individual_grow.table: Create a table of individual trees and their growth over two...
In forestgeo/ctfs: Tools for the Analysis of Forest Dynamics

Create a table of individual trees and their growth over two censuses, with many species included.

The flag ctr is used to center the time variable, which is the number of years since 1960 of the interval midpoint. There is a logarithmic transformation, for which all growth <= 0 is converted to mingrow. There is also a power transformation, where each growth rate is raised to the power given by powertransformation.

In the latter, negative growths are transformed to negative, so do not need to be corrected.

individual_grow.table(cnsdata, powertransformation = 0.45,
  rnd = c(FALSE, TRUE, rep(FALSE, 4)), mingrow = 0.1, mindbh = 10,
  maxdbh = 10000, maxerrorSD = 4, maxerrorGrow = 75, center = 1992,
  debug = FALSE)

`cnsdata`	A list of census data sets, for example: `list(bciex::bci12t1mini, bciex::bci12t2mini, bciex::bci12t3mini)`.
`rnd`	used to rounddown dbhs for certain intervals. This argument indicates that dbhs < 50 mm are rounded down to 5-mm, necessary because saplings at BCI in 1982 and 1985 were measuring in 5-mm increments. The growth functions in the CTFS R Package handle this.
`mindbh`	The minimum diameter above which the counts are done. Trees smaller than `mindbh` are excluded. If `NULL`, all living trees are included.
`debug`	Logical. If TRUE, call browser to debug.

A data frame where each row gives data from one individual, including:

tag: the tree’s tag number;
gx, gy: coordinates;
species: species;
dbh1, dbh2: tree diameter at census 1 and 2;
census, censusfact: census interval; 1 means growth between census 1 and 2, and 2 means growth from census 2 to 3, etc. census and censusfact store the same information but they differ in that their values are integers and factors;
time: mid-point of the interval over which growth was measured, centered on 1992. This is the format needed by lmer. The reason for centering time is that it is much better in linear regressions to have the x-axis near zero;
LnSize: log(dbh1);
incgr: untransformed growth increment; growth increment (difference in dbh) per year;
LnGrowth: log of the growth rate, with negatives and zeroes converted to 0.1 (from the argument mingrowth in the function);
CRGrowth: cube root of growth rate; power transformation of the growth rate, with negatives maintained negative (exponent = 0.45). Althouth you may use incgr or LnGrowth, CRGrowth allows cleaner analyses, that need no assumption about negative growths.