Nothing
build_datalist()
now works correctly with data.table datasets. (#34, #35, h/t Dan Schrage)build_datalist()
dropped factor levels when replacing a factor variable. (#39, h/t Tomasz Żółtak)find_data()
now respects subset
and na.actions
arguments for svyglm()
models. (#37, h/t Tomasz Żółtak)prediction_glm
with the data
argument (Issue #32).find_data()
and prediction.lm()
to check for correct behavior in the presence of missing data (na.action
) and subset
arguments. (#28)margex
, borrowed from Stata's identically named data.summary(prediction(...))
now reports variances of average predictions, along with test statistics, p-values, and confidence intervals, where supported. (#17)prediction_summary()
which simply calls summary(prediction(...))
.prediction.bigglm()
method (from biglm) due to failing tests. (#25)stats::poly()
rather than just poly()
in model formulae. (#22)prediction.glmnet()
method for "glmnet" objects from glmnet. (#1)prediction.merMod()
gains an re.form
argument to pass forward to predict.merMod()
.prediction.glmML()
method for "glimML" objects from aod. (#1)prediction.glmQL()
method for "glimQL" objects from aod. (#1)prediction.truncreg()
method for "truncreg" objects from truncreg. (#1)prediction.bruto()
method for "bruto" objects from mda. (#1)prediction.fda()
method for "fda" objects from mda. (#1)prediction.mars()
method for "mars" objects from mda. (#1)prediction.mda()
method for "mda" objects from mda. (#1)prediction.polyreg()
method for "polyreg" objects from mda. (#1)prediction.speedglm()
and prediction.speedlm()
methods for "speedglm" and "speedlm" objects from speedglm. (#1)prediction.bigLm()
method for "bigLm" objects from bigFastlm. (#1)prediction.biglm()
and prediction.bigglm()
methods for "biglm" and "bigglm" objects from biglm, including those based by "ffdf"
from ff. (#1)build_datalist()
. The function now returns an an at_specification
attribute, which is a data frame representation of the at
argument.prediction.gam()
is now prediction.Gam()
for "Gam" objects from gam. (#1)prediction.train()
method for "train" objects from caret. (#1)at
argument in build_datalist()
now accepts a data frame of combinations for limiting the set of levels.prediction()
methods gain a (experimental) calculate_se
argument, which regulates whether to calculate standard errors for predictions. Setting to FALSE
can improve performance if they are not needed.build_datalist()
gains an as.data.frame
argument, which - if TRUE
- returns a stacked data frame rather than a list. This argument is now used internally in most prediction()
functions in an effort to improve performance. (#18)summary.prediction()
method to interact with the average predicted values that are printed when at != NULL
.prediction.knnreg()
method for "knnreg" objects from caret. (#1)prediction.gausspr()
method for "gausspr" objects from kernlab. (#1)prediction.ksvm()
method for "ksvm" objects from kernlab. (#1)prediction.kqr()
method for "kqr" objects from kernlab. (#1)prediction.earth()
method for "earth" objects from earth. (#1)prediction.rpart()
method for "rpart" objects from rpart. (#1)mean_or_mode.data.frame()
and median_or_mode.data.frame()
methods.prediction.zeroinfl()
method for "zeroinfl" objects from pscl. (#1)prediction.hurdle()
method for "hurdle" objects from pscl. (#1)prediction.lme()
method for "lme" and "nlme" objects from nlme. (#1)prediction.merMod()
.prediction.plm()
method for "plm" objects from plm. (#1)CONTRIBUTING.md
to reflect expected test-driven development.prediction.mnp()
method for "mnp" objects from MNP. (#1)prediction.mnlogit()
method for "mnlogit" objects from mnlogit. (#1)prediction.gee()
method for "gee" objects from gee. (#1)prediction.lqs()
method for "lqs" objects from MASS. (#1)prediction.mca()
method for "mca" objects from MASS. (#1)prediction.glm()
method. (#1)category
argument to prediction()
methods for models of multilevel outcomes (e.g., ordered probit, etc.) to be dictate which level is expressed as the "fitted"
column. (#14)at
argument to prediction()
methods. (#13)mean_or_mode()
and median_or_mode()
S3 generics.mean_or_mode()
and median_or_mode()
where incorrect factor levels were being returned.prediction.princomp()
method for "princomp" objects from stats. (#1)prediction.ppr()
method for "ppr" objects from stats. (#1)prediction.naiveBayes()
method for "naiveBayes" objects from e1071. (#1)prediction.rlm()
method for "rlm" objects from MASS. (#1)prediction.qda()
method for "qda" objects from MASS. (#1)prediction.lda()
method for "lda" objects from MASS. (#1)find_data()
now respects the subset
argument in an original model call. (#15)find_data()
now respects the na.action
argument in an original model call. (#15)find_data()
now gracefully fails when a model is specified without a formula. (#16)prediction()
methods no longer add a "fit" or "se.fit" class to any columns. Fitted values are identifiable by the column name only.build_datalist()
now returns at
value combinations as a list.prediction.nnet()
method for "nnet" and "multinom" objects from nnet. (#1)prediction()
methods now return the value of data
as part of the response data frame. (#8, h/t Ben Whalley)find_data()
methods for "crch"
and "hxlr"
. (#5)prediction.glmx()
and prediction.hetglm()
methods for "glmx" and "hetglm" objects from glmx. (#1)prediction.betareg()
method for "betareg" objects from betareg. (#1)prediction.rq()
method for "rq" objects from quantreg. (#1)prediction.gam()
method for "gam" objects from gam. (#1)prediction()
and find_data()
methods for "crch"
"hxlr"
objects from crch. (#4, h/t Carl Ganz)prediction()
and find_data()
methods for "merMod"
objects from lme4. (#1)seq_range()
function from margins to prediction.build_datalist()
function from margins to prediction. This will simplify the ability to calculate arbitrary predictions.prediction.svm()
method for objects of class "svm"
from e1071. (#1)prediction.polr()
when attempting to pass a type
argument, which is always ignored. A warning is now issued when attempting to override this.mean_or_mode()
and median_or_mode()
functions, which provide a simple way to aggregate a variable of factor or numeric type. (#3)prediction()
methods for various time-series model classes: "ar", "arima0", and "Arima".find_data()
is now a generic, methods for "lm", "glm", and "svyglm" classes. (#2, h/t Carl Ganz)Any scripts or data that you put into this service are public.
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