Estimates don't move far enough from poor starts in CSOLNP

tbates's picture
Project:OpenMx
Component:Code
Category:bug report
Priority:normal
Assigned:Unassigned
Status:active
Description

Hi, The current trunk of OpenMx (999.0.0-3521) seems to be not estimating parameters correctly.

I've uploaded a gist that shows the issues here: It's a simple independence model with 3 variables in the built-in mtcars data set.

https://gist.github.com/tbates/d2261e8c5daf426e1c8e

You can see that the means are borderline OK, but the variances are way off.

# ===================================
# = 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 
 
summary(m1)$parameters
        name matrix  row  col     Estimate  Std.Error lbound ubound
1 ind.S[1,1]      S  mpg  mpg 6.955250e+05         NA              
2 ind.S[2,2]      S disp disp 1.089073e+08         NA              
3 ind.S[3,3]      S gear gear 1.879208e+04         NA              
4 ind.M[1,1]      M    1  mpg 2.001955e+01  145.28108              
5 ind.M[1,2]      M    1 disp 1.307563e+02 1833.42676              
6 ind.M[1,3]      M    1 gear 3.693204e+00   24.14331   

Comments

neale's picture

#1

I took a look at this today. It is a CSOLNP issue. NPSOL miraculously gets to the correct solution from the awful starting values provided (zeroes for everything basically). CSOLNP does not. Even given halfway decent ones with something like

Note that the correct ML estimates of the variances deviate from those calculated using var() or cov() a non-trivial amount (31/32) due to the small N of 32. This is definitely not the whole issue with CSOLNP.

> s = summary(m1)$parameters; s$Estimate = round(s$Estimate,2); s[,3:5]; diag(cov(mtcars[,manifests]))*31/32; colMeans(mtcars[,manifests])
   row  col Estimate
1  mpg  mpg    35.19
2 disp disp 14880.81
3 gear gear     0.53
4    1  mpg    20.09
5    1 disp   230.72
6    1 gear     3.69
         mpg         disp         gear 
3.518897e+01 1.488077e+04 5.273438e-01 
      mpg      disp      gear 
 20.09062 230.72188   3.68750  

Even given halfway decent starting values, CSOLNP fails to get var(disp) correct:

mxOption(NULL,"Default optimizer", "CSOLNP")
manifests = c("mpg", "disp", "gear")
 
# =============================
# = simple independence model =
# =============================
m1 <- mxModel("ind", type = "RAM",
	manifestVars = manifests,
	mxPath(from = manifests, arrows = 2, values=diag(cov(mtcars[,manifests]))*1.05),
	mxPath(from = "one", to = manifests, values=colMeans(mtcars[,manifests])*1.1),
	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    35.19
2 disp disp 16128.81
3 gear gear     0.53
4    1  mpg    20.09
5    1 disp   230.72
6    1 gear     3.69

Although the estimates are off, the -2lnL is only off by .2 or so, this is likely due to flattish gradients on account of the lack of data. But it should do better.

Useful test case, should probably go in models/failing in a CSOLNPmtcars.R

mxOption(NULL,"Default optimizer", "CSOLNP")
manifests = c("mpg", "disp", "gear")
 
# =============================
# = simple independence model =
# =============================
m1 <- mxModel("ind", type = "RAM",
	manifestVars = manifests,
	mxPath(from = manifests, arrows = 2, values=diag(cov(mtcars[,manifests]))*1.05),
	mxPath(from = "one", to = manifests, values=colMeans(mtcars[,manifests])*1.1),
	mxData(mtcars[,manifests], type="raw")
)
 
m1 = mxRun(m1)
 
# ===========================
# = Our parameter estiamtes =
# ===========================
s = summary(m1)$parameters; s$Estimate = round(s$Estimate,2); s[,3:5]; summary(m1); diag(cov(mtcars[,manifests]))*31/32; colMeans(mtcars[,manifests])

mhunter's picture

#2

This is not a raw data issue. The problem is the same with covariance data.

 # =============================
m1 <- mxModel("ind", type = "RAM",
manifestVars = manifests,
mxPath(from = manifests, arrows = 2),
mxPath(from = "one", to = manifests),
mxData(cov(mtcars[,manifests]), means=colMeans(mtcars[,manifests]), numObs=nrow(mtcars), type="cov")
 )
 
m1 = mxRun(m1)
#Running ind 
s = summary(m1)$parameters; s$Estimate = round(s$Estimate,2); s[,3:5]
#   row  col     Estimate
#1  mpg  mpg    695012.58
#2 disp disp 108907335.59
#3 gear gear         0.54
#4    1  mpg        20.09
#5    1 disp       230.59
#6    1 gear         3.69

Scaling is almost certainly the major problem. When free parameters span five orders of magnitude (1e-1 to 1e+4), only the most robust optimizers will be able to handle that on their own without blinking. It really screws with the magnitude of the gradient because you have to make tiny steps in some directions (for the 1e-1 parameters) and giant steps in other directions (for the 1e+4 parameters). Physicists suggest that you re-parameterize the problem to avoid parameter estimation at very different orders of magnitude.

Another part of the problem is definitely the starting values. Zero is just about the worst starting value for a variance. With that being said, CSOLNP does wiggle off from zero starting values. It just doesn't wiggle far enough.

Re-scaling the data (ala physics) and letting CSOLNP wiggle the starting values off zero resolves the behavioral issue.

library(OpenMx)
manifests = c("mpg", "disp", "gear")
 
mtcars$mpg <- mtcars$mpg/10
mtcars$disp <- mtcars$disp/300
mtcars$gear <- mtcars$gear
 
# =============================
# = simple independence model =
# =============================
m1 <- mxModel("ind", type = "RAM",
	manifestVars = manifests,
	mxPath(from = manifests, arrows = 2, values=0),
	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    35.19
#2 disp disp    16.53
#3 gear gear    52.73
#4    1  mpg    20.09
#5    1 disp     7.69
#6    1 gear    36.87
 
round(diag(cov(mtcars[,manifests])),2)
#  mpg  disp  gear 
#36.32 17.07 54.44

Of course, we would probably want CSOLNP to more robustly find the correct solution. Does anyone know if NPSOL rescales the gradient similar to CSOLNP?

tbates's picture

#3

is it ok to use library(testthat) in failing models?

if so, I can upload this to "models/failing/CSOLNPmtcars.R"

# models/failing/CSOLNPmtcars.R
# Not taking big enough steps?
 
library(OpenMx)
library(testthat)
# ===================
# = test with NPSOL =
# ===================
 
mxOption(NULL, "Default optimizer", "NPSOL")
 
# ====================================
# = make a simple independence model =
# ====================================
manifests = c("mpg", "disp", "gear")
m1 <- mxModel("ind", type = "RAM",
	manifestVars = manifests,
	mxPath(from = manifests, arrows = 2),
	mxPath(from = "one", to = manifests),
	mxData(mtcars[,manifests], type="raw")
)
 
# ======================================
# = get parameter estimated parameters =
# ======================================
m1 = mxRun(m1)
obtainedVars = summary(m1)$parameters[1:3,5]
expectedVars = as.numeric(diag(cov(mtcars[,manifests])))
mlexpected = expectedVars*(31/32) # n-1/n
 
testthat::expect_equal(obtainedVars[1], expected = mlexpected[1], tolerance = 0.01)
testthat::expect_equal(obtainedVars[2], expected = mlexpected[2], tolerance = 0.01)
testthat::expect_equal(obtainedVars[3], expected = mlexpected[3], tolerance = 0.01)
 
# ====================
# = switch to CSOLNP =
# ====================
 
mxOption(NULL, "Default optimizer", "CSOLNP")
manifests = c("mpg", "disp", "gear")
# re-build to set to crappy starts that are fine in NPSOL
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)
obtainedVars = summary(m1)$parameters[1:3,5]
expectedVars = as.numeric(diag(cov(mtcars[,manifests])))
mlexpected = expectedVars*(31/32) # n-1/n
 
testthat::expect_equal(obtainedVars[1], expected = mlexpected[1], tolerance = 0.01)
testthat::expect_equal(obtainedVars[2], expected = mlexpected[2], tolerance = 0.01)
testthat::expect_equal(obtainedVars[3], expected = mlexpected[3], tolerance = 0.01)
# e.g
# Error: obtainedVars[3] not equal to expectedVars[3]
# Mean relative difference: 34520.75

AttachmentSize
CSOLNPmtcars.R 1.94 KB
neale's picture

#4

Oh, we use our own omx routine for that, which reduces the number of required libraries.
E.g.,

omxCheckCloseEnough(growthCurveFit$output$estimate[["meani"]], 9.930, 0.01)

so a translation would be:
omxCheckCloseEnough(obtainedVars[1],  mlexpected[1], epsilon =  0.01)
omxCheckCloseEnough(obtainedVars[2],  mlexpected[2], epsilon =  0.05)
omxCheckCloseEnough(obtainedVars[3],  mlexpected[3], epsilon =  0.01)

This passes with NPSOL but not with CSOLNP.

However, omxCheckCloseEnough() only works on absolute differences, whereas testthat() can do relative as well. I like the flexibility of testthat() though -- not sure if it's worth changing over to using it more broadly, a question for the developers generally.

mhunter's picture

#5

OpenMx has omxCheckCloseEnough() for absolute differences and omxCheckWithinPercentError() for relative (percent) differences. These functions are defined in trunk/R/MxUnitTesting.R.

tbates's picture

#6

Title:values way off in simple mxModel() with raw data» Estimates don't move far enough from poor starts in CSOLNP

OK. uploaded the failing model using omxCheckWithinPercentError

tim:OpenMx tim$ svn commit trunk/models/failing/CSOLNPmtcars.R -m "Estimates are good in NPSOL but poor in CSOLNP - step size issue?"
Adding         trunk/models/failing/CSOLNPmtcars.R
Transmitting file data .
Committed revision 3526.

tbates's picture

#7

library(testthat) also has a wealth of other tests, like boolean, warning, string, error, comparison, type, name, class etc.
Really worthwhile.
Plus dovetails with devtools

mhunter's picture

#8

OpenMx has

omxCheckIdentical # uses R's identical() function
omxCheckEquals # uses R's == function
omxCheckSetEquals # checks for equality of sets (order independent)
omxCheckTrue # similar to boolean test
omxCheckCloseEnough # approximate equality on absolute scale
omxCheckWithinPercentError # approximate equality on relative scale
omxCheckWarning # verify existence of warning and the right message is printed
omxCheckError # verify existence of error and the right message is printed

As far as I can tell we've had all of these functions for 3+ years.