The phrase "also when the model" is correct and usable in written English.
It can be used in contexts where you are discussing conditions or scenarios related to a model, such as in research, data analysis, or machine learning.
Example: "The results were significant, also when the model was adjusted for external variables."
Alternatives: "additionally when the model" or "likewise when the model".
The onsets of aeration were set for 7 00 a.m. 30 July and 0 00 7 August, respectively, and these times were also when the model started.
Also, when the model was assessed with tap water nitrate levels as a continuous variable, there was no statistically significant association found with methemoglobin levels and nitrate levels at 36 weeks gestation among women who drank tap water at home (β = 0.787, p = 0.086).
Also, when the model of evolution assumed is oversimplified, phylogenetic methods may lose accuracy and consistency leading to incorrect trees more often or converging to an incorrect tree with increased amounts of data (Felsenstein 1978; Huelsenbeck and Hillis 1993; Penny et al. 1994; Bruno and Halpern 1999).
However, in our analyses also, when the models is adjusted for other variables the effect of institutional delivery has disappeared which supports the findings of EDHS.
Also, when the logistic regression model was adjusted for occurrence of severe or mild hypoglycemia, the OR for mortality remained significantly higher for those patients with a mean glucose in the lowest quintile.
This happens regardless of the training set used to train the model, also when the training set is the same as validation set.
The importance of the nuisance parameters in the retrospective models was also investigated by examining the estimates obtained when they are removed from the model and also when the offspring genotype is used as a substitute.
Good quantitative agreement between DEM and two-fluid predictions of minimum bubbling velocity was also observed when the model was used to predict minimum bubbling velocity, in contrast to the predictions from a non-cohesive, Wen Yu model.
The diversity in these relations across industries is also explored; when the model is applied to each Revised Pavitt class, different mechanisms of technological change and effects on jobs emerge.
In particular, we first identify a simple set of necessary and sufficient conditions for both input-state stability and Lyapunov stability of the system, and then prove that the same conditions also hold when the model parameters are subject to unknown but bounded perturbations.
Consistent with previous work by Greene [ 31, 39] for the case of unaccounted for heterogeneity where the effects show up as inefficiency, the resulting estimates from the spatial stochastic frontier model revealed that the same problem also arises when the model is not corrected for spill over effects.
