Why is this independence model not getting the means and variance in the data?

bad results.r

library(OpenMx)
manifests = c("mpg", "disp", "gear")

# =============================
# = simple independence model =
# =============================
m1 <- mxModel("ind", type = "RAM",
	manifestVars = manifests,
	mxPath(from = manifests, arrows = 2),
	mxPath(from = "one", to = manifests),
	mxData(mtcars[,manifests], type="raw")
)

m1 = mxRun(m1)

# ===========================
# = Our parameter estiamtes =
# ===========================
s = summary(m1)$parameters; s$Estimate = round(s$Estimate,2); s[,3:5]

   row  col     Estimate
1  mpg  mpg    695524.97
2 disp disp 108907330.80
3 gear gear     18792.08
4    1  mpg        20.02
5    1 disp       130.76
6    1 gear         3.69

# ===================================
# = Means and variances in the data =
# ===================================
round(diag(cov(mtcars[,manifests])),2)
     mpg     disp     gear 
   36.32 15360.80     0.54 

colMeans(mtcars[,manifests])
 mpg      disp      gear 
 20.09    230.72   3.69 

# switch optimiser
m1 = mxOption(m1, "Default optimizer", "NPSOL")

# set start values