errorStats {yaImpute} | R Documentation |
Error properties of estimates derived from imputation differ from those of regression-based estimates because the two methods include a different mix of error components. This function computes a partitioning of error statistics as proposed by Stage and Crookston (2007).
errorStats(mahal,...,scale=FALSE,pzero=0.1,plg=0.5,seeMethod="lm")
mahal |
An object of class yai computed with method="mahalanobis" . |
... |
Other objects of class yai for which statistics are desired. All
objects should be for the same data and variables used for the first argument. |
scale |
When TRUE , the errors are scaled by their respective standard deviations. |
pzero |
The lower tail p-value used to pick reference observations that are zero
distance from each other (used to compute rmmsd0 ). |
plg |
The upper tail p-value used to pick reference observations that are
substantially distant from each other (used to compute rmsdlg ). |
seeMethod |
Method used to compute SEE : seeMethod="lm" uses lm
and seeMethod="gam" uses gam . In both cases, the model formula is
a simple linear combination of the X-variables. |
See http://www.fs.fed.us/rm/pubs_other/rmrs_2007_stage_a001.pdf
A list that contains several data frames. The column names of each are a combination of the name of the object used to compute the statistics and the name of the statistic. The rownames correspond the the Y-variables from the first argument. The data frame names are as follows:
common |
statistics used to compute other statistics. |
name of first argument |
error statistics for the first yai object. |
names of ... arguments |
error statistics for each of the remaining yai objects,
if any. |
see |
standard error of estimate for individual regressions fit for corresponding Y-variables. |
rmmsd0 |
root mean square difference for imputations based on method="mahalanobis"
(always based on the first argument to the function). |
mlf |
square root of the model lack of fit: sqrt(see^2 - (rmmsd0^2/2)). |
rmsd |
root mean square error. |
rmsdlg |
root mean square error of the observations with larger distances. |
sei |
standard error of imputation sqrt(rmsd^2 - (rmmsd0^2/2)). |
dstc |
distance component: sqrt(rmsd^2 - rmmsd0^2). |
Note that unlike Stage and Crookston (2007), all statistics reported here are in the natural units, not squared units.
Nicholas L. Crookston ncrookston@fs.fed.us
Albert R. Stage astage@moscow.com
Stage, A.R.; Crookston, N.L. (2007). Partitioning error components for accuracy-assessment of near neighbor methods of imputation. For. Sci. 53(1):62-72. http://forest.moscowfsl.wsu.edu/gems/StagePartitioningFS.pdf
require (yaImpute) data(TallyLake) diag(cov(TallyLake[,1:8])) # see col A in Table 3 in Stage and Crookston mal=yai(x=TallyLake[,9:29],y=TallyLake[,1:8],ann=FALSE, noTrgs=TRUE,method="mahalanobis") msn=yai(x=TallyLake[,9:29],y=TallyLake[,1:8],ann=FALSE, noTrgs=TRUE,method="msn") # variable "see" for "mal" matches col B (when squared and scaled) # other columns don't match exactly as Stage and Crookston used different # software to compute values errorStats(mal,msn)