Description Usage Arguments Value Author(s) References Examples
A function to calculate distance and parameter estimates for SEM diagnostics
1 |
x |
Data frame or data matrix |
ram.path |
Ram path for R sem package |
software |
Software to be used. |
varphi |
Percentage of data to be down-weighted |
EQSmodel |
EQS input file name |
EQSdata |
Data file name used in EQS input file |
D |
How to treat prediction error. E: errors; F: factors |
delete |
A vector of data to be deleted. For example c(99,100) delete the 99th and 100th cases. |
max_it |
The maximum number of iterations |
EQSprog |
The path to where EQS program is installed. |
serial |
Serial no. for EQS. This is a string with spaces. Currently, it does not need to be supplied. |
d_f |
Distance for f |
d_r |
Distance for r |
mu |
Mean |
p |
Number of observed variables |
q |
Number of factors used in calculating f and r |
res |
Model fit and paramter estimates. nml: normal ML; tsr: two-stagte robust; dr: direct robust |
eqs |
Full EQS output for the above three models |
x |
Data |
Zhiyong Zhang and Ke-Hai Yuan
Maintainer: Zhiyong Zhang <zhiyongzhang@nd.edu>
Yuan, K.-H. and Zhang, Z. (2011). Structural Equation Modeling Diagnostics Using R Package semdiag and EQS. Structural Equation Modeling: An Interdisciplinary Journal.
Yuan, K.-H., and Hayashi, K. (2010). Fitting data to model: Structural equation modeling diagnosis using two scatter plots. Psychological Methods, 15, 335–351.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 | ## Not run:
## Examples based on EQS
library(semdiag)
## Example 1. Normally distributed data
data(N100)
## Not run
## The EQS input file is semplot.eqs
## Model 1: treating prediction errors as factors
N100out.1<-semdiag(N100, 'semplot.eqs', D='F')
## Diagnostics plot
semdiag.plot(N100out.1)
## Summary output
semdiag.summary(N100out.1)
## Model 0: treating prediction errors the same as measurement errors
N100out.0<-semdiag(N100, 'semplot.eqs')
## Diagnostics plot
semdiag.plot(N100out.0)
## Summary output
semdiag.summary(N100out.0)
## Example 2. Contaminated data
data(N85)
## The EQS input file is semplot.eqs
## Model 1: treating prediction errors as factors
N85out.1<-semdiag(N85, 'semplot.eqs', D='F')
## Diagnostics plot
semdiag.plot(N85out.1)
## Summary output
semdiag.summary(N85out.1)
## Model 0: treating prediction errors the same as measurement errors
N85out.0<-semdiag(N85, 'semplot.eqs', D='E')
## Diagnostics plot
semdiag.plot(N85out.0)
## Summary output
semdiag.summary(N85out.0)
## Case profile plot
semdiag.cpp(N85out.0, cases=c(86, 90, 98:100))
## Delete the 99th and 100th observations
N85out.1.del<-semdiag(N85, 'semplot.eqs', D='F', delete=c(99,100))
## Examples based on the sem package
library(sem)
data(N100)
## path diagram for the model
sem1<-specify.model()
f1 -> y1, NA, 1
f1 -> y2, l1, NA
f1 -> y3, l2, NA
f2 -> y4, NA, 1
f2 -> y5, l3, NA
f2 -> y6, l4, NA
f3 -> y7, NA, 1
f3 -> y8, l5, NA
f3 -> y9, l6, NA
f1 -> f2, g1, NA
f1 -> f3, g2, NA
f2 -> f3, g3, NA
y1 <-> y1, e1, NA
y2 <-> y2, e2, NA
y3 <-> y3, e3, NA
y4 <-> y4, e4, NA
y5 <-> y5, e5, NA
y6 <-> y6, e6, NA
y7 <-> y7, e7, NA
y8 <-> y8, e8, NA
y9 <-> y9, e9, NA
f1 <-> f1, e10, NA
f2 <-> f2, e11, NA
f3 <-> f3, e12, NA
## Model 1: treating prediction errors as factors
N100out.1<-semdiag(N100, ram.path=sem1, software='sem', D='F')
## Diagnostics plot
semdiag.plot(N100out.1)
## Summary output
semdiag.summary(N100out.1)
## Model 0: treating prediction errors the same as measurement errors
N100out.0<-semdiag(N100, ram.path=sem1, software='sem')
## Diagnostics plot
semdiag.plot(N100out.0)
## Summary output
semdiag.summary(N100out.0)
## Example 2. Contaminated data
data(N85)
## Model 1: treating prediction errors as factors
N85out.1<-semdiag(N85, ram.path=sem1, software='sem', D='F')
## Diagnostics plot
semdiag.plot(N85out.1)
## Summary output
semdiag.summary(N85out.1)
## Model 0: treating prediction errors the same as measurement errors
N85out.0<-semdiag(N85, ram.path=sem1, software='sem', D='E')
## Diagnostics plot
semdiag.plot(N85out.0)
## Summary output
semdiag.summary(N85out.0)
## Case profile plot
semdiag.cpp(N85out.0, cases=c(86, 90, 98:100))
## Delete the 99th and 100th observations
N85out.1.del<-semdiag(N85, ram.path=sem1, software='sem', D='F', delete=c(99,100))
## End(Not run)
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