Not able to run simex with model fit by nls()

[gjmzajac]

The simex documentation claims that it can be used to correct for measurement error in models fit by nls() but I encounter an error when I try to do so. I am attempting to use nls() to fit the same model and data as in the help page for the simex function.

> library(simex)
> 
> ## Seed
> set.seed(49494)
> 
> ## simulating the measurement error standard deviations
> sd_me <- 0.3
> sd_me2 <- 0.4
> temp <- runif(100, min = 0, max = 0.6)
> sd_me_het1 <- sort(temp)
> temp2 <- rnorm(100, sd = 0.1)
> sd_me_het2 <- abs(sd_me_het1 + temp2)
> 
> ## simulating the independent variables x (real and with measurement error):
> 
> x_real <- rnorm(100)
> x_real2 <- rpois(100, lambda = 2)
> x_real3 <- -4*x_real + runif(100, min = -10, max = 10)  # correlated to x_real
> 
> x_measured <- x_real + sd_me * rnorm(100)
> x_measured2 <- x_real2 + sd_me2 * rnorm(100)
> x_het1 <- x_real + sd_me_het1 * rnorm(100)
> x_het2 <- x_real3 + sd_me_het2 * rnorm(100)
> 
> ## calculating dependent variable y:
> y <- x_real + rnorm(100, sd = 0.05)
> y2 <- x_real + 2*x_real2 + rnorm(100, sd = 0.08)
> y3 <- x_real + 2*x_real3 + rnorm(100, sd = 0.08)
> 
> ### one variable with homoscedastic measurement error
> (model_real <- lm(y ~ x_real))

Call:
lm(formula = y ~ x_real)

Coefficients:
(Intercept)       x_real  
  -0.004752     0.994554  

> 
> (model_naiv <- lm(y ~ x_measured, x = TRUE))

Call:
lm(formula = y ~ x_measured, x = TRUE)

Coefficients:
(Intercept)   x_measured  
   -0.01217      0.90327  

> 
> (model_simex <- simex(model_naiv, SIMEXvariable = "x_measured", measurement.error = sd_me))

Naive model:
lm(formula = y ~ x_measured, x = TRUE)

SIMEX-Variables: x_measured
Number of Simulations: 100

Coefficients:
(Intercept)   x_measured  
   -0.01528      0.97007  

> 
> #for nls
> (nls_naiv = nls(y ~ intercept + beta * x_measured, start = list("intercept" = -0.01217, "beta" = 0.90327), model=T))
Nonlinear regression model
  model: y ~ intercept + beta * x_measured
   data: parent.frame()
intercept      beta 
 -0.01217   0.90327 
 residual sum-of-squares: 8.326

Number of iterations to convergence: 1 
Achieved convergence tolerance: 2.973e-08
> (nls_simex <- simex(nls_naiv, SIMEXvariable = "x_measured", measurement.error = sd_me, asymptotic = F))
Error in dimnames(x) <- dn : 
  length of 'dimnames' [2] not equal to array extent