Merge pull request #8 from bgctw/issue07

Issue07

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: lognorm
 Title: Functions for the Lognormal Distribution
-Version: 0.1.2
+Version: 0.1.3
 Author: Thomas Wutzler
 Maintainer: Thomas Wutzler <[email protected]>
 Description: Provides Moments and other statistics of the lognormal distribution. 

NEWS.md

@@ -1,3 +1,8 @@
+# lognorm 0.1.3
+
+estimateSumLognormal by default now returns NA if there are NA values in terms.
+New argument na.rm = TRUE allows for previous behaviour of neglecting those terms.
+
 # lognorm 0.1.2
 
 - computation of effective number of observations

R/autocorr.R

@@ -3,8 +3,9 @@
 computeEffectiveNumObs <- function(
   ### compute the effective number of observations taking into account autocorrelation
   res  ##<< numeric of autocorrelated numbers, usually observation - model residuals
-  , effAcf = computeEffectiveAutoCorr(res) ##<< may provide precomputed for efficiency
-  ## the first entry is fixed at 1 for zero distance
+  , effAcf = computeEffectiveAutoCorr(res) ##<< autocorrelation coefficients.
+  ## The first entry is fixed at 1 for zero distance.
+  ## May provide precomputed for efficiency or computed from a larger sample.
   , na.rm = FALSE  ##<< a logical value indicating whether NA values should be 
   ## stripped before the computation proceeds. 
 ){

R/lognormalSum.R

@@ -10,13 +10,14 @@ estimateSumLognormalSample <- function(
   ## of resLog is too small) set to 1 to assume uncorrelated sample
   , isGapFilled = logical(0) ##<< logical vector whether entry is gap-filled 
   ## rather than an original measurement, see details
+  , na.rm = TRUE  ##<< neglect terms with NA vlaues in mu or sigma
 ){
   # only one term, return the parameters
   if (length(mu) == 1 ) return(
     c(mu = as.vector(mu), sigma = as.vector(sigma), nEff = 1)
   )
   if (length(sigma) == 1) sigma <- rep(sigma, length(mu))
-  nEff <- computeEffectiveNumObs(resLog, effAcf = effAcf)
+  nEff <- computeEffectiveNumObs(resLog, effAcf = effAcf, na.rm = na.rm)
   ##details<<
   ## If there are no gap-filled values, i.e. \code{all(!isGapFilled)} or
   ## \code{!length(isGapFilled)} (the default), distribution parameters
@@ -39,19 +40,20 @@ estimateSumLognormalSample <- function(
       ans1 <- estimateSumLognormalSample(
         muMeas, sigmaMeas, resLogMeas
         ,effAcf = effAcf, isGapFilled = logical(0)
+        ,na.rm = na.rm
       )
       ans1["sigma"]
     }
     p <- estimateSumLognormal(
       # sigma will not be used only checked for is.finite()
-      mu, sigma = sigma, sigmaSum = sigmaSum, effAcf = effAcf)
+      mu, sigma = sigma, sigmaSum = sigmaSum, effAcf = effAcf, na.rm = na.rm)
     return(c(p , nEff = nEff))
   }
   ##value<< numeric vector with components "mu", "sigma", and "nEff"
   ## the parameters of the lognormal distribution at log scale
   ## (Result of \code{link{estimateSumLognormal}})
   ## and the number of effective observations.
-  p <- estimateSumLognormal( mu, sigma, effAcf = effAcf)
+  p <- estimateSumLognormal( mu, sigma, effAcf = effAcf, na.rm = na.rm)
   return(c(p , nEff = nEff))
 }
 
@@ -65,7 +67,7 @@ estimateSumLognormal <- function(
   ## of correlations between the random variables
   , sigmaSum = numeric(0) ##<< numeric scalar: possibility to specify
   ## of a precomputed scale parameter
-  , corrLength = if(inherits(corr, "ddiMatrix")) 0 else nTerm  ##<< integer 
+  , corrLength = if (inherits(corr, "ddiMatrix")) 0 else nTerm  ##<< integer 
   ## scalar: set correlation length to smaller values
   ## to speed up computation by neglecting correlations among terms
   ## further apart.
@@ -75,6 +77,9 @@ estimateSumLognormal <- function(
   ## Set this to TRUE to issue an error instead.
   , effAcf                 ##<< numeric vector of effective autocorrelation
   ## This overides arguments \code{corr} and \code{corrLength}
+  , na.rm = isStopOnNoTerm ##<< if there are terms with NA values in mu or sigma
+  ## by default also the sum coefficients are NA. Set to TRUE to 
+  ## neglect such terms in the sum.
 ){
   ##references<< 
   ## Lo C (2013) WKB approximation for the sum of two correlated lognormal 
@@ -84,6 +89,8 @@ estimateSumLognormal <- function(
   lengthMu <- length(mu)
   if (length(sigma) == 1) sigma <- rep(sigma, lengthMu)
   iFinite <- which( is.finite(mu) & is.finite(sigma))
+  if (!isTRUE(na.rm) && length(iFinite) != lengthMu) 
+    return(c(mu = NA_real_, sigma = NA_real_))
   muFin <- mu[iFinite]
   sigmaFin <- sigma[iFinite]
   nTerm = length(muFin)

man/computeEffectiveNumObs.Rd

@@ -6,8 +6,9 @@
     na.rm = FALSE)}
 \arguments{
   \item{res}{numeric of autocorrelated numbers, usually observation - model residuals}
-  \item{effAcf}{may provide precomputed for efficiency
-the first entry is fixed at 1 for zero distance}
+  \item{effAcf}{autocorrelation coefficients.
+The first entry is fixed at 1 for zero distance.
+May provide precomputed for efficiency or computed from a larger sample.}
   \item{na.rm}{a logical value indicating whether NA values should be
 stripped before the computation proceeds. }
 }

man/estimateSumLognormal.Rd

@@ -5,7 +5,7 @@
 \usage{estimateSumLognormal(mu, sigma, corr = Diagonal(length(mu)), 
     sigmaSum = numeric(0), corrLength = if (inherits(corr, 
         "ddiMatrix")) 0 else nTerm, isStopOnNoTerm = FALSE, 
-    effAcf)}
+    effAcf, na.rm = isStopOnNoTerm)}
 \arguments{
   \item{mu}{numeric vector of center parameters of terms at log scale}
   \item{sigma}{numeric vector of variance parameter of terms at log scale}
@@ -23,6 +23,9 @@ default, NA is returned for the sum.
 Set this to TRUE to issue an error instead.}
   \item{effAcf}{numeric vector of effective autocorrelation
 This overides arguments \code{corr} and \code{corrLength}}
+  \item{na.rm}{if there are terms with NA values in mu or sigma
+by default also the sum coefficients are NA. Set to TRUE to 
+neglect such terms in the sum.}
 }
 
 \value{numeric vector with two components mu and sigma

man/estimateSumLognormalSample.Rd

@@ -4,7 +4,7 @@
 \description{Estimate the parameters of the lognormal approximation to the sum}
 \usage{estimateSumLognormalSample(mu, sigma, resLog, 
     effAcf = computeEffectiveAutoCorr(resLog), 
-    isGapFilled = logical(0))}
+    isGapFilled = logical(0), na.rm = TRUE)}
 \arguments{
   \item{mu}{numeric vector of center parameters of terms at log scale}
   \item{sigma}{numeric vector of variance parameter of terms at log scale}
@@ -15,6 +15,7 @@ coefficients (may provide precomputed for efficiency or if the sample
 of resLog is too small) set to 1 to assume uncorrelated sample}
   \item{isGapFilled}{logical vector whether entry is gap-filled
 rather than an original measurement, see details}
+  \item{na.rm}{neglect terms with NA vlaues in mu or sigma}
 }
 \details{If there are no gap-filled values, i.e. \code{all(!isGapFilled)} or
 \code{!length(isGapFilled)} (the default), distribution parameters

tests/testthat/test_sumLognormal.R

@@ -1,6 +1,7 @@
 .tmp.f <- function(){
   require(testthat)
   #
+  require(Matrix)
   require(tidyr)
   require(dplyr)
   require(ggplot2)
@@ -103,13 +104,17 @@ test_that("estimateSumLognormal few terms",{
   sigma = log(rep(1.2, length(mu)))
   # no finite case
   sigma0 <- sigma; sigma0[] <- NA
+  coefSum <- estimateSumLognormal(mu, sigma0, na.rm = TRUE)
+  expect_equal(coefSum, c(mu = NA_real_, sigma = NA_real_))
   coefSum <- estimateSumLognormal(mu, sigma0)
   expect_equal(coefSum, c(mu = NA_real_, sigma = NA_real_))
   expect_error(
     coefSum <- estimateSumLognormal(mu, sigma0, isStopOnNoTerm = TRUE))
   # one finite case
   sigma1 <- sigma; sigma1[-1] <- NA
   coefSum <- estimateSumLognormal(mu, sigma1)
+  expect_equal(coefSum, c(mu = NA_real_, sigma = NA_real_))
+  coefSum <- estimateSumLognormal(mu, sigma1, na.rm = TRUE)
   expect_equal(coefSum, c(mu = mu[1], sigma = sigma1[1]))
 })