[Rd] predictions in nlme without fixed covariantes

[Prof Brian Ripley]

This should be fixed in nlme 3.1-112.

However, nlme has little support for formulae such as resp ~ 0, and does 
things like p:1 where p is the number of columns in the model matrix. 
3.1-112 does better but evidently the design did not consider this 
possibility.


On 30/09/2013 13:42, ONKELINX, Thierry wrote:
> 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
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-- 
Brian D. Ripley,                  ripley at stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
1 South Parks Road,                     +44 1865 272866 (PA)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595