Hello all,
I'm trying to replicate a confirmatory factor analysis done in Amos. The
idea is to compare a one-factor and a two-factor model. I get the following
warning message when I run either model:
"Could not compute QR decomposition of Hessian.
Optimization probably did not converge."
I have no idea what to do here. I believe posters reported the same
problem. It seems that the ability to invert the correlation matrix may
have something to do with this error, but solve(correl) yields a solution.
Here are my correlation matrix and model specifications:
--------------------------- R CODE BEGIN
------------------------------------------------
correl <- matrix(
c(1.0000000,-0.6657822,0.6702089,0.7997673,-0.7225454,0.8992372,
-0.8026491,-0.6715168,-0.5781565,-0.6657822,1.0000000,-0.5107568,
-0.5030886,0.6971188,-0.6306937,0.7200848,0.5121227,0.4496810,
0.6702089,-0.5107568,1.0000000,0.7276558,-0.3893792,0.6043672,
-0.7176532,-0.5247434,-0.4670362,0.7997673,-0.5030886,0.7276558,
1.0000000,-0.6251056,0.8164190,-0.6728515,-0.6371453,-0.5531964,
-0.7225454,0.6971188,-0.3893792,-0.6251056,1.0000000,-0.7760765,
0.6175124,0.5567924,0.4914176,0.8992372,-0.6306937,0.6043672,
0.8164190,-0.7760765,1.0000000,-0.7315507,-0.6625136,-0.5814590,
-0.8026491,0.7200848,-0.7176532,-0.6728515,0.6175124,-0.7315507,
1.0000000,0.5910688,0.5096898,-0.6715168,0.5121227,-0.5247434,
-0.6371453,0.5567924,-0.6625136,0.5910688,1.0000000,0.8106496,
-0.5781565,0.4496810,-0.4670362,-0.5531964,0.4914176,-0.5814590,
0.5096898,0.8106496,1.0000000), ,nrow=9,ncol=9)
rownames(correl) = c("pvote", "jmposaff", "jmnegaff",
"boposaff","bonegaff",
"obama.therm", "mccain.therm",
"oddcand.D", "evencand.D" )
colnames(correl) = c("pvote", "jmposaff", "jmnegaff",
"boposaff","bonegaff",
"obama.therm", "mccain.therm",
"oddcand.D", "evencand.D" )
#One Factor Model:
model.all <- specify.model()
allmeasures -> pvote, b1, NA
allmeasures -> obama.therm, b2, NA
allmeasures -> mccain.therm, b3, NA
allmeasures -> jmposaff, b4, NA
allmeasures -> jmnegaff, b5, NA
allmeasures -> boposaff, b6, NA
allmeasures -> bonegaff, b7, NA
allmeasures -> oddcand.D, b8, NA
allmeasures -> evencand.D, b9, NA
allmeasures <-> allmeasures, NA,1
pvote <-> pvote, v1, NA
obama.therm <-> obama.therm, v2, NA
mccain.therm <-> mccain.therm, v3, NA
jmposaff <-> jmposaff, v4, NA
jmnegaff <-> jmnegaff, v5, NA
boposaff <-> boposaff, v6, NA
bonegaff <-> bonegaff, v7, NA
oddcand.D <-> oddcand.D, v8, NA
evencand.D <-> evencand.D, v9, NA
sem.all <- sem(model.all, correl, 1100)
summary(sem.all)
#Two Factor Model:
model.vi <- specify.model()
verbal -> pvote, b1, NA
verbal -> obama.therm, b2, NA
verbal -> mccain.therm, b3, NA
verbal -> jmposaff, b4, NA
verbal -> jmnegaff, b5, NA
verbal -> boposaff, b6, NA
verbal -> bonegaff, b7, NA
imp -> oddcand.D, b8, NA
imp -> evencand.D, b9, NA
imp <-> imp, NA, 1
verbal <-> verbal, NA, 1
pvote <-> pvote, v1, NA
obama.therm <-> obama.therm, v2, NA
mccain.therm <-> mccain.therm, v3, NA
jmposaff <-> jmposaff, v4, NA
jmnegaff <-> jmnegaff, v5, NA
boposaff <-> boposaff, v6, NA
bonegaff <-> bonegaff, v7, NA
oddcand.D <-> oddcand.D, v8, NA
evencand.D <-> evencand.D, v9, NA
imp <-> verbal, civ, NA
sem.vi <- sem(model.vi, correl, 1100)
summary(sem.vi)
--------------------------- R CODE END
------------------------------------------------
Thanks very much.
-Solomon
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