Extract parameterEstimates from an estimated model
parameter_estimates.RdExtract parameterEstimates from an estimated model
Usage
parameter_estimates(object, ...)
# S3 method for class 'lavaan'
parameter_estimates(
object,
colon.pi = NULL,
high.order.as.measr = NULL,
rm.tmp.ov = NULL,
label.renamed.prod = NULL,
is.public = NULL,
...
)
# S3 method for class 'modsem_da'
parameter_estimates(
object,
high.order.as.measr = TRUE,
is.public = TRUE,
rm.tmp.ov = is.public,
label.renamed.prod = NULL,
...
)
# S3 method for class 'modsem_mplus'
parameter_estimates(
object,
colon.pi = NULL,
high.order.as.measr = NULL,
rm.tmp.ov = NULL,
label.renamed.prod = NULL,
is.public = NULL,
...
)
# S3 method for class 'modsem_pi'
parameter_estimates(
object,
colon.pi = FALSE,
label.renamed.prod = FALSE,
high.order.as.measr = NULL,
rm.tmp.ov = NULL,
is.public = NULL,
...
)Arguments
- object
An object of class
modsem_pi,modsem_da, ormodsem_mplus- ...
Additional arguments passed to other functions
- colon.pi
Should colons (
:) be added to the interaction terms (E.g., `XZ` -> `X:Z`)?- high.order.as.measr
Should higher order measurement model be denoted with the
=~operator? IfFALSEthe~operator is used.- rm.tmp.ov
Should temporary (hidden) variables be removed?
- label.renamed.prod
Should renamed product terms keep their old (implicit) labels?
- is.public
Should public version of parameter table be returned? If
FALSE, the internal version of the parameter table is returned.
Methods (by class)
parameter_estimates(lavaan): Get parameter estimates of alavaanobjectparameter_estimates(modsem_da): Get parameter estimates of amodsem_daobjectparameter_estimates(modsem_mplus): Get parameter estimates of amodsem_mplusobjectparameter_estimates(modsem_pi): Get parameter estimates of amodsem_piobject
Examples
m1 <- '
# Outer Model
X =~ x1 + x2 + x3
Z =~ z1 + z2 + z3
Y =~ y1 + y2 + y3
# Inner Model
Y ~ X + Z + X:Z
'
# Double centering approach
est_dca <- modsem(m1, oneInt)
pars <- parameter_estimates(est_dca) # no correction
# Pretty summary
summarize_partable(pars)
#> modsem (version 1.0.16)
#>
#> Number of model parameters 64
#> Number of latent variables 4
#> Number of observed variables 18
#>
#> Latent Variables:
#> Estimate Std.Error z.value P(>|z|)
#> X =~
#> x1 1.000 0.000
#> x2 0.804 0.013 63.612 0.000
#> x3 0.916 0.014 67.144 0.000
#> Z =~
#> z1 1.000 0.000
#> z2 0.812 0.013 64.763 0.000
#> z3 0.882 0.013 67.014 0.000
#> Y =~
#> y1 1.000 0.000
#> y2 0.798 0.007 107.428 0.000
#> y3 0.899 0.008 112.453 0.000
#> XZ =~
#> x1z1 1.000 0.000
#> x2z1 0.805 0.013 60.636 0.000
#> x3z1 0.877 0.014 62.680 0.000
#> x1z2 0.793 0.013 59.343 0.000
#> x2z2 0.646 0.015 43.672 0.000
#> x3z2 0.706 0.016 44.292 0.000
#> x1z3 0.887 0.014 63.700 0.000
#> x2z3 0.716 0.016 45.645 0.000
#> x3z3 0.781 0.017 45.339 0.000
#>
#> Regressions:
#> Estimate Std.Error z.value P(>|z|)
#> Y ~
#> X 0.675 0.027 25.379 0.000
#> Z 0.561 0.026 21.606 0.000
#> XZ 0.702 0.027 26.360 0.000
#>
#> Covariances:
#> Estimate Std.Error z.value P(>|z|)
#> .x1z1 ~~
#> .x1z2 0.115 0.008 14.802 0.000
#> .x1z3 0.114 0.008 13.947 0.000
#> .x2z1 0.125 0.008 16.095 0.000
#> .x3z1 0.140 0.009 16.135 0.000
#> .x1z2 ~~
#> .x1z3 0.103 0.007 14.675 0.000
#> .x2z2 0.128 0.006 20.850 0.000
#> .x3z2 0.146 0.007 21.243 0.000
#> .x1z3 ~~
#> .x2z3 0.116 0.007 17.818 0.000
#> .x3z3 0.135 0.007 18.335 0.000
#> .x2z1 ~~
#> .x2z2 0.135 0.006 20.905 0.000
#> .x2z3 0.145 0.007 21.145 0.000
#> .x3z1 0.114 0.007 16.058 0.000
#> .x2z2 ~~
#> .x2z3 0.117 0.006 20.419 0.000
#> .x3z2 0.116 0.006 20.586 0.000
#> .x2z3 ~~
#> .x3z3 0.109 0.006 18.059 0.000
#> .x3z1 ~~
#> .x3z2 0.138 0.007 19.331 0.000
#> .x3z3 0.158 0.008 20.269 0.000
#> .x3z2 ~~
#> .x3z3 0.131 0.007 19.958 0.000
#> X ~~
#> Z 0.201 0.024 8.271 0.000
#> XZ 0.016 0.025 0.628 0.530
#> Z ~~
#> XZ 0.062 0.025 2.449 0.014
#>
#> Variances:
#> Estimate Std.Error z.value P(>|z|)
#> .x1 0.160 0.009 17.871 0.000
#> .x2 0.162 0.007 22.969 0.000
#> .x3 0.163 0.008 20.161 0.000
#> .z1 0.168 0.009 18.143 0.000
#> .z2 0.158 0.007 22.264 0.000
#> .z3 0.158 0.008 20.389 0.000
#> .y1 0.159 0.009 17.896 0.000
#> .y2 0.154 0.007 22.640 0.000
#> .y3 0.164 0.008 20.698 0.000
#> .x1z1 0.311 0.014 22.227 0.000
#> .x2z1 0.292 0.011 27.287 0.000
#> .x3z1 0.327 0.012 26.275 0.000
#> .x1z2 0.290 0.011 26.910 0.000
#> .x2z2 0.239 0.008 29.770 0.000
#> .x3z2 0.270 0.009 29.117 0.000
#> .x1z3 0.272 0.012 23.586 0.000
#> .x2z3 0.245 0.009 27.979 0.000
#> .x3z3 0.297 0.011 28.154 0.000
#> X 0.981 0.036 26.895 0.000
#> Z 1.016 0.038 26.856 0.000
#> .Y 0.990 0.038 25.926 0.000
#> XZ 1.045 0.044 24.004 0.000
#>
# Only print the data.frame
pars
#> lhs op rhs est se z pvalue ci.lower ci.upper
#> 1 X =~ x1 1.000 0.000 NA NA 1.000 1.000
#> 2 X =~ x2 0.804 0.013 63.612 0.000 0.779 0.829
#> 3 X =~ x3 0.916 0.014 67.144 0.000 0.889 0.942
#> 4 Z =~ z1 1.000 0.000 NA NA 1.000 1.000
#> 5 Z =~ z2 0.812 0.013 64.763 0.000 0.787 0.836
#> 6 Z =~ z3 0.882 0.013 67.014 0.000 0.856 0.908
#> 7 Y =~ y1 1.000 0.000 NA NA 1.000 1.000
#> 8 Y =~ y2 0.798 0.007 107.428 0.000 0.784 0.813
#> 9 Y =~ y3 0.899 0.008 112.453 0.000 0.883 0.915
#> 10 Y ~ X 0.675 0.027 25.379 0.000 0.622 0.727
#> 11 Y ~ Z 0.561 0.026 21.606 0.000 0.510 0.612
#> 12 Y ~ XZ 0.702 0.027 26.360 0.000 0.650 0.754
#> 13 XZ =~ x1z1 1.000 0.000 NA NA 1.000 1.000
#> 14 XZ =~ x2z1 0.805 0.013 60.636 0.000 0.779 0.831
#> 15 XZ =~ x3z1 0.877 0.014 62.680 0.000 0.850 0.904
#> 16 XZ =~ x1z2 0.793 0.013 59.343 0.000 0.766 0.819
#> 17 XZ =~ x2z2 0.646 0.015 43.672 0.000 0.617 0.675
#> 18 XZ =~ x3z2 0.706 0.016 44.292 0.000 0.675 0.738
#> 19 XZ =~ x1z3 0.887 0.014 63.700 0.000 0.860 0.914
#> 20 XZ =~ x2z3 0.716 0.016 45.645 0.000 0.686 0.747
#> 21 XZ =~ x3z3 0.781 0.017 45.339 0.000 0.748 0.815
#> 22 x1z1 ~~ x1z2 0.115 0.008 14.802 0.000 0.100 0.131
#> 23 x1z1 ~~ x1z3 0.114 0.008 13.947 0.000 0.098 0.130
#> 24 x1z1 ~~ x2z1 0.125 0.008 16.095 0.000 0.110 0.140
#> 25 x1z1 ~~ x3z1 0.140 0.009 16.135 0.000 0.123 0.157
#> 26 x1z2 ~~ x1z3 0.103 0.007 14.675 0.000 0.089 0.117
#> 27 x1z2 ~~ x2z2 0.128 0.006 20.850 0.000 0.116 0.140
#> 28 x1z2 ~~ x3z2 0.146 0.007 21.243 0.000 0.132 0.159
#> 29 x1z3 ~~ x2z3 0.116 0.007 17.818 0.000 0.103 0.129
#> 30 x1z3 ~~ x3z3 0.135 0.007 18.335 0.000 0.120 0.149
#> 31 x2z1 ~~ x2z2 0.135 0.006 20.905 0.000 0.122 0.147
#> 32 x2z1 ~~ x2z3 0.145 0.007 21.145 0.000 0.131 0.158
#> 33 x2z1 ~~ x3z1 0.114 0.007 16.058 0.000 0.100 0.128
#> 34 x2z2 ~~ x2z3 0.117 0.006 20.419 0.000 0.106 0.129
#> 35 x2z2 ~~ x3z2 0.116 0.006 20.586 0.000 0.105 0.127
#> 36 x2z3 ~~ x3z3 0.109 0.006 18.059 0.000 0.097 0.121
#> 37 x3z1 ~~ x3z2 0.138 0.007 19.331 0.000 0.124 0.152
#> 38 x3z1 ~~ x3z3 0.158 0.008 20.269 0.000 0.143 0.173
#> 39 x3z2 ~~ x3z3 0.131 0.007 19.958 0.000 0.118 0.144
#> 40 x1 ~~ x1 0.160 0.009 17.871 0.000 0.142 0.177
#> 41 x2 ~~ x2 0.162 0.007 22.969 0.000 0.148 0.176
#> 42 x3 ~~ x3 0.163 0.008 20.161 0.000 0.147 0.179
#> 43 z1 ~~ z1 0.168 0.009 18.143 0.000 0.150 0.186
#> 44 z2 ~~ z2 0.158 0.007 22.264 0.000 0.144 0.172
#> 45 z3 ~~ z3 0.158 0.008 20.389 0.000 0.143 0.173
#> 46 y1 ~~ y1 0.159 0.009 17.896 0.000 0.142 0.177
#> 47 y2 ~~ y2 0.154 0.007 22.640 0.000 0.141 0.168
#> 48 y3 ~~ y3 0.164 0.008 20.698 0.000 0.148 0.179
#> 49 x1z1 ~~ x1z1 0.311 0.014 22.227 0.000 0.283 0.338
#> 50 x2z1 ~~ x2z1 0.292 0.011 27.287 0.000 0.271 0.313
#> 51 x3z1 ~~ x3z1 0.327 0.012 26.275 0.000 0.302 0.351
#> 52 x1z2 ~~ x1z2 0.290 0.011 26.910 0.000 0.269 0.311
#> 53 x2z2 ~~ x2z2 0.239 0.008 29.770 0.000 0.223 0.254
#> 54 x3z2 ~~ x3z2 0.270 0.009 29.117 0.000 0.252 0.288
#> 55 x1z3 ~~ x1z3 0.272 0.012 23.586 0.000 0.250 0.295
#> 56 x2z3 ~~ x2z3 0.245 0.009 27.979 0.000 0.228 0.262
#> 57 x3z3 ~~ x3z3 0.297 0.011 28.154 0.000 0.276 0.317
#> 58 X ~~ X 0.981 0.036 26.895 0.000 0.910 1.052
#> 59 Z ~~ Z 1.016 0.038 26.856 0.000 0.942 1.090
#> 60 Y ~~ Y 0.990 0.038 25.926 0.000 0.915 1.065
#> 61 XZ ~~ XZ 1.045 0.044 24.004 0.000 0.960 1.131
#> 62 X ~~ Z 0.201 0.024 8.271 0.000 0.153 0.249
#> 63 X ~~ XZ 0.016 0.025 0.628 0.530 -0.033 0.064
#> 64 Z ~~ XZ 0.062 0.025 2.449 0.014 0.012 0.112