Running examples in 'propagate-Ex.R' failed
The error most likely occurred in:

> ### Name: predictNLS
> ### Title: Confidence intervals for nonlinear models based on uncertainty
> ###   propagation
> ### Aliases: predictNLS
> ### Keywords: array algebra multivariate
> 
> ### ** Examples
> 
> ## Example from ?nls.
> DNase1 <- subset(DNase, Run == 1)
> fm3DNase1 <- nls(density ~ Asym/(1 + exp((xmid - log(conc))/scal)),
+                  data = DNase1, start = list(Asym = 3, xmid = 0, scal = 1))
> 
> ## Using a single predictor value without error.
> PROP1 <- predictNLS(fm3DNase1, newdata = data.frame(conc = 2))
Propagating predictor value #1 ...
> PRED1 <- predict(fm3DNase1, newdata = data.frame(conc = 2))
> PROP1$summary
  Prop.Mean.1 Prop.Mean.2   Prop.sd.1   Prop.sd.2 Prop.2.5% Prop.97.5%
1   0.7480472   0.7481514 0.008042515 0.008052591 0.7307548  0.7655479
   Sim.Mean      Sim.sd Sim.Median     Sim.MAD  Sim.2.5% Sim.97.5%
1 0.7481505 0.008041165  0.7481565 0.008040288 0.7323719 0.7639674
> PRED1
[1] 0.7480472
> ## => Prop.Mean.1 equal to PRED1
> 
> ## Not run: 
> ##D ## Using a sequence of predictor values without error.
> ##D CONC <- seq(1, 12, by = 1)
> ##D PROP2 <- predictNLS(fm3DNase1, newdata = data.frame(conc = CONC))
> ##D PRED2 <- predict(fm3DNase1, newdata = data.frame(conc = CONC))
> ##D PROP2$summary
> ##D PRED2
> ##D ## => Prop.Mean.1 equal to PRED2
> ##D 
> ##D ## Using a sequence of predictor values with error.
> ##D DAT <- data.frame(conc = CONC, error = rnorm(12, 0, 0.1))
> ##D PROP3 <- predictNLS(fm3DNase1, newdata = DAT)
> ##D PRED3 <- predict(fm3DNase1, newdata = DAT)
> ##D PROP3$summary
> ##D PRED3
> ##D ## => Prop.Mean.1 equal to PRED3
> ##D 
> ##D ## Plot predicted and confidence values from 
> ##D ## first-/second-order Taylor expansion 
> ##D ## and Monte Carlo simulation.
> ##D plot(DNase1$conc, DNase1$density)
> ##D lines(DNase1$conc, fitted(fm3DNase1), lwd = 2, col = 1)
> ##D points(CONC, PROP2$summary[, 1], col = 2, pch = 16)
> ##D lines(CONC, PROP2$summary[, 5], col = 2)
> ##D lines(CONC, PROP2$summary[, 6], col = 2)
> ## End(Not run)
> 
> ## Using multiple predictor values
> ## 1: Setup of response values
> ## with gaussian error of 10%.
> x <- seq(1, 10, by = 0.01)
> y <- seq(10, 1, by = -0.01)
> a <- 2
> b <- 5
> c <- 10
> z <- a * exp(b * x)^sin(y/c)
> z <- z + sapply(z, function(x) rnorm(1, x, 0.10 * x))
> ## 2: Fit 'nls' model.
> MOD <- nls(z ~ a * exp(b * x)^sin(y/c), 
+              start = list(a = 2, b = 5, c = 10))
> ## 3: newdata without errors and prediction.
> DAT1 <- data.frame(x = 4, y = 3)
> PROP4 <- predictNLS(MOD, newdata = DAT1)
Propagating predictor value #1 ...
> PROP4$summary
  Prop.Mean.1 Prop.Mean.2 Prop.sd.1 Prop.sd.2 Prop.2.5% Prop.97.5% Sim.Mean
1    1444.777    1442.775  54.96645  55.04016  1334.753   1550.798 1442.775
    Sim.sd Sim.Median  Sim.MAD Sim.2.5% Sim.97.5%
1 55.09055   1444.517 54.85169 1329.825  1545.365
> ## 4: newdata with errors and prediction.
> DAT2 <- data.frame(x = 4, y = 3, error.x = 0.2, error.y = 0.1)
> PROP5 <- predictNLS(MOD, newdata = DAT2)
Propagating predictor value #1 ...
Error in `rownames<-`(`*tmp*`, value = make.unique(nameVEC)) : 
  attempt to set 'rownames' on an object with no dimensions
Calls: predictNLS -> mixCov -> makeCov -> rownames<-
Execution halted