## reported by simon bond <shug0131@yahoo.co.uk> to R-help 2007-03-16
library(nlme)
x <- rnorm(10, 0.1, 1)
try(gls(x ~ 0))  # segfaulted in 3.1-79


## PR#10364
# copied verbatim from Pinheiro & Bates 8.3.3
fm1Dial.gnls <-
  gnls(rate ~ SSasympOff(pressure, Asym, lrc, c0),
       data = Dialyzer, params = list(Asym + lrc ~ QB, c0 ~ 1),
       start = c(53.6, 8.6, 0.51, -0.26, 0.225))
(p1 <- predict(fm1Dial.gnls))
(p2 <- predict(fm1Dial.gnls, newdata = Dialyzer))
# failed, factor levels complaint
# also, missed row names as names
stopifnot(all.equal(as.vector(p1), as.vector(p2)), # 'label' differs
          identical(names(p1), names(p2)))


## PR#13418
fm1 <- gls(weight ~ Time * Diet, BodyWeight)
(V10 <- Variogram(fm1, form = ~ Time | Rat)[1:10,])
## failed in 3.1-89
stopifnot(all.equal(V10$variog,
                    c(0.0072395216, 0.014584634, 0.014207936, 0.018442267,
                      0.011128505, 0.019910082, 0.027072311, 0.034140379,
                      0.028320657, 0.037525507)),
          V10$dist == c(1, 6, 7, 8, 13, 14, 15, 20, 21, 22),
          V10$n.pairs == 16*c(1, 1, 9, 1, 1, 8, 1, 1, 7, 1))

intervals(fm1)

## predict from model with factor and no intercept
fm1b <- gls(weight ~ Diet - 1, BodyWeight)
stopifnot(all.equal(predict(fm1b, BodyWeight[1,]), coef(fm1b)[1],
                    check.attributes = FALSE))
## in nlme <= 3.1-155, failed with
## Error in X[, names(cf), drop = FALSE] : subscript out of bounds

## predict.gls(): handling newdata for factor variables
stopifnot(all.equal(
    predict(fm1, newdata = data.frame(Time = 1, Diet = "1", stringsAsFactor = FALSE)),
    fitted(fm1)[1], check.attributes = FALSE))
## in nlme <= 3.1-155, predict() failed with
## Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) : 
##   contrasts can be applied only to factors with 2 or more levels
stopifnot(all.equal(
    predict(fm1, data.frame(Time = 71, Diet = c("2", "3"), stringsAsFactor = FALSE)),
    predict(fm1, data.frame(Time = 71, Diet = c("2", "3"), stringsAsFactor = TRUE))))
## in nlme <= 3.1-155, using character input failed with
## Error in X[, names(cf), drop = FALSE] : subscript out of bounds
tools::assertError(predict(fm1, data.frame(Time = 71, Diet = 2)), verbose = TRUE)
## more helpful error + warning now

## PR#17226: same for predict.gnls(), also without intercept
fm2 <- gnls(weight ~ f, data = BodyWeight, params = list(f ~ Diet - 1),
            start = rep(mean(BodyWeight$weight), 3))
stopifnot(all.equal(predict(fm2, head(BodyWeight)), head(fitted(fm2)),
                    check.attributes = FALSE))
## in nlme <= 3.1-155, failed with
## Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) : 
##   contrasts can be applied only to factors with 2 or more levels
stopifnot(all.equal(
    predict(fm2, data.frame(Time = 71, Diet = c("2", "3"), stringsAsFactor = FALSE)),
    predict(fm2, data.frame(Time = 71, Diet = c("2", "3"), stringsAsFactor = TRUE))))
## in nlme <= 3.1-155, failed with
## Error in p %*% beta[pmap[[nm]]] : non-conformable arguments


## PR#17880: offset() terms are (currently) not supported in package nlme
y <- 10:20; off <- rep(10, length(y))
tools::assertError(gls(y ~ 1 + offset(off)), verbose = TRUE)
## the following was TRUE in nlme <= 3.1-155, unfortunately:
## all.equal(coef(gls(y ~ 1 + offset(off))), coef(gls(y ~ 1)))


## PR#18283: gls() did not keep terms so predict() lacked "predvars"
fm_poly   <- gls(distance ~ poly(age, 1), data = Orthodont)
fm_nopoly <- gls(distance ~      age,     data = Orthodont)
stopifnot(all.equal(predict(fm_poly,   data.frame(age = 10)),
                    predict(fm_nopoly, data.frame(age = 10))))
## in nlme <= 3.1-155, prediction from fm_poly failed with
## Error in poly(age, 1) : 
##   'degree' must be less than number of unique points
stopifnot(all.equal(predict(fm_poly, head(Orthodont)),
                    head(fitted(fm_poly)), check.attributes = FALSE))
## predictions were wrong due to data-dependent bases


## R-help, From: Aaron Crowley; Jul 21, 2022  "Error generated by nlme::gnls"
df <- Soybean
n <- nrow(df)
set.seed(548)
for(j in 1:12)
    df[sprintf("x%02d", j)] <- sample(0:1, size=n, replace=TRUE)

gm12 <- gnls(weight ~ x, data = df,
             params = (x ~ -1 + x01 + x02 + x03 + x04 + x05 + x06 + x07 + x08 +
                           x09 + x10 + x11 + x12),
             start = rep(0, 12))
## gave error  Error in  if (deparse(params[[nm]][[3]]) != "1") { : 
##                the condition has length > 1
stopifnot(all.equal(
    c(0.652122, 0.454468, 1.01271, 1.20772, 1.35816, -0.207982,
      1.19959,  0.349636, 2.39818, 1.36285, 0.75055,  1.18374),
    unname(coef(gm12)), tol = 1e-5)) # Lnx x68_64: 1.18e-6


## PR#17988: corARMA() with default p=0=q fails, now with a clear error message
tools::assertError(
    gls(follicles ~ 1, Ovary, correlation = corARMA(form = ~ 1 | Mare))
  , verbose = TRUE)
## in nlme <= 3.1-164, corARMA() returned dysfunctional corIdent() which gave
## Error in getGroupsFormula.default(correlation) : 
##   'form' argument must be a formula


## PR#17227 (example originally posted by Wolfgang Viechtbauer, R-sig-ME, 2008q3)
gnls.exp <- gnls(circumference ~ Asym/(1 + exp(-(age-xmid)/scal)),
                 data = Orange, correlation = corExp(form = ~1 | Tree),
                 start = c(Asym=150, xmid=750, scal=300),
                 control = gnlsControl(returnObject = TRUE)) # warn, not stop
## using a spatial correlation structure would sometimes crash with varying
## memory errors in nlme <= 3.1-166
