NEWS.md
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(...)).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)