Repeatability of uplifting power experiment
Only in adults
AICs for lme repeatability
| 1 |
Weight~1 + (~ ID) |
3 |
68.26732 |
0.000000 |
1.0000000 |
0.3803738 |
-30.50208 |
0.3803738 |
| 4 |
Weight~(duration_of_trial) + (~ ID) |
4 |
69.40905 |
1.141730 |
0.5650365 |
0.2149251 |
-29.59341 |
0.5952989 |
| 2 |
Weight~(time_of_the_day) + (~ ID) |
4 |
70.51460 |
2.247276 |
0.3250949 |
0.1236576 |
-30.14619 |
0.7189565 |
| 7 |
Weight~(time_of_the_day) + (duration_of_trial) + (~ ID) |
5 |
70.84782 |
2.580497 |
0.2752025 |
0.1046798 |
-28.65920 |
0.8236363 |
| 3 |
Weight~(days_from_1st_trial) + (~ ID) |
4 |
71.08223 |
2.814913 |
0.2447650 |
0.0931022 |
-30.43001 |
0.9167385 |
| 6 |
Weight~(days_from_1st_trial) + (duration_of_trial) + (~ ID) |
5 |
72.65223 |
4.384913 |
0.1116421 |
0.0424657 |
-29.56141 |
0.9592042 |
| 5 |
Weight~(time_of_the_day) + (days_from_1st_trial) + (~ ID) |
5 |
73.80153 |
5.534208 |
0.0628437 |
0.0239041 |
-30.13606 |
0.9831083 |
| 8 |
Weight~(time_of_the_day) + (days_from_1st_trial) + (duration_of_trial) + (~ ID) |
6 |
74.49599 |
6.228664 |
0.0444082 |
0.0168917 |
-28.62299 |
1.0000000 |
| ####* |
same results with adult data |
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sample sizes
*Final uplift power was obtained using trials with the highest 2 flights from trials with at least 8 flights
Repeatability of morphology
Repeatability of morphology for ADULTS
Repeatability of morphology including ACROSS YEARS
Singing activity
AIC on singing activity repeatability models
| 3 |
rel.act~Lek. + (~ Bird.ID) |
5 |
7.457115 |
0.000000 |
1.0000000 |
0.4038797 |
1.7714424 |
0.4038797 |
| 4 |
rel.act~Date + (~ Bird.ID) |
4 |
9.310113 |
1.852998 |
0.3959374 |
0.1599111 |
-0.3271879 |
0.5637908 |
| 1 |
rel.act~1 + (~ Bird.ID) |
3 |
9.419563 |
1.962447 |
0.3748521 |
0.1513952 |
-1.5162330 |
0.7151860 |
| 5 |
rel.act~Date + Lek. + (~ Bird.ID) |
6 |
9.814742 |
2.357627 |
0.3076436 |
0.1242510 |
1.8044935 |
0.8394370 |
| 2 |
rel.act~1 + (~ Lek.) + (~ Bird.ID) |
4 |
10.564312 |
3.107197 |
0.2114856 |
0.0854147 |
-0.9542873 |
0.9248517 |
| 6 |
rel.act~1 + (~ Date) + (~ Bird.ID) |
4 |
11.688204 |
4.231089 |
0.1205677 |
0.0486948 |
-1.5162330 |
0.9735465 |
| 7 |
rel.act~1 + (~ Date) + (~ Lek.) + (~ Bird.ID) |
5 |
12.908575 |
5.451459 |
0.0654984 |
0.0264535 |
-0.9542873 |
1.0000000 |
| ##*ra |
ndom effects in paranthesis |
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Repeatability
Cognition
## DEFINEDNAME: 21 00 00 01 0b 00 00 00 03 00 00 00 00 00 00 0d 3b 02 00 00 00 0d 05 00 00 14 00
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## DEFINEDNAME: 20 00 00 01 0b 00 00 00 02 00 00 00 00 00 00 0d 3b 00 00 00 00 a9 02 00 00 09 00
##
## 143 2013 143 2014 178 2013 203 2013 221 2013 227 2014 231 2014 236 2014
## 29 33 47 20 50 46 57 73
## 265 2014 267 2014 271 2014 292 2014 292 2015 300 2014 301 2014 301 2015
## 27 45 58 134 130 60 42 165
## 303 2014 312 2014 313 2014 314 2014 316 2014 323 2014 324 2015 331 2014
## 93 106 32 55 60 65 37 58
## 336 2014 338 2014 353 2015 356 2015 357 2015 364 2015
## 35 25 35 88 48 123
repeatability of cognition at different bin sizes
with 18 visits bins
SIMULATIONS
Effect of visiting frequency
## DEFINEDNAME: 21 00 00 01 0b 00 00 00 03 00 00 00 00 00 00 0d 3b 02 00 00 00 0d 05 00 00 14 00
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## DEFINEDNAME: 21 00 00 01 0b 00 00 00 03 00 00 00 00 00 00 0d 3b 02 00 00 00 0d 05 00 00 14 00
## DEFINEDNAME: 21 00 00 01 0b 00 00 00 04 00 00 00 00 00 00 0d 3b 01 00 00 00 b4 05 00 00 20 00
## DEFINEDNAME: 20 00 00 01 0b 00 00 00 02 00 00 00 00 00 00 0d 3b 00 00 00 00 a9 02 00 00 09 00
## DEFINEDNAME: 21 00 00 01 0b 00 00 00 03 00 00 00 00 00 00 0d 3b 02 00 00 00 0d 05 00 00 14 00
## DEFINEDNAME: 21 00 00 01 0b 00 00 00 04 00 00 00 00 00 00 0d 3b 01 00 00 00 b4 05 00 00 20 00
## DEFINEDNAME: 20 00 00 01 0b 00 00 00 02 00 00 00 00 00 00 0d 3b 00 00 00 00 a9 02 00 00 09 00
## DEFINEDNAME: 21 00 00 01 0b 00 00 00 03 00 00 00 00 00 00 0d 3b 02 00 00 00 0d 05 00 00 14 00
## DEFINEDNAME: 21 00 00 01 0b 00 00 00 04 00 00 00 00 00 00 0d 3b 01 00 00 00 b4 05 00 00 20 00
## DEFINEDNAME: 20 00 00 01 0b 00 00 00 02 00 00 00 00 00 00 0d 3b 00 00 00 00 a9 02 00 00 09 00
## DEFINEDNAME: 21 00 00 01 0b 00 00 00 03 00 00 00 00 00 00 0d 3b 02 00 00 00 0d 05 00 00 14 00
## DEFINEDNAME: 21 00 00 01 0b 00 00 00 04 00 00 00 00 00 00 0d 3b 01 00 00 00 b4 05 00 00 20 00
## DEFINEDNAME: 20 00 00 01 0b 00 00 00 02 00 00 00 00 00 00 0d 3b 00 00 00 00 a9 02 00 00 09 00
##
## Confidence set for the best model
##
## Method: raw sum of model probabilities
##
## 95% confidence set:
## K AICc Delta_AICc AICcWt
## meancog~1 + (~ Bird.ID) 3 -9.85 0.00 0.39
## meancog~visit.interv + (~ Bird.ID) 4 -7.97 1.88 0.15
## meancog~vf.score + (~ Bird.ID) 4 -7.80 2.05 0.14
## meancog~n + (~ Bird.ID) 4 -7.64 2.21 0.13
## meancog~vf.score + n + (~ Bird.ID) 5 -6.08 3.77 0.06
## meancog~visit.interv + n + (~ Bird.ID) 5 -5.85 4.00 0.05
## meancog~vf.score + visit.interv + (~ Bird.ID) 5 -5.64 4.21 0.05
##
## Model probabilities sum to 0.98
##
## Multimodel inference on "visit.interv" based on AICc
##
## AICc table used to obtain model-averaged estimate:
##
## K AICc Delta_AICc AICcWt
## meancog~visit.interv + (~ Bird.ID) 4 -7.97 0.00 0.60
## meancog~visit.interv + n + (~ Bird.ID) 5 -5.85 2.11 0.21
## meancog~vf.score + visit.interv + (~ Bird.ID) 5 -5.64 2.32 0.19
## Estimate SE
## meancog~visit.interv + (~ Bird.ID) 0 0
## meancog~visit.interv + n + (~ Bird.ID) 0 0
## meancog~vf.score + visit.interv + (~ Bird.ID) 0 0
##
## Model-averaged estimate: 0
## Unconditional SE: 0
## 95% Unconditional confidence interval: 0, 0
##
## Multimodel inference on "vf.score" based on AICc
##
## AICc table used to obtain model-averaged estimate:
##
## K AICc Delta_AICc AICcWt
## meancog~vf.score + (~ Bird.ID) 4 -7.80 0.00 0.57
## meancog~vf.score + n + (~ Bird.ID) 5 -6.08 1.72 0.24
## meancog~vf.score + visit.interv + (~ Bird.ID) 5 -5.64 2.16 0.19
## Estimate SE
## meancog~vf.score + (~ Bird.ID) 0.01 0.01
## meancog~vf.score + n + (~ Bird.ID) 0.01 0.01
## meancog~vf.score + visit.interv + (~ Bird.ID) 0.00 0.01
##
## Model-averaged estimate: 0.01
## Unconditional SE: 0.01
## 95% Unconditional confidence interval: -0.02, 0.03
on the subset with high sample size
##
## Confidence set for the best model
##
## Method: raw sum of model probabilities
##
## 95% confidence set:
## K AICc Delta_AICc AICcWt
## meancog~1 + (~ Bird.ID) 3 -11.88 0.00 0.62
## meancog~vf.score + (~ Bird.ID) 4 -9.29 2.59 0.17
## meancog~visit.interv + (~ Bird.ID) 4 -8.19 3.70 0.10
## meancog~n + (~ Bird.ID) 4 -7.70 4.18 0.08
##
## Model probabilities sum to 0.96
##
## Multimodel inference on "visit.interv" based on AICc
##
## AICc table used to obtain model-averaged estimate:
##
## K AICc Delta_AICc AICcWt Estimate SE
## meancog~visit.interv + (~ Bird.ID) 4 -8.19 0 1 0 0
##
## Model-averaged estimate: 0
## Unconditional SE: 0
## 95% Unconditional confidence interval: 0, 0
##
## Multimodel inference on "vf.score" based on AICc
##
## AICc table used to obtain model-averaged estimate:
##
## K AICc Delta_AICc AICcWt Estimate SE
## meancog~vf.score + (~ Bird.ID) 4 -9.29 0 1 0.02 0.01
##
## Model-averaged estimate: 0.02
## Unconditional SE: 0.01
## 95% Unconditional confidence interval: -0.01, 0.04
jackknifed confidence intervals leaving one 10 test segment at the time
jackknifed confidence intervals removing one 10 test segment at the time
STATISTICAL ANALYSIS
discriminant analysis for sexing (based on DNA sexing)
| 3 |
10 |
3 |
Weight/Total.culmen/Central.rectriz |
0.0000000 |
0.9363057 |
1 |
| 14 |
10 |
3 |
Weight/Flattened.wing.length/Central.rectriz |
0.4000000 |
0.9473684 |
1 |
| 86 |
10 |
4 |
Weight/Total.culmen/Mean.tarsus.length/Central.rectriz |
0.0000000 |
0.9363057 |
1 |
| 91 |
10 |
4 |
Weight/Total.culmen/Flattened.wing.length/Central.rectriz |
0.4000000 |
0.9473684 |
1 |
| 96 |
10 |
4 |
Weight/Total.culmen/Central.rectriz/External.rectriz |
0.0000000 |
0.9358974 |
1 |
| 211 |
10 |
5 |
Weight/Total.culmen/Mean.tarsus.length/Flattened.wing.length/Central.rectriz |
0.4000000 |
0.9473684 |
1 |
| 217 |
10 |
5 |
Weight/Total.culmen/Mean.tarsus.length/Central.rectriz/Mandible.color.value |
0.0000000 |
0.9358974 |
1 |
| 25 |
9 |
3 |
Weight/External.rectriz/Curvature |
0.4000000 |
0.9536424 |
1 |
| 42 |
9 |
3 |
Total.culmen/Central.rectriz/bt |
0.0000000 |
0.9423077 |
1 |
| 61 |
9 |
3 |
Mean.tarsus.length/External.rectriz/Curvature |
0.3333333 |
0.9477124 |
1 |
| 3 |
0.9423077 |
0.0943396 |
0.9519231 |
0.1146497 |
0.1146497 |
0.3694268 |
| 14 |
0.9607843 |
0.2093023 |
0.9912281 |
0.1082803 |
0.1146497 |
0.2165605 |
| 86 |
0.9423077 |
0.0925926 |
0.9514563 |
0.1146497 |
0.1082803 |
0.3757962 |
| 91 |
0.9607843 |
0.2093023 |
0.9912281 |
0.1146497 |
0.1019108 |
0.2802548 |
| 96 |
0.9423077 |
0.1590909 |
0.9734513 |
0.1082803 |
0.1337580 |
0.3057325 |
| 211 |
0.9607843 |
0.2093023 |
0.9912281 |
0.1210191 |
0.1082803 |
0.2738854 |
| 217 |
0.9483871 |
0.2000000 |
0.9829060 |
0.1337580 |
0.1082803 |
0.2356688 |
| 25 |
0.9545455 |
0.2058824 |
0.9836066 |
0.1282051 |
0.1025641 |
0.2500000 |
| 42 |
0.9483871 |
0.0645161 |
0.9468085 |
0.1025641 |
0.1025641 |
0.4679487 |
| 61 |
0.9607843 |
0.1764706 |
0.9754098 |
0.1153846 |
0.1153846 |
0.3141026 |
Factor analysis for condition
## Importance of components:
## PC1 PC2 PC3 PC4
## Standard deviation 1.1945 1.0539 0.9321 0.7705
## Proportion of Variance 0.3567 0.2777 0.2172 0.1484
## Cumulative Proportion 0.3567 0.6344 0.8516 1.0000
##
## Call:
## factanal(x = log(cc10[, grep("Weight|Total.culmen|wing|Parasites|Mandible|bt", names(cc10))] + 1), factors = 1, scores = "Bartlett", rotation = "varimax")
##
## Uniquenesses:
## bt Parasites Weight
## 0.51 1.00 1.00
## Total.culmen Flattened.wing.length Mandible.color.value
## 0.69 0.69 0.92
##
## Loadings:
## [1] 0.70 0.56 0.56
##
## Factor1
## SS loadings 1.2
## Proportion Var 0.2
##
## Test of the hypothesis that 1 factor is sufficient.
## The chi square statistic is 8.52 on 9 degrees of freedom.
## The p-value is 0.483
##
## Loadings:
## Factor1
## bt 0.697
## Parasites
## Weight
## Total.culmen 0.559
## Flattened.wing.length 0.560
## Mandible.color.value 0.287
##
## Factor1
## SS loadings 1.196
## Proportion Var 0.199
model selection
## Importance of components:
## PC1 PC2 PC3 PC4
## Standard deviation 1.3159 1.0812 0.8259 0.6460
## Proportion of Variance 0.4329 0.2922 0.1705 0.1043
## Cumulative Proportion 0.4329 0.7251 0.8957 1.0000
##
## Call:
## factanal(x = log(cc10[, grep("Weight|Total.culmen|wing|Parasites|Mandible|bt", names(cc10))] + 1), factors = 1, scores = "Bartlett", rotation = "varimax")
##
## Uniquenesses:
## bt Parasites Weight
## 0.51 1.00 1.00
## Total.culmen Flattened.wing.length Mandible.color.value
## 0.69 0.69 0.92
##
## Loadings:
## [1] 0.70 0.56 0.56
##
## Factor1
## SS loadings 1.2
## Proportion Var 0.2
##
## Test of the hypothesis that 1 factor is sufficient.
## The chi square statistic is 8.52 on 9 degrees of freedom.
## The p-value is 0.483
## condition cog.score upmax2
## condition 1.00000000 -0.3085422 0.01464896
## cog.score -0.30854224 1.0000000 0.11572615
## upmax2 0.01464896 0.1157261 1.00000000
##
## Pearson's product-moment correlation
##
## data: cc$condition and cc$cog.score
## t = -1.5214, df = 22, p-value = 0.1424
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.6331358 0.1083389
## sample estimates:
## cor
## -0.3085422
## [1] 0.0003231572
## [1] 8.258265e-06
## [1] 1.960477e-06
## [1] 0.0001382611
##
## Confidence set for the best model
##
## Method: raw sum of model probabilities
##
## 95% confidence set:
## K AICc
## terr~condition + cog.score + (1 | lek) + (1 | age) 5 22.30
## terr~upmax2 + condition + cog.score + (1 | lek) + (1 | age) 6 25.67
## Delta_AICc
## terr~condition + cog.score + (1 | lek) + (1 | age) 0.00
## terr~upmax2 + condition + cog.score + (1 | lek) + (1 | age) 3.37
## AICcWt
## terr~condition + cog.score + (1 | lek) + (1 | age) 0.84
## terr~upmax2 + condition + cog.score + (1 | lek) + (1 | age) 0.15
##
## Model probabilities sum to 0.99
## [[1]]
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: terr ~ upmax2 + condition + cog.score + (1 | lek) + (1 | age)
## Data: cc
## Control:
## glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+06))
##
## AIC BIC logLik deviance df.resid
## 20.4 27.2 -4.2 8.4 17
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.045232 -0.000024 0.000000 0.000000 0.036919
##
## Random effects:
## Groups Name Variance Std.Dev.
## lek (Intercept) 14115 118.8
## age (Intercept) 19015 137.9
## Number of obs: 23, groups: lek, 3; age, 2
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 4.969 21.769 0.228 0.81943
## upmax2 4.527 8.209 0.551 0.58129
## condition 19.914 7.167 2.779 0.00546 **
## cog.score 32.356 9.954 3.250 0.00115 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) upmax2 condtn
## upmax2 -0.417
## condition 0.175 -0.342
## cog.score 0.505 -0.101 0.755
##
## [[2]]
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: terr ~ condition + cog.score + (1 | lek) + (1 | age)
## Data: cc
## Control:
## glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+06))
##
## AIC BIC logLik deviance df.resid
## 18.8 24.4 -4.4 8.8 18
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.038791 -0.000001 0.000000 0.000000 0.038044
##
## Random effects:
## Groups Name Variance Std.Dev.
## lek (Intercept) 19175 138.5
## age (Intercept) 25339 159.2
## Number of obs: 23, groups: lek, 3; age, 2
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.1526 25.6366 -0.006 0.995250
## condition 28.3821 9.5566 2.970 0.002979 **
## cog.score 38.9752 11.1476 3.496 0.000472 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) condtn
## condition -0.260
## cog.score 0.112 0.770
## param ES SE lower.CI upper.CI
## 1 cog.score 37.941063 11.229894 15.930876 59.95125
## 2 upmax2 4.527077 8.208689 -11.561657 20.61581
## 3 condition 27.059099 9.723060 8.002251 46.11595
## [1] 23
## geom_path: Each group consists of only one observation. Do you need to
## adjust the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to
## adjust the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to
## adjust the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to
## adjust the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to
## adjust the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to
## adjust the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to
## adjust the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to
## adjust the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to
## adjust the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to
## adjust the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to
## adjust the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to
## adjust the group aesthetic?
ideas
talvez mejor usar actividad como variable respuesta en analisis aparte?
prob de ser territorial el ano siguiente es relacionada a condicion o a cognicion?
