calisgfi | Produce a readable display of CALIS GFI statistics | calisgfi |
PROC CALIS, like other structural equation model software, produces
a table of goodness of fit statistics that is hard to read because it
simply presents a long list of many statistics without any structure.
The CALISGFI macro takes an OUTRAM=
data set from PROC CALIS, extracts
the goodness-of-fit statistics, and produces a more easily understood
version.
Very ugly contortions are used to extract and reform the statistics from the OUTRAM data set. The main innovation here is to group and relabel the plethora of fit statistics into coherent categories. This macro was written before ODS was available. I'm not sure if ODS would make this any easier.
The CALISGFI macro is defined with keyword parameters. The RAM=
parameter is required.
The arguments may be listed within parentheses in any order, separated
by commas. For example:
%calisgfi(ram=ram1, nv=5);
OUTRAM=
data set from PROC CALIS
OUTRAM=
data set.
The following lines read a covariance matrix of four variables,
X1, X2, Y1, Y2, and fit a two-factor model, capturing the
GFI statistics in an OUTRAM=
dataset, RAM4, which is input
to the CALISGFI macro.
data lord(type=cov); input _type_ $ _name_ $ x1 x2 y1 y2; cards; n . 649 . . . cov x1 86.3937 . . . cov x2 57.7751 86.2632 . . cov y1 56.8651 59.3177 97.2850 . cov y2 58.8986 59.6683 73.8201 97.8192 ; title "Lord's data: H4- unconstrained two-factor model"; proc calis data=lord cov noprint outram=ram4; lineqs x1 = beta1 F1 + e1, x2 = beta2 F1 + e2, y1 = beta3 F2 + e3, y2 = beta4 F2 + e4; std F1 F2 = 1, e1 e2 e3 e4 = ve1 ve2 ve3 ve4; cov F1 F2 = rho; run;
%include macros(calisgfi); *-- or include in an autocall library; %calisgfi(ram=ram4, nv=4);Output:
Model Information Sample size 649 Number of parameters 9 Number of active constraints 0 Null Model Chi-Square (df=6) 1466.590 (Pr>ChiSq= 0.000) Absolute Indices Fit Function 0.001085 Goodness of Fit Index 0.999 Root Mean Square Residual 0.272 Chi-Square (df=1) 0.703 (Pr>ChiSq= 0.402) Normal Theory Reweighted LS Chi-Square (df=1) 0.703 (Pr>ChiSq= 0.402) Z-Test for Chi-Square (df=1) 0.236 (Pr>ChiSq= 0.407) Critical N 3542 Absolute, Adjusted for Parsimony GFI Adjusted for Degrees of Freedom 0.995 Parsimonious GFI 0.167 RMS Error of Approx Estimate (RMSEA) 0.000 90% CI: ( . ,0.097) Probability of Close Fit [Pr(RMSEA <= 0.05)] 0.685 Expected Cross Validation Index (ECVI) 0.029 90% CI: ( . ,0.039) Akaike's Information Criterion -1.297 Consistent AIC -6.772 Schwarz's Bayesian Criterion -5.772 Centrality Index 1 Incremental Indices (vs. Null Model) Comparative Fit Index 1 Non-normed Index (Rho) 1 Normed Fit Index (NFI) 1 Parsimonious NFI 0.167 Normed Index Rho1 0.997 Non-normed Index Delta2 1