# Analysis of killdeer data illustrating basic RMark features
# Incorportating an age variable
# 2019-05-01 CJS Initial code
# This is the killdeer data that ships with RMark
# I added arbitrary nest ages.
library(ggplot2)
## Registered S3 methods overwritten by 'ggplot2':
## method from
## [.quosures rlang
## c.quosures rlang
## print.quosures rlang
library(readxl)
library(RMark)
## This is RMark 2.2.6
## Documentation available at http://www.phidot.org/software/mark/rmark/RMarkDocumentation.zip
# The dataframe must contain the following fields with the following names
#
# FirstFound: day the nest was first found
# LastPresent: last day that a chick was present in the nest
# LastChecked: last day the nest was checked
# Fate: fate of the nest; 0=hatch an
# Freq: number of nests with this data
# AgeDay1: age of nest at day 1 of study (can be negative)
#
# In this example, the multiple visits to a nest have been collapsed
# to a single record for each nest.
# In more complex examples, you may have multple records per nest
# as shown in the mallard example.
#
killdata <- readxl::read_excel(file.path("..","Killdeer.xlsx"),
sheet="killdeer-age")
killdata <- as.data.frame(killdata) # sometimes doesn't play nice with tibbles
head(killdata)
## id FirstFound LastPresent LastChecked Fate Freq AgeDay1
## 1 /*A*/ 1 9 9 0 1 0
## 2 /*B*/ 5 5 9 1 1 -2
## 3 /*C*/ 5 40 40 0 1 -3
## 4 /*D*/ 9 32 32 0 1 -4
## 5 /*E*/ 7 8 8 0 1 -4
## 6 /*F*/ 3 15 15 0 1 1
# what are the parameters of the model
# There is only one parameter, the daily survival probality (S)
setup.parameters("Nest", check=TRUE)
## [1] "S"
# 1. Process the data.
# The nocc variable is the data at which hatching occurs
kill.proc <- process.data(killdata, model="Nest", nocc=max(killdata$LastChecked))
kill.proc
## $data
## id FirstFound LastPresent LastChecked Fate freq AgeDay1
## 1 /*A*/ 1 9 9 0 1 0
## 2 /*B*/ 5 5 9 1 1 -2
## 3 /*C*/ 5 40 40 0 1 -3
## 4 /*D*/ 9 32 32 0 1 -4
## 5 /*E*/ 7 8 8 0 1 -4
## 6 /*F*/ 3 15 15 0 1 1
## 7 /*G*/ 8 32 32 0 1 -7
## 8 /*H*/ 14 14 16 1 1 -10
## 9 /*I*/ 8 14 14 0 1 -7
## 10 /*J*/ 13 14 14 0 1 -12
## 11 /*K*/ 14 33 33 0 1 -13
## 12 /*L*/ 15 37 37 0 1 -10
## 13 /*M*/ 16 37 40 1 1 -11
## 14 /*N*/ 16 28 32 1 1 -11
## 15 /*O*/ 16 17 17 0 1 -12
## 16 /*P*/ 21 28 33 1 1 -15
## 17 /*Q*/ 23 33 34 1 1 -17
## 18 /*R*/ 27 29 29 0 1 -23
##
## $model
## [1] "Nest"
##
## $mixtures
## [1] 1
##
## $freq
## group1
## 1 1
## 2 1
## 3 1
## 4 1
## 5 1
## 6 1
## 7 1
## 8 1
## 9 1
## 10 1
## 11 1
## 12 1
## 13 1
## 14 1
## 15 1
## 16 1
## 17 1
## 18 1
##
## $nocc
## [1] 40
##
## $nocc.secondary
## NULL
##
## $time.intervals
## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [36] 1 1 1 1 1
##
## $begin.time
## [1] 1
##
## $age.unit
## [1] 1
##
## $initial.ages
## [1] 0
##
## $group.covariates
## NULL
##
## $nstrata
## [1] 1
##
## $strata.labels
## [1] ""
##
## $counts
## NULL
##
## $reverse
## [1] FALSE
##
## $areas
## NULL
##
## $events
## NULL
# 2. Examine and/or modify the ddl. (Not done here)
# The Age variable here is time since the start of the study which is not useful.
kill.ddl <- make.design.data(kill.proc)
str(kill.ddl)
## List of 2
## $ S :'data.frame': 39 obs. of 7 variables:
## ..$ par.index : int [1:39] 1 2 3 4 5 6 7 8 9 10 ...
## ..$ model.index: num [1:39] 1 2 3 4 5 6 7 8 9 10 ...
## ..$ group : Factor w/ 1 level "1": 1 1 1 1 1 1 1 1 1 1 ...
## ..$ age : Factor w/ 39 levels "0","1","2","3",..: 1 2 3 4 5 6 7 8 9 10 ...
## ..$ time : Factor w/ 39 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8 9 10 ...
## ..$ Age : num [1:39] 0 1 2 3 4 5 6 7 8 9 ...
## ..$ Time : num [1:39] 0 1 2 3 4 5 6 7 8 9 ...
## $ pimtypes:List of 1
## ..$ S:List of 1
## .. ..$ pim.type: chr "all"
kill.ddl
## $S
## par.index model.index group age time Age Time
## 1 1 1 1 0 1 0 0
## 2 2 2 1 1 2 1 1
## 3 3 3 1 2 3 2 2
## 4 4 4 1 3 4 3 3
## 5 5 5 1 4 5 4 4
## 6 6 6 1 5 6 5 5
## 7 7 7 1 6 7 6 6
## 8 8 8 1 7 8 7 7
## 9 9 9 1 8 9 8 8
## 10 10 10 1 9 10 9 9
## 11 11 11 1 10 11 10 10
## 12 12 12 1 11 12 11 11
## 13 13 13 1 12 13 12 12
## 14 14 14 1 13 14 13 13
## 15 15 15 1 14 15 14 14
## 16 16 16 1 15 16 15 15
## 17 17 17 1 16 17 16 16
## 18 18 18 1 17 18 17 17
## 19 19 19 1 18 19 18 18
## 20 20 20 1 19 20 19 19
## 21 21 21 1 20 21 20 20
## 22 22 22 1 21 22 21 21
## 23 23 23 1 22 23 22 22
## 24 24 24 1 23 24 23 23
## 25 25 25 1 24 25 24 24
## 26 26 26 1 25 26 25 25
## 27 27 27 1 26 27 26 26
## 28 28 28 1 27 28 27 27
## 29 29 29 1 28 29 28 28
## 30 30 30 1 29 30 29 29
## 31 31 31 1 30 31 30 30
## 32 32 32 1 31 32 31 31
## 33 33 33 1 32 33 32 32
## 34 34 34 1 33 34 33 33
## 35 35 35 1 34 35 34 34
## 36 36 36 1 35 36 35 35
## 37 37 37 1 36 37 36 36
## 38 38 38 1 37 38 37 37
## 39 39 39 1 38 39 38 38
##
## $pimtypes
## $pimtypes$S
## $pimtypes$S$pim.type
## [1] "all"
# 3. Fit a particular model
# This is a model with S a function of nest age
mod.res <- RMark::mark(kill.proc, ddl=kill.ddl,
model="Nest",
model.parameters=list(
S =list(formula=~NestAge)
)
)
##
## Output summary for Nest model
## Name : S(~NestAge)
##
## Npar : 2
## -2lnL: 42.38081
## AICc : 46.43878
##
## Beta
## estimate se lcl ucl
## S:(Intercept) 3.7952906 0.7988475 2.2295494 5.3610318
## S:NestAge -0.0188125 0.0516282 -0.1200038 0.0823789
##
##
## Real Parameter S
## 1 2 3 4 5 6 7
## 0.9813396 0.9809919 0.9806379 0.9802775 0.9799105 0.9795368 0.9791563
## 8 9 10 11 12 13 14
## 0.9787688 0.9783744 0.9779727 0.9775638 0.9771475 0.9767236 0.976292
## 15 16 17 18 19 20 21
## 0.9758527 0.9754054 0.97495 0.9744864 0.9740145 0.9735341 0.973045
## 22 23 24 25 26 27 28
## 0.9725472 0.9720404 0.9715246 0.9709995 0.970465 0.969921 0.9693673
## 29 30 31 32 33 34 35
## 0.9688037 0.9682301 0.9676463 0.9670521 0.9664474 0.965832 0.9652057
## 36 37 38 39
## 0.9645684 0.9639198 0.9632598 0.9625882
summary(mod.res)
## Output summary for Nest model
## Name : S(~NestAge)
##
## Npar : 2
## -2lnL: 42.38081
## AICc : 46.43878
##
## Beta
## estimate se lcl ucl
## S:(Intercept) 3.7952906 0.7988475 2.2295494 5.3610318
## S:NestAge -0.0188125 0.0516282 -0.1200038 0.0823789
##
##
## Real Parameter S
## 1 2 3 4 5 6 7
## 0.9813396 0.9809919 0.9806379 0.9802775 0.9799105 0.9795368 0.9791563
## 8 9 10 11 12 13 14
## 0.9787688 0.9783744 0.9779727 0.9775638 0.9771475 0.9767236 0.976292
## 15 16 17 18 19 20 21
## 0.9758527 0.9754054 0.97495 0.9744864 0.9740145 0.9735341 0.973045
## 22 23 24 25 26 27 28
## 0.9725472 0.9720404 0.9715246 0.9709995 0.970465 0.969921 0.9693673
## 29 30 31 32 33 34 35
## 0.9688037 0.9682301 0.9676463 0.9670521 0.9664474 0.965832 0.9652057
## 36 37 38 39
## 0.9645684 0.9639198 0.9632598 0.9625882
# Look the objects returned in more details
names(mod.res)
## [1] "data" "model" "title"
## [4] "model.name" "links" "mixtures"
## [7] "call" "parameters" "time.intervals"
## [10] "number.of.groups" "group.labels" "nocc"
## [13] "begin.time" "covariates" "fixed"
## [16] "design.matrix" "pims" "design.data"
## [19] "strata.labels" "mlogit.list" "profile.int"
## [22] "simplify" "model.parameters" "results"
## [25] "output"
names(mod.res$results)
## [1] "lnl" "deviance" "deviance.df"
## [4] "npar" "n" "AICc"
## [7] "beta" "real" "beta.vcv"
## [10] "derived" "derived.vcv" "covariate.values"
## [13] "singular" "real.vcv"
# look at estimates on beta and original scale
mod.res$results$beta # on the logit scale
## estimate se lcl ucl
## S:(Intercept) 3.7952906 0.7988475 2.2295494 5.3610318
## S:NestAge -0.0188125 0.0516282 -0.1200038 0.0823789
mod.res$results$real# on the regular 0-1 scale for each day AVERAGE OVER NEST AGE
## estimate se lcl ucl fixed note
## S g1 a0 t1 0.9813396 0.0222450 0.8294276 0.9982449
## S g1 a1 t2 0.9809919 0.0217489 0.8399191 0.9980340
## S g1 a2 t3 0.9806379 0.0212325 0.8498100 0.9977991
## S g1 a3 t4 0.9802775 0.0206960 0.8591143 0.9975377
## S g1 a4 t5 0.9799105 0.0201399 0.8678479 0.9972474
## S g1 a5 t6 0.9795368 0.0195650 0.8760270 0.9969256
## S g1 a6 t7 0.9791563 0.0189723 0.8836684 0.9965696
## S g1 a7 t8 0.9787688 0.0183631 0.8907887 0.9961768
## S g1 a8 t9 0.9783744 0.0177393 0.8974038 0.9957447
## S g1 a9 t10 0.9779727 0.0171033 0.9035286 0.9952712
## S g1 a10 t11 0.9775638 0.0164582 0.9091765 0.9947547
## S g1 a11 t12 0.9771475 0.0158081 0.9143584 0.9941943
## S g1 a12 t13 0.9767236 0.0151581 0.9190826 0.9935907
## S g1 a13 t14 0.9762920 0.0145151 0.9233535 0.9929461
## S g1 a14 t15 0.9758527 0.0138875 0.9271708 0.9922652
## S g1 a15 t16 0.9754054 0.0132860 0.9305287 0.9915559
## S g1 a16 t17 0.9749500 0.0127240 0.9334147 0.9908305
## S g1 a17 t18 0.9744864 0.0122177 0.9358083 0.9901058
## S g1 a18 t19 0.9740145 0.0117867 0.9376809 0.9894041
## S g1 a19 t20 0.9735341 0.0114526 0.9389956 0.9887524
## S g1 a20 t21 0.9730450 0.0112391 0.9397091 0.9881808
## S g1 a21 t22 0.9725472 0.0111688 0.9397744 0.9877192
## S g1 a22 t23 0.9720404 0.0112615 0.9391457 0.9873927
## S g1 a23 t24 0.9715246 0.0115309 0.9377822 0.9872170
## S g1 a24 t25 0.9709995 0.0119834 0.9356512 0.9871959
## S g1 a25 t26 0.9704650 0.0126179 0.9327279 0.9873208
## S g1 a26 t27 0.9699210 0.0134272 0.9289924 0.9875740
## S g1 a27 t28 0.9693673 0.0144004 0.9244262 0.9879324
## S g1 a28 t29 0.9688037 0.0155251 0.9190078 0.9883713
## S g1 a29 t30 0.9682301 0.0167890 0.9127108 0.9888677
## S g1 a30 t31 0.9676463 0.0181808 0.9055021 0.9894013
## S g1 a31 t32 0.9670521 0.0196910 0.8973426 0.9899553
## S g1 a32 t33 0.9664474 0.0213116 0.8881873 0.9905165
## S g1 a33 t34 0.9658320 0.0230363 0.8779872 0.9910747
## S g1 a34 t35 0.9652057 0.0248601 0.8666906 0.9916223
## S g1 a35 t36 0.9645684 0.0267792 0.8542456 0.9921539
## S g1 a36 t37 0.9639198 0.0287907 0.8406018 0.9926656
## S g1 a37 t38 0.9632598 0.0308927 0.8257131 0.9931549
## S g1 a38 t39 0.9625882 0.0330837 0.8095401 0.9936204
# The real estimates are not useful because the value for each day is averaged over the
# nest ages at that time.
# For example, the beta values are shown above and the
# logit(DSR) for nest 1 day old is
logit_DSR_1 = sum( mod.res$results$beta$estimate *c(1,1))
logit_DSR_1
## [1] 3.776478
# and estimate of survival of nest 1 day old is
1/(1+exp(-logit_DSR_1))
## [1] 0.9776096
# compared to
head(mod.res$results$real)
## estimate se lcl ucl fixed note
## S g1 a0 t1 0.9813396 0.0222450 0.8294276 0.9982449
## S g1 a1 t2 0.9809919 0.0217489 0.8399191 0.9980340
## S g1 a2 t3 0.9806379 0.0212325 0.8498100 0.9977991
## S g1 a3 t4 0.9802775 0.0206960 0.8591143 0.9975377
## S g1 a4 t5 0.9799105 0.0201399 0.8678479 0.9972474
## S g1 a5 t6 0.9795368 0.0195650 0.8760270 0.9969256
# The average nest age at day 1 is
average_nest_age_1 = mean(killdata$AgeDay1)
average_nest_age_1
## [1] -8.888889
logit_DSR_1_avg = sum( mod.res$results$beta$estimate *c(1,average_nest_age_1))
logit_DSR_1_avg
## [1] 3.962513
# and estimate of survival of nest on day 1 at average age of nests is
1/(1+exp(-logit_DSR_1_avg)) # now matches the real estimates
## [1] 0.9813396
# You want to predict survival as a function of nest age.
# This is a bit tricker
# First get the all.diff.index values for each day of the study.
get.real(mod.res, param="S", se=TRUE)
## all.diff.index par.index estimate se lcl
## S g1 a0 t1 1 1 0.9813396 0.0222450 0.8294276
## S g1 a1 t2 2 2 0.9809919 0.0217489 0.8399191
## S g1 a2 t3 3 3 0.9806379 0.0212325 0.8498100
## S g1 a3 t4 4 4 0.9802775 0.0206960 0.8591143
## S g1 a4 t5 5 5 0.9799105 0.0201399 0.8678479
## S g1 a5 t6 6 6 0.9795368 0.0195650 0.8760270
## S g1 a6 t7 7 7 0.9791563 0.0189723 0.8836684
## S g1 a7 t8 8 8 0.9787688 0.0183631 0.8907887
## S g1 a8 t9 9 9 0.9783744 0.0177393 0.8974038
## S g1 a9 t10 10 10 0.9779727 0.0171033 0.9035286
## S g1 a10 t11 11 11 0.9775638 0.0164582 0.9091765
## S g1 a11 t12 12 12 0.9771475 0.0158081 0.9143584
## S g1 a12 t13 13 13 0.9767236 0.0151581 0.9190826
## S g1 a13 t14 14 14 0.9762920 0.0145151 0.9233535
## S g1 a14 t15 15 15 0.9758527 0.0138875 0.9271708
## S g1 a15 t16 16 16 0.9754054 0.0132860 0.9305287
## S g1 a16 t17 17 17 0.9749500 0.0127240 0.9334147
## S g1 a17 t18 18 18 0.9744864 0.0122177 0.9358083
## S g1 a18 t19 19 19 0.9740145 0.0117867 0.9376809
## S g1 a19 t20 20 20 0.9735341 0.0114526 0.9389956
## S g1 a20 t21 21 21 0.9730450 0.0112391 0.9397091
## S g1 a21 t22 22 22 0.9725472 0.0111688 0.9397744
## S g1 a22 t23 23 23 0.9720404 0.0112615 0.9391457
## S g1 a23 t24 24 24 0.9715246 0.0115309 0.9377822
## S g1 a24 t25 25 25 0.9709995 0.0119834 0.9356512
## S g1 a25 t26 26 26 0.9704650 0.0126179 0.9327279
## S g1 a26 t27 27 27 0.9699210 0.0134272 0.9289924
## S g1 a27 t28 28 28 0.9693673 0.0144004 0.9244262
## S g1 a28 t29 29 29 0.9688037 0.0155251 0.9190078
## S g1 a29 t30 30 30 0.9682301 0.0167890 0.9127108
## S g1 a30 t31 31 31 0.9676463 0.0181808 0.9055021
## S g1 a31 t32 32 32 0.9670521 0.0196910 0.8973426
## S g1 a32 t33 33 33 0.9664474 0.0213116 0.8881873
## S g1 a33 t34 34 34 0.9658320 0.0230363 0.8779872
## S g1 a34 t35 35 35 0.9652057 0.0248601 0.8666906
## S g1 a35 t36 36 36 0.9645684 0.0267792 0.8542456
## S g1 a36 t37 37 37 0.9639198 0.0287907 0.8406018
## S g1 a37 t38 38 38 0.9632598 0.0308927 0.8257131
## S g1 a38 t39 39 39 0.9625882 0.0330837 0.8095401
## ucl fixed note group age time Age Time
## S g1 a0 t1 0.9982449 1 0 1 0 0
## S g1 a1 t2 0.9980340 1 1 2 1 1
## S g1 a2 t3 0.9977991 1 2 3 2 2
## S g1 a3 t4 0.9975377 1 3 4 3 3
## S g1 a4 t5 0.9972474 1 4 5 4 4
## S g1 a5 t6 0.9969256 1 5 6 5 5
## S g1 a6 t7 0.9965696 1 6 7 6 6
## S g1 a7 t8 0.9961768 1 7 8 7 7
## S g1 a8 t9 0.9957447 1 8 9 8 8
## S g1 a9 t10 0.9952712 1 9 10 9 9
## S g1 a10 t11 0.9947547 1 10 11 10 10
## S g1 a11 t12 0.9941943 1 11 12 11 11
## S g1 a12 t13 0.9935907 1 12 13 12 12
## S g1 a13 t14 0.9929461 1 13 14 13 13
## S g1 a14 t15 0.9922652 1 14 15 14 14
## S g1 a15 t16 0.9915559 1 15 16 15 15
## S g1 a16 t17 0.9908305 1 16 17 16 16
## S g1 a17 t18 0.9901058 1 17 18 17 17
## S g1 a18 t19 0.9894041 1 18 19 18 18
## S g1 a19 t20 0.9887524 1 19 20 19 19
## S g1 a20 t21 0.9881808 1 20 21 20 20
## S g1 a21 t22 0.9877192 1 21 22 21 21
## S g1 a22 t23 0.9873927 1 22 23 22 22
## S g1 a23 t24 0.9872170 1 23 24 23 23
## S g1 a24 t25 0.9871959 1 24 25 24 24
## S g1 a25 t26 0.9873208 1 25 26 25 25
## S g1 a26 t27 0.9875740 1 26 27 26 26
## S g1 a27 t28 0.9879324 1 27 28 27 27
## S g1 a28 t29 0.9883713 1 28 29 28 28
## S g1 a29 t30 0.9888677 1 29 30 29 29
## S g1 a30 t31 0.9894013 1 30 31 30 30
## S g1 a31 t32 0.9899553 1 31 32 31 31
## S g1 a32 t33 0.9905165 1 32 33 32 32
## S g1 a33 t34 0.9910747 1 33 34 33 33
## S g1 a34 t35 0.9916223 1 34 35 34 34
## S g1 a35 t36 0.9921539 1 35 36 35 35
## S g1 a36 t37 0.9926656 1 36 37 36 36
## S g1 a37 t38 0.9931549 1 37 38 37 37
## S g1 a38 t39 0.9936204 1 38 39 38 38
# we see that all.diff.index==1 is for nest survival on day 1 of the study
# we will predict then the DSR for day 1 at various ages
pred.ages <- data.frame(NestAge1=1:20, index=1)
covariate.predictions(mod.res, data=pred.ages )$estimates[1:10,]
## vcv.index model.index par.index NestAge1 index estimate se
## 1 1 1 1 1 1 0.9776096 0.01653021
## 2 1 1 1 2 1 0.9771941 0.01588040
## 3 1 1 1 3 1 0.9767711 0.01523016
## 4 1 1 1 4 1 0.9763404 0.01458597
## 5 1 1 1 5 1 0.9759019 0.01395614
## 6 1 1 1 6 1 0.9754555 0.01335112
## 7 1 1 1 7 1 0.9750010 0.01278398
## 8 1 1 1 8 1 0.9745384 0.01227066
## 9 1 1 1 9 1 0.9740674 0.01183018
## 10 1 1 1 10 1 0.9735879 0.01148422
## lcl ucl fixed
## 1 0.9085721 0.9948142
## 2 0.9138054 0.9942588
## 3 0.9185801 0.9936599
## 4 0.9229012 0.9930196
## 5 0.9267692 0.9923424
## 6 0.9301786 0.9916358
## 7 0.9331179 0.9909114
## 8 0.9355674 0.9901857
## 9 0.9374996 0.9894801
## 10 0.9388784 0.9888214
# because the model does NOT include a term for day effects, these predictions will be the same
# for all days of the study
pred.ages <- data.frame(NestAge4=1:20, index=4)
covariate.predictions(mod.res, data=pred.ages )$estimates[1:10,]
## vcv.index model.index par.index NestAge4 index estimate se
## 1 4 4 4 1 4 0.9776096 0.01653021
## 2 4 4 4 2 4 0.9771941 0.01588040
## 3 4 4 4 3 4 0.9767711 0.01523016
## 4 4 4 4 4 4 0.9763404 0.01458597
## 5 4 4 4 5 4 0.9759019 0.01395614
## 6 4 4 4 6 4 0.9754555 0.01335112
## 7 4 4 4 7 4 0.9750010 0.01278398
## 8 4 4 4 8 4 0.9745384 0.01227066
## 9 4 4 4 9 4 0.9740674 0.01183018
## 10 4 4 4 10 4 0.9735879 0.01148422
## lcl ucl fixed
## 1 0.9085721 0.9948142
## 2 0.9138054 0.9942588
## 3 0.9185801 0.9936599
## 4 0.9229012 0.9930196
## 5 0.9267692 0.9923424
## 6 0.9301786 0.9916358
## 7 0.9331179 0.9909114
## 8 0.9355674 0.9901857
## 9 0.9374996 0.9894801
## 10 0.9388784 0.9888214
plotdata <-covariate.predictions(mod.res, data=pred.ages )$estimates
dsr.age <- ggplot(data=plotdata, aes(x=NestAge4, y=estimate, group=1))+
ggtitle("Survival as a function of nest age")+
geom_line()+
geom_ribbon(aes(ymin=lcl, ymax=ucl), alpha=0.1)
dsr.age
ggsave(dsr.age,
file=file.path("..","..","..","..","MyStuff","Images","killdear-age.png"),h=4, w=6, units="in", dpi=300)
#_----------------------------------------------------------------------
# Compare to the null model
mod.null <- RMark::mark(kill.proc, ddl=kill.ddl,
model="Nest",
model.parameters=list(
S =list(formula=~1)
)
)
##
## Output summary for Nest model
## Name : S(~1)
##
## Npar : 1
## -2lnL: 42.51028
## AICc : 44.52951
##
## Beta
## estimate se lcl ucl
## S:(Intercept) 3.557002 0.4141776 2.745214 4.368791
##
##
## Real Parameter S
## 1 2 3 4 5 6 7
## 0.9722669 0.9722669 0.9722669 0.9722669 0.9722669 0.9722669 0.9722669
## 8 9 10 11 12 13 14
## 0.9722669 0.9722669 0.9722669 0.9722669 0.9722669 0.9722669 0.9722669
## 15 16 17 18 19 20 21
## 0.9722669 0.9722669 0.9722669 0.9722669 0.9722669 0.9722669 0.9722669
## 22 23 24 25 26 27 28
## 0.9722669 0.9722669 0.9722669 0.9722669 0.9722669 0.9722669 0.9722669
## 29 30 31 32 33 34 35
## 0.9722669 0.9722669 0.9722669 0.9722669 0.9722669 0.9722669 0.9722669
## 36 37 38 39
## 0.9722669 0.9722669 0.9722669 0.9722669
collect.models(type="Nest")
## model npar AICc DeltaAICc weight Deviance
## 1 S(~1) 1 44.52951 0.000000 0.7220459 42.51028
## 2 S(~NestAge) 2 46.43878 1.909265 0.2779541 42.38081
cleanup(ask=FALSE)