Respiratory motion can be a source of errors and uncertainties when delivering radiotherapy treatment. Precise knowledge of the respiratory induced anatomical motion may lead to more accurate and effective treatments. 4DCT can be used to account for respiratory motion during planning, but this may not give a good representation of the motion at treatment time due to inter-fraction variations in the motion and anatomy. 4D-CBCT can be acquired just prior to treatment to provide a better estimate of the motion at treatment time. However, 4D-CBCT can suffer from poor image quality due to the assumption of regular breathing and the need to bin the projection data. Another solution is to use surrogate-driven respiratory motion models to estimate the motion. Typically these models are built in two stages: 1) use image registration to determine the motion of the internal anatomy; 2) fit a correspondence model that relates the motion to the surrogate signal(s). In this work we have utilised a recently developed generalised framework that unifies image registration and correspondence model fitting into a single optimisation. This enables the model to be fitted directly to unsorted/unreconstructed data. This work presents the first application of this framework to CBCT projection data. Since evaluation of the model on real data is difficult because the ground truth motion is unknown, we have used an anthropomorphic software phantom to simulate CBCT projection data and evaluate the generated motion model. Results from the generated model were assessed both quantitatively and qualitatively. We compared the results of the motion model to the ground truth motion using sum squared differences, Dice coefficient and the centre of mass of the tumour in the volumes. All the results obtained indicated that the model generated with the CBCT projection data was able to estimate ground truth motion well.