Raw data modelling (LMM) using population
#exclude the acclimation time-point
calotriton_resp1_subset<-calotriton_resp1 %>%
filter(time_point!="0")
#Create linear model formula
formula <- formula(o2_normalized ~ time_point*temperature*population+(1|individual))
#Fit the linear model
model <- lmer(formula, data = calotriton_resp1_subset)
boundary (singular) fit: see help('isSingular')
#Print the model summary
summary(model)
Linear mixed model fit by REML ['lmerMod']
Formula: o2_normalized ~ time_point * temperature * population + (1 | individual)
Data: calotriton_resp1_subset
REML criterion at convergence: -540.2
Scaled residuals:
Min 1Q Median 3Q Max
-1.9238 -0.5697 0.1368 0.6823 1.8585
Random effects:
Groups Name Variance Std.Dev.
individual (Intercept) 0.00e+00 0.0000000
Residual 9.37e-08 0.0003061
Number of obs: 58, groups: individual, 31
Fixed effects:
Estimate Std. Error t value
(Intercept) 8.460e-04 1.767e-04 4.787
time_point2 -2.513e-05 2.338e-04 -0.108
temperature20 3.534e-04 2.338e-04 1.512
populationGoizueta 2.336e-04 2.235e-04 1.045
populationHarpea -3.588e-04 2.499e-04 -1.436
populationLeitzaran 1.300e-05 2.499e-04 0.052
time_point2:temperature20 -3.960e-04 3.186e-04 -1.243
time_point2:populationGoizueta -2.728e-04 3.112e-04 -0.877
time_point2:populationHarpea 3.292e-04 3.422e-04 0.962
time_point2:populationLeitzaran 4.449e-04 3.643e-04 1.221
temperature20:populationGoizueta -8.314e-04 3.112e-04 -2.672
temperature20:populationHarpea 2.267e-04 3.306e-04 0.686
temperature20:populationLeitzaran 6.616e-05 3.306e-04 0.200
time_point2:temperature20:populationGoizueta 1.275e-03 4.365e-04 2.920
time_point2:temperature20:populationHarpea 1.560e-04 4.676e-04 0.334
time_point2:temperature20:populationLeitzaran -3.016e-04 4.758e-04 -0.634
Correlation matrix not shown by default, as p = 16 > 12.
Use print(x, correlation=TRUE) or
vcov(x) if you need it
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')
MuMIn::r.squaredGLMM(model)
R2m R2c
[1,] 0.3534154 0.3534154
Raw data modelling (LMM) without interaction
#Create linear model formula
formula3 <- formula(o2_normalized ~ time_point*temperature+population+(1|individual))
#Fit the linear model
model3 <- lmer(formula3, data = calotriton_resp1_subset)
boundary (singular) fit: see help('isSingular')
#Print the model summary
summary(model3)
Linear mixed model fit by REML ['lmerMod']
Formula: o2_normalized ~ time_point * temperature + population + (1 | individual)
Data: calotriton_resp1_subset
REML criterion at convergence: -650.5
Scaled residuals:
Min 1Q Median 3Q Max
-2.07343 -0.72830 0.05987 0.69854 2.38744
Random effects:
Groups Name Variance Std.Dev.
individual (Intercept) 0.000e+00 0.0000000
Residual 1.205e-07 0.0003472
Number of obs: 58, groups: individual, 31
Fixed effects:
Estimate Std. Error t value
(Intercept) 8.195e-04 1.240e-04 6.611
time_point2 2.876e-05 1.342e-04 0.214
temperature20 1.782e-04 1.275e-04 1.398
populationGoizueta 1.699e-05 1.234e-04 0.138
populationHarpea -3.175e-05 1.317e-04 -0.241
populationLeitzaran 1.705e-04 1.321e-04 1.290
time_point2:temperature20 -5.430e-05 1.834e-04 -0.296
Correlation of Fixed Effects:
(Intr) tm_pn2 tmpr20 ppltnG ppltnH ppltnL
time_point2 -0.566
temperatr20 -0.567 0.506
populatnGzt -0.582 0.067 0.067
populatnHrp -0.510 0.034 -0.003 0.497
popltnLtzrn -0.510 0.075 -0.003 0.495 0.464
tm_pnt2:t20 0.409 -0.732 -0.693 -0.048 0.000 -0.055
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')
MuMIn::r.squaredGLMM(model3)
R2m R2c
[1,] 0.09324329 0.09324329