fix todo, and roxygenise

DESCRIPTION

@@ -2,7 +2,8 @@ Package: faoutlier
 Version: 0.5.1
 Date: 2015-04-26
 Type: Package
-Title: Influential Case Detection Methods for Factor Analysis and Structural Equation Models
+Title: Influential Case Detection Methods for Factor Analysis and Structural
+    Equation Models
 Authors@R: c(
     person("Phil", "Chalmers",
     email = "[email protected]", role = c("aut", "cre")))
@@ -27,3 +28,4 @@ LazyData: yes
 Repository: github
 License: GPL (>= 2)
 URL: https://github.com/philchalmers/faoutlier
+RoxygenNote: 5.0.1

NAMESPACE

@@ -1,4 +1,4 @@
-# Generated by roxygen2 (4.1.1): do not edit by hand
+# Generated by roxygen2: do not edit by hand
 
 S3method(plot,GOF)
 S3method(plot,LD)

R/LD.R

@@ -87,8 +87,8 @@ LD <- function(data, model, ...)
         Sigma <- res$loadings %*% t(res$loadings) + diag(res$uniquenesses)
         sum(dmvnorm(data, sigma=Sigma, log=TRUE))
     }
-    f_sem <- function(ind, data, model, objective, ...){
-        logLik(sem::sem(model, data=data[-ind, ], objective=objective, ...))
+    f_sem <- function(ind, data, model, ...){
+        logLik(sem::sem(model, data=data[-ind, ], ...))
     }
     f_lavaan <- function(ind, data, model, ...){
         lavaan::logLik(lavaan::sem(model, data=data[-ind, ], ...))
@@ -107,11 +107,9 @@ LD <- function(data, model, ...)
 		MLmod <- f_numeric(nrow(data) + 1, data=data, model=model, ...)
 		LR <- myApply(index, MARGIN=1L, FUN=f_numeric, data=data, model=model, ...)
 	} else if(class(model) == "semmod"){
-	    stop('semmod objects under construction. Use GOF() for now instead') #TODO
-        objective <- if(any(is.na(data))) sem::objectiveFIML else sem::objectiveML
-	    MLmod <- sem::sem(model, data=data, objective=objective, ...)
-        MLmod <- logLik(MLmod) ## TODO, this doesn't resolve correctly
-	    LR <- myApply(index, MARGIN=1L, FUN=f_sem, data=data, model=model, objective=objective, ...)
+	    MLmod <- sem::sem(model, data=data, ...)
+        MLmod <- logLik(MLmod)
+	    LR <- myApply(index, MARGIN=1L, FUN=f_sem, data=data, model=model, ...)
 	} else if(class(model) == "character"){
         MLmod <- lavaan::sem(model, data=data, ...)
         MLmod <- lavaan::logLik(MLmod)

R/faoutlier.R

@@ -1,10 +1,10 @@
 #' Influential case detection methods for factor analysis and SEM
-#' 
+#'
 #' Implements robust Mahalanobis methods, generalized Cook's distances,
-#' likelihood ratio tests, model implied residuals, and various 
-#' graphical methods to help detect and summarize influential 
-#' cases that can affect exploratory and confirmatory factor analyses. 
-#' 
+#' likelihood ratio tests, model implied residuals, and various
+#' graphical methods to help detect and summarize influential
+#' cases that can affect exploratory and confirmatory factor analyses.
+#'
 #' @name faoutlier
 #' @docType package
 #' @title Influential case detection methods for FA and SEM
@@ -15,25 +15,25 @@
 NULL
 
 #' Description of holzinger data
-#' 
+#'
 #' A sample of 100 simulated cases from the infamous Holzinger dataset
-#' using 9 variables. 
-#' 
-#' 
+#' using 9 variables.
+#'
+#'
 #' @name holzinger
 #' @docType data
 #' @author Phil Chalmers \email{rphilip.chalmers@@gmail.com}
 #' @keywords data
 NULL
 
 #' Description of holzinger data with 1 outlier
-#' 
+#'
 #' A sample of 100 simulated cases from the infamous Holzinger dataset
-#' using 9 variables, but with 1 outlier added to the dataset. The first row was replaced 
-#' by adding 2 to five of the observed variables (odd-numbered items) and subtracting 2 from 
-#' the other four observed variables (even-numbered items). 
-#' 
-#' 
+#' using 9 variables, but with 1 outlier added to the dataset. The first row was replaced
+#' by adding 2 to five of the observed variables (odd-numbered items) and subtracting 2 from
+#' the other four observed variables (even-numbered items).
+#'
+#'
 #' @name holzinger.outlier
 #' @docType data
 #' @author Phil Chalmers \email{rphilip.chalmers@@gmail.com}

tests/testthat/test-LD.R

@@ -14,15 +14,15 @@ test_that('LD run', {
     expect_is(plot(LDresult.outlier), 'trellis')
 
     #-------------------------------------------------------------------
-#     suppressMessages(model <- sem::specifyModel(file='sem-model/sem-model.txt', quiet=TRUE))
-#     LDresult <- LD(holzinger, model)
-#     LDresult.outlier <- LD(holzinger.outlier, model)
-#     expect_equal(as.numeric(LDresult[1:3]), c(-4.945440, -1.943843, -3.330193),
-#                  tolerance=1e-5)
-#     expect_is(LDresult, 'LD')
-#     expect_is(LDresult.outlier, 'LD')
-#     expect_is(plot(LDresult), 'trellis')
-#     expect_is(plot(LDresult.outlier), 'trellis')
+    suppressMessages(model <- sem::specifyModel(file='sem-model/sem-model.txt', quiet=TRUE))
+    LDresult <- LD(holzinger, model)
+    LDresult.outlier <- LD(holzinger.outlier, model)
+    expect_equal(as.numeric(LDresult[1:3]), c(-4.945440, -1.943843, -3.330193),
+                 tolerance=1e-5)
+    expect_is(LDresult, 'LD')
+    expect_is(LDresult.outlier, 'LD')
+    expect_is(plot(LDresult), 'trellis')
+    expect_is(plot(LDresult.outlier), 'trellis')
 
     #-------------------------------------------------------------------
     #Confirmatory with lavaan