fit_clutter | R Documentation |
Fit the Clutter model for growth and yield using the two stage least squares method (2SLS).
fit_clutter(
df,
age,
dh,
basal_area,
volume,
site,
plot,
.groups = NA,
model = "full",
keep_model = FALSE
)
df |
A data frame. |
age |
Quoted name for the age variable. |
dh |
Quoted name for the dominant height variable. |
basal_area |
Quoted name for the basal area variable. |
volume |
Quoted name for the volume area variable. |
site |
Quoted name for the site variable. |
plot |
Quoted name for the plot variable. |
.groups |
Optional argument. Quoted name(s) of grouping variables used to fit multiple regressions, one for each level of the provided variable(s). Default |
model |
Character variable for the type of the model fitted. If |
keep_model |
If |
A data frame with the regression coefficients.
Sollano Rabelo Braga sollanorb@gmail.com
Clutter, J. L. (1963) Compatible Growth For Loblolly by the Southeastern, Forest Science, 9(3), pp. 354–371. Sullivan, A. D. and Clutter, J. L. (1972) A Simultaneous Growth and Yield for Loblolly Pine, Forest Science, 18(1), pp. 76–86. Campos, J. C. C. and Leite, H. G. (2017) Mensuracao Florestal: Perguntas e Respostas. 5a. Vicosa: UFV.
other sampling functions:
est_clutter
for estimating the Clutter growth and yield model variables, and
classify_site
for classifying data according to site.
library(forestmangr)
data("exfm17")
head(exfm17)
# To fit the Clutter model we just need to define the data, and age, dominant height,
# basal area, volume, site and plot variables:
fit_clutter(exfm17, "age", "DH", "B", "V", "S", "plot")
# To fit the alternate model (without a1) just use model="mod":
fit_clutter(exfm17, "age", "DH", "B", "V", "S", "plot",model="mod")
# To keep the regression model, use keep_model=TRUE:
fit_clutter(exfm17, "age", "DH", "B", "V", "S", "plot", keep_model=TRUE)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.