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library (ggplot2)library (lme4)
Loading required package: Matrix
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Please re-install glmmTMB from source or restore original 'TMB' package (see '?reinstalling' for more information)
Loading required package: carData
lattice theme set by effectsTheme()
See ?effectsTheme for details.
library (performance)library (car)
Attaching package: 'car'
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library (rsample)library (data.table)
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# Original code for feeding effects <- read.csv ("behavior_23Feb23.csv" )<- behavior_23Feb23drop_na (behavior)
replicate microcolony date date_withyr time temp moving feeding stationary
1 28 23A1 2/23 2/23/22 11:08 23 0 0 0
incubating fanning total_alive observer_initials infected parent
1 2 0 2 EF 0 1
day_of_experiment
1 9
notes drop
1 there were 10 alive bees, something is funny about this data. Drop. 1
<- behavior %>% mutate (observer_initials= if_else (observer_initials== 'JM' ,'JLM' ,observer_initials))
1.) H0: there is no effect of infection status and temp on incubating behavior, and no interactive effect of the two on behavior
HA: there is an interactive effect of infection status and temp on incubating behavior
#Step 1: make incubating count data into props = (behavior$ incubating/ behavior$ total_alive)<- cbind (behavior,incubatingprop)$ temp= as.character (behavior$ temp)$ infected= as.factor (behavior$ infected)#Step 2: run an ANOVA <- aov (incubatingprop~ temp* infected, data= behavior)summary (aov1)
Df Sum Sq Mean Sq F value Pr(>F)
temp 4 29.18 7.295 219.58 <2e-16 ***
infected 1 5.93 5.928 178.42 <2e-16 ***
temp:infected 4 3.22 0.805 24.23 <2e-16 ***
Residuals 1270 42.20 0.033
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
728 observations deleted due to missingness
According to the summary, there are significant main effects of temperature and infection status, as well as a significant interactive effect of temp and infection status on incubating behavior.
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = incubatingprop ~ temp * infected, data = behavior)
$temp
diff lwr upr p adj
30-23 -0.15987824 -0.2012131 -0.11854335 0.0000000
34-23 -0.27766899 -0.3228906 -0.23244739 0.0000000
37-23 -0.33436341 -0.3739777 -0.29474911 0.0000000
40-23 -0.41391575 -0.4584101 -0.36942139 0.0000000
34-30 -0.11779075 -0.1654707 -0.07011077 0.0000000
37-30 -0.17448518 -0.2168842 -0.13208610 0.0000000
40-30 -0.25403751 -0.3010283 -0.20704671 0.0000000
37-34 -0.05669443 -0.1028908 -0.01049810 0.0073545
40-34 -0.13624676 -0.1866903 -0.08580318 0.0000000
40-37 -0.07955234 -0.1250370 -0.03406766 0.0000195
$infected
diff lwr upr p adj
1-0 -0.1306659 -0.1507389 -0.110593 0
$`temp:infected`
diff lwr upr p adj
30:0-23:0 -0.18373961 -0.24342261 -0.124056604 0.0000000
34:0-23:0 -0.36644385 -0.44926100 -0.283626703 0.0000000
37:0-23:0 -0.40547760 -0.46355719 -0.347398004 0.0000000
40:0-23:0 -0.50677743 -0.58959458 -0.423960285 0.0000000
23:1-23:0 -0.25804682 -0.32264051 -0.193453137 0.0000000
30:1-23:0 -0.42667980 -0.50227080 -0.351088792 0.0000000
34:1-23:0 -0.38590119 -0.45142019 -0.320382182 0.0000000
37:1-23:0 -0.49169105 -0.56091891 -0.422463184 0.0000000
40:1-23:0 -0.51974283 -0.58375831 -0.455727344 0.0000000
34:0-30:0 -0.18270424 -0.26715946 -0.098249021 0.0000000
37:0-30:0 -0.22173799 -0.28213040 -0.161345575 0.0000000
40:0-30:0 -0.32303782 -0.40749305 -0.238582603 0.0000000
23:1-30:0 -0.07430721 -0.14098816 -0.007626267 0.0154972
30:1-30:0 -0.24294019 -0.32032239 -0.165557991 0.0000000
34:1-30:0 -0.20216158 -0.26973927 -0.134583886 0.0000000
37:1-30:0 -0.30795144 -0.37913080 -0.236772077 0.0000000
40:1-30:0 -0.33600322 -0.40212422 -0.269882220 0.0000000
37:0-34:0 -0.03903375 -0.12236359 0.044296095 0.8983498
40:0-34:0 -0.14033358 -0.24245754 -0.038209621 0.0006042
23:1-34:0 0.10839703 0.02040289 0.196391162 0.0039292
30:1-34:0 -0.06023595 -0.15659270 0.036120804 0.6124732
34:1-34:0 -0.01945734 -0.10813295 0.069218271 0.9995417
37:1-34:0 -0.12524720 -0.21669730 -0.033797099 0.0006440
40:1-34:0 -0.15329898 -0.24086955 -0.065728403 0.0000016
40:0-37:0 -0.10129983 -0.18462968 -0.017969993 0.0047947
23:1-37:0 0.14743078 0.08218105 0.212680502 0.0000000
30:1-37:0 -0.02120220 -0.09735457 0.054950165 0.9969761
34:1-37:0 0.01957641 -0.04658946 0.085742283 0.9952058
37:1-37:0 -0.08621345 -0.15605384 -0.016373062 0.0038062
40:1-37:0 -0.11426523 -0.17894262 -0.049587840 0.0000012
23:1-40:0 0.24873061 0.16073648 0.336724743 0.0000000
30:1-40:0 0.08009763 -0.01625912 0.176454385 0.2025862
34:1-40:0 0.12087624 0.03220064 0.209551852 0.0007040
37:1-40:0 0.01508638 -0.07636371 0.106536483 0.9999576
40:1-40:0 -0.01296539 -0.10053597 0.074605179 0.9999832
30:1-23:1 -0.16863298 -0.24986282 -0.087403130 0.0000000
34:1-23:1 -0.12785437 -0.19980593 -0.055902800 0.0000010
37:1-23:1 -0.23364423 -0.30898867 -0.158299784 0.0000000
40:1-23:1 -0.26169600 -0.33228120 -0.191110810 0.0000000
34:1-30:1 0.04077861 -0.04118897 0.122746189 0.8593736
37:1-30:1 -0.06501125 -0.14997266 0.019950159 0.3115546
40:1-30:1 -0.09306303 -0.17383385 -0.012292206 0.0101452
37:1-34:1 -0.10578986 -0.18192908 -0.029650640 0.0004878
40:1-34:1 -0.13384164 -0.20527458 -0.062408694 0.0000002
40:1-37:1 -0.02805178 -0.10290111 0.046797554 0.9742279
Variable `Component` is not in your data frame :/
<- behavior %>% group_by (temp,infected) %>% drop_na (incubatingprop) %>% summarize (meaninc = mean (incubatingprop), sd= sd (incubatingprop),n= n (),se= sd/ sqrt (n))
`summarise()` has grouped output by 'temp'. You can override using the
`.groups` argument.
# A tibble: 10 × 6
# Groups: temp [5]
temp infected meaninc sd n se
<chr> <fct> <dbl> <dbl> <int> <dbl>
1 23 0 0.527 0.286 203 0.0201
2 23 1 0.268 0.257 132 0.0223
3 30 0 0.343 0.220 174 0.0167
4 30 1 0.0999 0.107 82 0.0118
5 34 0 0.160 0.154 64 0.0193
6 34 1 0.141 0.131 126 0.0117
7 37 0 0.121 0.130 193 0.00938
8 37 1 0.0349 0.0604 106 0.00587
9 40 0 0.0198 0.0702 64 0.00877
10 40 1 0.00680 0.0257 136 0.00221
<- position_dodge (width= 0.2 )ggplot (data= behavior_meanincubating, aes (x= temp, y= meaninc, color= infected)) + geom_point (position= pd) + geom_errorbar (data= behavior_meanincubating, aes (x= temp, ymin= meaninc- se,ymax= meaninc+ se),size= 0.5 ,position= pd) + theme_bw () + labs (x= "Temperature" , y= "Average Bees Incubating" , color= "Infected" ) + scale_color_manual (labels= c ("No" ,"Yes" ), values= c ("red" ,"blue" ))
Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.
2.) H0: there is no difference in mean fanning behavior based on observer
HA: there is a difference in mean fanning behavior based on observer
= (behavior$ fanning/ behavior$ total_alive)<- cbind (behavior,fanningprop)<- aov (fanningprop~ observer_initials, data= behavior)summary (aov2)
Df Sum Sq Mean Sq F value Pr(>F)
observer_initials 12 1.53 0.12762 2.723 0.0012 **
Residuals 1267 59.38 0.04687
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
728 observations deleted due to missingness
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = fanningprop ~ observer_initials, data = behavior)
$observer_initials
diff lwr upr p adj
DC-AS 0.0314177380 -0.054291021 0.117126498 0.9921733
EF-AS 0.0083038258 -0.095885687 0.112493338 1.0000000
EM-AS 0.0138482424 -0.083137337 0.110833822 0.9999996
JLM-AS 0.0288634071 -0.094405948 0.152132763 0.9999064
JVW-AS 0.0517680419 -0.124711367 0.228247451 0.9990442
MC-AS 0.1516636142 0.009868840 0.293458388 0.0236174
MF-AS 0.0630386688 -0.016584431 0.142661769 0.2945225
MF -AS -0.1042887668 -0.825584486 0.617006953 0.9999996
ML-AS 0.0665745966 -0.006684549 0.139833742 0.1191698
TS-AS 0.0132515558 -0.076943706 0.103446817 0.9999995
TS -AS 0.0068223443 -0.714473375 0.728118064 1.0000000
WR-AS -0.0917887668 -0.282156853 0.098579319 0.9299462
EF-DC -0.0231139122 -0.124397822 0.078169998 0.9999289
EM-DC -0.0175694956 -0.111426720 0.076287729 0.9999918
JLM-DC -0.0025543309 -0.123377797 0.118269135 1.0000000
JVW-DC 0.0203503039 -0.154429437 0.195130045 1.0000000
MC-DC 0.1202458761 -0.019427793 0.259919546 0.1777143
MF-DC 0.0316209307 -0.044160426 0.107402287 0.9764866
MF -DC -0.1357065048 -0.856588250 0.585175241 0.9999913
ML-DC 0.0351568585 -0.033907447 0.104221164 0.8988831
TS-DC -0.0181661822 -0.104988767 0.068656402 0.9999722
TS -DC -0.0245953937 -0.745477139 0.696286352 1.0000000
WR-DC -0.1232065048 -0.312000001 0.065586991 0.6163589
EM-EF 0.0055444166 -0.105444959 0.116533793 1.0000000
JLM-EF 0.0205595813 -0.114005544 0.155124707 0.9999992
JVW-EF 0.0434642161 -0.141082311 0.228010744 0.9999005
MC-EF 0.1433597884 -0.008357702 0.295077279 0.0858509
MF-EF 0.0547348430 -0.041453915 0.150923600 0.8020542
MF -EF -0.1125925926 -0.835904389 0.610719203 0.9999990
ML-EF 0.0582707708 -0.032720115 0.149261657 0.6458014
TS-EF 0.0049477300 -0.100159949 0.110055409 1.0000000
TS -EF -0.0014814815 -0.724793277 0.721830314 1.0000000
WR-EF -0.1000925926 -0.297962366 0.097777181 0.9029741
JLM-EM 0.0150151647 -0.114052729 0.144083058 1.0000000
JVW-EM 0.0379197995 -0.142657522 0.218497121 0.9999711
MC-EM 0.1378153718 -0.009048324 0.284679067 0.0912299
MF-EM 0.0491904264 -0.039144307 0.137525160 0.8246743
MF -EM -0.1181370092 -0.840446294 0.604172275 0.9999982
ML-EM 0.0527263542 -0.029918001 0.135370709 0.6515640
TS-EM -0.0005966866 -0.098567970 0.097374596 1.0000000
TS -EM -0.0070258981 -0.729335182 0.715283386 1.0000000
WR-EM -0.1056370092 -0.299810116 0.088536097 0.8469319
JVW-JLM 0.0229046348 -0.173047656 0.218856926 1.0000000
MC-JLM 0.1228002070 -0.042602563 0.288202978 0.3995005
MF-JLM 0.0341752616 -0.082410143 0.150760666 0.9990507
MF -JLM -0.1331521739 -0.859457780 0.593153432 0.9999935
ML-JLM 0.0377111895 -0.074624116 0.150046495 0.9964890
TS-JLM -0.0156118513 -0.139658229 0.108434526 0.9999999
TS -JLM -0.0220410628 -0.748346669 0.704264543 1.0000000
WR-JLM -0.1206521739 -0.329200314 0.087895966 0.7834429
MC-JVW 0.0998955723 -0.108209508 0.308000652 0.9320970
MF-JVW 0.0112706269 -0.160606668 0.183147921 1.0000000
MF -JVW -0.1560568087 -0.893260635 0.581147018 0.9999684
ML-JVW 0.0148065547 -0.154216724 0.183829833 1.0000000
TS-JVW -0.0385164861 -0.215539512 0.138506539 0.9999575
TS -JVW -0.0449456976 -0.782149524 0.692258129 1.0000000
WR-JVW -0.1435568087 -0.387364057 0.100250440 0.7624167
MF-MC -0.0886249454 -0.224649136 0.047399246 0.6189374
MF -MC -0.2559523810 -0.985630569 0.473725807 0.9947340
ML-MC -0.0850890176 -0.217488583 0.047310548 0.6403665
TS-MC -0.1384120584 -0.280882854 0.004058738 0.0665287
TS -MC -0.1448412698 -0.874519458 0.584836918 0.9999844
WR-MC -0.2434523810 -0.463458632 -0.023446129 0.0154049
MF -MF -0.1673274356 -0.887510980 0.552856109 0.9999139
ML-MF 0.0035359278 -0.057813277 0.064885133 1.0000000
TS-MF -0.0497871130 -0.130607951 0.031033725 0.7028837
TS -MF -0.0562163245 -0.776399869 0.663967220 1.0000000
WR-MF -0.1548274356 -0.340937163 0.031282292 0.2219726
ML-MF 0.1708633634 -0.548644386 0.890371113 0.9998913
TS-MF 0.1175403226 -0.603888596 0.838969241 0.9999983
TS -MF 0.1111111111 -0.905054237 1.127276459 1.0000000
WR-MF 0.0125000000 -0.728151408 0.753151408 1.0000000
TS-ML -0.0533230408 -0.127882226 0.021236144 0.4633815
TS -ML -0.0597522523 -0.779260002 0.659755498 1.0000000
WR-ML -0.1583633634 -0.341840596 0.025113870 0.1746091
TS -TS -0.0064292115 -0.727858130 0.714999707 1.0000000
WR-TS -0.1050403226 -0.295912473 0.085831828 0.8362401
WR-TS -0.0986111111 -0.839262519 0.642040297 0.9999998
# Homogeneity of variance assumption is not met leveneTest (fanningprop~ observer_initials,data= behavior)
Warning in leveneTest.default(y = y, group = group, ...): group coerced to
factor.
Levene's Test for Homogeneity of Variance (center = median)
Df F value Pr(>F)
group 12 2.3842 0.004828 **
1267
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
<- behavior %>% group_by (observer_initials) %>% drop_na (fanningprop) %>% summarize (meanfan = mean (fanningprop), sd= sd (fanningprop),n= n (),se= sd/ sqrt (n))<- behavior_meanfan[- c (9 ,12 ),]
# A tibble: 11 × 5
observer_initials meanfan sd n se
<chr> <dbl> <dbl> <int> <dbl>
1 AS 0.104 0.199 130 0.0175
2 DC 0.136 0.214 153 0.0173
3 EF 0.113 0.211 75 0.0243
4 EM 0.118 0.148 95 0.0152
5 JLM 0.133 0.215 46 0.0317
6 JVW 0.156 0.259 19 0.0594
7 MC 0.256 0.470 32 0.0831
8 MF 0.167 0.281 218 0.0190
9 ML 0.171 0.165 370 0.00857
10 TS 0.118 0.193 124 0.0174
11 WR 0.0125 0.0342 16 0.00854
$ n= as.factor (behavior_meanfan$ n)ggplot (data= behavior_meanfan, aes (x= observer_initials, y= meanfan,color= n)) + geom_point (position= pd) + geom_errorbar (data= behavior_meanfan, aes (x= observer_initials, ymin= meanfan- se,ymax= meanfan+ se),size= 0.5 ,position= pd) + theme_bw () + labs (x= "Observer" , y= "Average Bees Fanning" )
3.) H0: there is no effect of infection status and temp on feeding behavior, and no interactive effect of the two on behavior
HA: there is an interactive effect of infection status and temp on feeding behavior
= (behavior$ feeding/ behavior$ total_alive)<- cbind (behavior,feedingprop)<- aov (feedingprop~ temp* infected, data= behavior)summary (aov3)
Df Sum Sq Mean Sq F value Pr(>F)
temp 4 29.070 7.267 544.745 < 2e-16 ***
infected 1 0.078 0.078 5.873 0.01552 *
temp:infected 4 0.179 0.045 3.357 0.00961 **
Residuals 1270 16.943 0.013
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
728 observations deleted due to missingness
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = feedingprop ~ temp * infected, data = behavior)
$temp
diff lwr upr p adj
30-23 -0.01124301 -0.037435630 0.01494960 0.7671130
34-23 0.03504866 0.006393161 0.06370416 0.0076455
37-23 0.15094378 0.125841446 0.17604612 0.0000000
40-23 0.42403613 0.395841458 0.45223080 0.0000000
34-30 0.04629167 0.016078372 0.07650497 0.0002937
37-30 0.16218680 0.135319842 0.18905375 0.0000000
40-30 0.43527914 0.405502551 0.46505573 0.0000000
37-34 0.11589512 0.086621966 0.14516828 0.0000000
40-34 0.38898747 0.357022959 0.42095198 0.0000000
40-37 0.27309234 0.244270139 0.30191455 0.0000000
$infected
diff lwr upr p adj
1-0 0.01502172 0.002302145 0.02774129 0.020667
$`temp:infected`
diff lwr upr p adj
30:0-23:0 -0.012440378 -0.050259617 0.02537886 0.9895660
34:0-23:0 0.043185947 -0.009292669 0.09566456 0.2147083
37:0-23:0 0.141861600 0.105058393 0.17866481 0.0000000
40:0-23:0 0.378664618 0.326186002 0.43114323 0.0000000
23:1-23:0 0.001539009 -0.039391973 0.04246999 1.0000000
30:1-23:0 -0.006809062 -0.054708701 0.04109058 0.9999882
34:1-23:0 0.031829871 -0.009687458 0.07334720 0.3088037
37:1-23:0 0.169190762 0.125323247 0.21305828 0.0000000
40:1-23:0 0.446279216 0.405714622 0.48684381 0.0000000
34:0-30:0 0.055626326 0.002109713 0.10914294 0.0340380
37:0-30:0 0.154301979 0.116033209 0.19257075 0.0000000
40:0-30:0 0.391104996 0.337588384 0.44462161 0.0000000
23:1-30:0 0.013979387 -0.028274228 0.05623300 0.9891264
30:1-30:0 0.005631316 -0.043403345 0.05466598 0.9999982
34:1-30:0 0.044270250 0.001448394 0.08709211 0.0359936
37:1-30:0 0.181631141 0.136527021 0.22673526 0.0000000
40:1-30:0 0.458719594 0.416820801 0.50061839 0.0000000
37:0-34:0 0.098675653 0.045872158 0.15147915 0.0000002
40:0-34:0 0.335478671 0.270765935 0.40019141 0.0000000
23:1-34:0 -0.041646938 -0.097406048 0.01411217 0.3466184
30:1-34:0 -0.049995009 -0.111053247 0.01106323 0.2208696
34:1-34:0 -0.011356076 -0.067547015 0.04483486 0.9997668
37:1-34:0 0.126004815 0.068055769 0.18395386 0.0000000
40:1-34:0 0.403093269 0.347602555 0.45858398 0.0000000
40:0-37:0 0.236803018 0.183999522 0.28960651 0.0000000
23:1-37:0 -0.140322591 -0.181669287 -0.09897590 0.0000000
30:1-37:0 -0.148670663 -0.196926018 -0.10041531 0.0000000
34:1-37:0 -0.110031729 -0.151958958 -0.06810450 0.0000000
37:1-37:0 0.027329162 -0.016926491 0.07158482 0.6294695
40:1-37:0 0.304417615 0.263433592 0.34540164 0.0000000
23:1-40:0 -0.377125609 -0.432884719 -0.32136650 0.0000000
30:1-40:0 -0.385473680 -0.446531918 -0.32441544 0.0000000
34:1-40:0 -0.346834747 -0.403025686 -0.29064381 0.0000000
37:1-40:0 -0.209473855 -0.267422901 -0.15152481 0.0000000
40:1-40:0 0.067614598 0.012123885 0.12310531 0.0046317
30:1-23:1 -0.008348071 -0.059820866 0.04312472 0.9999634
34:1-23:1 0.030290862 -0.015302578 0.07588430 0.5237805
37:1-23:1 0.167651753 0.119908355 0.21539515 0.0000000
40:1-23:1 0.444740207 0.400012593 0.48946782 0.0000000
34:1-30:1 0.038638933 -0.013301338 0.09057921 0.3525499
37:1-30:1 0.175999825 0.122162457 0.22983719 0.0000000
40:1-30:1 0.453088278 0.401906353 0.50427020 0.0000000
37:1-34:1 0.137360891 0.089113866 0.18560792 0.0000000
40:1-34:1 0.414449345 0.369184538 0.45971415 0.0000000
40:1-37:1 0.277088453 0.229658790 0.32451812 0.0000000
Variable `Component` is not in your data frame :/
<- behavior %>% group_by (temp,infected) %>% drop_na (feedingprop) %>% summarize (meanfeed = mean (feedingprop), sd= sd (feedingprop),n= n (),se= sd/ sqrt (n))
`summarise()` has grouped output by 'temp'. You can override using the
`.groups` argument.
# A tibble: 10 × 6
# Groups: temp [5]
temp infected meanfeed sd n se
<chr> <fct> <dbl> <dbl> <int> <dbl>
1 23 0 0.0374 0.0629 203 0.00442
2 23 1 0.0390 0.0645 132 0.00561
3 30 0 0.0250 0.0516 174 0.00391
4 30 1 0.0306 0.0575 82 0.00635
5 34 0 0.0806 0.0944 64 0.0118
6 34 1 0.0692 0.0934 126 0.00832
7 37 0 0.179 0.152 193 0.0109
8 37 1 0.207 0.136 106 0.0132
9 40 0 0.416 0.180 64 0.0225
10 40 1 0.484 0.189 136 0.0162
ggplot (data= behavior_meanfeed, aes (x= temp, y= meanfeed, color= infected)) + geom_point (position= pd) + geom_errorbar (data= behavior_meanfeed, aes (x= temp, ymin= meanfeed- se,ymax= meanfeed+ se),size= 0.5 ,position= pd) + theme_bw () + labs (x= "Temperature" , y= "Average Bees Feeding" , color= "Infected" ) + scale_color_manual (labels= c ("No" ,"Yes" ), values= c ("red" ,"blue" ))
