From Thierry.ONKELINX at inbo.be  Mon Sep 30 14:42:43 2013
From: Thierry.ONKELINX at inbo.be (ONKELINX, Thierry)
Date: Mon, 30 Sep 2013 12:42:43 +0000
Subject: [Rd] predictions in nlme without fixed covariantes
Message-ID: <AA818EAD2576BC488B4F623941DA7427DFAED0B1@inbomail.inbo.be>

Dear all,

predict.lme() throws an error when the fixed part consists of only an intercept and using newdata. See the reproducible example below. I've tracked the error down to asOneFormula() which returns in this case NULL instead of a formula. Changing NULL instead of ~1 in that function (see below) solves the problem in the case of an intercept only model (m1). It does not solve the problem in case of a model without intercept nor covariates (m2). The package with altered asOneFormula() passes R CMD check on my machine.

Best regards,

Thierry Onkelinx

library(nlme)
data(Orthodont)
m0 <- lme(distance ~ Sex, random = ~1|Subject, data = Orthodont)
m1 <- lme(distance ~ 1, random = ~1|Subject, data = Orthodont)
m2 <- lme(distance ~ 0, random = ~1|Subject, data = Orthodont)

test.data <- Orthodont

test.data$Fitted <- predict(m0, level = 0)
test.data$Fitted.Newdata <- predict(m0, level = 0, newdata = test.data)
sum(abs(test.data$Fitted - test.data$Fitted.Newdata))

test.data$Fitted <- predict(m0, level = 1)
test.data$Fitted.Newdata <- predict(m0, level = 1, newdata = test.data)
sum(abs(test.data$Fitted - test.data$Fitted.Newdata))

test.data$Fitted <- predict(m1, level = 0)
test.data$Fitted.Newdata <- predict(m1, level = 0, newdata = test.data)
sum(abs(test.data$Fitted - test.data$Fitted.Newdata))

test.data$Fitted <- predict(m1, level = 1)
test.data$Fitted.Newdata <- predict(m1, level = 1, newdata = test.data)
sum(abs(test.data$Fitted - test.data$Fitted.Newdata))

test.data$Fitted <- predict(m2, level = 0)
test.data$Fitted.Newdata <- predict(m2, level = 0, newdata = test.data)
sum(abs(test.data$Fitted - test.data$Fitted.Newdata))

test.data$Fitted <- predict(m2, level = 1)
test.data$Fitted.Newdata <- predict(m2, level = 1, newdata = test.data)
sum(abs(test.data$Fitted - test.data$Fitted.Newdata))



#new version
asOneFormula <-
  ## Constructs a linear formula with all the variables used in a
  ## list of formulas, except for the names in omit
  function(..., omit = c(".", "pi"))
{
  names <- unique(allVarsRec((list(...))))
  names <- names[is.na(match(names, omit))]
  if (length(names)) {
    eval(parse(text = paste("~", paste(names, collapse = "+")))[[1]])
  } else {
    ~ 1 #this was NULL
  }
}



sessionInfo()
R Under development (unstable) (2013-08-24 r63687)
Platform: i386-w64-mingw32/i386 (32-bit)

locale:
[1] LC_COLLATE=Dutch_Belgium.1252  LC_CTYPE=Dutch_Belgium.1252
[3] LC_MONETARY=Dutch_Belgium.1252 LC_NUMERIC=C
[5] LC_TIME=Dutch_Belgium.1252

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base

loaded via a namespace (and not attached):
[1] grid_3.1.0      lattice_0.20-15 tools_3.1.0
* * * * * * * * * * * * * D I S C L A I M E R * * * * * * * * * * * * *
Dit bericht en eventuele bijlagen geven enkel de visie van de schrijver weer en binden het INBO onder geen enkel beding, zolang dit bericht niet bevestigd is door een geldig ondertekend document.
The views expressed in this message and any annex are purely those of the writer and may not be regarded as stating an official position of INBO, as long as the message is not confirmed by a duly signed document.



[Rd] predictions in nlme without fixed covariantes
ONKELINX, Thierry