Documentation

DESCRIPTION

@@ -24,11 +24,11 @@ Suggests:
     formatR,
     patchwork,
     spelling
-Description: Algorithm to select variables and functional forms through 
+Description: Algorithm to select variables and functional forms simultaneously through 
   fractional polynomials in regression models (Generalized linear models 
-  and Cox). In addition, it has the ability to model a sigmoid relationship
-  between x and an outcome variable y using the approximate cumulative 
-  distribution transformation proposed by "Royston" in 2014.
+  and Cox models). In addition, it can model a 'sigmoid' relationship
+  between variable x and an outcome variable y using the approximate cumulative 
+  distribution transformation- something that a standard fractional polynomial function cannot do.
 License: GPL-3 + file LICENSE
 URL: https://github.com/EdwinKipruto/mfp2
 BugReports: https://github.com/EdwinKipruto/mfp2/issues

NEWS.md

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 # mfp2 1.0.0
 
-# mfp2 0.2
-
 * Initial CRAN submission.

R/mfp2.R

@@ -338,8 +338,6 @@
 #' fit1 = mfp2(x, y, verbose = FALSE)
 #' fit1$fp_terms
 #' fracplot(fit1) # generate plots
-#' coef(fit1)
-#' print(fit1)
 #' summary(fit1)
 #' # formula interface
 #' fit1b = mfp2(lpsa ~ fp(age) + fp(svi, df = 1) + fp(pgg45) + fp(cavol) + fp(weight) +
@@ -351,7 +349,6 @@
 #' yy <- as.vector(pima$y)
 #' fit2 <- mfp2(xx, yy, family = "binomial", verbose = FALSE)
 #' fit2$fp_terms
-#' fracplot(fit2)
 #' 
 #' # Cox regression model
 #' data("gbsg")
@@ -362,7 +359,6 @@
 #' # fit mfp and keep hormon in the model
 #' fit3 <- mfp2(xd, yd, family = "cox", keep = "hormon", verbose = FALSE)
 #' fit3$fp_terms
-#' fracplot(fit3)
 #' 
 #' @return 
 #' `mfp2()` returns an object of class inheriting from `glm` or `copxh`, 

README.md

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 extensions. It allows the selection of variables and functional forms when
 modelling the relationship of a data matrix `x` and some outcome `y`. Currently
 supports generalized linear models and Cox proportional hazards models.
+It also has the ability to model a sigmoid relationship between x and an outcome variable y
+using approximate cumulative distribution transformation- something that a standard fractional
+polynomial function cannot achieve (Royston 2014). 
 
 ## Compatibility with existing software packages
 
-`mfp2` closely emulates the functionality of the [`mfp` package in
-stata].
+`mfp2` closely emulates the functionality of the `mfp` package in
+Stata.
 
-It augments the functionality of the existing [`mfp` R
-package]by:
+It augments the functionality of the existing `mfp` R
+package by:
 
 -   a matrix and a formula interface for input
 -   sigmoid transformations via the approximate cumulative distribution (ACD)

cran-comments.md

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 ## R CMD check results
 
-0 errors | 0 warnings | 4 note
+0 errors | 0 warnings | 1 note
 
 * This is a new release.