knn_tools | R Documentation |
Series of helpers which in combination with the package yaImpute can be used to perform tree-list imputation.
yai_id( xNms = NULL, yNms = NULL, idNm = NULL, data = NULL, omity = NULL, dup_ids_remove = T, ... ) newtargets_id(yai_mod, idNm, data, k = NULL, ann = NULL) impute_id(newtargs_id, ...) yai_weights( yaimod, dtype = c("invdist", "invdist2", "eq"), zero_dist = c("small", "min", "NA") ) tl_impute( wts, idNm, cols_knn_id = "col_id", cols_knn_wt = "col_wt", trees, sort_targets = F, debug = F ) tl_impute_2( wts, idNm, cols_knn_id = "col_id", cols_knn_wt = "col_wt", trees, debug = F ) yai_cv( omit = 5, idNm, xNm, yNm, pdNm = NA, data, iter_max = 500, k = 5, debug = FALSE, method = "msn", min_rows = 15, method_impute = c("closest", "mean", "median", "dstWeighted"), ... )
idNm |
name of id field |
data |
input data |
omity |
optional: records to omit |
... |
other arguments to ?? |
yai_mod |
model returned by yai_id |
k |
?number of neighbors? |
ann |
T/Fsee yai documentation
|
newtargs_id |
? |
yaimod |
model returned by yai_id |
dtype |
distance type |
wts |
weights from ? |
cols_knn_id |
col names of knn ids |
cols_knn_wt |
col names of knn wts |
debug |
T/F |
omit |
observation to omit at time i |
yNm |
?names of response fields |
iter_max |
number of iterations max |
method |
? distance approach? |
id_x |
vector of auxiliary variable column names led by id variable |
id_y |
vector of response variable column names led by id variable |
id |
id column |
zero_dist() |
how to deal with zero distances (e.g. when everything is zero)
|
env |
environment with weights in it |
tr_pl |
tree records |
x |
? |
max_comb |
?? |
yaImpute has some funkiness (e.g using row names as an ID field... bad...) and cannot support tree list imputation. The functions here facilitate that process
yai_id: Update of yai that replaces row names with record ids newtargets_id: wrapper for newtargets that handles yai_id object impute_id: impute response values to target locations - wrapper for 'impute()' in yaImpute handles id field more explicitly yai_weights: Get weights associated with imputation objects tl_impute: function to impute tree list based on knn model fitted between aerial attributes yai_cv: peform crossvalidation on knn model
This program is free software but it is provided WITHOUT WARRANTY and with ABSOLUTELY NO GUARANTEE of fitness or functionality for any purpose; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version.
Revision History
1.0 | 2014 July 04 Import from Jacob's R Library |
1.0 | 2015 Jan 06 Add roxygen header |
yai_id:
newtargets_id:
impute_id:
yai_weights:
tl_impute:
yai_cv:
Jacob Strunk <Jacob.strunk@usgda.gov>
yaImpute
fit_pdf
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