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I'm currently using lme4 to fit the following model:

Model = lmer(CA ~ P + T + S + (1 | Study), Data)

P and T refer to pressure and temperature, and there is an a priori reason to expect a different relationship at low pressure and temperature compared to high pressure and temperature. So I've partitioned my data into two, one for subcritical CO2 conditions (P < 7.37 and T < 31.1) and one for supercritical CO2 conditions (P >= 7.37 and T >= 31.1). Which means having two models...

ModelSub = lmer(CA ~ P + T + S + (1 | Study), DataSub)
ModelSuper = lmer(CA ~ P + T + S + (1 | Study), DataSuper)

I'm wondering, though, if there is a way to have a single model but include the 'phase' category somehow (Sub versus Supercritical) that doesn't introduce problems, and even if a single model would make it more difficult to interpret the results (at the model the results are easy to interpret because the estimates for T and S are very close across both models)?

Neither of these yielded what I expected...

ModelCombined = lmer(CA ~ P*Phase + T + S + (1|Study),Data)
ModelCombined = lmer(CA ~ P:Phase + T + S + (1|Study),Data)

In the second version (P:Phase) the T and S estimates were okay but the P:Phase estimates were the same for both categories whereas there is a marked difference when separate models are made...

I'm currently using lme4 to fit the following model:

Model = lmer(CA ~ P + T + S + (1 | Study), Data)

P and T refer to pressure and temperature, and there is an a priori reason to expect a different relationship at low pressure and temperature compared to high pressure and temperature. So I've partitioned my data into two, one for supercritical CO2 conditions (P > 7.37 and T > 31.1) and one for everything else. Which means having two models...

ModelSub = lmer(CA ~ P + T + S + (1 | Study), DataSub)
ModelSuper = lmer(CA ~ P + T + S + (1 | Study), DataSuper)

I'm wondering, though, if there is a way to have a single model but include the 'phase' category somehow (Sub versus Supercritical) that doesn't introduce problems, and even if a single model would make it more difficult to interpret the results (at the model the results are easy to interpret because the estimates for T and S are very close across both models)?

Neither of these yielded what I expected...

ModelCombined = lmer(CA ~ P*Phase + T + S + (1|Study),Data)
ModelCombined = lmer(CA ~ P:Phase + T + S + (1|Study),Data)

In the second version (P:Phase) the T and S estimates were okay but the P:Phase estimates were the same for both categories whereas there is a marked difference when separate models are made...