Raw data modelling (LMM) using elevation
#Create linear model formula
formula4 <- formula(o2_normalized ~ time_point*temperature+elevation+(1|individual)) #QC_normalized=o2 consumption normalized (ml/s/g)
#Fit the linear model
model4 <- lmer(formula4, data = calotriton_resp1_subset)
boundary (singular) fit: see help('isSingular')
#Print the model summary
summary(model4)
Linear mixed model fit by REML ['lmerMod']
Formula: o2_normalized ~ time_point * temperature + elevation + (1 | individual)
Data: calotriton_resp1_subset
REML criterion at convergence: -681.1
Scaled residuals:
Min 1Q Median 3Q Max
-1.94738 -0.63126 0.07569 0.73963 2.66971
Random effects:
Groups Name Variance Std.Dev.
individual (Intercept) 0.000e+00 0.0000000
Residual 1.194e-07 0.0003455
Number of obs: 58, groups: individual, 31
Fixed effects:
Estimate Std. Error t value
(Intercept) 7.992e-04 1.060e-04 7.540
time_point2 2.789e-05 1.334e-04 0.209
temperature20 1.884e-04 1.266e-04 1.488
elevationLow 9.820e-05 9.107e-05 1.078
time_point2:temperature20 -5.198e-05 1.825e-04 -0.285
Correlation of Fixed Effects:
(Intr) tm_pn2 tmpr20 elvtnL
time_point2 -0.640
temperatr20 -0.661 0.509
elevationLw -0.491 0.075 0.051
tm_pnt2:t20 0.476 -0.733 -0.696 -0.071
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')
MuMIn::r.squaredGLMM(model4)
R2m R2c
[1,] 0.07199619 0.07199619
Raw data modelling (LMM) with interaction
#Create linear model formula
formula5 <- formula(o2_normalized ~ time_point*temperature*elevation+(1|individual)) #QC_normalized=o2 consumption normalized (ml/s/g)
#Fit the linear model
model5 <- lmer(formula5, data = calotriton_resp1_subset)
boundary (singular) fit: see help('isSingular')
#Print the model summary
summary(model5)
Linear mixed model fit by REML ['lmerMod']
Formula: o2_normalized ~ time_point * temperature * elevation + (1 | individual)
Data: calotriton_resp1_subset
REML criterion at convergence: -641
Scaled residuals:
Min 1Q Median 3Q Max
-2.13722 -0.68608 0.06061 0.56779 2.64762
Random effects:
Groups Name Variance Std.Dev.
individual (Intercept) 0.000e+00 0.00000
Residual 1.156e-07 0.00034
Number of obs: 58, groups: individual, 31
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.0006666 0.0001388 4.802
time_point2 0.0001416 0.0001892 0.748
temperature20 0.0004667 0.0001836 2.541
elevationLow 0.0003303 0.0001836 1.798
time_point2:temperature20 -0.0003454 0.0002584 -1.337
time_point2:elevationLow -0.0001911 0.0002637 -0.725
temperature20:elevationLow -0.0005235 0.0002503 -2.092
time_point2:temperature20:elevationLow 0.0005462 0.0003597 1.518
Correlation of Fixed Effects:
(Intr) tm_pn2 tmpr20 elvtnL tm_2:20 tm_2:L tm20:L
time_point2 -0.734
temperatr20 -0.756 0.555
elevationLw -0.756 0.555 0.571
tm_pnt2:t20 0.537 -0.732 -0.711 -0.406
tm_pnt2:lvL 0.527 -0.718 -0.398 -0.697 0.525
tmprtr20:lL 0.555 -0.407 -0.734 -0.734 0.522 0.511
tm_pn2:20:L -0.386 0.526 0.511 0.511 -0.718 -0.733 -0.696
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')
MuMIn::r.squaredGLMM(model5)
R2m R2c
[1,] 0.139987 0.139987