Running examples in ‘semEff-Ex.R’ failed
The error most likely occurred in:

> ### Name: stdEff
> ### Title: Standardised Effects
> ### Aliases: stdEff
> 
> ### ** Examples
> 
> library(lme4)
Loading required package: Matrix

Attaching package: ‘lme4’

The following object is masked from ‘package:semEff’:

    getData

> 
> # Standardised (direct) effects for SEM
> m <- shipley.sem
> stdEff(m)
$DD
(Intercept)         lat 
-0.05600661 -0.68772025 

$Date
(Intercept)          DD 
-0.01493651 -0.62813666 

$Growth
(Intercept)        Date 
 -0.2917507   0.3824224 

$Live
(Intercept)      Growth 
  0.3105220   0.3681961 

> stdEff(m, cen.y = FALSE, std.y = FALSE)  # x-only
$DD
(Intercept)         lat 
 143.183138   -7.066909 

$Date
(Intercept)          DD 
  126.79703    -5.11375 

$Growth
(Intercept)        Date 
  48.955514    2.448161 

$Live
(Intercept)      Growth 
   5.452982    2.226862 

> stdEff(m, std.x = FALSE, std.y = FALSE)  # centred only
$DD
(Intercept)         lat 
 -0.5755154  -0.8354729 

$Date
(Intercept)          DD 
 -0.1216002  -0.4976475 

$Growth
(Intercept)        Date 
 -1.8677067   0.3007147 

$Live
(Intercept)      Growth 
  1.8780467   0.3478536 

> stdEff(m, cen.x = FALSE, cen.y = FALSE)  # scaled only
$DD
(Intercept)         lat 
 19.1373370  -0.6877202 

$Date
(Intercept)          DD 
 24.3624481  -0.6281367 

$Growth
(Intercept)        Date 
  1.6853620   0.3824224 

$Live
(Intercept)      Growth 
 -2.0214940   0.3681961 

> stdEff(m, unique.eff = FALSE)  # include multicollinearity
$DD
(Intercept)         lat 
-0.05600661 -0.68772025 

$Date
(Intercept)          DD 
-0.01493651 -0.62813666 

$Growth
(Intercept)        Date 
 -0.2917507   0.3824224 

$Live
(Intercept)      Growth 
  0.3105220   0.3681961 

> stdEff(m, R.squared = TRUE)  # add R-squared
$DD
     (Intercept)              lat      (R.squared)  (R.squared.adj) 
     -0.05600661      -0.68772025       0.70800826       0.70747906 
(R.squared.pred) 
      0.68356361 

$Date
     (Intercept)               DD      (R.squared)  (R.squared.adj) 
     -0.01493651      -0.62813666       0.98555565       0.98553510 
(R.squared.pred) 
      0.98202414 

$Growth
     (Intercept)             Date      (R.squared)  (R.squared.adj) 
      -0.2917507        0.3824224        0.7938367        0.7934879 
(R.squared.pred) 
       0.7552854 

$Live
     (Intercept)           Growth      (R.squared)  (R.squared.adj) 
       0.3105220        0.3681961        0.2668652        0.2655971 
(R.squared.pred) 
              NA 

> stdEff(m, incl.raw = TRUE)  # add unstandardised
$DD
      (Intercept)               lat (raw)_(Intercept)         (raw)_lat 
      -0.05600661       -0.68772025      196.65237838       -0.83547294 

$Date
      (Intercept)                DD (raw)_(Intercept)          (raw)_DD 
      -0.01493651       -0.62813666      198.33816379       -0.49764747 

$Growth
      (Intercept)              Date (raw)_(Intercept)        (raw)_Date 
       -0.2917507         0.3824224        10.7892162         0.3007147 

$Live
      (Intercept)            Growth (raw)_(Intercept)      (raw)_Growth 
        0.3105220         0.3681961       -12.2260588         0.3478536 

> 
> # Demonstrate equality with effects from manually-standardised variables
> # (gaussian models only)
> m <- shipley.growth[[3]]
> d <- data.frame(scale(na.omit(shipley)))
> e1 <- stdEff(m, unique.eff = FALSE)
> e2 <- coef(summary(update(m, data = d)))[, 1]
Warning in optwrap(optimizer, devfun, getStart(start, rho$pp), lower = rho$lower,  :
  convergence code -2 from nloptwrap: NLOPT_INVALID_ARGS: Invalid arguments (e.g. lower bounds are bigger than upper bounds, an unknown algorithm was specified, etcetera).
Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :
  Model failed to converge with max|grad| = 8.25548 (tol = 0.002, component 1)
> stopifnot(all.equal(e1, e2))
Error: e1 and e2 are not equal:
  Mean relative difference: 0.1456697
Execution halted