Handle missing values

DESCRIPTION

@@ -2,7 +2,7 @@ Package: stdBeta
 Type: Package
 Title: Standardized Coefficients for Linear Models
 Description: Updates a model fit by substituting standarized variables.
-Version: 0.2.2
+Version: 0.2.3
 Authors@R: person("Doug", "Hemken", email="[email protected]", 
         role=c("aut", "cre"), comment="SSCC, Univ. of Wisconsin-Madison")
 Maintainer: Doug Hemken <[email protected]>

R/stdBeta.r

@@ -15,7 +15,8 @@ stdBeta <- function(lmfit) {
   # print(sapply(stddata, data.class))
   stopifnot(all(sapply(stddata, data.class) %in% c("numeric", "factor", "AsIs")))
   numvars <- sapply(stddata[, modelvars], is.numeric)
-  stddata[, modelvars][, numvars] <- sapply(stddata[, modelvars][, numvars], scale)
+  stddata <- stats::na.omit(stddata[, modelvars])
+  stddata[, numvars] <- sapply(stddata[, numvars], scale)
   # head(df)
   stats::update(lmfit, data=stddata)
 }

inst/doc/polnomials.r

@@ -34,8 +34,9 @@ mtcars[, modelvars] <- sapply(mtcars[, modelvars], scale)
 summary(update(fit4, data=mtcars))
 rm(mtcars)
 
-stdBeta::stdBeta(fit4) # blocked
 QuantPsyc::lm.beta(fit4) # Wrong, with WARNING
 lm.beta::lm.beta(fit4) # wrong, no warning
 lsr::standardCoefs(fit4) # again wrong, no warning
 arm::standardize(fit4, standardize.y=TRUE) # wrong, but no error
+stdBeta::stdBeta(fit4) # good
+

vignettes/Why_stdBeta.rmd

@@ -1,7 +1,7 @@
 ---
 title: "stdBeta"
 author: "Doug Hemken"
-date: "July 2017"
+date: "August 2017"
 output: rmarkdown::html_vignette
 vignette: >
   %\VignetteIndexEntry{Why stdBeta?}
@@ -11,9 +11,14 @@ vignette: >
 
 ## Do we really need another function that standardizes coefficients?
 There are several R functions out there that will return \'standardized
-coefficients\' of one sort or another.  My chief complaint with them
-all is that they are difficult to interpret when the model includes
-interaction terms (or polynomial terms, for that matter).
+coefficients\' of one sort or another.  Three of these work fine for
+models with no interaction or polynomial terms, but fail to produce
+the expected results where these higher order terms are present.
+Two of these produce valid models, in the sense that they produce
+the correct predicted values and residuals.  However, they are
+difficult to interpret when the model includes
+higher order terms, because similar-looking variables are not
+on the same scales.
 
 Other package::functions that return standardized coefficients: