Case study 6: Field studies to evaluate the benefits on milk production of providing smallholder farmers in Kenya access to credit to allow shifting of concentrate feeding to early lactation

Statistical modelling / Fitting Wood's curve
If we now look at the GenStat Input file we can see how the instructions are represented

MODEL logmilk_1[1]
TERMS [FACT=9] logday_1[1],day_1[1]
FIT [PRINT=model, summary, estimates; CONSTANT=estimate;\
FPROB=yes; TPROB=yes; FACT=9] logday_1[1],day_1[1]
RKEEP ; ESTIMATES=Est[1]

We copy these statements into an Input window and modify them by incorporating a loop 'For [NTIMES=77]' and replacing the [1] in each of the variate names by [I]. As we shall need to calculate average values for the three estimates we can incorporate statements to do this with the final values stored in the variate 'MeanEst'.

VARIATE [NVALUES=3;VALUES=0,0,0] SumEst,MeanEst
SCALAR [VALUE=0] I
FOR [NTIMES=77]
CALCULATE I=I+1
MODEL logmilk_1[I]
TERMS [FACT=9] logday_1[I],day_1[I]
FIT [PRINT=model, summary, estimates; CONSTANT=estimate;\
FPROB=yes; TPROB=yes; FACT=9] logday_1[I],day_1[I]
RKEEP ; ESTIMATES=Est[I]
CALCULATE SumEst = SumEst + Est[I]
ENDFOR
CALCULATE MeanEst = SumEst/77
PRINT MeanEst

We then use Run Submit Selection to complete the calculations.