Summary of first visit
for (i in 1:6){
print(paste("********** Trial",i,"**********"))
suppressMessages({
raw <- read_csv(as.character(z[i,3]))
key <- read_csv(as.character(z[i,2]))
flagged.good <- start_fire(raw, key,start=as.data.frame(z)[i,4]%>%ymd())%>%power_flag()
flagged.good %>% meta_fire()
# 1.1. File records filter by unit
flagged.fil<-flagged.good%>%filter(!is.na(unit))
flagged.fil%>%pyro_plot()
flagged.fil<-arrange(flagged.fil,Location,Date,Entry)%>%
filter(Ear_Tag!="000000000000000")%>%
mutate(follower=lead(Ear_Tag), follower_loc=lead(Location), follower_Date=lead(Date),
follower_Entry=lead(as.numeric(Entry)))%>%
mutate(to_next=as.numeric(follower_Entry)-as.numeric(Exit))%>%
mutate(to_next=to_next+(to_next<0)*86400)%>%
filter(Location==follower_loc,follower_Date-Date<=1)%>%
select(Location,Date,Entry,Exit, Consumed,Weight,Ear_Tag,visit.length,
intake.rate,barcode,trial.day,follower:to_next)
})
# 1.3. Hour of first entry to feeder
h1<-flagged.fil%>%
group_by(Ear_Tag)%>%arrange(Location,Date,Entry,Exit,Ear_Tag)%>%
distinct(Ear_Tag, .keep_all = T)%>%ungroup(h1)%>%
select(Location,Date,Entry,Exit,Ear_Tag,Consumed,trial.day)
# 2. Hour to first food, minimun consumed it 0.100 gr
l1<-flagged.fil%>%filter(Consumed>=0.100)%>%
group_by(Ear_Tag)%>%arrange(Location,Date,Entry,Exit,Ear_Tag)%>%
distinct(Ear_Tag, .keep_all = T)%>%ungroup(l1)%>%
select(Location,Date,Entry,Exit,Ear_Tag,Consumed,trial.day)
if(!exists("event_log")){
event_log<-mutate(flagged.fil,trial=i)
} else {
event_log<-rbind(event_log,mutate(flagged.fil,trial=i))
}
if(!exists("first.visit")){
first.visit<-mutate(h1,trial=i,visit.type="V")
} else {
first.visit<-rbind(first.visit,mutate(h1,trial=i,visit.type="V"))
}
first.visit<-rbind(first.visit,mutate(l1,trial=i,visit.type="M"))
print("end of trial----------")
plot(0,0,"n")
}## [1] "********** Trial 1 **********"## Warning in rq.fit.br(x, y, tau = tau, ...): Solution may be nonunique
## Warning in rq.fit.br(x, y, tau = tau, ...): Solution may be nonunique
## Warning in rq.fit.br(x, y, tau = tau, ...): Solution may be nonunique
## Warning in rq.fit.br(x, y, tau = tau, ...): Solution may be nonunique## Warning: Removed 130 rows containing missing values (geom_point).
## Warning: Removed 130 rows containing missing values (geom_point).
## [1] "end of trial----------"## [1] "********** Trial 2 **********"## Warning in rq.fit.br(x, y, tau = tau, ...): Solution may be nonunique
## Warning in rq.fit.br(wx, wy, tau = tau, ...): Solution may be nonunique## Warning: Removed 1395 rows containing missing values (geom_point).
## Warning: Removed 1395 rows containing missing values (geom_point).
## [1] "end of trial----------"
## [1] "********** Trial 3 **********"## Warning in rq.fit.br(wx, wy, tau = tau, ...): Solution may be nonunique## Warning: Removed 756 rows containing missing values (geom_point).
## Warning: Removed 756 rows containing missing values (geom_point).
## [1] "end of trial----------"## [1] "********** Trial 4 **********"## Warning in rq.fit.br(x, y, tau = tau, ...): Solution may be nonunique## Warning in rq.fit.br(x, y, tau = tau, ...): Solution may be nonunique## Warning in is.na(unit): is.na() applied to non-(list or vector) of type
## 'closure'
## Warning: Removed 647 rows containing missing values (geom_point).
## Warning: Removed 647 rows containing missing values (geom_point).
## [1] "end of trial----------"## [1] "********** Trial 5 **********"## Warning in rq.fit.br(x, y, tau = tau, ...): Solution may be nonunique## Warning in rq.fit.br(x, y, tau = tau, ...): Solution may be nonunique## Warning in is.na(unit): is.na() applied to non-(list or vector) of type
## 'closure'
## Warning: Removed 130 rows containing missing values (geom_point).
## Warning: Removed 130 rows containing missing values (geom_point).
## [1] "end of trial----------"## [1] "********** Trial 6 **********"## Warning in rq.fit.br(x, y, tau = tau, ...): Solution may be nonunique## Warning in rq.fit.br(x, y, tau = tau, ...): Solution may be nonunique
## Warning in rq.fit.br(x, y, tau = tau, ...): Solution may be nonunique
## Warning in rq.fit.br(x, y, tau = tau, ...): Solution may be nonunique
## Warning in rq.fit.br(x, y, tau = tau, ...): Solution may be nonunique## Warning in is.na(unit): is.na() applied to non-(list or vector) of type
## 'closure'
## Warning in rq.fit.br(wx, wy, tau = tau, ...): Solution may be nonunique## Warning in rq.fit.br(wx, wy, tau = tau, ...): Solution may be nonunique## Warning: Removed 158 rows containing missing values (geom_point).
## Warning: Removed 158 rows containing missing values (geom_point).
## [1] "end of trial----------"
Time of first visit to the feeder, and time to first food, minimun consumed it 0.100 gr
table(first.visit$trial.day)##
## 0 1 2 3 4 5 6
## 132 65 11 10 14 3 1first.visit<-mutate(first.visit,hfm=Entry%>%as.numeric()/3600,trial.hour=24*trial.day+hfm)
first_visit_hours<-first.visit%>%filter(trial.hour<=72)
earliest<-first_visit_hours%>%group_by(trial,Location)%>%summarise(earliest=min(trial.hour))
first_visit_hours<-left_join(first_visit_hours,earliest,by=c("Location","trial"))%>%mutate(trial.hour=trial.hour-earliest)
by(first_visit_hours$trial.hour,INDICES = first_visit_hours$visit.type,summary)## first_visit_hours$visit.type: M
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 3.854 7.692 13.134 20.734 57.346
## --------------------------------------------------------
## first_visit_hours$visit.type: V
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.5708 4.6342 10.1679 18.0017 50.0731by(first_visit_hours$trial.hour,INDICES = first_visit_hours$visit.type,sd)## first_visit_hours$visit.type: M
## [1] 12.68536
## --------------------------------------------------------
## first_visit_hours$visit.type: V
## [1] 12.15547by(first_visit_hours$trial.hour,INDICES = first_visit_hours$visit.type,IQR)## first_visit_hours$visit.type: M
## [1] 16.87944
## --------------------------------------------------------
## first_visit_hours$visit.type: V
## [1] 17.43083ggplot(first_visit_hours,aes(x=trial.hour))+geom_density()+facet_wrap(.~visit.type)
ggplot(first_visit_hours,aes(y=trial.hour,x=visit.type))+geom_boxplot()
Eventlog Analysis and Interaction matrices by trial and location
interaction_table<-event_log%>%select(Ear_Tag,follower)%>%by(data = .,INDICES = list (event_log$Location,event_log$trial),FUN = table)
print(interaction_table)## : 1
## : 1
## follower
## Ear_Tag Y64-16 Y64-17 Y66-12 Y66-14 Y67-14 Y68-11 Y68-12 Y69-12 Y70-13
## Y64-16 0 12 13 14 9 23 27 12 27
## Y64-17 19 2 21 16 9 14 12 11 22
## Y66-12 13 15 5 9 18 13 17 6 18
## Y66-14 10 20 6 3 14 12 21 12 21
## Y67-14 14 15 13 16 5 15 27 15 17
## Y68-11 15 18 17 16 12 8 16 12 18
## Y68-12 16 23 22 26 16 19 4 16 27
## Y69-12 10 14 10 12 14 13 9 4 19
## Y70-13 27 20 14 13 30 20 35 16 9
## Y71-12 18 14 11 22 39 15 21 26 34
## Y77-10 24 12 12 12 19 9 23 15 16
## Y77-11 14 17 14 8 8 19 26 5 14
## follower
## Ear_Tag Y71-12 Y77-10 Y77-11
## Y64-16 18 15 10
## Y64-17 23 12 21
## Y66-12 16 16 12
## Y66-14 18 17 12
## Y67-14 32 12 11
## Y68-11 23 16 9
## Y68-12 27 19 23
## Y69-12 19 17 10
## Y70-13 22 19 17
## Y71-12 6 21 18
## Y77-10 25 2 11
## Y77-11 15 14 1
## --------------------------------------------------------
## : 2
## : 1
## follower
## Ear_Tag Y64-14 Y65-10 Y66-10 Y67-11 Y68-14 Y70-11 Y70-12 Y74-14 Y76-13
## Y64-14 4 18 9 22 18 13 25 7 38
## Y65-10 21 5 17 18 12 7 14 12 24
## Y66-10 14 8 5 31 25 13 26 26 75
## Y67-11 21 25 28 12 25 24 29 29 34
## Y68-14 11 20 26 20 1 11 19 14 50
## Y70-11 17 8 15 25 15 2 12 21 18
## Y70-12 19 17 22 31 15 16 7 19 47
## Y74-14 14 18 21 23 19 18 28 1 41
## Y76-13 29 18 61 54 48 26 38 48 5
## Y76-15 29 23 19 38 17 16 24 34 50
## Y77-12 11 13 23 26 10 14 15 10 44
## Y77-13 9 7 18 15 17 18 10 11 17
## follower
## Ear_Tag Y76-15 Y77-12 Y77-13
## Y64-14 27 7 11
## Y65-10 17 20 14
## Y66-10 19 13 8
## Y67-11 46 27 14
## Y68-14 21 14 15
## Y70-11 21 9 15
## Y70-12 28 14 12
## Y74-14 27 13 9
## Y76-13 61 28 27
## Y76-15 1 19 16
## Y77-12 11 2 7
## Y77-13 7 19 0
## --------------------------------------------------------
## : 1
## : 2
## follower
## Ear_Tag 1353 1354 1355 1356 1357 1358 1359 1360 1361 1365 1367
## 1353 47 76 62 62 84 72 72 97 93 169 86
## 1354 70 31 67 59 43 67 79 55 64 75 56
## 1355 65 75 97 87 85 56 85 55 88 69 56
## 1356 62 67 88 36 73 53 103 60 60 91 65
## 1357 89 63 68 71 30 54 59 76 85 76 72
## 1358 84 46 68 59 71 11 98 58 80 86 63
## 1359 92 61 92 102 68 91 46 76 78 92 77
## 1360 83 44 67 65 74 86 85 27 81 74 58
## 1361 89 71 80 82 69 78 68 70 55 129 105
## 1365 135 69 72 76 75 100 105 103 125 59 95
## 1367 104 63 57 59 71 57 74 67 87 94 21
## --------------------------------------------------------
## : 2
## : 2
## follower
## Ear_Tag 1302 1304 1305 1306 1307 1310 1311 1312 1313 1314 1316
## 1302 23 46 55 41 45 53 69 49 51 62 60
## 1304 40 59 78 66 40 77 102 67 50 58 89
## 1305 62 66 25 81 81 71 94 77 77 59 66
## 1306 54 82 96 73 53 53 90 85 59 53 64
## 1307 50 70 58 48 21 83 75 80 116 63 44
## 1310 43 74 54 78 81 88 87 67 71 54 68
## 1311 53 69 85 89 87 89 65 84 134 79 99
## 1312 66 72 81 76 59 70 85 18 83 45 65
## 1313 60 65 86 76 99 57 112 74 39 76 82
## 1314 51 64 66 53 76 53 75 55 70 34 42
## 1316 51 59 75 82 66 71 79 64 76 56 100
## --------------------------------------------------------
## : 1
## : 3
## follower
## Ear_Tag 1102 1413 1415 1418 1419 1598 1653 1666 1668 1672 1691
## 1102 0 28 40 35 34 31 49 59 54 79 44
## 1413 27 8 27 44 43 70 67 67 54 78 72
## 1415 45 32 9 52 33 60 72 72 90 73 78
## 1418 37 44 54 6 51 56 84 58 91 88 76
## 1419 42 35 55 41 2 55 70 64 73 92 69
## 1598 51 56 39 56 45 7 102 67 129 94 80
## 1653 57 76 77 73 75 84 40 92 194 181 119
## 1666 45 89 72 62 61 106 84 5 117 92 83
## 1668 43 68 84 93 85 102 194 152 31 131 121
## 1672 58 55 107 88 99 89 183 97 144 19 132
## 1691 48 66 52 95 70 66 124 82 127 144 8
## --------------------------------------------------------
## : 2
## : 3
## follower
## Ear_Tag 1106 1129 1132 1200 1371 1421 1688 1689 1699 2034 2346
## 1106 8 61 61 138 70 95 96 50 64 67 77
## 1129 64 10 30 72 62 74 57 20 24 42 49
## 1132 52 29 22 44 54 54 63 32 32 57 45
## 1200 127 71 62 37 85 186 102 57 63 65 90
## 1371 74 54 71 79 17 72 45 45 33 58 43
## 1421 72 69 61 190 82 3 105 51 63 44 48
## 1688 96 57 38 137 41 95 2 48 57 35 58
## 1689 50 37 36 48 38 51 38 2 27 36 33
## 1699 71 39 28 50 40 63 56 21 6 36 38
## 2034 65 29 45 61 56 47 53 46 40 7 29
## 2346 108 48 30 89 46 49 47 24 39 31 6
## --------------------------------------------------------
## : 1
## : 4
## follower
## Ear_Tag 1127 1408 1412 1416 2337 2338 2339 2342
## 1127 3 54 82 45 85 81 105 76
## 1408 53 24 108 48 87 88 93 69
## 1412 101 93 10 85 100 103 169 109
## 1416 50 52 76 6 85 76 88 76
## 2337 89 74 136 71 18 120 140 109
## 2338 69 82 98 68 116 38 144 110
## 2339 94 113 149 105 136 121 34 148
## 2342 72 78 111 81 130 98 127 18
## --------------------------------------------------------
## : 2
## : 4
## follower
## Ear_Tag 1376 1378 1379 1403 1410 2336 2340 2341
## 1376 75 166 102 97 86 99 88 109
## 1378 125 20 146 235 110 123 90 142
## 1379 119 136 13 125 120 174 131 157
## 1403 119 154 151 37 118 163 93 107
## 1410 94 122 160 80 30 68 96 89
## 2336 105 136 160 135 82 40 90 143
## 2340 87 107 117 76 78 99 79 92
## 2341 98 150 126 156 115 125 68 34
## --------------------------------------------------------
## : 1
## : 5
## follower
## Ear_Tag 2084 2111 2129 2138 2146 2345 2347 2349
## 2084 11 28 42 40 73 41 27 34
## 2111 53 6 50 39 87 98 55 39
## 2129 42 53 7 41 113 52 40 46
## 2138 27 52 36 9 58 68 26 30
## 2146 46 154 107 59 22 94 66 79
## 2345 41 48 72 37 132 2 57 51
## 2347 26 53 40 31 73 40 1 28
## 2349 51 33 40 49 69 45 20 2
## --------------------------------------------------------
## : 2
## : 5
## follower
## Ear_Tag 2082 2089 2134 2144 2343 2344 2348 2350
## 2082 32 49 63 53 56 52 55 27
## 2089 37 7 73 63 47 26 55 26
## 2134 82 52 18 85 61 45 63 37
## 2144 73 59 65 22 50 49 82 55
## 2343 47 46 70 65 10 32 49 39
## 2344 20 38 46 43 52 7 31 31
## 2348 60 50 64 75 49 31 11 31
## 2350 36 33 44 50 33 25 25 3
## --------------------------------------------------------
## : 1
## : 6
## follower
## Ear_Tag 1317 1318 1319 1320 1321 1322 2005 2097 2098
## 1317 1 21 9 21 14 20 14 5 23
## 1318 20 9 25 14 13 14 9 11 19
## 1319 25 14 0 9 22 12 8 7 19
## 1320 12 23 15 1 15 21 13 16 21
## 1321 11 12 16 22 2 23 14 19 13
## 1322 16 22 11 20 18 0 17 13 23
## 2005 12 9 6 12 13 11 1 7 24
## 2097 11 14 16 14 8 13 8 2 18
## 2098 20 10 18 24 26 26 12 24 2
## --------------------------------------------------------
## : 2
## : 6
## follower
## Ear_Tag 1323 1324 1325 1368 1369 1370 1371 1372 1373
## 1323 0 10 18 25 7 11 23 14 23
## 1324 12 1 11 14 9 8 11 11 11
## 1325 11 10 8 18 14 14 16 16 14
## 1368 21 20 17 4 13 11 28 21 27
## 1369 11 6 8 11 1 12 5 10 13
## 1370 13 9 9 17 9 0 18 15 5
## 1371 26 7 17 30 6 10 9 18 17
## 1372 14 12 20 15 6 15 13 4 20
## 1373 23 13 13 28 12 14 17 10 1Linear fixed effects models for length of visit variable
Length visit < 600 seconds
Linear regression of length of visit on ID pig and follower pig ID
tr<-600
close_visit<-event_log%>%filter(to_next<tr)
dim(close_visit)## [1] 51855 17lmf<-close_visit%>%by(data=.,INDICES=close_visit$trial,function(x) lm(visit.length~Ear_Tag+follower,data=x))
lapply(lmf,anova)## $`1`
## Analysis of Variance Table
##
## Response: visit.length
## Df Sum Sq Mean Sq F value Pr(>F)
## Ear_Tag 23 178747682 7771638 31.8940 < 2.2e-16 ***
## follower 22 29756917 1352587 5.5509 1.255e-15 ***
## Residuals 4594 1119425539 243671
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`2`
## Analysis of Variance Table
##
## Response: visit.length
## Df Sum Sq Mean Sq F value Pr(>F)
## Ear_Tag 21 102383514 4875405 28.3820 < 2.2e-16 ***
## follower 20 30907221 1545361 8.9963 < 2.2e-16 ***
## Residuals 15292 2626831216 171778
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`3`
## Analysis of Variance Table
##
## Response: visit.length
## Df Sum Sq Mean Sq F value Pr(>F)
## Ear_Tag 21 880573711 41932081 173.364 < 2.2e-16 ***
## follower 20 72317364 3615868 14.949 < 2.2e-16 ***
## Residuals 13951 3374379504 241874
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`4`
## Analysis of Variance Table
##
## Response: visit.length
## Df Sum Sq Mean Sq F value Pr(>F)
## Ear_Tag 15 208620904 13908060 93.481 < 2.2e-16 ***
## follower 14 28291915 2020851 13.583 < 2.2e-16 ***
## Residuals 10904 1622297374 148780
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`5`
## Analysis of Variance Table
##
## Response: visit.length
## Df Sum Sq Mean Sq F value Pr(>F)
## Ear_Tag 15 77078776 5138585 29.8988 < 2.2e-16 ***
## follower 14 10758980 768499 4.4715 4.642e-08 ***
## Residuals 4993 858125448 171866
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`6`
## Analysis of Variance Table
##
## Response: visit.length
## Df Sum Sq Mean Sq F value Pr(>F)
## Ear_Tag 17 20363601 1197859 9.1150 < 2.2e-16 ***
## follower 16 10900902 681306 5.1843 8.974e-11 ***
## Residuals 1897 249296946 131416
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Linear mixed model for lenght visit variable
lmer(visit.length~(1|Ear_Tag:trial)+(1|follower:trial),data=close_visit)%>%summary()## Linear mixed model fit by REML ['lmerMod']
## Formula: visit.length ~ (1 | Ear_Tag:trial) + (1 | follower:trial)
## Data: close_visit
##
## REML criterion at convergence: 778374.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0784 -0.6959 -0.1553 0.5406 8.3411
##
## Random effects:
## Groups Name Variance Std.Dev.
## Ear_Tag:trial (Intercept) 39187 198.0
## follower:trial (Intercept) 3820 61.8
## Residual 190778 436.8
## Number of obs: 51855, groups: Ear_Tag:trial, 118; follower:trial, 118
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 601.74 19.26 31.24Length visit >= 600 seconds
Linear regression of length of visit on ID pig and follower pig ID
distant_visit<-event_log%>%filter(to_next>=tr)
dim(distant_visit)## [1] 6146 17lmf<-distant_visit%>%by(data=.,INDICES=distant_visit$trial,function(x) lm(visit.length~Ear_Tag+follower,data=x))
lapply(lmf,anova)## $`1`
## Analysis of Variance Table
##
## Response: visit.length
## Df Sum Sq Mean Sq F value Pr(>F)
## Ear_Tag 23 40014964 1739781 6.3447 <2e-16 ***
## follower 22 7402517 336478 1.2271 0.2186
## Residuals 482 132169433 274210
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`2`
## Analysis of Variance Table
##
## Response: visit.length
## Df Sum Sq Mean Sq F value Pr(>F)
## Ear_Tag 21 19824292 944014 3.4812 1.686e-07 ***
## follower 20 8011361 400568 1.4772 0.07937 .
## Residuals 1707 462894351 271174
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`3`
## Analysis of Variance Table
##
## Response: visit.length
## Df Sum Sq Mean Sq F value Pr(>F)
## Ear_Tag 21 82520084 3929528 16.6403 <2e-16 ***
## follower 20 4973896 248695 1.0531 0.3951
## Residuals 1103 260467810 236145
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`4`
## Analysis of Variance Table
##
## Response: visit.length
## Df Sum Sq Mean Sq F value Pr(>F)
## Ear_Tag 15 45812825 3054188 23.440 <2e-16 ***
## follower 14 2096045 149718 1.149 0.3094
## Residuals 1480 192839260 130297
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`5`
## Analysis of Variance Table
##
## Response: visit.length
## Df Sum Sq Mean Sq F value Pr(>F)
## Ear_Tag 15 9945142 663009 3.1242 5.289e-05 ***
## follower 14 1648395 117742 0.5548 0.9002
## Residuals 903 191633268 212218
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`6`
## Analysis of Variance Table
##
## Response: visit.length
## Df Sum Sq Mean Sq F value Pr(>F)
## Ear_Tag 17 6064118 356713 4.0816 3.386e-07 ***
## follower 16 1094292 68393 0.7826 0.7048
## Residuals 247 21586401 87394
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Linear mixed model for lenght visit variable
lmer(visit.length~(1|Ear_Tag:trial)+(1|follower:trial),data=distant_visit)%>%summary()## Linear mixed model fit by REML ['lmerMod']
## Formula: visit.length ~ (1 | Ear_Tag:trial) + (1 | follower:trial)
## Data: distant_visit
##
## REML criterion at convergence: 93117.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7565 -0.6472 -0.1359 0.4845 14.2155
##
## Random effects:
## Groups Name Variance Std.Dev.
## Ear_Tag:trial (Intercept) 42673.9 206.58
## follower:trial (Intercept) 590.7 24.31
## Residual 213075.7 461.60
## Number of obs: 6146, groups: Ear_Tag:trial, 118; follower:trial, 118
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 642.44 20.56 31.25#linear model length visit < 300 seconds
tr<-300
close_visit<-event_log%>%filter(to_next<tr)
dim(close_visit)## [1] 49423 17lmf<-close_visit%>%by(data=.,INDICES=close_visit$trial,function(x) lm(visit.length~Ear_Tag+follower,data=x))
lapply(lmf,anova)## $`1`
## Analysis of Variance Table
##
## Response: visit.length
## Df Sum Sq Mean Sq F value Pr(>F)
## Ear_Tag 23 172544692 7501943 31.020 < 2.2e-16 ***
## follower 22 30247403 1374882 5.685 3.85e-16 ***
## Residuals 4393 1062424150 241845
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`2`
## Analysis of Variance Table
##
## Response: visit.length
## Df Sum Sq Mean Sq F value Pr(>F)
## Ear_Tag 21 94039265 4478060 26.6022 < 2.2e-16 ***
## follower 20 31183348 1559167 9.2623 < 2.2e-16 ***
## Residuals 14445 2431583453 168334
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`3`
## Analysis of Variance Table
##
## Response: visit.length
## Df Sum Sq Mean Sq F value Pr(>F)
## Ear_Tag 21 855134173 40720675 168.845 < 2.2e-16 ***
## follower 20 71211158 3560558 14.764 < 2.2e-16 ***
## Residuals 13518 3260165750 241172
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`4`
## Analysis of Variance Table
##
## Response: visit.length
## Df Sum Sq Mean Sq F value Pr(>F)
## Ear_Tag 15 201576449 13438430 91.443 < 2.2e-16 ***
## follower 14 26969479 1926391 13.108 < 2.2e-16 ***
## Residuals 10359 1522360980 146960
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`5`
## Analysis of Variance Table
##
## Response: visit.length
## Df Sum Sq Mean Sq F value Pr(>F)
## Ear_Tag 15 73275429 4885029 28.3100 < 2.2e-16 ***
## follower 14 11475504 819679 4.7503 9.645e-09 ***
## Residuals 4687 808763693 172555
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`6`
## Analysis of Variance Table
##
## Response: visit.length
## Df Sum Sq Mean Sq F value Pr(>F)
## Ear_Tag 17 18499827 1088225 8.1603 < 2.2e-16 ***
## follower 16 10613642 663353 4.9743 3.537e-10 ***
## Residuals 1797 239641540 133356
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Linear mixed model for lenght visit variable
lmer(visit.length~(1|Ear_Tag:trial)+(1|follower:trial),data=close_visit)%>%summary()## Linear mixed model fit by REML ['lmerMod']
## Formula: visit.length ~ (1 | Ear_Tag:trial) + (1 | follower:trial)
## Data: close_visit
##
## REML criterion at convergence: 741573.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1422 -0.6961 -0.1549 0.5438 8.2906
##
## Random effects:
## Groups Name Variance Std.Dev.
## Ear_Tag:trial (Intercept) 39529 198.82
## follower:trial (Intercept) 3984 63.12
## Residual 189528 435.35
## Number of obs: 49423, groups: Ear_Tag:trial, 118; follower:trial, 118
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 601.08 19.38 31.02Linear model length visit >= 300 seconds
distant_visit<-event_log%>%filter(to_next>=tr)
dim(distant_visit)## [1] 8578 17lmf<-distant_visit%>%by(data=.,INDICES=distant_visit$trial,function(x) lm(visit.length~Ear_Tag+follower,data=x))
lapply(lmf,anova)## $`1`
## Analysis of Variance Table
##
## Response: visit.length
## Df Sum Sq Mean Sq F value Pr(>F)
## Ear_Tag 23 45872027 1994436 7.2427 <2e-16 ***
## follower 22 8243908 374723 1.3608 0.1253
## Residuals 683 188079160 275372
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`2`
## Analysis of Variance Table
##
## Response: visit.length
## Df Sum Sq Mean Sq F value Pr(>F)
## Ear_Tag 21 29588878 1408994 5.4617 1.589e-14 ***
## follower 20 5598994 279950 1.0852 0.3577
## Residuals 2554 658872298 257977
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`3`
## Analysis of Variance Table
##
## Response: visit.length
## Df Sum Sq Mean Sq F value Pr(>F)
## Ear_Tag 21 111168241 5293726 21.9899 < 2e-16 ***
## follower 20 7224875 361244 1.5006 0.07156 .
## Residuals 1536 369767458 240734
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`4`
## Analysis of Variance Table
##
## Response: visit.length
## Df Sum Sq Mean Sq F value Pr(>F)
## Ear_Tag 15 53410042 3560669 24.7876 < 2e-16 ***
## follower 14 3848521 274894 1.9137 0.02109 *
## Residuals 2025 290885297 143647
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`5`
## Analysis of Variance Table
##
## Response: visit.length
## Df Sum Sq Mean Sq F value Pr(>F)
## Ear_Tag 15 12661830 844122 4.2377 1.018e-07 ***
## follower 14 1915257 136804 0.6868 0.7891
## Residuals 1209 240826698 199195
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`6`
## Analysis of Variance Table
##
## Response: visit.length
## Df Sum Sq Mean Sq F value Pr(>F)
## Ear_Tag 17 7850852 461815 5.0367 8.174e-10 ***
## follower 16 810334 50646 0.5524 0.9174
## Residuals 347 31816466 91690
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Linear mixed model for lenght visit variable
lmer(visit.length~(1|Ear_Tag:trial)+(1|follower:trial),data=distant_visit)%>%summary()## Linear mixed model fit by REML ['lmerMod']
## Formula: visit.length ~ (1 | Ear_Tag:trial) + (1 | follower:trial)
## Data: distant_visit
##
## REML criterion at convergence: 129898.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8311 -0.6566 -0.1378 0.4855 14.2761
##
## Random effects:
## Groups Name Variance Std.Dev.
## Ear_Tag:trial (Intercept) 40305.0 200.8
## follower:trial (Intercept) 718.5 26.8
## Residual 213127.2 461.7
## Number of obs: 8578, groups: Ear_Tag:trial, 118; follower:trial, 118
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 633.93 19.71 32.17Length visit < 150 seconds
tr<-150
close_visit<-event_log%>%filter(to_next<tr)
dim(close_visit)## [1] 46988 17lmf<-close_visit%>%by(data=.,INDICES=close_visit$trial,function(x) lm(visit.length~Ear_Tag+follower,data=x))
lapply(lmf,anova)## $`1`
## Analysis of Variance Table
##
## Response: visit.length
## Df Sum Sq Mean Sq F value Pr(>F)
## Ear_Tag 23 164516650 7152898 29.5927 < 2.2e-16 ***
## follower 22 28501625 1295528 5.3598 7.299e-15 ***
## Residuals 4203 1015912872 241711
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`2`
## Analysis of Variance Table
##
## Response: visit.length
## Df Sum Sq Mean Sq F value Pr(>F)
## Ear_Tag 21 87772526 4179644 25.203 < 2.2e-16 ***
## follower 20 32659843 1632992 9.847 < 2.2e-16 ***
## Residuals 13606 2256369441 165836
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`3`
## Analysis of Variance Table
##
## Response: visit.length
## Df Sum Sq Mean Sq F value Pr(>F)
## Ear_Tag 21 836423110 39829672 166.384 < 2.2e-16 ***
## follower 20 71467011 3573351 14.927 < 2.2e-16 ***
## Residuals 13143 3146218236 239384
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`4`
## Analysis of Variance Table
##
## Response: visit.length
## Df Sum Sq Mean Sq F value Pr(>F)
## Ear_Tag 15 190375865 12691724 87.776 < 2.2e-16 ***
## follower 14 26753276 1910948 13.216 < 2.2e-16 ***
## Residuals 9782 1414396611 144592
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`5`
## Analysis of Variance Table
##
## Response: visit.length
## Df Sum Sq Mean Sq F value Pr(>F)
## Ear_Tag 15 75034303 5002287 28.9389 < 2.2e-16 ***
## follower 14 11696471 835462 4.8333 6.075e-09 ***
## Residuals 4377 756594460 172857
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`6`
## Analysis of Variance Table
##
## Response: visit.length
## Df Sum Sq Mean Sq F value Pr(>F)
## Ear_Tag 17 19077141 1122185 8.3537 < 2.2e-16 ***
## follower 16 10575989 660999 4.9206 5.218e-10 ***
## Residuals 1653 222053157 134333
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Linear mixed model for lenght visit variable
lmer(visit.length~(1|Ear_Tag:trial)+(1|follower:trial),data=close_visit)%>%summary()## Linear mixed model fit by REML ['lmerMod']
## Formula: visit.length ~ (1 | Ear_Tag:trial) + (1 | follower:trial)
## Data: close_visit
##
## REML criterion at convergence: 704790.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0488 -0.6962 -0.1540 0.5475 8.4037
##
## Random effects:
## Groups Name Variance Std.Dev.
## Ear_Tag:trial (Intercept) 39822 199.55
## follower:trial (Intercept) 4155 64.46
## Residual 188417 434.07
## Number of obs: 46988, groups: Ear_Tag:trial, 118; follower:trial, 118
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 601.29 19.49 30.85Length visit >= 150 seconds
distant_visit<-event_log%>%filter(to_next>=tr)
dim(distant_visit)## [1] 11013 17lmf<-distant_visit%>%by(data=.,INDICES=distant_visit$trial,function(x) lm(visit.length~Ear_Tag+follower,data=x))
lapply(lmf,anova)## $`1`
## Analysis of Variance Table
##
## Response: visit.length
## Df Sum Sq Mean Sq F value Pr(>F)
## Ear_Tag 23 57576815 2503340 9.3620 < 2e-16 ***
## follower 22 8467995 384909 1.4395 0.08704 .
## Residuals 873 233434201 267393
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`2`
## Analysis of Variance Table
##
## Response: visit.length
## Df Sum Sq Mean Sq F value Pr(>F)
## Ear_Tag 21 42191384 2009114 8.2034 <2e-16 ***
## follower 20 3318927 165946 0.6776 0.8521
## Residuals 3393 830991113 244913
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`3`
## Analysis of Variance Table
##
## Response: visit.length
## Df Sum Sq Mean Sq F value Pr(>F)
## Ear_Tag 21 136481144 6499102 26.2441 < 2.2e-16 ***
## follower 20 10289969 514498 2.0776 0.003409 **
## Residuals 1911 473241103 247641
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`4`
## Analysis of Variance Table
##
## Response: visit.length
## Df Sum Sq Mean Sq F value Pr(>F)
## Ear_Tag 15 74148995 4943266 33.3202 < 2.2e-16 ***
## follower 14 5722369 408741 2.7551 0.0004549 ***
## Residuals 2602 386023259 148356
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`5`
## Analysis of Variance Table
##
## Response: visit.length
## Df Sum Sq Mean Sq F value Pr(>F)
## Ear_Tag 15 13581292 905419 4.7330 5.091e-09 ***
## follower 14 1651162 117940 0.6165 0.8534
## Residuals 1519 290585067 191300
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`6`
## Analysis of Variance Table
##
## Response: visit.length
## Df Sum Sq Mean Sq F value Pr(>F)
## Ear_Tag 17 10601991 623647 6.7602 1.135e-14 ***
## follower 16 1791827 111989 1.2139 0.2525
## Residuals 491 45295857 92252
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Linear mixed model for lenght visit variable
lmer(visit.length~(1|Ear_Tag:trial)+(1|follower:trial),data=distant_visit)%>%summary()## Linear mixed model fit by REML ['lmerMod']
## Formula: visit.length ~ (1 | Ear_Tag:trial) + (1 | follower:trial)
## Data: distant_visit
##
## REML criterion at convergence: 166546.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8386 -0.6528 -0.1481 0.4936 14.3394
##
## Random effects:
## Groups Name Variance Std.Dev.
## Ear_Tag:trial (Intercept) 42632.5 206.48
## follower:trial (Intercept) 851.9 29.19
## Residual 209590.3 457.81
## Number of obs: 11013, groups: Ear_Tag:trial, 118; follower:trial, 118
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 625.15 19.99 31.27Proportion variance explain by individuals and followers
prpvar<-matrix(c(16.76,1.63,16.64,0.23,
16.96,1.7,15.8,0.28,
17.1,1.8, 16.8,0.33), ncol = 2, byrow = T)
rownames(prpvar)<-c("< 600 s", ">= 600 s", "< 300 s",">= 300 s", "< 150 s", ">= 150 s")
colnames(prpvar)<-c("Ear_Tag","Follower")
print(prpvar)## Ear_Tag Follower
## < 600 s 16.76 1.63
## >= 600 s 16.64 0.23
## < 300 s 16.96 1.70
## >= 300 s 15.80 0.28
## < 150 s 17.10 1.80
## >= 150 s 16.80 0.33Length visit < 60 seconds
tr<-60
close_visit<-event_log%>%filter(to_next<tr)
dim(close_visit)## [1] 42050 17lmf<-close_visit%>%by(data=.,INDICES=close_visit$trial,function(x) lm(visit.length~Ear_Tag+follower,data=x))
lapply(lmf,anova)## $`1`
## Analysis of Variance Table
##
## Response: visit.length
## Df Sum Sq Mean Sq F value Pr(>F)
## Ear_Tag 23 142866734 6211597 26.0101 < 2.2e-16 ***
## follower 22 24617616 1118983 4.6856 2.93e-12 ***
## Residuals 3734 891735405 238815
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`2`
## Analysis of Variance Table
##
## Response: visit.length
## Df Sum Sq Mean Sq F value Pr(>F)
## Ear_Tag 21 80351381 3826256 23.351 < 2.2e-16 ***
## follower 20 34047410 1702371 10.389 < 2.2e-16 ***
## Residuals 12104 1983348445 163859
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`3`
## Analysis of Variance Table
##
## Response: visit.length
## Df Sum Sq Mean Sq F value Pr(>F)
## Ear_Tag 21 790005276 37619299 159.458 < 2.2e-16 ***
## follower 20 71641492 3582075 15.184 < 2.2e-16 ***
## Residuals 12349 2913371063 235920
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`4`
## Analysis of Variance Table
##
## Response: visit.length
## Df Sum Sq Mean Sq F value Pr(>F)
## Ear_Tag 15 182647651 12176510 87.773 < 2.2e-16 ***
## follower 14 28379451 2027104 14.612 < 2.2e-16 ***
## Residuals 8501 1179324364 138728
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`5`
## Analysis of Variance Table
##
## Response: visit.length
## Df Sum Sq Mean Sq F value Pr(>F)
## Ear_Tag 15 73574736 4904982 27.7358 < 2.2e-16 ***
## follower 14 11364902 811779 4.5903 2.48e-08 ***
## Residuals 3750 663173936 176846
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`6`
## Analysis of Variance Table
##
## Response: visit.length
## Df Sum Sq Mean Sq F value Pr(>F)
## Ear_Tag 17 18477340 1086902 7.8452 < 2.2e-16 ***
## follower 16 9767190 610449 4.4062 1.427e-08 ***
## Residuals 1388 192299692 138544
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1lapply(lmf,summary)## $`1`
##
## Call:
## lm(formula = visit.length ~ Ear_Tag + follower, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1223.1 -341.8 -49.0 302.2 3659.3
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 651.2466 61.8079 10.537 < 2e-16 ***
## Ear_TagY64-16 100.1618 89.3834 1.121 0.26254
## Ear_TagY64-17 -2.4327 86.3872 -0.028 0.97754
## Ear_TagY65-10 582.5834 62.6412 9.300 < 2e-16 ***
## Ear_TagY66-10 -27.4892 56.4084 -0.487 0.62606
## Ear_TagY66-12 276.6407 90.5344 3.056 0.00226 **
## Ear_TagY66-14 420.5878 90.2767 4.659 3.29e-06 ***
## Ear_TagY67-11 62.6766 56.1978 1.115 0.26480
## Ear_TagY67-14 114.5409 87.6644 1.307 0.19143
## Ear_TagY68-11 369.9564 89.4336 4.137 3.60e-05 ***
## Ear_TagY68-12 -106.6545 85.1343 -1.253 0.21036
## Ear_TagY68-14 44.4096 58.3083 0.762 0.44633
## Ear_TagY69-12 387.2707 89.0177 4.350 1.39e-05 ***
## Ear_TagY70-11 65.0668 61.4901 1.058 0.29005
## Ear_TagY70-12 -93.1451 57.4255 -1.622 0.10488
## Ear_TagY70-13 -107.8442 84.7814 -1.272 0.20344
## Ear_TagY71-12 92.8666 85.4077 1.087 0.27696
## Ear_TagY74-14 95.4411 57.8424 1.650 0.09902 .
## Ear_TagY76-13 -165.7148 52.2466 -3.172 0.00153 **
## Ear_TagY76-15 -11.6780 57.2674 -0.204 0.83843
## Ear_TagY77-10 -2.6664 88.3484 -0.030 0.97592
## Ear_TagY77-11 78.4717 92.6185 0.847 0.39691
## Ear_TagY77-12 184.3512 61.4519 3.000 0.00272 **
## Ear_TagY77-13 184.5658 65.2417 2.829 0.00469 **
## followerY64-16 80.4279 64.9187 1.239 0.21546
## followerY64-17 101.4693 65.4887 1.549 0.12137
## followerY65-10 105.7387 60.1041 1.759 0.07862 .
## followerY66-10 -59.3411 53.4277 -1.111 0.26678
## followerY66-12 93.6621 63.6902 1.471 0.14149
## followerY66-14 45.4418 64.1142 0.709 0.47852
## followerY67-11 -72.9748 52.8453 -1.381 0.16739
## followerY67-14 55.2141 61.8654 0.892 0.37219
## followerY68-11 32.0989 72.1994 0.445 0.65664
## followerY68-12 -21.9344 61.3647 -0.357 0.72078
## followerY68-14 -65.3922 55.0096 -1.189 0.23462
## followerY69-12 54.6752 68.8634 0.794 0.42727
## followerY70-11 39.8615 57.0771 0.698 0.48498
## followerY70-12 -39.2375 54.1984 -0.724 0.46913
## followerY70-13 -4.7071 60.3255 -0.078 0.93781
## followerY71-12 0.2261 59.8492 0.004 0.99699
## followerY74-14 -11.4261 53.6018 -0.213 0.83121
## followerY76-13 -265.0863 48.4407 -5.472 4.73e-08 ***
## followerY76-15 -104.4549 53.6527 -1.947 0.05162 .
## followerY77-10 76.8362 63.3327 1.213 0.22512
## followerY77-11 NA NA NA NA
## followerY77-12 3.2984 57.9362 0.057 0.95460
## followerY77-13 -5.1530 62.8671 -0.082 0.93468
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 488.7 on 3734 degrees of freedom
## Multiple R-squared: 0.1581, Adjusted R-squared: 0.148
## F-statistic: 15.58 on 45 and 3734 DF, p-value: < 2.2e-16
##
##
## $`2`
##
## Call:
## lm(formula = visit.length ~ Ear_Tag + follower, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -734.0 -300.1 -81.5 207.7 3313.3
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 616.305 30.203 20.406 < 2e-16 ***
## Ear_Tag1304 147.686 27.907 5.292 1.23e-07 ***
## Ear_Tag1305 -91.821 27.886 -3.293 0.000995 ***
## Ear_Tag1306 43.843 27.415 1.599 0.109800
## Ear_Tag1307 -86.509 28.579 -3.027 0.002475 **
## Ear_Tag1310 71.717 28.264 2.537 0.011181 *
## Ear_Tag1311 -25.218 26.788 -0.941 0.346531
## Ear_Tag1312 97.415 28.314 3.441 0.000582 ***
## Ear_Tag1313 -34.496 27.727 -1.244 0.213474
## Ear_Tag1314 70.071 29.267 2.394 0.016672 *
## Ear_Tag1316 -64.205 27.818 -2.308 0.021013 *
## Ear_Tag1353 -106.524 37.431 -2.846 0.004436 **
## Ear_Tag1354 121.341 38.623 3.142 0.001684 **
## Ear_Tag1355 7.484 37.954 0.197 0.843675
## Ear_Tag1356 -34.197 37.954 -0.901 0.367609
## Ear_Tag1357 -44.661 38.068 -1.173 0.240744
## Ear_Tag1358 -127.619 38.228 -3.338 0.000845 ***
## Ear_Tag1359 -91.050 37.565 -2.424 0.015373 *
## Ear_Tag1360 -84.843 38.378 -2.211 0.027075 *
## Ear_Tag1361 -6.590 37.375 -0.176 0.860051
## Ear_Tag1365 -100.483 37.086 -2.709 0.006749 **
## Ear_Tag1367 -207.028 38.732 -5.345 9.20e-08 ***
## follower1304 -157.581 27.353 -5.761 8.56e-09 ***
## follower1305 -83.585 27.220 -3.071 0.002141 **
## follower1306 -141.111 26.496 -5.326 1.02e-07 ***
## follower1307 -120.723 28.126 -4.292 1.78e-05 ***
## follower1310 -116.512 27.533 -4.232 2.34e-05 ***
## follower1311 -168.007 25.922 -6.481 9.46e-11 ***
## follower1312 -97.072 27.299 -3.556 0.000378 ***
## follower1313 -208.342 26.773 -7.782 7.74e-15 ***
## follower1314 -178.124 28.314 -6.291 3.26e-10 ***
## follower1316 -174.818 26.891 -6.501 8.29e-11 ***
## follower1353 -48.212 22.782 -2.116 0.034340 *
## follower1354 -139.747 24.843 -5.625 1.89e-08 ***
## follower1355 -171.599 23.748 -7.226 5.28e-13 ***
## follower1356 -117.524 23.763 -4.946 7.69e-07 ***
## follower1357 -183.420 23.896 -7.676 1.77e-14 ***
## follower1358 -88.191 23.972 -3.679 0.000235 ***
## follower1359 -113.963 23.185 -4.915 8.98e-07 ***
## follower1360 -162.361 24.272 -6.689 2.34e-11 ***
## follower1361 -54.192 22.845 -2.372 0.017701 *
## follower1365 -113.203 22.218 -5.095 3.54e-07 ***
## follower1367 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 404.8 on 12104 degrees of freedom
## Multiple R-squared: 0.05453, Adjusted R-squared: 0.05133
## F-statistic: 17.03 on 41 and 12104 DF, p-value: < 2.2e-16
##
##
## $`3`
##
## Call:
## lm(formula = visit.length ~ Ear_Tag + follower, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1345.67 -328.43 -52.79 288.61 2376.07
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 976.078 35.136 27.780 < 2e-16 ***
## Ear_Tag1106 -386.251 44.960 -8.591 < 2e-16 ***
## Ear_Tag1129 -155.276 47.322 -3.281 0.001036 **
## Ear_Tag1132 -184.588 48.286 -3.823 0.000133 ***
## Ear_Tag1200 -365.982 44.564 -8.213 2.38e-16 ***
## Ear_Tag1371 -53.215 47.166 -1.128 0.259241
## Ear_Tag1413 -362.474 33.323 -10.877 < 2e-16 ***
## Ear_Tag1415 -205.704 32.412 -6.347 2.28e-10 ***
## Ear_Tag1418 -418.355 32.860 -12.732 < 2e-16 ***
## Ear_Tag1419 -366.595 32.761 -11.190 < 2e-16 ***
## Ear_Tag1421 -431.263 45.449 -9.489 < 2e-16 ***
## Ear_Tag1598 -661.807 31.343 -21.115 < 2e-16 ***
## Ear_Tag1653 -778.545 30.000 -25.951 < 2e-16 ***
## Ear_Tag1666 -583.351 30.756 -18.967 < 2e-16 ***
## Ear_Tag1668 -826.845 29.474 -28.054 < 2e-16 ***
## Ear_Tag1672 -779.758 29.918 -26.063 < 2e-16 ***
## Ear_Tag1688 -40.287 45.695 -0.882 0.377988
## Ear_Tag1689 167.235 48.900 3.420 0.000628 ***
## Ear_Tag1691 -504.994 30.705 -16.447 < 2e-16 ***
## Ear_Tag1699 281.111 48.492 5.797 6.91e-09 ***
## Ear_Tag2034 -117.754 48.209 -2.443 0.014597 *
## Ear_Tag2346 -319.546 48.235 -6.625 3.62e-11 ***
## follower1106 96.136 29.599 3.248 0.001166 **
## follower1129 -103.985 32.659 -3.184 0.001457 **
## follower1132 67.766 33.767 2.007 0.044783 *
## follower1200 -128.647 28.563 -4.504 6.73e-06 ***
## follower1371 99.482 32.318 3.078 0.002087 **
## follower1413 88.819 33.732 2.633 0.008472 **
## follower1415 203.370 32.542 6.249 4.25e-10 ***
## follower1418 178.041 33.230 5.358 8.58e-08 ***
## follower1419 200.404 33.292 6.020 1.80e-09 ***
## follower1421 44.633 29.345 1.521 0.128291
## follower1598 132.766 31.102 4.269 1.98e-05 ***
## follower1653 195.576 29.584 6.611 3.97e-11 ***
## follower1666 99.866 30.982 3.223 0.001270 **
## follower1668 117.179 29.303 3.999 6.40e-05 ***
## follower1672 221.207 29.930 7.391 1.55e-13 ***
## follower1688 6.322 30.705 0.206 0.836876
## follower1689 -50.797 34.242 -1.483 0.137980
## follower1691 223.300 30.906 7.225 5.30e-13 ***
## follower1699 -75.776 33.537 -2.259 0.023871 *
## follower2034 190.752 33.509 5.693 1.28e-08 ***
## follower2346 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 485.7 on 12349 degrees of freedom
## Multiple R-squared: 0.2282, Adjusted R-squared: 0.2257
## F-statistic: 89.08 on 41 and 12349 DF, p-value: < 2.2e-16
##
##
## $`4`
##
## Call:
## lm(formula = visit.length ~ Ear_Tag + follower, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -910.2 -256.3 -71.4 193.3 2697.9
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 588.622 28.431 20.704 < 2e-16 ***
## Ear_Tag1376 -4.792 35.850 -0.134 0.893678
## Ear_Tag1378 -246.887 34.827 -7.089 1.46e-12 ***
## Ear_Tag1379 -145.891 34.494 -4.229 2.37e-05 ***
## Ear_Tag1403 -224.798 35.166 -6.393 1.72e-10 ***
## Ear_Tag1408 -216.066 27.564 -7.839 5.11e-15 ***
## Ear_Tag1410 122.358 35.425 3.454 0.000555 ***
## Ear_Tag1412 -200.850 25.520 -7.870 3.97e-15 ***
## Ear_Tag1416 231.034 29.223 7.906 2.99e-15 ***
## Ear_Tag2336 -203.747 34.887 -5.840 5.40e-09 ***
## Ear_Tag2337 -142.393 25.578 -5.567 2.67e-08 ***
## Ear_Tag2338 -324.045 26.908 -12.043 < 2e-16 ***
## Ear_Tag2339 -199.912 24.874 -8.037 1.04e-15 ***
## Ear_Tag2340 92.450 35.633 2.594 0.009489 **
## Ear_Tag2341 -112.251 36.145 -3.106 0.001905 **
## Ear_Tag2342 -180.990 26.616 -6.800 1.12e-11 ***
## follower1376 -93.768 21.915 -4.279 1.90e-05 ***
## follower1378 46.003 20.970 2.194 0.028277 *
## follower1379 -66.154 20.606 -3.210 0.001330 **
## follower1403 -135.327 21.608 -6.263 3.96e-10 ***
## follower1408 124.246 27.165 4.574 4.86e-06 ***
## follower1410 -69.234 22.356 -3.097 0.001962 **
## follower1412 54.924 26.313 2.087 0.036891 *
## follower1416 103.039 29.502 3.493 0.000481 ***
## follower2336 -14.925 21.377 -0.698 0.485075
## follower2337 84.561 25.173 3.359 0.000785 ***
## follower2338 108.550 26.605 4.080 4.54e-05 ***
## follower2339 20.129 25.943 0.776 0.437826
## follower2340 -182.425 22.137 -8.241 < 2e-16 ***
## follower2341 NA NA NA NA
## follower2342 79.740 26.422 3.018 0.002552 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 372.5 on 8501 degrees of freedom
## Multiple R-squared: 0.1518, Adjusted R-squared: 0.1489
## F-statistic: 52.45 on 29 and 8501 DF, p-value: < 2.2e-16
##
##
## $`5`
##
## Call:
## lm(formula = visit.length ~ Ear_Tag + follower, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -895.8 -305.0 -51.5 234.5 3468.8
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 453.066 49.746 9.108 < 2e-16 ***
## Ear_Tag2084 502.817 64.946 7.742 1.25e-14 ***
## Ear_Tag2089 123.889 44.260 2.799 0.005150 **
## Ear_Tag2111 202.253 62.314 3.246 0.001182 **
## Ear_Tag2129 228.215 63.029 3.621 0.000298 ***
## Ear_Tag2134 -28.482 41.502 -0.686 0.492583
## Ear_Tag2138 134.124 64.786 2.070 0.038497 *
## Ear_Tag2144 -39.720 41.739 -0.952 0.341346
## Ear_Tag2146 -32.682 59.648 -0.548 0.583788
## Ear_Tag2343 10.773 43.623 0.247 0.804955
## Ear_Tag2344 299.339 44.723 6.693 2.51e-11 ***
## Ear_Tag2345 83.986 61.263 1.371 0.170489
## Ear_Tag2347 137.504 63.958 2.150 0.031627 *
## Ear_Tag2348 237.429 45.659 5.200 2.10e-07 ***
## Ear_Tag2349 216.325 65.077 3.324 0.000896 ***
## Ear_Tag2350 226.274 48.060 4.708 2.59e-06 ***
## follower2084 -35.666 44.805 -0.796 0.426075
## follower2089 -29.727 45.114 -0.659 0.509975
## follower2111 -26.449 39.465 -0.670 0.502779
## follower2129 -84.867 39.237 -2.163 0.030608 *
## follower2134 95.199 44.056 2.161 0.030769 *
## follower2138 -50.704 43.457 -1.167 0.243380
## follower2144 147.563 44.576 3.310 0.000941 ***
## follower2146 -52.539 35.131 -1.496 0.134860
## follower2343 88.511 44.335 1.996 0.045964 *
## follower2344 8.268 47.249 0.175 0.861101
## follower2345 -154.058 37.054 -4.158 3.29e-05 ***
## follower2347 15.915 41.626 0.382 0.702242
## follower2348 127.196 44.804 2.839 0.004551 **
## follower2349 NA NA NA NA
## follower2350 98.618 50.283 1.961 0.049920 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 420.5 on 3750 degrees of freedom
## Multiple R-squared: 0.1135, Adjusted R-squared: 0.1067
## F-statistic: 16.56 on 29 and 3750 DF, p-value: < 2.2e-16
##
##
## $`6`
##
## Call:
## lm(formula = visit.length ~ Ear_Tag + follower, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -875.55 -260.92 -69.19 214.04 1749.09
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 434.4333 56.4605 7.694 2.68e-14 ***
## Ear_Tag1318 -139.3468 58.8002 -2.370 0.01793 *
## Ear_Tag1319 0.8263 56.0267 0.015 0.98824
## Ear_Tag1320 27.7813 54.5982 0.509 0.61095
## Ear_Tag1321 150.1278 55.8245 2.689 0.00725 **
## Ear_Tag1322 -113.8212 56.2587 -2.023 0.04325 *
## Ear_Tag1323 59.5671 76.6537 0.777 0.43724
## Ear_Tag1324 96.8429 84.9192 1.140 0.25431
## Ear_Tag1325 -54.3440 83.3254 -0.652 0.51439
## Ear_Tag1368 -166.8266 75.8395 -2.200 0.02799 *
## Ear_Tag1369 267.5194 83.6927 3.196 0.00142 **
## Ear_Tag1370 153.0762 83.0662 1.843 0.06557 .
## Ear_Tag1371 82.8926 78.0981 1.061 0.28870
## Ear_Tag1372 153.7799 80.4822 1.911 0.05624 .
## Ear_Tag1373 -59.0913 81.0904 -0.729 0.46630
## Ear_Tag2005 -93.5791 61.9834 -1.510 0.13134
## Ear_Tag2097 -55.1008 59.8271 -0.921 0.35721
## Ear_Tag2098 -60.0330 52.5409 -1.143 0.25340
## follower1318 42.0472 62.5394 0.672 0.50148
## follower1319 81.3235 59.6564 1.363 0.17304
## follower1320 179.8351 56.0099 3.211 0.00135 **
## follower1321 -42.7690 55.1504 -0.775 0.43818
## follower1322 158.1958 55.5810 2.846 0.00449 **
## follower1323 183.5998 56.4254 3.254 0.00117 **
## follower1324 99.8209 68.0092 1.468 0.14240
## follower1325 52.7183 64.5994 0.816 0.41459
## follower1368 -78.1490 52.9456 -1.476 0.14016
## follower1369 128.9120 67.8059 1.901 0.05748 .
## follower1370 28.5884 62.9958 0.454 0.65003
## follower1371 25.1889 55.8658 0.451 0.65214
## follower1372 38.7894 57.8862 0.670 0.50291
## follower1373 NA NA NA NA
## follower2005 28.6303 65.5043 0.437 0.66213
## follower2097 134.0759 59.6531 2.248 0.02476 *
## follower2098 -77.7113 52.1881 -1.489 0.13670
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 372.2 on 1388 degrees of freedom
## Multiple R-squared: 0.1281, Adjusted R-squared: 0.1073
## F-statistic: 6.178 on 33 and 1388 DF, p-value: < 2.2e-16Linear mixed model for lenght visit variable
lmer(visit.length~(1|Ear_Tag:trial)+(1|follower:trial),data=close_visit)%>%summary()## Linear mixed model fit by REML ['lmerMod']
## Formula: visit.length ~ (1 | Ear_Tag:trial) + (1 | follower:trial)
## Data: close_visit
##
## REML criterion at convergence: 630471.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0803 -0.6940 -0.1537 0.5498 8.5339
##
## Random effects:
## Groups Name Variance Std.Dev.
## Ear_Tag:trial (Intercept) 40275 200.69
## follower:trial (Intercept) 4504 67.11
## Residual 187030 432.47
## Number of obs: 42050, groups: Ear_Tag:trial, 118; follower:trial, 118
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 602.23 19.69 30.58Length visit < 30 seconds
tr<-30
close_visit<-event_log%>%filter(to_next<tr)
dim(close_visit)## [1] 34774 17lmf<-close_visit%>%by(data=.,INDICES=close_visit$trial,function(x) lm(visit.length~Ear_Tag+follower,data=x))
lapply(lmf,anova)## $`1`
## Analysis of Variance Table
##
## Response: visit.length
## Df Sum Sq Mean Sq F value Pr(>F)
## Ear_Tag 23 120625888 5244604 22.2196 < 2.2e-16 ***
## follower 22 26208888 1191313 5.0472 1.458e-13 ***
## Residuals 2869 677185805 236035
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`2`
## Analysis of Variance Table
##
## Response: visit.length
## Df Sum Sq Mean Sq F value Pr(>F)
## Ear_Tag 21 74782247 3561059 22.143 < 2.2e-16 ***
## follower 20 34916180 1745809 10.856 < 2.2e-16 ***
## Residuals 10098 1623949259 160819
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`3`
## Analysis of Variance Table
##
## Response: visit.length
## Df Sum Sq Mean Sq F value Pr(>F)
## Ear_Tag 21 713565908 33979329 145.988 < 2.2e-16 ***
## follower 20 74555232 3727762 16.016 < 2.2e-16 ***
## Residuals 11011 2562848678 232753
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`4`
## Analysis of Variance Table
##
## Response: visit.length
## Df Sum Sq Mean Sq F value Pr(>F)
## Ear_Tag 15 164053786 10936919 79.936 < 2.2e-16 ***
## follower 14 25490961 1820783 13.308 < 2.2e-16 ***
## Residuals 6774 926818722 136820
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`5`
## Analysis of Variance Table
##
## Response: visit.length
## Df Sum Sq Mean Sq F value Pr(>F)
## Ear_Tag 15 63075486 4205032 24.1871 < 2.2e-16 ***
## follower 14 10673070 762362 4.3851 8.378e-08 ***
## Residuals 2674 464886716 173854
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`6`
## Analysis of Variance Table
##
## Response: visit.length
## Df Sum Sq Mean Sq F value Pr(>F)
## Ear_Tag 17 15302782 900164 6.6585 2.247e-15 ***
## follower 16 9042842 565178 4.1806 6.344e-08 ***
## Residuals 1124 151954191 135191
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1lapply(lmf,summary)## $`1`
##
## Call:
## lm(formula = visit.length ~ Ear_Tag + follower, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1284.70 -342.59 -51.13 302.58 2072.78
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 691.906 70.100 9.870 < 2e-16 ***
## Ear_TagY64-16 91.108 102.752 0.887 0.375326
## Ear_TagY64-17 -4.812 101.004 -0.048 0.962003
## Ear_TagY65-10 672.185 70.776 9.497 < 2e-16 ***
## Ear_TagY66-10 -66.192 63.262 -1.046 0.295504
## Ear_TagY66-12 320.238 108.655 2.947 0.003232 **
## Ear_TagY66-14 492.025 104.513 4.708 2.62e-06 ***
## Ear_TagY67-11 58.143 63.474 0.916 0.359737
## Ear_TagY67-14 80.924 100.896 0.802 0.422590
## Ear_TagY68-11 314.001 106.028 2.961 0.003087 **
## Ear_TagY68-12 -110.821 101.373 -1.093 0.274400
## Ear_TagY68-14 14.377 66.037 0.218 0.827667
## Ear_TagY69-12 388.750 102.942 3.776 0.000162 ***
## Ear_TagY70-11 36.922 68.907 0.536 0.592122
## Ear_TagY70-12 -148.064 65.377 -2.265 0.023601 *
## Ear_TagY70-13 -87.488 97.944 -0.893 0.371803
## Ear_TagY71-12 93.795 101.589 0.923 0.355941
## Ear_TagY74-14 81.991 67.393 1.217 0.223855
## Ear_TagY76-13 -181.777 60.222 -3.018 0.002563 **
## Ear_TagY76-15 -24.563 65.555 -0.375 0.707918
## Ear_TagY77-10 22.699 101.370 0.224 0.822832
## Ear_TagY77-11 37.597 107.026 0.351 0.725394
## Ear_TagY77-12 148.565 69.796 2.129 0.033375 *
## Ear_TagY77-13 112.736 74.006 1.523 0.127786
## followerY64-16 59.068 78.376 0.754 0.451124
## followerY64-17 79.316 78.751 1.007 0.313936
## followerY65-10 143.241 66.631 2.150 0.031657 *
## followerY66-10 -78.645 59.368 -1.325 0.185376
## followerY66-12 30.618 76.559 0.400 0.689246
## followerY66-14 5.945 75.750 0.078 0.937454
## followerY67-11 -73.392 58.442 -1.256 0.209289
## followerY67-14 6.877 74.521 0.092 0.926478
## followerY68-11 -131.910 97.701 -1.350 0.177077
## followerY68-12 -94.182 71.798 -1.312 0.189707
## followerY68-14 -60.292 61.032 -0.988 0.323294
## followerY69-12 -26.882 85.788 -0.313 0.754039
## followerY70-11 36.837 62.053 0.594 0.552793
## followerY70-12 -60.048 59.262 -1.013 0.311018
## followerY70-13 -90.605 70.914 -1.278 0.201471
## followerY71-12 -38.532 71.423 -0.539 0.589592
## followerY74-14 -16.155 58.182 -0.278 0.781296
## followerY76-13 -282.216 52.399 -5.386 7.79e-08 ***
## followerY76-15 -103.463 59.500 -1.739 0.082165 .
## followerY77-10 86.621 76.087 1.138 0.255031
## followerY77-11 NA NA NA NA
## followerY77-12 -26.682 64.297 -0.415 0.678188
## followerY77-13 -38.070 68.369 -0.557 0.577684
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 485.8 on 2869 degrees of freedom
## Multiple R-squared: 0.1782, Adjusted R-squared: 0.1653
## F-statistic: 13.82 on 45 and 2869 DF, p-value: < 2.2e-16
##
##
## $`2`
##
## Call:
## lm(formula = visit.length ~ Ear_Tag + follower, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -748.4 -292.9 -82.9 202.6 3341.5
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 640.394 33.543 19.092 < 2e-16 ***
## Ear_Tag1304 138.168 30.018 4.603 4.22e-06 ***
## Ear_Tag1305 -70.779 30.709 -2.305 0.021196 *
## Ear_Tag1306 49.755 29.590 1.681 0.092700 .
## Ear_Tag1307 -87.936 31.621 -2.781 0.005431 **
## Ear_Tag1310 80.796 30.772 2.626 0.008661 **
## Ear_Tag1311 -35.257 28.762 -1.226 0.220308
## Ear_Tag1312 117.966 30.579 3.858 0.000115 ***
## Ear_Tag1313 -9.979 30.247 -0.330 0.741461
## Ear_Tag1314 86.836 31.833 2.728 0.006385 **
## Ear_Tag1316 -71.313 31.138 -2.290 0.022029 *
## Ear_Tag1353 -142.833 41.294 -3.459 0.000545 ***
## Ear_Tag1354 120.025 42.331 2.835 0.004586 **
## Ear_Tag1355 -13.477 41.494 -0.325 0.745342
## Ear_Tag1356 -63.543 41.640 -1.526 0.127041
## Ear_Tag1357 -68.044 41.794 -1.628 0.103539
## Ear_Tag1358 -166.712 42.235 -3.947 7.96e-05 ***
## Ear_Tag1359 -119.886 41.204 -2.910 0.003627 **
## Ear_Tag1360 -104.289 42.461 -2.456 0.014061 *
## Ear_Tag1361 -56.772 41.225 -1.377 0.168497
## Ear_Tag1365 -109.452 40.735 -2.687 0.007223 **
## Ear_Tag1367 -236.230 42.734 -5.528 3.32e-08 ***
## follower1304 -197.693 30.453 -6.492 8.88e-11 ***
## follower1305 -116.096 30.301 -3.831 0.000128 ***
## follower1306 -181.342 29.633 -6.120 9.73e-10 ***
## follower1307 -142.761 31.764 -4.494 7.05e-06 ***
## follower1310 -152.216 31.202 -4.878 1.09e-06 ***
## follower1311 -207.046 28.729 -7.207 6.14e-13 ***
## follower1312 -132.219 30.183 -4.381 1.20e-05 ***
## follower1313 -250.659 29.955 -8.368 < 2e-16 ***
## follower1314 -239.572 32.392 -7.396 1.51e-13 ***
## follower1316 -216.352 30.161 -7.173 7.84e-13 ***
## follower1353 -60.511 24.608 -2.459 0.013949 *
## follower1354 -143.825 26.748 -5.377 7.74e-08 ***
## follower1355 -178.182 25.810 -6.903 5.38e-12 ***
## follower1356 -131.440 25.597 -5.135 2.87e-07 ***
## follower1357 -197.915 25.826 -7.663 1.98e-14 ***
## follower1358 -103.633 26.050 -3.978 6.99e-05 ***
## follower1359 -111.444 24.985 -4.461 8.27e-06 ***
## follower1360 -179.803 26.113 -6.886 6.09e-12 ***
## follower1361 -61.472 24.549 -2.504 0.012292 *
## follower1365 -125.779 24.061 -5.228 1.75e-07 ***
## follower1367 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 401 on 10098 degrees of freedom
## Multiple R-squared: 0.06328, Adjusted R-squared: 0.05947
## F-statistic: 16.64 on 41 and 10098 DF, p-value: < 2.2e-16
##
##
## $`3`
##
## Call:
## lm(formula = visit.length ~ Ear_Tag + follower, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1373.16 -327.88 -51.25 286.74 2382.88
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 959.324 36.209 26.494 < 2e-16 ***
## Ear_Tag1106 -406.882 46.747 -8.704 < 2e-16 ***
## Ear_Tag1129 -129.636 49.423 -2.623 0.008728 **
## Ear_Tag1132 -213.033 50.691 -4.203 2.66e-05 ***
## Ear_Tag1200 -358.975 46.263 -7.759 9.28e-15 ***
## Ear_Tag1371 -65.094 49.187 -1.323 0.185728
## Ear_Tag1413 -340.559 34.798 -9.787 < 2e-16 ***
## Ear_Tag1415 -194.081 33.578 -5.780 7.67e-09 ***
## Ear_Tag1418 -407.902 34.224 -11.918 < 2e-16 ***
## Ear_Tag1419 -360.975 34.390 -10.496 < 2e-16 ***
## Ear_Tag1421 -425.919 47.106 -9.042 < 2e-16 ***
## Ear_Tag1598 -654.966 32.492 -20.158 < 2e-16 ***
## Ear_Tag1653 -765.551 31.347 -24.422 < 2e-16 ***
## Ear_Tag1666 -581.063 31.714 -18.322 < 2e-16 ***
## Ear_Tag1668 -823.641 30.370 -27.120 < 2e-16 ***
## Ear_Tag1672 -770.522 31.251 -24.656 < 2e-16 ***
## Ear_Tag1688 -50.766 47.416 -1.071 0.284351
## Ear_Tag1689 186.031 50.909 3.654 0.000259 ***
## Ear_Tag1691 -497.692 32.060 -15.524 < 2e-16 ***
## Ear_Tag1699 307.271 50.262 6.113 1.01e-09 ***
## Ear_Tag2034 -120.436 50.267 -2.396 0.016594 *
## Ear_Tag2346 -302.868 49.943 -6.064 1.37e-09 ***
## follower1106 116.565 30.759 3.790 0.000152 ***
## follower1129 -88.063 34.018 -2.589 0.009646 **
## follower1132 51.388 36.896 1.393 0.163717
## follower1200 -140.782 30.155 -4.669 3.07e-06 ***
## follower1371 121.705 35.116 3.466 0.000531 ***
## follower1413 93.406 34.747 2.688 0.007196 **
## follower1415 216.572 33.929 6.383 1.81e-10 ***
## follower1418 205.576 34.775 5.912 3.49e-09 ***
## follower1419 219.089 34.481 6.354 2.18e-10 ***
## follower1421 58.218 30.771 1.892 0.058518 .
## follower1598 135.989 32.138 4.231 2.34e-05 ***
## follower1653 206.196 30.507 6.759 1.46e-11 ***
## follower1666 109.307 31.813 3.436 0.000593 ***
## follower1668 125.846 30.498 4.126 3.71e-05 ***
## follower1672 237.937 30.926 7.694 1.55e-14 ***
## follower1688 -7.546 32.382 -0.233 0.815733
## follower1689 -48.822 35.824 -1.363 0.172970
## follower1691 236.241 32.079 7.364 1.91e-13 ***
## follower1699 -63.638 34.844 -1.826 0.067825 .
## follower2034 207.623 34.732 5.978 2.33e-09 ***
## follower2346 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 482.4 on 11011 degrees of freedom
## Multiple R-squared: 0.2352, Adjusted R-squared: 0.2323
## F-statistic: 82.59 on 41 and 11011 DF, p-value: < 2.2e-16
##
##
## $`4`
##
## Call:
## lm(formula = visit.length ~ Ear_Tag + follower, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -925.04 -251.73 -72.41 195.68 2007.33
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 613.48 31.35 19.567 < 2e-16 ***
## Ear_Tag1376 -24.79 39.95 -0.620 0.534961
## Ear_Tag1378 -269.56 38.49 -7.004 2.73e-12 ***
## Ear_Tag1379 -154.11 38.37 -4.016 5.98e-05 ***
## Ear_Tag1403 -238.50 38.83 -6.143 8.58e-10 ***
## Ear_Tag1408 -235.56 30.69 -7.676 1.88e-14 ***
## Ear_Tag1410 108.66 39.18 2.774 0.005561 **
## Ear_Tag1412 -203.41 28.23 -7.205 6.42e-13 ***
## Ear_Tag1416 214.07 32.88 6.512 7.97e-11 ***
## Ear_Tag2336 -222.45 38.74 -5.742 9.77e-09 ***
## Ear_Tag2337 -167.65 28.30 -5.924 3.30e-09 ***
## Ear_Tag2338 -345.48 30.48 -11.336 < 2e-16 ***
## Ear_Tag2339 -238.28 27.68 -8.609 < 2e-16 ***
## Ear_Tag2340 122.22 39.59 3.087 0.002028 **
## Ear_Tag2341 -130.85 40.04 -3.268 0.001088 **
## Ear_Tag2342 -193.40 30.17 -6.411 1.54e-10 ***
## follower1376 -134.04 24.86 -5.393 7.17e-08 ***
## follower1378 23.55 23.43 1.005 0.314729
## follower1379 -82.03 22.98 -3.569 0.000361 ***
## follower1403 -156.90 24.37 -6.439 1.29e-10 ***
## follower1408 124.60 29.41 4.236 2.30e-05 ***
## follower1410 -106.30 25.20 -4.219 2.49e-05 ***
## follower1412 52.24 30.09 1.736 0.082580 .
## follower1416 91.65 34.97 2.621 0.008797 **
## follower2336 -25.04 23.52 -1.064 0.287189
## follower2337 83.61 27.21 3.073 0.002131 **
## follower2338 115.50 29.78 3.879 0.000106 ***
## follower2339 7.61 28.48 0.267 0.789298
## follower2340 -192.69 24.46 -7.877 3.89e-15 ***
## follower2341 NA NA NA NA
## follower2342 94.81 28.79 3.294 0.000994 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 369.9 on 6774 degrees of freedom
## Multiple R-squared: 0.1698, Adjusted R-squared: 0.1662
## F-statistic: 47.77 on 29 and 6774 DF, p-value: < 2.2e-16
##
##
## $`5`
##
## Call:
## lm(formula = visit.length ~ Ear_Tag + follower, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -896.8 -292.3 -51.8 224.9 3436.5
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 433.393 63.299 6.847 9.34e-12 ***
## Ear_Tag2084 524.867 81.792 6.417 1.64e-10 ***
## Ear_Tag2089 190.553 56.811 3.354 0.000807 ***
## Ear_Tag2111 244.562 78.112 3.131 0.001761 **
## Ear_Tag2129 260.660 77.552 3.361 0.000787 ***
## Ear_Tag2134 -68.081 53.743 -1.267 0.205342
## Ear_Tag2138 130.577 80.497 1.622 0.104895
## Ear_Tag2144 -6.006 54.204 -0.111 0.911780
## Ear_Tag2146 -22.670 73.538 -0.308 0.757897
## Ear_Tag2343 28.403 55.569 0.511 0.609298
## Ear_Tag2344 337.872 57.003 5.927 3.48e-09 ***
## Ear_Tag2345 123.334 75.525 1.633 0.102580
## Ear_Tag2347 207.094 79.432 2.607 0.009180 **
## Ear_Tag2348 283.919 58.121 4.885 1.10e-06 ***
## Ear_Tag2349 246.594 79.944 3.085 0.002059 **
## Ear_Tag2350 280.741 61.743 4.547 5.69e-06 ***
## follower2084 -116.765 52.669 -2.217 0.026710 *
## follower2089 -42.946 52.441 -0.819 0.412899
## follower2111 -32.990 46.755 -0.706 0.480507
## follower2129 -113.259 47.812 -2.369 0.017913 *
## follower2134 70.832 51.426 1.377 0.168513
## follower2138 -70.269 51.597 -1.362 0.173351
## follower2144 151.538 52.777 2.871 0.004120 **
## follower2146 -94.114 40.915 -2.300 0.021510 *
## follower2343 82.979 52.053 1.594 0.111022
## follower2344 -48.974 56.732 -0.863 0.388071
## follower2345 -179.162 42.623 -4.203 2.72e-05 ***
## follower2347 4.579 47.667 0.096 0.923475
## follower2348 112.561 53.161 2.117 0.034320 *
## follower2349 NA NA NA NA
## follower2350 49.322 65.868 0.749 0.454045
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 417 on 2674 degrees of freedom
## Multiple R-squared: 0.1369, Adjusted R-squared: 0.1276
## F-statistic: 14.63 on 29 and 2674 DF, p-value: < 2.2e-16
##
##
## $`6`
##
## Call:
## lm(formula = visit.length ~ Ear_Tag + follower, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -793.00 -260.55 -59.75 208.07 1653.28
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 378.80 60.07 6.306 4.1e-10 ***
## Ear_Tag1318 -109.69 63.44 -1.729 0.084071 .
## Ear_Tag1319 24.95 58.43 0.427 0.669448
## Ear_Tag1320 71.10 58.31 1.219 0.223007
## Ear_Tag1321 212.30 62.22 3.412 0.000668 ***
## Ear_Tag1322 -105.78 60.94 -1.736 0.082857 .
## Ear_Tag1323 159.35 83.38 1.911 0.056236 .
## Ear_Tag1324 215.33 95.37 2.258 0.024141 *
## Ear_Tag1325 66.11 89.08 0.742 0.458138
## Ear_Tag1368 -77.72 82.10 -0.947 0.343997
## Ear_Tag1369 290.03 94.20 3.079 0.002129 **
## Ear_Tag1370 177.20 91.36 1.939 0.052695 .
## Ear_Tag1371 197.66 84.54 2.338 0.019562 *
## Ear_Tag1372 256.54 85.72 2.993 0.002824 **
## Ear_Tag1373 31.37 88.81 0.353 0.723971
## Ear_Tag2005 -78.65 66.18 -1.188 0.234892
## Ear_Tag2097 -22.34 64.13 -0.348 0.727680
## Ear_Tag2098 -25.56 56.54 -0.452 0.651265
## follower1318 90.28 65.18 1.385 0.166316
## follower1319 98.78 63.79 1.548 0.121788
## follower1320 218.90 60.98 3.590 0.000345 ***
## follower1321 -43.49 59.40 -0.732 0.464206
## follower1322 170.31 60.24 2.827 0.004781 **
## follower1323 105.73 63.92 1.654 0.098373 .
## follower1324 115.92 78.76 1.472 0.141333
## follower1325 12.68 72.54 0.175 0.861240
## follower1368 -140.27 58.24 -2.409 0.016177 *
## follower1369 83.44 74.32 1.123 0.261797
## follower1370 -21.50 69.73 -0.308 0.757847
## follower1371 -14.58 62.13 -0.235 0.814444
## follower1372 24.13 64.68 0.373 0.709138
## follower1373 NA NA NA NA
## follower2005 58.59 74.17 0.790 0.429694
## follower2097 192.64 65.23 2.953 0.003212 **
## follower2098 -57.89 55.15 -1.050 0.294084
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 367.7 on 1124 degrees of freedom
## Multiple R-squared: 0.1381, Adjusted R-squared: 0.1128
## F-statistic: 5.457 on 33 and 1124 DF, p-value: < 2.2e-16Linear mixed model for lenght visit variable
lmer(visit.length~(1|Ear_Tag:trial)+(1|follower:trial),data=close_visit)%>%summary()## Linear mixed model fit by REML ['lmerMod']
## Formula: visit.length ~ (1 | Ear_Tag:trial) + (1 | follower:trial)
## Data: close_visit
##
## REML criterion at convergence: 521183.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1617 -0.6896 -0.1570 0.5488 8.0015
##
## Random effects:
## Groups Name Variance Std.Dev.
## Ear_Tag:trial (Intercept) 42093 205.17
## follower:trial (Intercept) 5572 74.64
## Residual 185444 430.63
## Number of obs: 34774, groups: Ear_Tag:trial, 118; follower:trial, 118
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 597.94 20.37 29.35Proportion variance explain by individuals and followers
prpvar<-matrix(c(17.37,1.94,18.05,2.39), ncol = 2, byrow = T)
rownames(prpvar)<-c("<= 60 s", "<= 300 s")
colnames(prpvar)<-c("Ear_Tag","Follower")
print(prpvar)## Ear_Tag Follower
## <= 60 s 17.37 1.94
## <= 300 s 18.05 2.39