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library(broom)library(lme4)
Loading required package: Matrix
library(dplyr)
Attaching package: 'dplyr'
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
library(lme4)library(lmerTest)
Attaching package: 'lmerTest'
The following object is masked from 'package:lme4':
lmer
The following object is masked from 'package:stats':
step
library(emmeans)library(car)
Loading required package: carData
Attaching package: 'car'
The following object is masked from 'package:dplyr':
recode
library(tidyverse)
-- Attaching core tidyverse packages ------------------------ tidyverse 2.0.0 --
v forcats 1.0.0 v readr 2.1.4
v ggplot2 3.4.3 v stringr 1.5.0
v lubridate 1.9.2 v tibble 3.2.1
v purrr 1.0.1 v tidyr 1.3.0
-- Conflicts ------------------------------------------ tidyverse_conflicts() --
x tidyr::expand() masks Matrix::expand()
x dplyr::filter() masks stats::filter()
x dplyr::lag() masks stats::lag()
x tidyr::pack() masks Matrix::pack()
x car::recode() masks dplyr::recode()
x purrr::some() masks car::some()
x tidyr::unpack() masks Matrix::unpack()
i Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
# A tibble: 8 x 7
# Groups: time_between_group [2]
time_between_group effect group term estimate std.error statistic
<chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
1 distant ran_pars ID sd__(In~ 0.241 NA NA
2 distant ran_pars Follower_ID sd__(In~ 0.154 NA NA
3 distant ran_pars Social_Group sd__(In~ 0.121 NA NA
4 distant ran_pars Residual sd__Obs~ 1.05 NA NA
5 immediate ran_pars ID sd__(In~ 0.365 NA NA
6 immediate ran_pars Follower_ID sd__(In~ 0.279 NA NA
7 immediate ran_pars Social_Group sd__(In~ 0 NA NA
8 immediate ran_pars Residual sd__Obs~ 1.32 NA NA
# A tibble: 8 x 7
# Groups: time_between_group [2]
time_between_group effect group term estimate std.error statistic
<chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
1 distant ran_pars ID sd__(In~ 0.230 NA NA
2 distant ran_pars Follower_ID sd__(In~ 0.131 NA NA
3 distant ran_pars Social_Group sd__(In~ 0.103 NA NA
4 distant ran_pars Residual sd__Obs~ 0.963 NA NA
5 immediate ran_pars ID sd__(In~ 0.305 NA NA
6 immediate ran_pars Follower_ID sd__(In~ 0.213 NA NA
7 immediate ran_pars Social_Group sd__(In~ 0.0309 NA NA
8 immediate ran_pars Residual sd__Obs~ 1.23 NA NA
# A tibble: 8 x 7
# Groups: time_between_group [2]
time_between_group effect group term estimate std.error statistic
<chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
1 distant ran_pars ID sd__(In~ 0.227 NA NA
2 distant ran_pars Follower_ID sd__(In~ 0.115 NA NA
3 distant ran_pars Social_Group sd__(In~ 0.0970 NA NA
4 distant ran_pars Residual sd__Obs~ 0.906 NA NA
5 immediate ran_pars ID sd__(In~ 0.298 NA NA
6 immediate ran_pars Follower_ID sd__(In~ 0.185 NA NA
7 immediate ran_pars Social_Group sd__(In~ 0.0867 NA NA
8 immediate ran_pars Residual sd__Obs~ 1.20 NA NA
# A tibble: 8 x 7
# Groups: time_between_group [2]
time_between_group effect group term estimate std.error statistic
<chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
1 distant ran_pars ID sd__(In~ 0.225 NA NA
2 distant ran_pars Follower_ID sd__(In~ 0.102 NA NA
3 distant ran_pars Social_Group sd__(In~ 0.0942 NA NA
4 distant ran_pars Residual sd__Obs~ 0.862 NA NA
5 immediate ran_pars ID sd__(In~ 0.295 NA NA
6 immediate ran_pars Follower_ID sd__(In~ 0.163 NA NA
7 immediate ran_pars Social_Group sd__(In~ 0.127 NA NA
8 immediate ran_pars Residual sd__Obs~ 1.20 NA NA
# A tibble: 8 x 7
# Groups: time_between_group [2]
time_between_group effect group term estimate std.error statistic
<chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
1 distant ran_pars ID sd__(In~ 0.223 NA NA
2 distant ran_pars Follower_ID sd__(In~ 0.0921 NA NA
3 distant ran_pars Social_Group sd__(In~ 0.0968 NA NA
4 distant ran_pars Residual sd__Obs~ 0.829 NA NA
5 immediate ran_pars ID sd__(In~ 0.296 NA NA
6 immediate ran_pars Follower_ID sd__(In~ 0.163 NA NA
7 immediate ran_pars Social_Group sd__(In~ 0.155 NA NA
8 immediate ran_pars Residual sd__Obs~ 1.21 NA NA
Warning: The `x` argument of `as_tibble.matrix()` must have unique column names if
`.name_repair` is omitted as of tibble 2.0.0.
i Using compatibility `.name_repair`.
'as(<dtTMatrix>, "dtCMatrix")' is deprecated.
Use 'as(., "CsparseMatrix")' instead.
See help("Deprecated") and help("Matrix-deprecated").
summary(A[upper.tri(A)])
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000000 0.000000 0.000000 0.008621 0.000000 0.500000
sum(A[upper.tri(A)]>0)
[1] 10535
length(A[upper.tri(A)] >0 )
[1] 351541
summary(diag(A))
Min. 1st Qu. Median Mean 3rd Qu. Max.
1 1 1 1 1 1
Pedigreemm models
#| warning: true#| echo: true# Creaando IDpe y Follower.pe en el data framedata_PIC_pvalues_ped1 <-within(data_PIC_pvalues, { IDpe <- ID Follower.pe <- Follower_ID sdL_time <- L_time /sd(L_time)})# Modelo completo (fm2), efectos directos e indirectos ambientales y geneticofm2 <-pedigreemm( L_time ~as.factor(Hour_ENTRY) + (1| ID) + (1| Follower_ID) + (1| IDpe) + (1| Follower.pe) + (1| Social_Group),data = data_PIC_pvalues_ped1,pedigree =list(ID = d, Follower_ID = d))
Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
unable to evaluate scaled gradient
Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model failed to converge: degenerate Hessian with 1 negative eigenvalues
Warning: Model failed to converge with 1 negative eigenvalue: -1.2e+00
Correlation matrix not shown by default, as p = 24 > 12.
Use print(x, correlation=TRUE) or
vcov(x) if you need it
## modelo sin pedigree modelo original efectos directos e indirectosreduced_model_Follower.pe <-lmer( L_time ~as.factor(Hour_ENTRY) + (1| ID) + (1| Follower_ID) + (1| Social_Group),data = data_PIC_pvalues_ped1)##reduced_model_Follower <-pedigreemm( L_time ~as.factor(Hour_ENTRY) + (1| ID) + (1| Follower_ID) + (1| Social_Group),data = data_PIC_pvalues,pedigree =list(ID = d, Follower_ID = d))## modelo sin pedigree modelo original efectos directos e indirectosreduced_model_Follower.pe <-lmer( L_time ~as.factor(Hour_ENTRY) + (1| ID) + (1| Follower_ID) + (1| Social_Group),data = data_PIC_pvalues)## full model usando lmer y sin ambiente permanent del followerfm.lmer <-lmer( L_time ~as.factor(Hour_ENTRY) + (1| ID) + (1| Follower_ID) + (1| IDpe) + (1| Social_Group),data = data_PIC_pvalues_ped1 )
Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model failed to converge with max|grad| = 0.0559996 (tol = 0.002, component 1)
Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: large eigenvalue ratio
- Rescale variables?
## full model usando lmer y sin ambiente permanente del idfm.lmer2 <-lmer( L_time ~as.factor(Hour_ENTRY) + (1| ID) + (1| Follower_ID) + (1| Follower.pe) + (1| Social_Group), data = data_PIC_pvalues_ped1 )
Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model failed to converge with max|grad| = 0.0223521 (tol = 0.002, component 1)
Correlation matrix not shown by default, as p = 24 > 12.
Use print(x, correlation=TRUE) or
vcov(x) if you need it
optimizer (nloptwrap) convergence code: 0 (OK)
Model failed to converge with max|grad| = 0.0559996 (tol = 0.002, component 1)
Model is nearly unidentifiable: large eigenvalue ratio
- Rescale variables?