correct typos

Author: bgctw

.typos.toml

@@ -4,4 +4,6 @@ SOM = "SOM"
 som = "som"
 tha = "tha"
 Tha = "Tha"
+Gam = "Gam"
+
 

inst/walkthrough_todo.jmd

@@ -270,7 +270,7 @@ g1_USO = stomatal_slope(thaf,model="USO",g0=0,robust_nls=true)
 g1_USO
 ```
 
-In this case, we have estimated $g_1$ from the USO (optimal stomatal optimization) model as described in Medlyn et al. 2011. The output is a model object that prints the model formulat that is used to estimate $g_1$, the estimated parameter value(s), as well as the weighted residual sum-of-squares. Further information on this model object can be obtained using the `summary` function.
+In this case, we have estimated $g_1$ from the USO (optimal stomatal optimization) model as described in Medlyn et al. 2011. The output is a model object that prints the model formula that is used to estimate $g_1$, the estimated parameter value(s), as well as the weighted residual sum-of-squares. Further information on this model object can be obtained using the `summary` function.
 In this case we have fixed the model intercept $g_0$ to 0 (this could also be any other value). We can also try to estimate $g_1$ and $g_0$ simultaneously (if we add `fitg0=true` to the function call above), but note that the two parameters are usually correlated, and that the values of $g_0$ are not straightforward to interpret (especially at ecosystem level). The option `robust_nls=true` specifies that $g_1$ is determined by a robust non-linear regression routine (from the `robustbase` package). We recommend to use this option since otherwise the parameter estimates are sensitive to outliers in $G_s$, which often occur even in filtered EC datasets.
 By default, the model takes VPD and atmospheric CO$_2$ concentration as measured at the tower as input. We can also calculate $g_1$ by taking the surface conditions, which are probably more relevant for plant physiological processes than those measured a certain distance above the canopy: