$sl_lag[ugs_ddl$Phi$time=="2016"]=-0.96 #--------------------------------------------------------------------------------------------------- # Create variables: climate ##### #--------------------------------------------------------------------------------------------------- #_ Winter temps: temp from Dec(t-1) thru emergence (t) affect survival t-1 --> t #### ugs_ddl$Phi$wint=0 ugs_ddl$Phi$wint[ugs_ddl$Phi$time==1964]= 1.17 # phi1964 is 64-65: affects survival 64-65 ugs_ddl$Phi$wint[ugs_ddl$Phi$time==1965]= -1.10 ugs_ddl$Phi$wint[ugs_ddl$Phi$time==1966]= -0.13 ugs_ddl$Phi$wint[ugs_ddl$Phi$time==1967]= -0.93 ugs_ddl$Phi$wint[ugs_ddl$Phi$time==1968]= -0.15 ugs_ddl$Phi$wint[ugs_ddl$Phi$time==2014]= 2.14 ugs_ddl$Phi$wint[ugs_ddl$Phi$time==2015]= 0.40 ugs_ddl$Phi$wint[ugs_ddl$Phi$time==2016]= -0.01 # Winter temps: temp from Dec(t-1) thru emergence (t) affect survival t --> t + 1 ugs_ddl$Phi$wint_lag=0 ugs_ddl$Phi$wint_lag[ugs_ddl$Phi$time==1964]= -1.58 # winter temp 63-64, affecting survival 64-65 ugs_ddl$Phi$wint_lag[ugs_ddl$Phi$time==1965]= 1.17 ugs_ddl$Phi$wint_lag[ugs_ddl$Phi$time==1966]= -1.10 ugs_ddl$Phi$wint_lag[ugs_ddl$Phi$time==1967]= -0.13 ugs_ddl$Phi$wint_lag[ugs_ddl$Phi$time==2014]= 0.59 ugs_ddl$Phi$wint_lag[ugs_ddl$Phi$time==2015]= 2.14 ugs_ddl$Phi$wint_lag[ugs_ddl$Phi$time==2016]= 0.40 #-------------------------------- # Snow depth (WINTER) #### ugs_ddl$Phi$snowd=0 ugs_ddl$Phi$snowd[ugs_ddl$Phi$time==1964]= -1.15 # snowd 1964-65, affecting survival 64-65 (phi1964 is 64-65 interval) ugs_ddl$Phi$snowd[ugs_ddl$Phi$time==1965]= 0.44 ugs_ddl$Phi$snowd[ugs_ddl$Phi$time==1966]= 0.10 ugs_ddl$Phi$snowd[ugs_ddl$Phi$time==1967]= 0.74 ugs_ddl$Phi$snowd[ugs_ddl$Phi$time==2014]= -1.49 ugs_ddl$Phi$snowd[ugs_ddl$Phi$time==2015]= 0.68 ugs_ddl$Phi$snowd[ugs_ddl$Phi$time==2016]= 1.62 ugs_ddl$Phi$snowd_lag=0 ugs_ddl$Phi$snowd_lag[ugs_ddl$Phi$time==1964]= 2.61 # snowd 1963-64, affecting survival 64-65 (phi1964 is 64-65 interval) ugs_ddl$Phi$snowd_lag[ugs_ddl$Phi$time==1965]= -1.15 ugs_ddl$Phi$snowd_lag[ugs_ddl$Phi$time==1966]= 0.44 ugs_ddl$Phi$snowd_lag[ugs_ddl$Phi$time==1967]= 0.10 ugs_ddl$Phi$snowd_lag[ugs_ddl$Phi$time==2014]= -0.43 ugs_ddl$Phi$snowd_lag[ugs_ddl$Phi$time==2015]= -1.49 ugs_ddl$Phi$snowd_lag[ugs_ddl$Phi$time==2016]= 0.68 #-------------------------------- # MAX march temperature Mar15-31: (SPRING): avg temp at year t affecting survival t-1 to t #### ugs_ddl$Phi$marm=0 ugs_ddl$Phi$marm[ugs_ddl$Phi$time==1964]= -0.50 # marm temp in 1965 affects survival from 64-65 ugs_ddl$Phi$marm[ugs_ddl$Phi$time==1965]= 0.34 ugs_ddl$Phi$marm[ugs_ddl$Phi$time==1966]= -0.15 ugs_ddl$Phi$marm[ugs_ddl$Phi$time==1967]= 0.22 ugs_ddl$Phi$marm[ugs_ddl$Phi$time==2014]= 1.83 # in 2015 affecting 2014-2015 ugs_ddl$Phi$marm[ugs_ddl$Phi$time==2015]= -0.63 ugs_ddl$Phi$marm[ugs_ddl$Phi$time==2016]= 1.03 # March temperature in spring, LAG (SPRING) ugs_ddl$Phi$marm_lag=0 ugs_ddl$Phi$marm_lag[ugs_ddl$Phi$time==1964]= -2.45 # 1964 affects 64-65 interval (phi1964) ugs_ddl$Phi$marm_lag[ugs_ddl$Phi$time==1965]= -0.50 ugs_ddl$Phi$marm_lag[ugs_ddl$Phi$time==1966]= 0.34 ugs_ddl$Phi$marm_lag[ugs_ddl$Phi$time==1967]= -0.15 ugs_ddl$Phi$marm_lag[ugs_ddl$Phi$time==2014]= 0.40 ugs_ddl$Phi$marm_lag[ugs_ddl$Phi$time==2015]= 1.83 ugs_ddl$Phi$marm_lag[ugs_ddl$Phi$time==2016]= -0.63 #-------------------------------- # Summer drought measure BG (SUMMER): BG in year t affects survival from t to t+1 #### ugs_ddl$Phi$bg=0 ugs_ddl$Phi$bg[ugs_ddl$Phi$time==1964]= 0.92 ugs_ddl$Phi$bg[ugs_ddl$Phi$time==1965]= 1.43 ugs_ddl$Phi$bg[ugs_ddl$Phi$time==1966]= -0.13 ugs_ddl$Phi$bg[ugs_ddl$Phi$time==1967]= 0.55 ugs_ddl$Phi$bg[ugs_ddl$Phi$time==1968]= 1.55 ugs_ddl$Phi$bg[ugs_ddl$Phi$time==2014]= 0.43 ugs_ddl$Phi$bg[ugs_ddl$Phi$time==2015]= -0.43 ugs_ddl$Phi$bg[ugs_ddl$Phi$time==2016]= -1.09 #-------------------------------- # GDD values (SUMMER): GDD in year t affects t to t + 1 survival; based on 0 degrees #### ugs_ddl$Phi$gdd=0 ugs_ddl$Phi$gdd[ugs_ddl$Phi$time==1964]= 2.30 ugs_ddl$Phi$gdd[ugs_ddl$Phi$time==1965]= 2.32 ugs_ddl$Phi$gdd[ugs_ddl$Phi$time==1966]= 1.59 ugs_ddl$Phi$gdd[ugs_ddl$Phi$time==1967]= -0.29 ugs_ddl$Phi$gdd[ugs_ddl$Phi$time==1968]= 0.43 ugs_ddl$Phi$gdd[ugs_ddl$Phi$time==2014]= 1.87 ugs_ddl$Phi$gdd[ugs_ddl$Phi$time==2015]= 1.46 ugs_ddl$Phi$gdd[ugs_ddl$Phi$time==2016]= 1.04 #-------------------------------- # Summer precip: in t affects survival from t to t+1 #### ugs_ddl$Phi$sprecip=0 ugs_ddl$Phi$sprecip[ugs_ddl$Phi$time==1964]= 0.92 ugs_ddl$Phi$sprecip[ugs_ddl$Phi$time==1965]= 0.60 ugs_ddl$Phi$sprecip[ugs_ddl$Phi$time==1966]= -1.27 ugs_ddl$Phi$sprecip[ugs_ddl$Phi$time==1967]= 0.65 ugs_ddl$Phi$sprecip[ugs_ddl$Phi$time==1968]= 1.98 ugs_ddl$Phi$sprecip[ugs_ddl$Phi$time==2014]= 0.55 ugs_ddl$Phi$sprecip[ugs_ddl$Phi$time==2015]= 0.27 ugs_ddl$Phi$sprecip[ugs_ddl$Phi$time==2016]= -1.35 #-------------------------------- # Summer temp: June, July, Aug (SUMMER): stemp in t affects survival from t to t+1 #### ugs_ddl$Phi$stemp=0 ugs_ddl$Phi$stemp[ugs_ddl$Phi$time==1964]= -1.03 ugs_ddl$Phi$stemp[ugs_ddl$Phi$time==1965]= -1.63 ugs_ddl$Phi$stemp[ugs_ddl$Phi$time==1966]= -0.09 ugs_ddl$Phi$stemp[ugs_ddl$Phi$time==1967]= -0.48 ugs_ddl$Phi$stemp[ugs_ddl$Phi$time==1968]= -1.47 ugs_ddl$Phi$stemp[ugs_ddl$Phi$time==2014]= -0.35 ugs_ddl$Phi$stemp[ugs_ddl$Phi$time==2015]= 0.48 ugs_ddl$Phi$stemp[ugs_ddl$Phi$time==2016]= 1.02 #--------------------------------------------------------------------------------------------------- # Create a GLOBAL MODEL for MARK ##### #--------------------------------------------------------------------------------------------------- # only on PC #--------------------------------------------------------------------------------------------------- # Run models: parameterize p ##### #--------------------------------------------------------------------------------------------------- Phi.t.fix <- list(formula = ~ -1 + sex, fixed = list(index=Phiind, value=Phival)) p.fix <-list(formula = ~ 1, fixed = list(index= pind, value= pval)) p.t.fix <-list(formula = ~ -1 + time, fixed = list(index=pind, value=pval)) p.sex.fix <-list(formula = ~ -1 + sex, fixed = list(index= pind, value= pval)) p.age.fix <-list(formula = ~ -1 + agegroup, fixed = list(index= pind, value= pval)) # Run models p0 <- mark(ugs_process,ugs_ddl,model.parameters=list(Phi=Phi.t.fix, p=p.fix)) p1 <- mark(ugs_process,ugs_ddl,model.parameters=list(Phi=Phi.t.fix, p=p.t.fix)) p2 <- mark(ugs_process,ugs_ddl,model.parameters=list(Phi=Phi.t.fix, p=p.sex.fix)) p3 <- mark(ugs_process,ugs_ddl,model.parameters=list(Phi=Phi.t.fix, p=p.age.fix)) UGS.results <- collect.models(type="CJS") UGS.results UGS.results_adj <- adjust.chat(3.9, UGS.results) UGS.results_adj # rm(list=collect.model.names(ls())) #--------------------------------------------------------------------------------------------------- # Test demographic variables ##### #--------------------------------------------------------------------------------------------------- # Top p: p.fix p.fix <-list(formula = ~-1 + sex, fixed = list(index= pind, value= pval)) Phi_null <- list(formula = ~1, fixed = list(index=Phiind, value=Phival)) Phid1 <- list(formula = ~-1 + time, fixed = list(index=Phiind, value=Phival)) Phid2 <- list(formula = ~-1 + sex, fixed = list(index=Phiind, value=Phival)) Phid3 <- list(formula = ~-1 + agegroup, fixed = list(index=Phiind, value=Phival)) Phid4 <- list(formula = ~-1 + ageclass, fixed = list(index=Phiind, value=Phival)) # Additive: Phid5 <- list(formula = ~-1 + time + sex, fixed = list(index=Phiind, value=Phival)) Phid6 <- list(formula = ~-1 + time + agegroup, fixed = list(index=Phiind, value=Phival)) Phid7 <- list(formula = ~-1 + time + ageclass, fixed = list(index=Phiind, value=Phival)) Phid8 <- list(formula = ~-1 + sex + agegroup, fixed = list(index=Phiind, value=Phival)) Phid9 <- list(formula = ~-1 + sex + ageclass, fixed = list(index=Phiind, value=Phival)) Phid10 <- list(formula = ~-1 + time + sex + agegroup, fixed = list(index=Phiind, value=Phival)) Phid11 <- list(formula = ~-1 + time + sex + ageclass, fixed = list(index=Phiind, value=Phival)) # Interaction Phid12 <- list(formula = ~-1 + time:sex + time + sex, fixed = list(index=Phiind, value=Phival)) Phid13 <- list(formula = ~-1 + time:agegroup + time + agegroup, fixed = list(index=Phiind, value=Phival)) Phid14 <- list(formula = ~-1 + time:ageclass + time + agegroup, fixed = list(index=Phiind, value=Phival)) Phid15 <- list(formula = ~-1 + sex:agegroup + sex + agegroup, fixed = list(index=Phiind, value=Phival)) Phid16 <- list(formula = ~-1 + sex:ageclass + sex + ageclass, fixed = list(index=Phiind, value=Phival)) Phid17 <- list(formula = ~-1 + sex:juv + adyr + juv, fixed = list(index=Phiind, value=Phival)) Phid18 <- list(formula = ~-1 + sex:adyr + juv + adyr, fixed = list(index=Phiind, value=Phival)) Phid19 <- list(formula = ~-1 + sex:juv + adyr + time + juv, fixed = list(index=Phiind, value=Phival)) Phid20 <- list(formula = ~-1 + sex:adyr + juv + time + adyr, fixed = list(index=Phiind, value=Phival)) # Run models phi_d0 <- mark(ugs_process,ugs_ddl,model.parameters=list(p=p.fix, Phi=Phi_null)) phi_d1 <- mark(ugs_process,ugs_ddl,model.parameters=list(p=p.fix, Phi=Phid1)) phi_d2 <- mark(ugs_process,ugs_ddl,model.parameters=list(p=p.fix, Phi=Phid2)) phi_d3 <- mark(ugs_process,ugs_ddl,model.parameters=list(p=p.fix, Phi=Phid3)) phi_d4 <- mark(ugs_process,ugs_ddl,model.parameters=list(p=p.fix, Phi=Phid4)) phi_d5 <- mark(ugs_process,ugs_ddl,model.parameters=list(p=p.fix, Phi=Phid5)) phi_d6 <- mark(ugs_process,ugs_ddl,model.parameters=list(p=p.fix, Phi=Phid6)) phi_d7 <- mark(ugs_process,ugs_ddl,model.parameters=list(p=p.fix, Phi=Phid7)) phi_d8 <- mark(ugs_process,ugs_ddl,model.parameters=list(p=p.fix, Phi=Phid8)) phi_d9 <- mark(ugs_process,ugs_ddl,model.parameters=list(p=p.fix, Phi=Phid9)) phi_d10 <- mark(ugs_process,ugs_ddl,model.parameters=list(p=p.fix, Phi=Phid10)) phi_d11 <- mark(ugs_process,ugs_ddl,model.parameters=list(p=p.fix, Phi=Phid11)) phi_d12 <- mark(ugs_process,ugs_ddl,model.parameters=list(p=p.fix, Phi=Phid12)) phi_d13 <- mark(ugs_process,ugs_ddl,model.parameters=list(p=p.fix, Phi=Phid13)) phi_d14 <- mark(ugs_process,ugs_ddl,model.parameters=list(p=p.fix, Phi=Phid14)) phi_d15 <- mark(ugs_process,ugs_ddl,model.parameters=list(p=p.fix, Phi=Phid15)) phi_d16 <- mark(ugs_process,ugs_ddl,model.parameters=list(p=p.fix, Phi=Phid16)) phi_d17 <- mark(ugs_process,ugs_ddl,model.parameters=list(p=p.fix, Phi=Phid17)) phi_d18 <- mark(ugs_process,ugs_ddl,model.parameters=list(p=p.fix, Phi=Phid18)) phi_d19 <- mark(ugs_process,ugs_ddl,model.parameters=list(p=p.fix, Phi=Phid19)) phi_d20 <- mark(ugs_process,ugs_ddl,model.parameters=list(p=p.fix, Phi=Phid20)) phi_d21 <- mark(ugs_process,ugs_ddl,model.parameters=list(p=p.fix, Phi=Phid21)) phi_d22 <- mark(ugs_process,ugs_ddl,model.parameters=list(p=p.fix, Phi=Phid22)) phi_d23 <- mark(ugs_process,ugs_ddl,model.parameters=list(p=p.fix, Phi=Phid23)) phi_d24 <- mark(ugs_process,ugs_ddl,model.parameters=list(p=p.fix, Phi=Phid24)) phi_d25 <- mark(ugs_process,ugs_ddl,model.parameters=list(p=p.fix, Phi=Phid25)) phi_d26 <- mark(ugs_process,ugs_ddl,model.parameters=list(p=p.fix, Phi=Phid26)) phi_d27 <- mark(ugs_process,ugs_ddl,model.parameters=list(p=p.fix, Phi=Phid27)) phi_d28 <- mark(ugs_process,ugs_ddl,model.parameters=list(p=p.fix, Phi=Phid28)) UGS.results <- collect.models(type="CJS") UGS.results UGS.results_adj <- adjust.chat(3.9, UGS.results) UGS.results_adj # rm(list=collect.model.names(ls())) #--------------------------------------------------------------------------------------------------- # Run models: wint, snowd, marm, and sl_lag (sl_lag only makes sense for adyr though) #### p.fix <- list(formula = ~ -1 + sex, fixed = list(index=pind, value=pval)) Phi_null <- list(formula = ~ 1, fixed = list(index=Phiind, value=Phival)) Phi_d <- list(formula=~ -1 + agegroup, fixed = list(index=Phiind, value=Phival)) # Phi1 <- list(formula=~ -1 + agegroup*wint, fixed = list(index=Phiind, value=Phival)) # Phi2 <- list(formula=~ -1 + agegroup*snowd, fixed = list(index=Phiind, value=Phival)) # Phi3 <- list(formula=~ -1 + agegroup*marm, fixed = list(index=Phiind, value=Phival)) # Phi4<- list(formula=~ -1 + adyr + juv + juv:wint, fixed = list(index=Phiind, value=Phival)) # Phi5<- list(formula=~ -1 + adyr + juv + juv:snowd, fixed = list(index=Phiind, value=Phival)) # Phi6<- list(formula=~ -1 + adyr + juv + juv:marm, fixed = list(index=Phiind, value=Phival)) # Phi7 <- list(formula=~ -1 + juv + adyr + adyr:wint, fixed = list(index=Phiind, value=Phival)) # Phi8 <- list(formula=~ -1 + juv + adyr + adyr:snowd, fixed = list(index=Phiind, value=Phival)) # Phi9 <- list(formula=~ -1 + juv + adyr + adyr:marm, fixed = list(index=Phiind, value=Phival)) # Phi10<- list(formula=~ -1 + juv + adyr + adyr:sl_lag, fixed = list(index=Phiind, value=Phival)) # # Run models Phi.n <- mark(ugs_process,ugs_ddl,model.parameters=list(Phi=Phi_null, p=p.fix)) Phi.d <- mark(ugs_process,ugs_ddl,model.parameters=list(Phi=Phi_d, p=p.fix)) Phi.1 <- mark(ugs_process,ugs_ddl,model.parameters=list(Phi=Phi1, p=p.fix)) Phi.2 <- mark(ugs_process,ugs_ddl,model.parameters=list(Phi=Phi2, p=p.fix)) Phi.3 <- mark(ugs_process,ugs_ddl,model.parameters=list(Phi=Phi3, p=p.fix)) Phi.4 <- mark(ugs_process,ugs_ddl,model.parameters=list(Phi=Phi4, p=p.fix)) Phi.5 <- mark(ugs_process,ugs_ddl,model.parameters=list(Phi=Phi5, p=p.fix)) Phi.6 <- mark(ugs_process,ugs_ddl,model.parameters=list(Phi=Phi6, p=p.fix)) Phi.7 <- mark(ugs_process,ugs_ddl,model.parameters=list(Phi=Phi7, p=p.fix)) Phi.8 <- mark(ugs_process,ugs_ddl,model.parameters=list(Phi=Phi8, p=p.fix)) Phi.9 <- mark(ugs_process,ugs_ddl,model.parameters=list(Phi=Phi9, p=p.fix)) Phi.10<- mark(ugs_process,ugs_ddl,model.parameters=list(Phi=Phi10, p=p.fix)) UGS.results <- collect.models(type="CJS") UGS.results UGS.results_adj <- adjust.chat(3.9,UGS.results) UGS.results_adj # rm(list=collect.model.names(ls()))