# Analysis of Redstart data illustrating basic RMark features
# Reproduce Figure 2 where the Nest survival is computed for each year.
# 2019-05-01 CJS Initial code
# Sherry TW, Wilson S, Hunter S, Holmes RT (2015)
# Impacts of nest predators and weather on reproductive success and
# population limitation in a long-distance migratory songbird.
# Journal of Avian Biology 46(6): 559-569. https://doi.org/10.1111/jav.00536
# Data from
# Sherry TW, Wilson S, Hunter S, Holmes RT (2015)
# Data from: Impacts of nest predators and weather on reproductive success and
# population limitation in a long-distance migratory songbird.
# Dryad Digital Repository. https://doi.org/10.5061/dryad.73870
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
#
# 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.
#
reddata <- readxl::read_excel(file.path("..","Sherry.xlsx"),
sheet="NestData")
head(reddata)
## # A tibble: 6 x 11
## NestId FirstFound LastPresent LastChecked Fate Freq Height BaffleStatus
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 /*198~ 11 11 18 1 1 16.9 N
## 2 /*198~ 13 22 23 1 1 10.5 N
## 3 /*198~ 7 26 26 0 1 4.8 N
## 4 /*198~ 12 14 20 1 1 3.7 N
## 5 /*198~ 6 20 25 1 1 10.7 N
## 6 /*198~ 11 19 20 1 1 17.8 N
## # ... with 3 more variables: DBH <dbl>, AgeDay1 <dbl>, Year <chr>
reddata <- as.data.frame(reddata)
# get the yearly date and merge with the nest data
annual.data <- readxl::read_excel(file.path("..","Sherry.xlsx"),
sheet="AnnualCovariates", skip=9)
head(annual.data)
## # A tibble: 6 x 7
## Year Density Predators MayTemp JuneTemp MayRain JuneRain
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1983 0.867 2.4 8.89 17.1 194. 35.3
## 2 1984 0.467 0.5 9.06 16.2 312. 180.
## 3 1985 0.589 3.4 10.6 13.2 107. 130
## 4 1986 0.439 6 12.2 13.4 128. 89.6
## 5 1987 0.372 1.7 10.9 15.4 91.6 200.
## 6 1988 0.361 0.3 12 14.4 97.8 70.2
dim(reddata)
## [1] 537 11
reddata <- merge(reddata, annual.data, all.x=TRUE)
dim(reddata)
## [1] 537 17
# any missing data?
sum(!complete.cases(reddata))
## [1] 0
# as noted in the paper, we remove nests with baffeling
dim(reddata)
## [1] 537 17
reddata <- reddata[ !(reddata$BaffleStatus=="Y"),]
dim(reddata)
## [1] 466 17
# create factor variable for year
reddata$YearF <- factor(reddata$Year)
# what are the parameters of the model
# There is only one parameter, the daily survival probality (S)
setup.parameters("Nest", check=TRUE)
## [1] "S"
# Now we look through each year of the study to compute the DSR using a S(.) model
# and compute the nest survival probability
fits <- plyr::dlply(reddata, c("Year","MayTemp"), function(x){
cat("Fitting ", x$Year[1], x$MayTemp[1], "\n")
# 1. Process the data.
# The nocc variable is the data at which hatching occurs
red.proc <- process.data(x, model="Nest", group=c("YearF"), nocc=max(x$LastChecked))
red.proc
# 2. Examine and/or modify the ddl. (Not done here)
red.ddl <- make.design.data(red.proc)
str(red.ddl)
red.ddl
# 3. Fit the dot model
fit <- RMark::mark(red.proc, ddl=red.ddl,
model="Nest",
model.parameters=list(
S =list(formula=~1))
)
print(summary(fit))
# 4. Estimate the nest success for 20 days
dsr <- get.real(fit, "S", se=TRUE, vcv=TRUE, expand=TRUE)
ns <- prod(dsr$estimates$estimate[1:20])
ns.se <- deltamethod.special("prod",
dsr$estimates$estimate[1:20],
dsr$vcv.real[1:20, 1:20])
list(red.proc=red.proc, red.ddl=red.ddl, fit=fit, dsr=dsr, ns=ns, ns.se=ns.se)
})
## Fitting 1983 8.89
## List of 2
## $ S :'data.frame': 55 obs. of 8 variables:
## ..$ par.index : int [1:55] 1 2 3 4 5 6 7 8 9 10 ...
## ..$ model.index: num [1:55] 1 2 3 4 5 6 7 8 9 10 ...
## ..$ group : Factor w/ 1 level "1983": 1 1 1 1 1 1 1 1 1 1 ...
## ..$ age : Factor w/ 55 levels "0","1","2","3",..: 1 2 3 4 5 6 7 8 9 10 ...
## ..$ time : Factor w/ 55 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8 9 10 ...
## ..$ Age : num [1:55] 0 1 2 3 4 5 6 7 8 9 ...
## ..$ Time : num [1:55] 0 1 2 3 4 5 6 7 8 9 ...
## ..$ YearF : Factor w/ 13 levels "1983","1984",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ pimtypes:List of 1
## ..$ S:List of 1
## .. ..$ pim.type: chr "all"
##
## Output summary for Nest model
## Name : S(~1)
##
## Npar : 1
## -2lnL: 133.5122
## AICc : 135.525
##
## Beta
## estimate se lcl ucl
## S:(Intercept) 2.570125 0.2124018 2.153818 2.986433
##
##
## Real Parameter S
## 1 2 3 4 5 6 7 8
## 0.928914 0.928914 0.928914 0.928914 0.928914 0.928914 0.928914 0.928914
## 9 10 11 12 13 14 15 16
## 0.928914 0.928914 0.928914 0.928914 0.928914 0.928914 0.928914 0.928914
## 17 18 19 20 21 22 23 24
## 0.928914 0.928914 0.928914 0.928914 0.928914 0.928914 0.928914 0.928914
## 25 26 27 28 29 30 31 32
## 0.928914 0.928914 0.928914 0.928914 0.928914 0.928914 0.928914 0.928914
## 33 34 35 36 37 38 39 40
## 0.928914 0.928914 0.928914 0.928914 0.928914 0.928914 0.928914 0.928914
## 41 42 43 44 45 46 47 48
## 0.928914 0.928914 0.928914 0.928914 0.928914 0.928914 0.928914 0.928914
## 49 50 51 52 53 54 55
## 0.928914 0.928914 0.928914 0.928914 0.928914 0.928914 0.928914
## Output summary for Nest model
## Name : S(~1)
##
## Npar : 1
## -2lnL: 133.5122
## AICc : 135.525
##
## Beta
## estimate se lcl ucl
## S:(Intercept) 2.570125 0.2124018 2.153818 2.986433
##
##
## Real Parameter S
## 1 2 3 4 5 6 7 8
## 0.928914 0.928914 0.928914 0.928914 0.928914 0.928914 0.928914 0.928914
## 9 10 11 12 13 14 15 16
## 0.928914 0.928914 0.928914 0.928914 0.928914 0.928914 0.928914 0.928914
## 17 18 19 20 21 22 23 24
## 0.928914 0.928914 0.928914 0.928914 0.928914 0.928914 0.928914 0.928914
## 25 26 27 28 29 30 31 32
## 0.928914 0.928914 0.928914 0.928914 0.928914 0.928914 0.928914 0.928914
## 33 34 35 36 37 38 39 40
## 0.928914 0.928914 0.928914 0.928914 0.928914 0.928914 0.928914 0.928914
## 41 42 43 44 45 46 47 48
## 0.928914 0.928914 0.928914 0.928914 0.928914 0.928914 0.928914 0.928914
## 49 50 51 52 53 54 55
## 0.928914 0.928914 0.928914 0.928914 0.928914 0.928914 0.928914
## Fitting 1984 9.06
## List of 2
## $ S :'data.frame': 53 obs. of 8 variables:
## ..$ par.index : int [1:53] 1 2 3 4 5 6 7 8 9 10 ...
## ..$ model.index: num [1:53] 1 2 3 4 5 6 7 8 9 10 ...
## ..$ group : Factor w/ 1 level "1984": 1 1 1 1 1 1 1 1 1 1 ...
## ..$ age : Factor w/ 53 levels "0","1","2","3",..: 1 2 3 4 5 6 7 8 9 10 ...
## ..$ time : Factor w/ 53 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8 9 10 ...
## ..$ Age : num [1:53] 0 1 2 3 4 5 6 7 8 9 ...
## ..$ Time : num [1:53] 0 1 2 3 4 5 6 7 8 9 ...
## ..$ YearF : Factor w/ 13 levels "1983","1984",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ pimtypes:List of 1
## ..$ S:List of 1
## .. ..$ pim.type: chr "all"
##
## Output summary for Nest model
## Name : S(~1)
##
## Npar : 1
## -2lnL: 128.8802
## AICc : 130.8888
##
## Beta
## estimate se lcl ucl
## S:(Intercept) 3.292025 0.2470683 2.807771 3.776279
##
##
## Real Parameter S
## 1 2 3 4 5 6 7
## 0.9641542 0.9641542 0.9641542 0.9641542 0.9641542 0.9641542 0.9641542
## 8 9 10 11 12 13 14
## 0.9641542 0.9641542 0.9641542 0.9641542 0.9641542 0.9641542 0.9641542
## 15 16 17 18 19 20 21
## 0.9641542 0.9641542 0.9641542 0.9641542 0.9641542 0.9641542 0.9641542
## 22 23 24 25 26 27 28
## 0.9641542 0.9641542 0.9641542 0.9641542 0.9641542 0.9641542 0.9641542
## 29 30 31 32 33 34 35
## 0.9641542 0.9641542 0.9641542 0.9641542 0.9641542 0.9641542 0.9641542
## 36 37 38 39 40 41 42
## 0.9641542 0.9641542 0.9641542 0.9641542 0.9641542 0.9641542 0.9641542
## 43 44 45 46 47 48 49
## 0.9641542 0.9641542 0.9641542 0.9641542 0.9641542 0.9641542 0.9641542
## 50 51 52 53
## 0.9641542 0.9641542 0.9641542 0.9641542
## Output summary for Nest model
## Name : S(~1)
##
## Npar : 1
## -2lnL: 128.8802
## AICc : 130.8888
##
## Beta
## estimate se lcl ucl
## S:(Intercept) 3.292025 0.2470683 2.807771 3.776279
##
##
## Real Parameter S
## 1 2 3 4 5 6 7
## 0.9641542 0.9641542 0.9641542 0.9641542 0.9641542 0.9641542 0.9641542
## 8 9 10 11 12 13 14
## 0.9641542 0.9641542 0.9641542 0.9641542 0.9641542 0.9641542 0.9641542
## 15 16 17 18 19 20 21
## 0.9641542 0.9641542 0.9641542 0.9641542 0.9641542 0.9641542 0.9641542
## 22 23 24 25 26 27 28
## 0.9641542 0.9641542 0.9641542 0.9641542 0.9641542 0.9641542 0.9641542
## 29 30 31 32 33 34 35
## 0.9641542 0.9641542 0.9641542 0.9641542 0.9641542 0.9641542 0.9641542
## 36 37 38 39 40 41 42
## 0.9641542 0.9641542 0.9641542 0.9641542 0.9641542 0.9641542 0.9641542
## 43 44 45 46 47 48 49
## 0.9641542 0.9641542 0.9641542 0.9641542 0.9641542 0.9641542 0.9641542
## 50 51 52 53
## 0.9641542 0.9641542 0.9641542 0.9641542
## Fitting 1985 10.6
## List of 2
## $ S :'data.frame': 55 obs. of 8 variables:
## ..$ par.index : int [1:55] 1 2 3 4 5 6 7 8 9 10 ...
## ..$ model.index: num [1:55] 1 2 3 4 5 6 7 8 9 10 ...
## ..$ group : Factor w/ 1 level "1985": 1 1 1 1 1 1 1 1 1 1 ...
## ..$ age : Factor w/ 55 levels "0","1","2","3",..: 1 2 3 4 5 6 7 8 9 10 ...
## ..$ time : Factor w/ 55 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8 9 10 ...
## ..$ Age : num [1:55] 0 1 2 3 4 5 6 7 8 9 ...
## ..$ Time : num [1:55] 0 1 2 3 4 5 6 7 8 9 ...
## ..$ YearF : Factor w/ 13 levels "1983","1984",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ pimtypes:List of 1
## ..$ S:List of 1
## .. ..$ pim.type: chr "all"
##
## Output summary for Nest model
## Name : S(~1)
##
## Npar : 1
## -2lnL: 80.16544
## AICc : 82.18065
##
## Beta
## estimate se lcl ucl
## S:(Intercept) 3.089103 0.2954554 2.51001 3.668196
##
##
## Real Parameter S
## 1 2 3 4 5 6 7 8
## 0.956441 0.956441 0.956441 0.956441 0.956441 0.956441 0.956441 0.956441
## 9 10 11 12 13 14 15 16
## 0.956441 0.956441 0.956441 0.956441 0.956441 0.956441 0.956441 0.956441
## 17 18 19 20 21 22 23 24
## 0.956441 0.956441 0.956441 0.956441 0.956441 0.956441 0.956441 0.956441
## 25 26 27 28 29 30 31 32
## 0.956441 0.956441 0.956441 0.956441 0.956441 0.956441 0.956441 0.956441
## 33 34 35 36 37 38 39 40
## 0.956441 0.956441 0.956441 0.956441 0.956441 0.956441 0.956441 0.956441
## 41 42 43 44 45 46 47 48
## 0.956441 0.956441 0.956441 0.956441 0.956441 0.956441 0.956441 0.956441
## 49 50 51 52 53 54 55
## 0.956441 0.956441 0.956441 0.956441 0.956441 0.956441 0.956441
## Output summary for Nest model
## Name : S(~1)
##
## Npar : 1
## -2lnL: 80.16544
## AICc : 82.18065
##
## Beta
## estimate se lcl ucl
## S:(Intercept) 3.089103 0.2954554 2.51001 3.668196
##
##
## Real Parameter S
## 1 2 3 4 5 6 7 8
## 0.956441 0.956441 0.956441 0.956441 0.956441 0.956441 0.956441 0.956441
## 9 10 11 12 13 14 15 16
## 0.956441 0.956441 0.956441 0.956441 0.956441 0.956441 0.956441 0.956441
## 17 18 19 20 21 22 23 24
## 0.956441 0.956441 0.956441 0.956441 0.956441 0.956441 0.956441 0.956441
## 25 26 27 28 29 30 31 32
## 0.956441 0.956441 0.956441 0.956441 0.956441 0.956441 0.956441 0.956441
## 33 34 35 36 37 38 39 40
## 0.956441 0.956441 0.956441 0.956441 0.956441 0.956441 0.956441 0.956441
## 41 42 43 44 45 46 47 48
## 0.956441 0.956441 0.956441 0.956441 0.956441 0.956441 0.956441 0.956441
## 49 50 51 52 53 54 55
## 0.956441 0.956441 0.956441 0.956441 0.956441 0.956441 0.956441
## Fitting 1986 12.16
## List of 2
## $ S :'data.frame': 50 obs. of 8 variables:
## ..$ par.index : int [1:50] 1 2 3 4 5 6 7 8 9 10 ...
## ..$ model.index: num [1:50] 1 2 3 4 5 6 7 8 9 10 ...
## ..$ group : Factor w/ 1 level "1986": 1 1 1 1 1 1 1 1 1 1 ...
## ..$ age : Factor w/ 50 levels "0","1","2","3",..: 1 2 3 4 5 6 7 8 9 10 ...
## ..$ time : Factor w/ 50 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8 9 10 ...
## ..$ Age : num [1:50] 0 1 2 3 4 5 6 7 8 9 ...
## ..$ Time : num [1:50] 0 1 2 3 4 5 6 7 8 9 ...
## ..$ YearF : Factor w/ 13 levels "1983","1984",..: 4 4 4 4 4 4 4 4 4 4 ...
## $ pimtypes:List of 1
## ..$ S:List of 1
## .. ..$ pim.type: chr "all"
##
## Output summary for Nest model
## Name : S(~1)
##
## Npar : 1
## -2lnL: 93.88375
## AICc : 95.89121
##
## Beta
## estimate se lcl ucl
## S:(Intercept) 3.881376 0.3046328 3.284295 4.478456
##
##
## Real Parameter S
## 1 2 3 4 5 6 7
## 0.9797943 0.9797943 0.9797943 0.9797943 0.9797943 0.9797943 0.9797943
## 8 9 10 11 12 13 14
## 0.9797943 0.9797943 0.9797943 0.9797943 0.9797943 0.9797943 0.9797943
## 15 16 17 18 19 20 21
## 0.9797943 0.9797943 0.9797943 0.9797943 0.9797943 0.9797943 0.9797943
## 22 23 24 25 26 27 28
## 0.9797943 0.9797943 0.9797943 0.9797943 0.9797943 0.9797943 0.9797943
## 29 30 31 32 33 34 35
## 0.9797943 0.9797943 0.9797943 0.9797943 0.9797943 0.9797943 0.9797943
## 36 37 38 39 40 41 42
## 0.9797943 0.9797943 0.9797943 0.9797943 0.9797943 0.9797943 0.9797943
## 43 44 45 46 47 48 49
## 0.9797943 0.9797943 0.9797943 0.9797943 0.9797943 0.9797943 0.9797943
## 50
## 0.9797943
## Output summary for Nest model
## Name : S(~1)
##
## Npar : 1
## -2lnL: 93.88375
## AICc : 95.89121
##
## Beta
## estimate se lcl ucl
## S:(Intercept) 3.881376 0.3046328 3.284295 4.478456
##
##
## Real Parameter S
## 1 2 3 4 5 6 7
## 0.9797943 0.9797943 0.9797943 0.9797943 0.9797943 0.9797943 0.9797943
## 8 9 10 11 12 13 14
## 0.9797943 0.9797943 0.9797943 0.9797943 0.9797943 0.9797943 0.9797943
## 15 16 17 18 19 20 21
## 0.9797943 0.9797943 0.9797943 0.9797943 0.9797943 0.9797943 0.9797943
## 22 23 24 25 26 27 28
## 0.9797943 0.9797943 0.9797943 0.9797943 0.9797943 0.9797943 0.9797943
## 29 30 31 32 33 34 35
## 0.9797943 0.9797943 0.9797943 0.9797943 0.9797943 0.9797943 0.9797943
## 36 37 38 39 40 41 42
## 0.9797943 0.9797943 0.9797943 0.9797943 0.9797943 0.9797943 0.9797943
## 43 44 45 46 47 48 49
## 0.9797943 0.9797943 0.9797943 0.9797943 0.9797943 0.9797943 0.9797943
## 50
## 0.9797943
## Fitting 1987 10.92
## List of 2
## $ S :'data.frame': 38 obs. of 8 variables:
## ..$ par.index : int [1:38] 1 2 3 4 5 6 7 8 9 10 ...
## ..$ model.index: num [1:38] 1 2 3 4 5 6 7 8 9 10 ...
## ..$ group : Factor w/ 1 level "1987": 1 1 1 1 1 1 1 1 1 1 ...
## ..$ age : Factor w/ 38 levels "0","1","2","3",..: 1 2 3 4 5 6 7 8 9 10 ...
## ..$ time : Factor w/ 38 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8 9 10 ...
## ..$ Age : num [1:38] 0 1 2 3 4 5 6 7 8 9 ...
## ..$ Time : num [1:38] 0 1 2 3 4 5 6 7 8 9 ...
## ..$ YearF : Factor w/ 13 levels "1983","1984",..: 5 5 5 5 5 5 5 5 5 5 ...
## $ pimtypes:List of 1
## ..$ S:List of 1
## .. ..$ pim.type: chr "all"
##
## Output summary for Nest model
## Name : S(~1)
##
## Npar : 1
## -2lnL: 75.14853
## AICc : 77.15906
##
## Beta
## estimate se lcl ucl
## S:(Intercept) 3.73995 0.3373295 3.078784 4.401116
##
##
## Real Parameter S
## 1 2 3 4 5 6 7
## 0.9767959 0.9767959 0.9767959 0.9767959 0.9767959 0.9767959 0.9767959
## 8 9 10 11 12 13 14
## 0.9767959 0.9767959 0.9767959 0.9767959 0.9767959 0.9767959 0.9767959
## 15 16 17 18 19 20 21
## 0.9767959 0.9767959 0.9767959 0.9767959 0.9767959 0.9767959 0.9767959
## 22 23 24 25 26 27 28
## 0.9767959 0.9767959 0.9767959 0.9767959 0.9767959 0.9767959 0.9767959
## 29 30 31 32 33 34 35
## 0.9767959 0.9767959 0.9767959 0.9767959 0.9767959 0.9767959 0.9767959
## 36 37 38
## 0.9767959 0.9767959 0.9767959
## Output summary for Nest model
## Name : S(~1)
##
## Npar : 1
## -2lnL: 75.14853
## AICc : 77.15906
##
## Beta
## estimate se lcl ucl
## S:(Intercept) 3.73995 0.3373295 3.078784 4.401116
##
##
## Real Parameter S
## 1 2 3 4 5 6 7
## 0.9767959 0.9767959 0.9767959 0.9767959 0.9767959 0.9767959 0.9767959
## 8 9 10 11 12 13 14
## 0.9767959 0.9767959 0.9767959 0.9767959 0.9767959 0.9767959 0.9767959
## 15 16 17 18 19 20 21
## 0.9767959 0.9767959 0.9767959 0.9767959 0.9767959 0.9767959 0.9767959
## 22 23 24 25 26 27 28
## 0.9767959 0.9767959 0.9767959 0.9767959 0.9767959 0.9767959 0.9767959
## 29 30 31 32 33 34 35
## 0.9767959 0.9767959 0.9767959 0.9767959 0.9767959 0.9767959 0.9767959
## 36 37 38
## 0.9767959 0.9767959 0.9767959
## Fitting 1988 12
## List of 2
## $ S :'data.frame': 53 obs. of 8 variables:
## ..$ par.index : int [1:53] 1 2 3 4 5 6 7 8 9 10 ...
## ..$ model.index: num [1:53] 1 2 3 4 5 6 7 8 9 10 ...
## ..$ group : Factor w/ 1 level "1988": 1 1 1 1 1 1 1 1 1 1 ...
## ..$ age : Factor w/ 53 levels "0","1","2","3",..: 1 2 3 4 5 6 7 8 9 10 ...
## ..$ time : Factor w/ 53 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8 9 10 ...
## ..$ Age : num [1:53] 0 1 2 3 4 5 6 7 8 9 ...
## ..$ Time : num [1:53] 0 1 2 3 4 5 6 7 8 9 ...
## ..$ YearF : Factor w/ 13 levels "1983","1984",..: 6 6 6 6 6 6 6 6 6 6 ...
## $ pimtypes:List of 1
## ..$ S:List of 1
## .. ..$ pim.type: chr "all"
##
## Output summary for Nest model
## Name : S(~1)
##
## Npar : 1
## -2lnL: 48.32463
## AICc : 50.33463
##
## Beta
## estimate se lcl ucl
## S:(Intercept) 4.380721 0.4500158 3.49869 5.262752
##
##
## Real Parameter S
## 1 2 3 4 5 6 7
## 0.9876384 0.9876384 0.9876384 0.9876384 0.9876384 0.9876384 0.9876384
## 8 9 10 11 12 13 14
## 0.9876384 0.9876384 0.9876384 0.9876384 0.9876384 0.9876384 0.9876384
## 15 16 17 18 19 20 21
## 0.9876384 0.9876384 0.9876384 0.9876384 0.9876384 0.9876384 0.9876384
## 22 23 24 25 26 27 28
## 0.9876384 0.9876384 0.9876384 0.9876384 0.9876384 0.9876384 0.9876384
## 29 30 31 32 33 34 35
## 0.9876384 0.9876384 0.9876384 0.9876384 0.9876384 0.9876384 0.9876384
## 36 37 38 39 40 41 42
## 0.9876384 0.9876384 0.9876384 0.9876384 0.9876384 0.9876384 0.9876384
## 43 44 45 46 47 48 49
## 0.9876384 0.9876384 0.9876384 0.9876384 0.9876384 0.9876384 0.9876384
## 50 51 52 53
## 0.9876384 0.9876384 0.9876384 0.9876384
## Output summary for Nest model
## Name : S(~1)
##
## Npar : 1
## -2lnL: 48.32463
## AICc : 50.33463
##
## Beta
## estimate se lcl ucl
## S:(Intercept) 4.380721 0.4500158 3.49869 5.262752
##
##
## Real Parameter S
## 1 2 3 4 5 6 7
## 0.9876384 0.9876384 0.9876384 0.9876384 0.9876384 0.9876384 0.9876384
## 8 9 10 11 12 13 14
## 0.9876384 0.9876384 0.9876384 0.9876384 0.9876384 0.9876384 0.9876384
## 15 16 17 18 19 20 21
## 0.9876384 0.9876384 0.9876384 0.9876384 0.9876384 0.9876384 0.9876384
## 22 23 24 25 26 27 28
## 0.9876384 0.9876384 0.9876384 0.9876384 0.9876384 0.9876384 0.9876384
## 29 30 31 32 33 34 35
## 0.9876384 0.9876384 0.9876384 0.9876384 0.9876384 0.9876384 0.9876384
## 36 37 38 39 40 41 42
## 0.9876384 0.9876384 0.9876384 0.9876384 0.9876384 0.9876384 0.9876384
## 43 44 45 46 47 48 49
## 0.9876384 0.9876384 0.9876384 0.9876384 0.9876384 0.9876384 0.9876384
## 50 51 52 53
## 0.9876384 0.9876384 0.9876384 0.9876384
## Fitting 1989 11.89
## List of 2
## $ S :'data.frame': 50 obs. of 8 variables:
## ..$ par.index : int [1:50] 1 2 3 4 5 6 7 8 9 10 ...
## ..$ model.index: num [1:50] 1 2 3 4 5 6 7 8 9 10 ...
## ..$ group : Factor w/ 1 level "1989": 1 1 1 1 1 1 1 1 1 1 ...
## ..$ age : Factor w/ 50 levels "0","1","2","3",..: 1 2 3 4 5 6 7 8 9 10 ...
## ..$ time : Factor w/ 50 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8 9 10 ...
## ..$ Age : num [1:50] 0 1 2 3 4 5 6 7 8 9 ...
## ..$ Time : num [1:50] 0 1 2 3 4 5 6 7 8 9 ...
## ..$ YearF : Factor w/ 13 levels "1983","1984",..: 7 7 7 7 7 7 7 7 7 7 ...
## $ pimtypes:List of 1
## ..$ S:List of 1
## .. ..$ pim.type: chr "all"
##
## Output summary for Nest model
## Name : S(~1)
##
## Npar : 1
## -2lnL: 88.57516
## AICc : 90.58755
##
## Beta
## estimate se lcl ucl
## S:(Intercept) 3.360827 0.306726 2.759644 3.96201
##
##
## Real Parameter S
## 1 2 3 4 5 6 7
## 0.9664576 0.9664576 0.9664576 0.9664576 0.9664576 0.9664576 0.9664576
## 8 9 10 11 12 13 14
## 0.9664576 0.9664576 0.9664576 0.9664576 0.9664576 0.9664576 0.9664576
## 15 16 17 18 19 20 21
## 0.9664576 0.9664576 0.9664576 0.9664576 0.9664576 0.9664576 0.9664576
## 22 23 24 25 26 27 28
## 0.9664576 0.9664576 0.9664576 0.9664576 0.9664576 0.9664576 0.9664576
## 29 30 31 32 33 34 35
## 0.9664576 0.9664576 0.9664576 0.9664576 0.9664576 0.9664576 0.9664576
## 36 37 38 39 40 41 42
## 0.9664576 0.9664576 0.9664576 0.9664576 0.9664576 0.9664576 0.9664576
## 43 44 45 46 47 48 49
## 0.9664576 0.9664576 0.9664576 0.9664576 0.9664576 0.9664576 0.9664576
## 50
## 0.9664576
## Output summary for Nest model
## Name : S(~1)
##
## Npar : 1
## -2lnL: 88.57516
## AICc : 90.58755
##
## Beta
## estimate se lcl ucl
## S:(Intercept) 3.360827 0.306726 2.759644 3.96201
##
##
## Real Parameter S
## 1 2 3 4 5 6 7
## 0.9664576 0.9664576 0.9664576 0.9664576 0.9664576 0.9664576 0.9664576
## 8 9 10 11 12 13 14
## 0.9664576 0.9664576 0.9664576 0.9664576 0.9664576 0.9664576 0.9664576
## 15 16 17 18 19 20 21
## 0.9664576 0.9664576 0.9664576 0.9664576 0.9664576 0.9664576 0.9664576
## 22 23 24 25 26 27 28
## 0.9664576 0.9664576 0.9664576 0.9664576 0.9664576 0.9664576 0.9664576
## 29 30 31 32 33 34 35
## 0.9664576 0.9664576 0.9664576 0.9664576 0.9664576 0.9664576 0.9664576
## 36 37 38 39 40 41 42
## 0.9664576 0.9664576 0.9664576 0.9664576 0.9664576 0.9664576 0.9664576
## 43 44 45 46 47 48 49
## 0.9664576 0.9664576 0.9664576 0.9664576 0.9664576 0.9664576 0.9664576
## 50
## 0.9664576
## Fitting 1990 9.15
## List of 2
## $ S :'data.frame': 56 obs. of 8 variables:
## ..$ par.index : int [1:56] 1 2 3 4 5 6 7 8 9 10 ...
## ..$ model.index: num [1:56] 1 2 3 4 5 6 7 8 9 10 ...
## ..$ group : Factor w/ 1 level "1990": 1 1 1 1 1 1 1 1 1 1 ...
## ..$ age : Factor w/ 56 levels "0","1","2","3",..: 1 2 3 4 5 6 7 8 9 10 ...
## ..$ time : Factor w/ 56 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8 9 10 ...
## ..$ Age : num [1:56] 0 1 2 3 4 5 6 7 8 9 ...
## ..$ Time : num [1:56] 0 1 2 3 4 5 6 7 8 9 ...
## ..$ YearF : Factor w/ 13 levels "1983","1984",..: 8 8 8 8 8 8 8 8 8 8 ...
## $ pimtypes:List of 1
## ..$ S:List of 1
## .. ..$ pim.type: chr "all"
##
## Output summary for Nest model
## Name : S(~1)
##
## Npar : 1
## -2lnL: 140.1545
## AICc : 142.16
##
## Beta
## estimate se lcl ucl
## S:(Intercept) 3.65866 0.2324495 3.203059 4.114261
##
##
## Real Parameter S
## 1 2 3 4 5 6 7
## 0.9748802 0.9748802 0.9748802 0.9748802 0.9748802 0.9748802 0.9748802
## 8 9 10 11 12 13 14
## 0.9748802 0.9748802 0.9748802 0.9748802 0.9748802 0.9748802 0.9748802
## 15 16 17 18 19 20 21
## 0.9748802 0.9748802 0.9748802 0.9748802 0.9748802 0.9748802 0.9748802
## 22 23 24 25 26 27 28
## 0.9748802 0.9748802 0.9748802 0.9748802 0.9748802 0.9748802 0.9748802
## 29 30 31 32 33 34 35
## 0.9748802 0.9748802 0.9748802 0.9748802 0.9748802 0.9748802 0.9748802
## 36 37 38 39 40 41 42
## 0.9748802 0.9748802 0.9748802 0.9748802 0.9748802 0.9748802 0.9748802
## 43 44 45 46 47 48 49
## 0.9748802 0.9748802 0.9748802 0.9748802 0.9748802 0.9748802 0.9748802
## 50 51 52 53 54 55 56
## 0.9748802 0.9748802 0.9748802 0.9748802 0.9748802 0.9748802 0.9748802
## Output summary for Nest model
## Name : S(~1)
##
## Npar : 1
## -2lnL: 140.1545
## AICc : 142.16
##
## Beta
## estimate se lcl ucl
## S:(Intercept) 3.65866 0.2324495 3.203059 4.114261
##
##
## Real Parameter S
## 1 2 3 4 5 6 7
## 0.9748802 0.9748802 0.9748802 0.9748802 0.9748802 0.9748802 0.9748802
## 8 9 10 11 12 13 14
## 0.9748802 0.9748802 0.9748802 0.9748802 0.9748802 0.9748802 0.9748802
## 15 16 17 18 19 20 21
## 0.9748802 0.9748802 0.9748802 0.9748802 0.9748802 0.9748802 0.9748802
## 22 23 24 25 26 27 28
## 0.9748802 0.9748802 0.9748802 0.9748802 0.9748802 0.9748802 0.9748802
## 29 30 31 32 33 34 35
## 0.9748802 0.9748802 0.9748802 0.9748802 0.9748802 0.9748802 0.9748802
## 36 37 38 39 40 41 42
## 0.9748802 0.9748802 0.9748802 0.9748802 0.9748802 0.9748802 0.9748802
## 43 44 45 46 47 48 49
## 0.9748802 0.9748802 0.9748802 0.9748802 0.9748802 0.9748802 0.9748802
## 50 51 52 53 54 55 56
## 0.9748802 0.9748802 0.9748802 0.9748802 0.9748802 0.9748802 0.9748802
## Fitting 1991 12.34
## List of 2
## $ S :'data.frame': 55 obs. of 8 variables:
## ..$ par.index : int [1:55] 1 2 3 4 5 6 7 8 9 10 ...
## ..$ model.index: num [1:55] 1 2 3 4 5 6 7 8 9 10 ...
## ..$ group : Factor w/ 1 level "1991": 1 1 1 1 1 1 1 1 1 1 ...
## ..$ age : Factor w/ 55 levels "0","1","2","3",..: 1 2 3 4 5 6 7 8 9 10 ...
## ..$ time : Factor w/ 55 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8 9 10 ...
## ..$ Age : num [1:55] 0 1 2 3 4 5 6 7 8 9 ...
## ..$ Time : num [1:55] 0 1 2 3 4 5 6 7 8 9 ...
## ..$ YearF : Factor w/ 13 levels "1983","1984",..: 9 9 9 9 9 9 9 9 9 9 ...
## $ pimtypes:List of 1
## ..$ S:List of 1
## .. ..$ pim.type: chr "all"
##
## Output summary for Nest model
## Name : S(~1)
##
## Npar : 1
## -2lnL: 103.0282
## AICc : 105.0359
##
## Beta
## estimate se lcl ucl
## S:(Intercept) 3.672934 0.2809117 3.122347 4.223521
##
##
## Real Parameter S
## 1 2 3 4 5 6 7
## 0.9752274 0.9752274 0.9752274 0.9752274 0.9752274 0.9752274 0.9752274
## 8 9 10 11 12 13 14
## 0.9752274 0.9752274 0.9752274 0.9752274 0.9752274 0.9752274 0.9752274
## 15 16 17 18 19 20 21
## 0.9752274 0.9752274 0.9752274 0.9752274 0.9752274 0.9752274 0.9752274
## 22 23 24 25 26 27 28
## 0.9752274 0.9752274 0.9752274 0.9752274 0.9752274 0.9752274 0.9752274
## 29 30 31 32 33 34 35
## 0.9752274 0.9752274 0.9752274 0.9752274 0.9752274 0.9752274 0.9752274
## 36 37 38 39 40 41 42
## 0.9752274 0.9752274 0.9752274 0.9752274 0.9752274 0.9752274 0.9752274
## 43 44 45 46 47 48 49
## 0.9752274 0.9752274 0.9752274 0.9752274 0.9752274 0.9752274 0.9752274
## 50 51 52 53 54 55
## 0.9752274 0.9752274 0.9752274 0.9752274 0.9752274 0.9752274
## Output summary for Nest model
## Name : S(~1)
##
## Npar : 1
## -2lnL: 103.0282
## AICc : 105.0359
##
## Beta
## estimate se lcl ucl
## S:(Intercept) 3.672934 0.2809117 3.122347 4.223521
##
##
## Real Parameter S
## 1 2 3 4 5 6 7
## 0.9752274 0.9752274 0.9752274 0.9752274 0.9752274 0.9752274 0.9752274
## 8 9 10 11 12 13 14
## 0.9752274 0.9752274 0.9752274 0.9752274 0.9752274 0.9752274 0.9752274
## 15 16 17 18 19 20 21
## 0.9752274 0.9752274 0.9752274 0.9752274 0.9752274 0.9752274 0.9752274
## 22 23 24 25 26 27 28
## 0.9752274 0.9752274 0.9752274 0.9752274 0.9752274 0.9752274 0.9752274
## 29 30 31 32 33 34 35
## 0.9752274 0.9752274 0.9752274 0.9752274 0.9752274 0.9752274 0.9752274
## 36 37 38 39 40 41 42
## 0.9752274 0.9752274 0.9752274 0.9752274 0.9752274 0.9752274 0.9752274
## 43 44 45 46 47 48 49
## 0.9752274 0.9752274 0.9752274 0.9752274 0.9752274 0.9752274 0.9752274
## 50 51 52 53 54 55
## 0.9752274 0.9752274 0.9752274 0.9752274 0.9752274 0.9752274
## Fitting 1992 11.56
## List of 2
## $ S :'data.frame': 61 obs. of 8 variables:
## ..$ par.index : int [1:61] 1 2 3 4 5 6 7 8 9 10 ...
## ..$ model.index: num [1:61] 1 2 3 4 5 6 7 8 9 10 ...
## ..$ group : Factor w/ 1 level "1992": 1 1 1 1 1 1 1 1 1 1 ...
## ..$ age : Factor w/ 61 levels "0","1","2","3",..: 1 2 3 4 5 6 7 8 9 10 ...
## ..$ time : Factor w/ 61 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8 9 10 ...
## ..$ Age : num [1:61] 0 1 2 3 4 5 6 7 8 9 ...
## ..$ Time : num [1:61] 0 1 2 3 4 5 6 7 8 9 ...
## ..$ YearF : Factor w/ 13 levels "1983","1984",..: 10 10 10 10 10 10 10 10 10 10 ...
## $ pimtypes:List of 1
## ..$ S:List of 1
## .. ..$ pim.type: chr "all"
##
## Output summary for Nest model
## Name : S(~1)
##
## Npar : 1
## -2lnL: 241.1084
## AICc : 243.1143
##
## Beta
## estimate se lcl ucl
## S:(Intercept) 2.895561 0.1690079 2.564306 3.226817
##
##
## Real Parameter S
## 1 2 3 4 5 6 7
## 0.9476266 0.9476266 0.9476266 0.9476266 0.9476266 0.9476266 0.9476266
## 8 9 10 11 12 13 14
## 0.9476266 0.9476266 0.9476266 0.9476266 0.9476266 0.9476266 0.9476266
## 15 16 17 18 19 20 21
## 0.9476266 0.9476266 0.9476266 0.9476266 0.9476266 0.9476266 0.9476266
## 22 23 24 25 26 27 28
## 0.9476266 0.9476266 0.9476266 0.9476266 0.9476266 0.9476266 0.9476266
## 29 30 31 32 33 34 35
## 0.9476266 0.9476266 0.9476266 0.9476266 0.9476266 0.9476266 0.9476266
## 36 37 38 39 40 41 42
## 0.9476266 0.9476266 0.9476266 0.9476266 0.9476266 0.9476266 0.9476266
## 43 44 45 46 47 48 49
## 0.9476266 0.9476266 0.9476266 0.9476266 0.9476266 0.9476266 0.9476266
## 50 51 52 53 54 55 56
## 0.9476266 0.9476266 0.9476266 0.9476266 0.9476266 0.9476266 0.9476266
## 57 58 59 60 61
## 0.9476266 0.9476266 0.9476266 0.9476266 0.9476266
## Output summary for Nest model
## Name : S(~1)
##
## Npar : 1
## -2lnL: 241.1084
## AICc : 243.1143
##
## Beta
## estimate se lcl ucl
## S:(Intercept) 2.895561 0.1690079 2.564306 3.226817
##
##
## Real Parameter S
## 1 2 3 4 5 6 7
## 0.9476266 0.9476266 0.9476266 0.9476266 0.9476266 0.9476266 0.9476266
## 8 9 10 11 12 13 14
## 0.9476266 0.9476266 0.9476266 0.9476266 0.9476266 0.9476266 0.9476266
## 15 16 17 18 19 20 21
## 0.9476266 0.9476266 0.9476266 0.9476266 0.9476266 0.9476266 0.9476266
## 22 23 24 25 26 27 28
## 0.9476266 0.9476266 0.9476266 0.9476266 0.9476266 0.9476266 0.9476266
## 29 30 31 32 33 34 35
## 0.9476266 0.9476266 0.9476266 0.9476266 0.9476266 0.9476266 0.9476266
## 36 37 38 39 40 41 42
## 0.9476266 0.9476266 0.9476266 0.9476266 0.9476266 0.9476266 0.9476266
## 43 44 45 46 47 48 49
## 0.9476266 0.9476266 0.9476266 0.9476266 0.9476266 0.9476266 0.9476266
## 50 51 52 53 54 55 56
## 0.9476266 0.9476266 0.9476266 0.9476266 0.9476266 0.9476266 0.9476266
## 57 58 59 60 61
## 0.9476266 0.9476266 0.9476266 0.9476266 0.9476266
## Fitting 1993 10.56
## List of 2
## $ S :'data.frame': 57 obs. of 8 variables:
## ..$ par.index : int [1:57] 1 2 3 4 5 6 7 8 9 10 ...
## ..$ model.index: num [1:57] 1 2 3 4 5 6 7 8 9 10 ...
## ..$ group : Factor w/ 1 level "1993": 1 1 1 1 1 1 1 1 1 1 ...
## ..$ age : Factor w/ 57 levels "0","1","2","3",..: 1 2 3 4 5 6 7 8 9 10 ...
## ..$ time : Factor w/ 57 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8 9 10 ...
## ..$ Age : num [1:57] 0 1 2 3 4 5 6 7 8 9 ...
## ..$ Time : num [1:57] 0 1 2 3 4 5 6 7 8 9 ...
## ..$ YearF : Factor w/ 13 levels "1983","1984",..: 11 11 11 11 11 11 11 11 11 11 ...
## $ pimtypes:List of 1
## ..$ S:List of 1
## .. ..$ pim.type: chr "all"
##
## Output summary for Nest model
## Name : S(~1)
##
## Npar : 1
## -2lnL: 173.5447
## AICc : 175.5551
##
## Beta
## estimate se lcl ucl
## S:(Intercept) 2.435438 0.1819113 2.078892 2.791984
##
##
## Real Parameter S
## 1 2 3 4 5 6 7 8 9
## 0.91949 0.91949 0.91949 0.91949 0.91949 0.91949 0.91949 0.91949 0.91949
## 10 11 12 13 14 15 16 17 18
## 0.91949 0.91949 0.91949 0.91949 0.91949 0.91949 0.91949 0.91949 0.91949
## 19 20 21 22 23 24 25 26 27
## 0.91949 0.91949 0.91949 0.91949 0.91949 0.91949 0.91949 0.91949 0.91949
## 28 29 30 31 32 33 34 35 36
## 0.91949 0.91949 0.91949 0.91949 0.91949 0.91949 0.91949 0.91949 0.91949
## 37 38 39 40 41 42 43 44 45
## 0.91949 0.91949 0.91949 0.91949 0.91949 0.91949 0.91949 0.91949 0.91949
## 46 47 48 49 50 51 52 53 54
## 0.91949 0.91949 0.91949 0.91949 0.91949 0.91949 0.91949 0.91949 0.91949
## 55 56 57
## 0.91949 0.91949 0.91949
## Output summary for Nest model
## Name : S(~1)
##
## Npar : 1
## -2lnL: 173.5447
## AICc : 175.5551
##
## Beta
## estimate se lcl ucl
## S:(Intercept) 2.435438 0.1819113 2.078892 2.791984
##
##
## Real Parameter S
## 1 2 3 4 5 6 7 8 9
## 0.91949 0.91949 0.91949 0.91949 0.91949 0.91949 0.91949 0.91949 0.91949
## 10 11 12 13 14 15 16 17 18
## 0.91949 0.91949 0.91949 0.91949 0.91949 0.91949 0.91949 0.91949 0.91949
## 19 20 21 22 23 24 25 26 27
## 0.91949 0.91949 0.91949 0.91949 0.91949 0.91949 0.91949 0.91949 0.91949
## 28 29 30 31 32 33 34 35 36
## 0.91949 0.91949 0.91949 0.91949 0.91949 0.91949 0.91949 0.91949 0.91949
## 37 38 39 40 41 42 43 44 45
## 0.91949 0.91949 0.91949 0.91949 0.91949 0.91949 0.91949 0.91949 0.91949
## 46 47 48 49 50 51 52 53 54
## 0.91949 0.91949 0.91949 0.91949 0.91949 0.91949 0.91949 0.91949 0.91949
## 55 56 57
## 0.91949 0.91949 0.91949
## Fitting 1994 10.16
## List of 2
## $ S :'data.frame': 43 obs. of 8 variables:
## ..$ par.index : int [1:43] 1 2 3 4 5 6 7 8 9 10 ...
## ..$ model.index: num [1:43] 1 2 3 4 5 6 7 8 9 10 ...
## ..$ group : Factor w/ 1 level "1994": 1 1 1 1 1 1 1 1 1 1 ...
## ..$ age : Factor w/ 43 levels "0","1","2","3",..: 1 2 3 4 5 6 7 8 9 10 ...
## ..$ time : Factor w/ 43 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8 9 10 ...
## ..$ Age : num [1:43] 0 1 2 3 4 5 6 7 8 9 ...
## ..$ Time : num [1:43] 0 1 2 3 4 5 6 7 8 9 ...
## ..$ YearF : Factor w/ 13 levels "1983","1984",..: 12 12 12 12 12 12 12 12 12 12 ...
## $ pimtypes:List of 1
## ..$ S:List of 1
## .. ..$ pim.type: chr "all"
##
## Output summary for Nest model
## Name : S(~1)
##
## Npar : 1
## -2lnL: 46.86583
## AICc : 48.88574
##
## Beta
## estimate se lcl ucl
## S:(Intercept) 3.251288 0.3606861 2.544343 3.958233
##
##
## Real Parameter S
## 1 2 3 4 5 6 7
## 0.9627194 0.9627194 0.9627194 0.9627194 0.9627194 0.9627194 0.9627194
## 8 9 10 11 12 13 14
## 0.9627194 0.9627194 0.9627194 0.9627194 0.9627194 0.9627194 0.9627194
## 15 16 17 18 19 20 21
## 0.9627194 0.9627194 0.9627194 0.9627194 0.9627194 0.9627194 0.9627194
## 22 23 24 25 26 27 28
## 0.9627194 0.9627194 0.9627194 0.9627194 0.9627194 0.9627194 0.9627194
## 29 30 31 32 33 34 35
## 0.9627194 0.9627194 0.9627194 0.9627194 0.9627194 0.9627194 0.9627194
## 36 37 38 39 40 41 42
## 0.9627194 0.9627194 0.9627194 0.9627194 0.9627194 0.9627194 0.9627194
## 43
## 0.9627194
## Output summary for Nest model
## Name : S(~1)
##
## Npar : 1
## -2lnL: 46.86583
## AICc : 48.88574
##
## Beta
## estimate se lcl ucl
## S:(Intercept) 3.251288 0.3606861 2.544343 3.958233
##
##
## Real Parameter S
## 1 2 3 4 5 6 7
## 0.9627194 0.9627194 0.9627194 0.9627194 0.9627194 0.9627194 0.9627194
## 8 9 10 11 12 13 14
## 0.9627194 0.9627194 0.9627194 0.9627194 0.9627194 0.9627194 0.9627194
## 15 16 17 18 19 20 21
## 0.9627194 0.9627194 0.9627194 0.9627194 0.9627194 0.9627194 0.9627194
## 22 23 24 25 26 27 28
## 0.9627194 0.9627194 0.9627194 0.9627194 0.9627194 0.9627194 0.9627194
## 29 30 31 32 33 34 35
## 0.9627194 0.9627194 0.9627194 0.9627194 0.9627194 0.9627194 0.9627194
## 36 37 38 39 40 41 42
## 0.9627194 0.9627194 0.9627194 0.9627194 0.9627194 0.9627194 0.9627194
## 43
## 0.9627194
## Fitting 1995 9.63
## List of 2
## $ S :'data.frame': 42 obs. of 8 variables:
## ..$ par.index : int [1:42] 1 2 3 4 5 6 7 8 9 10 ...
## ..$ model.index: num [1:42] 1 2 3 4 5 6 7 8 9 10 ...
## ..$ group : Factor w/ 1 level "1995": 1 1 1 1 1 1 1 1 1 1 ...
## ..$ age : Factor w/ 42 levels "0","1","2","3",..: 1 2 3 4 5 6 7 8 9 10 ...
## ..$ time : Factor w/ 42 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8 9 10 ...
## ..$ Age : num [1:42] 0 1 2 3 4 5 6 7 8 9 ...
## ..$ Time : num [1:42] 0 1 2 3 4 5 6 7 8 9 ...
## ..$ YearF : Factor w/ 13 levels "1983","1984",..: 13 13 13 13 13 13 13 13 13 13 ...
## $ pimtypes:List of 1
## ..$ S:List of 1
## .. ..$ pim.type: chr "all"
##
## Output summary for Nest model
## Name : S(~1)
##
## Npar : 1
## -2lnL: 53.09551
## AICc : 55.12067
##
## Beta
## estimate se lcl ucl
## S:(Intercept) 2.884334 0.3430137 2.212027 3.55664
##
##
## Real Parameter S
## 1 2 3 4 5 6 7
## 0.9470665 0.9470665 0.9470665 0.9470665 0.9470665 0.9470665 0.9470665
## 8 9 10 11 12 13 14
## 0.9470665 0.9470665 0.9470665 0.9470665 0.9470665 0.9470665 0.9470665
## 15 16 17 18 19 20 21
## 0.9470665 0.9470665 0.9470665 0.9470665 0.9470665 0.9470665 0.9470665
## 22 23 24 25 26 27 28
## 0.9470665 0.9470665 0.9470665 0.9470665 0.9470665 0.9470665 0.9470665
## 29 30 31 32 33 34 35
## 0.9470665 0.9470665 0.9470665 0.9470665 0.9470665 0.9470665 0.9470665
## 36 37 38 39 40 41 42
## 0.9470665 0.9470665 0.9470665 0.9470665 0.9470665 0.9470665 0.9470665
## Output summary for Nest model
## Name : S(~1)
##
## Npar : 1
## -2lnL: 53.09551
## AICc : 55.12067
##
## Beta
## estimate se lcl ucl
## S:(Intercept) 2.884334 0.3430137 2.212027 3.55664
##
##
## Real Parameter S
## 1 2 3 4 5 6 7
## 0.9470665 0.9470665 0.9470665 0.9470665 0.9470665 0.9470665 0.9470665
## 8 9 10 11 12 13 14
## 0.9470665 0.9470665 0.9470665 0.9470665 0.9470665 0.9470665 0.9470665
## 15 16 17 18 19 20 21
## 0.9470665 0.9470665 0.9470665 0.9470665 0.9470665 0.9470665 0.9470665
## 22 23 24 25 26 27 28
## 0.9470665 0.9470665 0.9470665 0.9470665 0.9470665 0.9470665 0.9470665
## 29 30 31 32 33 34 35
## 0.9470665 0.9470665 0.9470665 0.9470665 0.9470665 0.9470665 0.9470665
## 36 37 38 39 40 41 42
## 0.9470665 0.9470665 0.9470665 0.9470665 0.9470665 0.9470665 0.9470665
# Extract the nest success and create the plot
ns<- plyr::ldply(fits, function(x){
data.frame(ns=x$ns, ns.se=x$ns.se)
})
ns$lcl <- ns$ns - 1.95*ns$ns.se
ns$ucl <- ns$ns + 1.96*ns$ns.se
ns
## Year MayTemp ns ns.se lcl ucl
## 1 1983 8.89 0.2288284 0.06910069 0.09408203 0.3642657
## 2 1984 9.06 0.4818701 0.08535231 0.31543313 0.6491606
## 3 1985 10.60 0.4103588 0.10562422 0.20439161 0.6173823
## 4 1986 12.16 0.6648103 0.08184257 0.50521732 0.8252218
## 5 1987 10.92 0.6252834 0.09788706 0.43440360 0.8171420
## 6 1988 12.00 0.7797570 0.08675448 0.61058578 0.9497958
## 7 1989 11.89 0.5054243 0.10399939 0.30262548 0.7092631
## 8 1990 9.15 0.6012089 0.07021007 0.46429930 0.7388207
## 9 1991 12.34 0.6055058 0.08427310 0.44117322 0.7706811
## 10 1992 11.56 0.3409924 0.06036602 0.22327867 0.4593098
## 11 1993 10.56 0.1866123 0.05466127 0.08002280 0.2937484
## 12 1994 10.16 0.4677289 0.12578730 0.22244363 0.7142720
## 13 1995 9.63 0.3369844 0.12237186 0.09835929 0.5768333
ns.vs.temp <- ggplot(data=ns, aes(x=MayTemp, y=ns))+
ggtitle("Nest success vs. May temp", subtitle="S(.) model fit to each year separately")+
geom_point()+
geom_errorbar(aes(ymin=lcl, ymax=ucl), width=0.01)+
geom_smooth(method="lm")+
ylim(0,1)
ns.vs.temp
ggsave(ns.vs.temp,
file=file.path("..","..","..","..","MyStuff","Images","sherry-ns-vs-temp.png"), h=4, w=6, units="in", dpi=300)
cleanup(ask=FALSE)