13  Weight [Pups & Teens]

❖ Data

❖ Description

Variable Description
Mouse

Mouse unique identifier

Condition

Hypoxia condition: Normoxia (N) vs Intermittent Hypoxia (IH)

Stage

Developmental stage

Weight_Gain

Weight gain between two successive stages (starting at P2) (g)

Variable Description
Mouse

Mouse unique identifier

Condition

Hypoxia condition: Normoxia (N) vs Intermittent Hypoxia (IH)

Stage

Developmental stage

Weight_Gain

Weight gain between two successive stages (starting at P2) (g)

❖ Correlations

13.1 Weight evolution in time (Pups & Teens)

13.1.1 Data Exploration

❖ Distribution:

Condition Mean SD Variance CoV IQR Min Max Skewness Kurtosis n
N 0.427 0.055 0.003 0.129 0.1 0.3 0.5 0.109 −0.264 22
IH 0.2 0.059 0.004 0.297 0 0.1 0.3 0 0.425 18
Condition Mean SD Variance CoV IQR Min Max Skewness Kurtosis n
N 0.427 0.055 0.003 0.129 0.1 0.3 0.5 0.109 −0.264 22
IH 0.2 0.059 0.004 0.297 0 0.1 0.3 0 0.425 18
Condition Mean SD Variance CoV IQR Min Max Skewness Kurtosis n
N 0.964 0.114 0.013 0.118 0.125 0.7 1.1 −0.908 0.907 22
IH 0.394 0.064 0.004 0.162 0.025 0.3 0.5 0.041 −0.143 18
Condition Mean SD Variance CoV IQR Min Max Skewness Kurtosis n
N 0.964 0.114 0.013 0.118 0.125 0.7 1.1 −0.908 0.907 22
IH 0.394 0.064 0.004 0.162 0.025 0.3 0.5 0.041 −0.143 18
Condition Mean SD Variance CoV IQR Min Max Skewness Kurtosis n
N 1.455 0.202 0.041 0.139 0.25 0.9 1.7 −0.953 1.525 22
IH 0.622 0.111 0.012 0.179 0.1 0.4 0.8 −0.204 0.709 18
Condition Mean SD Variance CoV IQR Min Max Skewness Kurtosis n
N 1.455 0.202 0.041 0.139 0.25 0.9 1.7 −0.953 1.525 22
IH 0.622 0.111 0.012 0.179 0.1 0.4 0.8 −0.204 0.709 18
Condition Mean SD Variance CoV IQR Min Max Skewness Kurtosis n
N 2.032 0.208 0.043 0.102 0.325 1.7 2.4 0.196 −1.341 22
IH 0.867 0.157 0.025 0.181 0.225 0.7 1.2 0.829 −0.527 18
Condition Mean SD Variance CoV IQR Min Max Skewness Kurtosis n
N 2.032 0.208 0.043 0.102 0.325 1.7 2.4 0.196 −1.341 22
IH 0.867 0.157 0.025 0.181 0.225 0.7 1.2 0.829 −0.527 18
Condition Mean SD Variance CoV IQR Min Max Skewness Kurtosis n
N 2.586 0.236 0.056 0.091 0.3 2.1 3.1 0.203 0.072 22
IH 1.15 0.195 0.038 0.169 0.325 0.9 1.5 0.591 −0.636 18
Condition Mean SD Variance CoV IQR Min Max Skewness Kurtosis n
N 2.586 0.236 0.056 0.091 0.3 2.1 3.1 0.203 0.072 22
IH 1.15 0.195 0.038 0.169 0.325 0.9 1.5 0.591 −0.636 18
Condition Mean SD Variance CoV IQR Min Max Skewness Kurtosis n
N 3.036 0.313 0.098 0.103 0.325 2.3 3.6 −0.197 0.433 22
IH 1.372 0.263 0.069 0.192 0.325 0.9 1.8 0.1 −0.441 18
Condition Mean SD Variance CoV IQR Min Max Skewness Kurtosis n
N 3.036 0.313 0.098 0.103 0.325 2.3 3.6 −0.197 0.433 22
IH 1.372 0.263 0.069 0.192 0.325 0.9 1.8 0.1 −0.441 18
Condition Mean SD Variance CoV IQR Min Max Skewness Kurtosis n
N 3.409 0.385 0.148 0.113 0.525 2.7 4.3 0.545 0.127 22
IH 1.617 0.3 0.09 0.185 0.5 1 2.1 0.026 −0.337 18
Condition Mean SD Variance CoV IQR Min Max Skewness Kurtosis n
N 3.409 0.385 0.148 0.113 0.525 2.7 4.3 0.545 0.127 22
IH 1.617 0.3 0.09 0.185 0.5 1 2.1 0.026 −0.337 18
Condition Mean SD Variance CoV IQR Min Max Skewness Kurtosis n
N 3.677 0.529 0.28 0.144 0.8 3 4.7 0.632 −0.929 22
IH 1.789 0.312 0.098 0.175 0.5 1.3 2.3 0.194 −0.755 18
Condition Mean SD Variance CoV IQR Min Max Skewness Kurtosis n
N 3.677 0.529 0.28 0.144 0.8 3 4.7 0.632 −0.929 22
IH 1.789 0.312 0.098 0.175 0.5 1.3 2.3 0.194 −0.755 18
Condition Mean SD Variance CoV IQR Min Max Skewness Kurtosis n
N 3.868 0.618 0.382 0.16 1.125 3.2 5.1 0.646 −1.152 22
IH 1.983 0.405 0.164 0.204 0.625 1.4 2.6 0.37 −1.015 18
Condition Mean SD Variance CoV IQR Min Max Skewness Kurtosis n
N 3.868 0.618 0.382 0.16 1.125 3.2 5.1 0.646 −1.152 22
IH 1.983 0.405 0.164 0.204 0.625 1.4 2.6 0.37 −1.015 18
Condition Mean SD Variance CoV IQR Min Max Skewness Kurtosis n
N 6.05 0.129 0.017 0.021 0.25 5.9 6.2 0 −1.2 4
IH 4.467 0.651 0.423 0.146 1.3 3.8 5.1 −0.23 −1.5 3
Condition Mean SD Variance CoV IQR Min Max Skewness Kurtosis n
N 6.05 0.129 0.017 0.021 0.25 5.9 6.2 0 −1.2 4
IH 4.467 0.651 0.423 0.146 1.3 3.8 5.1 −0.23 −1.5 3
Condition Mean SD Variance CoV IQR Min Max Skewness Kurtosis n
N 6.7 0.455 0.207 0.068 0.85 6.1 7.1 −0.894 −0.748 4
IH 5.233 0.153 0.023 0.029 0.3 5.1 5.4 0.935 −1.5 3
Condition Mean SD Variance CoV IQR Min Max Skewness Kurtosis n
N 6.7 0.455 0.207 0.068 0.85 6.1 7.1 −0.894 −0.748 4
IH 5.233 0.153 0.023 0.029 0.3 5.1 5.4 0.935 −1.5 3

❖ Evolution:

13.1.2 Models & Diagnostics

Exploring some Generalized Linear (Mixed) model candidates:

❖ Model call:

```{r}
glmmTMB(formula = Weight_Gain ~ Condition * Stage + ar1(Stage + 
    0 | Mouse), data = data, family = Gamma("log"), REML = TRUE, 
    ziformula = ~0, dispformula = ~1)
```

❖ Performance:

performance::performance(mod)
AIC AICc BIC R2_conditional R2_marginal ICC RMSE Sigma
-266.52 -262.78 -168.41 1.00 1.00 0 0.05 0.05
AIC AICc BIC R2_conditional R2_marginal ICC RMSE Sigma
-266.52 -262.78 -168.41 1.00 1.00 0 0.05 0.05

❖ Residuals:

performance::check_model(
  mod, panel = FALSE,
  check = c("pp_check", "qq", "reqq", "linearity", "homogeneity")
)

❖ Predictions:

Simulating data from the model for pseudo “Posterior Predictive” plots.

♦ Simulated data vs observed data:

♦ Simulated statistics vs observed ones:

❖ Potential outliers:

13.1.3 Effects Analysis

```{r}
glmmTMB(formula = Weight_Gain ~ Condition * Stage + ar1(Stage + 
    0 | Mouse), data = data, family = Gamma("log"), REML = TRUE, 
    ziformula = ~0, dispformula = ~1)
```

13.1.3.1 Coefficients

❖ All effects (Wald):

parameters::parameters(
  mod, component = "conditional", effects = "fixed",
  exponentiate = should_exp(mod), p_adjust = "none", summary = TRUE, digits = 3
)
Parameter Coefficient SE 95% CI z p
(Intercept) 1.746 0.045 (1.66, 1.84) 21.616 < .001
Condition1 1.417 0.037 (1.35, 1.49) 13.515 < .001
Stage1 0.163 0.003 (0.16, 0.17) -103.160 < .001
Stage2 0.350 0.006 (0.34, 0.36) -65.651 < .001
Stage3 0.538 0.008 (0.52, 0.55) -42.298 < .001
Stage4 0.753 0.010 (0.73, 0.77) -20.803 < .001
Stage5 0.980 0.013 (0.96, 1.00) -1.579 0.114
Stage6 1.156 0.015 (1.13, 1.19) 11.395 < .001
Stage7 1.330 0.017 (1.30, 1.36) 22.000 < .001
Stage8 1.452 0.020 (1.41, 1.49) 27.381 < .001
Stage9 1.562 0.023 (1.52, 1.61) 30.450 < .001
Stage10 3.338 0.099 (3.15, 3.54) 40.657 < .001
Condition1 * Stage1 1.051 0.018 (1.02, 1.09) 2.845 0.004 **
Condition1 * Stage2 1.106 0.018 (1.07, 1.14) 6.284 < .001
Condition1 * Stage3 1.082 0.016 (1.05, 1.11) 5.406 < .001
Condition1 * Stage4 1.086 0.015 (1.06, 1.11) 6.040 < .001
Condition1 * Stage5 1.063 0.014 (1.04, 1.09) 4.746 < .001
Condition1 * Stage6 1.057 0.013 (1.03, 1.08) 4.332 < .001
Condition1 * Stage7 1.030 0.013 (1.00, 1.06) 2.318 0.020 *
Condition1 * Stage8 1.015 0.014 (0.99, 1.04) 1.064 0.287
Condition1 * Stage9 0.990 0.014 (0.96, 1.02) -0.708 0.479
Condition1 * Stage10 0.806 0.024 (0.76, 0.85) -7.300 < .001
Model: Weight_Gain ~ Condition * Stage (374 Observations)
Parameter Coefficient SE 95% CI z p
(Intercept) 1.746 0.045 (1.66, 1.84) 21.616 < .001
Condition1 1.417 0.037 (1.35, 1.49) 13.515 < .001
Stage1 0.163 0.003 (0.16, 0.17) -103.160 < .001
Stage2 0.350 0.006 (0.34, 0.36) -65.651 < .001
Stage3 0.538 0.008 (0.52, 0.55) -42.298 < .001
Stage4 0.753 0.010 (0.73, 0.77) -20.803 < .001
Stage5 0.980 0.013 (0.96, 1.00) -1.579 0.114
Stage6 1.156 0.015 (1.13, 1.19) 11.395 < .001
Stage7 1.330 0.017 (1.30, 1.36) 22.000 < .001
Stage8 1.452 0.020 (1.41, 1.49) 27.381 < .001
Stage9 1.562 0.023 (1.52, 1.61) 30.450 < .001
Stage10 3.338 0.099 (3.15, 3.54) 40.657 < .001
Condition1 * Stage1 1.051 0.018 (1.02, 1.09) 2.845 0.004 **
Condition1 * Stage2 1.106 0.018 (1.07, 1.14) 6.284 < .001
Condition1 * Stage3 1.082 0.016 (1.05, 1.11) 5.406 < .001
Condition1 * Stage4 1.086 0.015 (1.06, 1.11) 6.040 < .001
Condition1 * Stage5 1.063 0.014 (1.04, 1.09) 4.746 < .001
Condition1 * Stage6 1.057 0.013 (1.03, 1.08) 4.332 < .001
Condition1 * Stage7 1.030 0.013 (1.00, 1.06) 2.318 0.020 *
Condition1 * Stage8 1.015 0.014 (0.99, 1.04) 1.064 0.287
Condition1 * Stage9 0.990 0.014 (0.96, 1.02) -0.708 0.479
Condition1 * Stage10 0.806 0.024 (0.76, 0.85) -7.300 < .001
Model: Weight_Gain ~ Condition * Stage (374 Observations)

❖ Main effects (Wald Chi-Square):

car::Anova(mod, type = 3)
term statistic df p.value
Condition 182.66 1 <0.001 ***
Stage 10730.80 10 <0.001 ***
Condition:Stage 87.92 10 <0.001 ***
term statistic df p.value
Condition 182.66 1 <0.001 ***
Stage 10730.80 10 <0.001 ***
Condition:Stage 87.92 10 <0.001 ***

❖ Main effects (Likelihood Ratio Test):

LRT(mod, pred = "Condition")
model df aic bic log_lik deviance chisq chi_df pr_chisq
mod_reduced 14 -291.60 -236.67 159.80 -319.60
mod_full 25 -415.64 -317.54 232.82 -465.64 146.04 11 <0.001 ***
model df aic bic log_lik deviance chisq chi_df pr_chisq
mod_reduced 14 -291.60 -236.67 159.80 -319.60
mod_full 25 -415.64 -317.54 232.82 -465.64 146.04 11 <0.001 ***
Important

Our LRT() method removes the predictor plus all its interactions

13.1.3.2 Marginal Effects

Marginal means & Contrasts for each predictor:

❖ Marginal Means:

emmeans(mod, specs = pred, type = "response")
Condition response SE df lower.CL upper.CL
N 2.473 0.085 371 2.311 2.647
IH 1.232 0.047 371 1.143 1.329
Condition response SE df lower.CL upper.CL
N 2.473 0.085 371 2.311 2.647
IH 1.232 0.047 371 1.143 1.329 
- Results are averaged over the levels of: Stage
- Confidence level used: 0.95
- Intervals are back-transformed from the log scale

❖ Contrasts:

emmeans(mod, specs = pred, type = "response") |> 
  contrast(method = "consec", adjust = "none", infer = TRUE)
contrast ratio SE df lower.CL upper.CL null t.ratio p.value
IH / N 0.498 0.026 371 0.45 0.551 1 −13.515 <0.001 ***
contrast ratio SE df lower.CL upper.CL null t.ratio p.value
IH / N 0.498 0.026 371 0.45 0.551 1 −13.515 <0.001 *** 
- Results are averaged over the levels of: Stage
- Confidence level used: 0.95
- Intervals are back-transformed from the log scale
- Tests are performed on the log scale

❖ Boxplot:

❖ Marginal Means:

emmeans(mod, specs = pred, type = "response")
Stage response SE df lower.CL upper.CL
P3 0.285 0.008 371 0.269 0.301
P4 0.611 0.018 371 0.577 0.647
P5 0.939 0.027 371 0.888 0.994
P6 1.315 0.038 371 1.242 1.392
P7 1.71 0.049 371 1.616 1.81
P8 2.018 0.058 371 1.907 2.136
P9 2.322 0.067 371 2.194 2.457
P10 2.534 0.073 371 2.395 2.682
P11 2.727 0.079 371 2.577 2.887
P16 5.828 0.241 371 5.372 6.322
P21 6.613 0.299 371 6.049 7.229
Stage response SE df lower.CL upper.CL
P3 0.285 0.008 371 0.269 0.301
P4 0.611 0.018 371 0.577 0.647
P5 0.939 0.027 371 0.888 0.994
P6 1.315 0.038 371 1.242 1.392
P7 1.71 0.049 371 1.616 1.81
P8 2.018 0.058 371 1.907 2.136
P9 2.322 0.067 371 2.194 2.457
P10 2.534 0.073 371 2.395 2.682
P11 2.727 0.079 371 2.577 2.887
P16 5.828 0.241 371 5.372 6.322
P21 6.613 0.299 371 6.049 7.229 
- Results are averaged over the levels of: Condition
- Confidence level used: 0.95
- Intervals are back-transformed from the log scale

❖ Contrasts:

emmeans(mod, specs = pred, type = "response") |> 
  contrast(method = "consec", adjust = "none", infer = TRUE)
contrast ratio SE df lower.CL upper.CL null t.ratio p.value
P4 / P3 2.146 0.031 371 2.086 2.209 1 52.523 <0.001 ***
P5 / P4 1.537 0.022 371 1.494 1.582 1 29.589 <0.001 ***
P6 / P5 1.4 0.02 371 1.36 1.44 1 23.147 <0.001 ***
P7 / P6 1.301 0.019 371 1.264 1.338 1 18.091 <0.001 ***
P8 / P7 1.18 0.017 371 1.147 1.214 1 11.397 <0.001 ***
P9 / P8 1.15 0.017 371 1.118 1.184 1 9.637 <0.001 ***
P10 / P9 1.092 0.016 371 1.061 1.123 1 6.038 <0.001 ***
P11 / P10 1.076 0.016 371 1.046 1.107 1 5.052 <0.001 ***
P16 / P11 2.137 0.071 371 2.002 2.28 1 22.931 <0.001 ***
P21 / P16 1.135 0.04 371 1.06 1.215 1 3.629 <0.001 ***
contrast ratio SE df lower.CL upper.CL null t.ratio p.value
P4 / P3 2.146 0.031 371 2.086 2.209 1 52.523 <0.001 ***
P5 / P4 1.537 0.022 371 1.494 1.582 1 29.589 <0.001 ***
P6 / P5 1.4 0.02 371 1.36 1.44 1 23.147 <0.001 ***
P7 / P6 1.301 0.019 371 1.264 1.338 1 18.091 <0.001 ***
P8 / P7 1.18 0.017 371 1.147 1.214 1 11.397 <0.001 ***
P9 / P8 1.15 0.017 371 1.118 1.184 1 9.637 <0.001 ***
P10 / P9 1.092 0.016 371 1.061 1.123 1 6.038 <0.001 ***
P11 / P10 1.076 0.016 371 1.046 1.107 1 5.052 <0.001 ***
P16 / P11 2.137 0.071 371 2.002 2.28 1 22.931 <0.001 ***
P21 / P16 1.135 0.04 371 1.06 1.215 1 3.629 <0.001 *** 
- Results are averaged over the levels of: Condition
- Confidence level used: 0.95
- Intervals are back-transformed from the log scale
- Tests are performed on the log scale

❖ Boxplot:

❖ Marginal Means:

emmeans(mod, specs = emmeans_formula, type = "response")
Condition response SE df lower.CL upper.CL
N 0.424 0.016 371 0.393 0.458
IH 0.191 0.008 371 0.176 0.208
Condition response SE df lower.CL upper.CL
N 0.424 0.016 371 0.393 0.458
IH 0.191 0.008 371 0.176 0.208
Condition response SE df lower.CL upper.CL
N 0.957 0.037 371 0.887 1.033
IH 0.39 0.017 371 0.359 0.424
Condition response SE df lower.CL upper.CL
N 0.957 0.037 371 0.887 1.033
IH 0.39 0.017 371 0.359 0.424
Condition response SE df lower.CL upper.CL
N 1.44 0.056 371 1.335 1.554
IH 0.613 0.026 371 0.563 0.666
Condition response SE df lower.CL upper.CL
N 1.44 0.056 371 1.335 1.554
IH 0.613 0.026 371 0.563 0.666
Condition response SE df lower.CL upper.CL
N 2.022 0.078 371 1.874 2.182
IH 0.855 0.037 371 0.786 0.93
Condition response SE df lower.CL upper.CL
N 2.022 0.078 371 1.874 2.182
IH 0.855 0.037 371 0.786 0.93
Condition response SE df lower.CL upper.CL
N 2.577 0.1 371 2.388 2.78
IH 1.135 0.049 371 1.044 1.235
Condition response SE df lower.CL upper.CL
N 2.577 0.1 371 2.388 2.78
IH 1.135 0.049 371 1.044 1.235
Condition response SE df lower.CL upper.CL
N 3.021 0.117 371 2.8 3.26
IH 1.348 0.058 371 1.239 1.467
Condition response SE df lower.CL upper.CL
N 3.021 0.117 371 2.8 3.26
IH 1.348 0.058 371 1.239 1.467
Condition response SE df lower.CL upper.CL
N 3.389 0.131 371 3.141 3.657
IH 1.59 0.068 371 1.462 1.73
Condition response SE df lower.CL upper.CL
N 3.389 0.131 371 3.141 3.657
IH 1.59 0.068 371 1.462 1.73
Condition response SE df lower.CL upper.CL
N 3.643 0.141 371 3.376 3.931
IH 1.763 0.075 371 1.621 1.918
Condition response SE df lower.CL upper.CL
N 3.643 0.141 371 3.376 3.931
IH 1.763 0.075 371 1.621 1.918
Condition response SE df lower.CL upper.CL
N 3.824 0.148 371 3.544 4.126
IH 1.945 0.083 371 1.788 2.116
Condition response SE df lower.CL upper.CL
N 3.824 0.148 371 3.544 4.126
IH 1.945 0.083 371 1.788 2.116
Condition response SE df lower.CL upper.CL
N 6.655 0.364 371 5.976 7.411
IH 5.103 0.317 371 4.517 5.765
Condition response SE df lower.CL upper.CL
N 6.655 0.364 371 5.976 7.411
IH 5.103 0.317 371 4.517 5.765
Condition response SE df lower.CL upper.CL
N 7.319 0.436 371 6.509 8.23
IH 5.975 0.406 371 5.227 6.829
Condition response SE df lower.CL upper.CL
N 7.319 0.436 371 6.509 8.23
IH 5.975 0.406 371 5.227 6.829 
- Confidence level used: 0.95
- Intervals are back-transformed from the log scale

❖ Contrasts:

emmeans(mod, specs = emmeans_formula, type = "response") |> 
  contrast(method = "pairwise", adjust = "none", infer = TRUE)
contrast ratio SE df lower.CL upper.CL null t.ratio p.value
N / IH 2.218 0.128 371 1.98 2.484 1 13.803 <0.001 ***
contrast ratio SE df lower.CL upper.CL null t.ratio p.value
N / IH 2.218 0.128 371 1.98 2.484 1 13.803 <0.001 ***
contrast ratio SE df lower.CL upper.CL null t.ratio p.value
N / IH 2.453 0.142 371 2.19 2.748 1 15.554 <0.001 ***
contrast ratio SE df lower.CL upper.CL null t.ratio p.value
N / IH 2.453 0.142 371 2.19 2.748 1 15.554 <0.001 ***
contrast ratio SE df lower.CL upper.CL null t.ratio p.value
N / IH 2.351 0.136 371 2.099 2.634 1 14.818 <0.001 ***
contrast ratio SE df lower.CL upper.CL null t.ratio p.value
N / IH 2.351 0.136 371 2.099 2.634 1 14.818 <0.001 ***
contrast ratio SE df lower.CL upper.CL null t.ratio p.value
N / IH 2.365 0.136 371 2.112 2.65 1 14.923 <0.001 ***
contrast ratio SE df lower.CL upper.CL null t.ratio p.value
N / IH 2.365 0.136 371 2.112 2.65 1 14.923 <0.001 ***
contrast ratio SE df lower.CL upper.CL null t.ratio p.value
N / IH 2.269 0.131 371 2.026 2.542 1 14.206 <0.001 ***
contrast ratio SE df lower.CL upper.CL null t.ratio p.value
N / IH 2.269 0.131 371 2.026 2.542 1 14.206 <0.001 ***
contrast ratio SE df lower.CL upper.CL null t.ratio p.value
N / IH 2.241 0.129 371 2.001 2.51 1 13.986 <0.001 ***
contrast ratio SE df lower.CL upper.CL null t.ratio p.value
N / IH 2.241 0.129 371 2.001 2.51 1 13.986 <0.001 ***
contrast ratio SE df lower.CL upper.CL null t.ratio p.value
N / IH 2.131 0.123 371 1.903 2.387 1 13.115 <0.001 ***
contrast ratio SE df lower.CL upper.CL null t.ratio p.value
N / IH 2.131 0.123 371 1.903 2.387 1 13.115 <0.001 ***
contrast ratio SE df lower.CL upper.CL null t.ratio p.value
N / IH 2.066 0.119 371 1.844 2.314 1 12.577 <0.001 ***
contrast ratio SE df lower.CL upper.CL null t.ratio p.value
N / IH 2.066 0.119 371 1.844 2.314 1 12.577 <0.001 ***
contrast ratio SE df lower.CL upper.CL null t.ratio p.value
N / IH 1.966 0.113 371 1.755 2.202 1 11.715 <0.001 ***
contrast ratio SE df lower.CL upper.CL null t.ratio p.value
N / IH 1.966 0.113 371 1.755 2.202 1 11.715 <0.001 ***
contrast ratio SE df lower.CL upper.CL null t.ratio p.value
N / IH 1.304 0.108 371 1.109 1.534 1 3.214 0.001 **
contrast ratio SE df lower.CL upper.CL null t.ratio p.value
N / IH 1.304 0.108 371 1.109 1.534 1 3.214 0.001 **
contrast ratio SE df lower.CL upper.CL null t.ratio p.value
N / IH 1.225 0.111 371 1.026 1.463 1 2.247 0.025 *
contrast ratio SE df lower.CL upper.CL null t.ratio p.value
N / IH 1.225 0.111 371 1.026 1.463 1 2.247 0.025 * 
- Confidence level used: 0.95
- Intervals are back-transformed from the log scale
- Tests are performed on the log scale

❖ Temporal plot: