README.md
In asqm/digavaan: Easily dig relevant information from `lavaan`, `cfa`, `sem` and `growth` objects
digavaan
The goal of digavaan is to extract information as R data structures from
a lavaan object.
Installation
Install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("asqm/digavaan")
Warning: digavaan will most likely never go to CRAN because it
uses internal functions from lavaan. This hinders the package as
unstable. However, because most of the inner functions that are used in
this package are the same used by lavaan::summary, unless there’s a
breaking change (very unlikely with such a mature package) the same
information should be stable.
Example
Using the same example from lavaan:
library(lavaan)
#> This is lavaan 0.6-2
#> lavaan is BETA software! Please report any bugs.
library(digavaan)
model <- "
# latent variables
ind60 =~ x1 + x2 + x3
dem60 =~ y1 + y2 + y3 + y4
dem65 =~ y5 + y6 + y7 + y8
# regressions
dem60 ~ ind60
dem65 ~ ind60 + dem60
# residual covariances
y1 ~~ y5
y2 ~~ y4 + y6
y3 ~~ y7
y4 ~~ y8
y6 ~~ y8"
fit <- sem(model, data=PoliticalDemocracy)
# Traditional print
summary(fit)
#> lavaan 0.6-2 ended normally after 68 iterations
#>
#> Optimization method NLMINB
#> Number of free parameters 31
#>
#> Number of observations 75
#>
#> Estimator ML
#> Model Fit Test Statistic 38.125
#> Degrees of freedom 35
#> P-value (Chi-square) 0.329
#>
#> Parameter Estimates:
#>
#> Information Expected
#> Information saturated (h1) model Structured
#> Standard Errors Standard
#>
#> Latent Variables:
#> Estimate Std.Err z-value P(>|z|)
#> ind60 =~
#> x1 1.000
#> x2 2.180 0.139 15.742 0.000
#> x3 1.819 0.152 11.967 0.000
#> dem60 =~
#> y1 1.000
#> y2 1.257 0.182 6.889 0.000
#> y3 1.058 0.151 6.987 0.000
#> y4 1.265 0.145 8.722 0.000
#> dem65 =~
#> y5 1.000
#> y6 1.186 0.169 7.024 0.000
#> y7 1.280 0.160 8.002 0.000
#> y8 1.266 0.158 8.007 0.000
#>
#> Regressions:
#> Estimate Std.Err z-value P(>|z|)
#> dem60 ~
#> ind60 1.483 0.399 3.715 0.000
#> dem65 ~
#> ind60 0.572 0.221 2.586 0.010
#> dem60 0.837 0.098 8.514 0.000
#>
#> Covariances:
#> Estimate Std.Err z-value P(>|z|)
#> .y1 ~~
#> .y5 0.624 0.358 1.741 0.082
#> .y2 ~~
#> .y4 1.313 0.702 1.871 0.061
#> .y6 2.153 0.734 2.934 0.003
#> .y3 ~~
#> .y7 0.795 0.608 1.308 0.191
#> .y4 ~~
#> .y8 0.348 0.442 0.787 0.431
#> .y6 ~~
#> .y8 1.356 0.568 2.386 0.017
#>
#> Variances:
#> Estimate Std.Err z-value P(>|z|)
#> .x1 0.082 0.019 4.184 0.000
#> .x2 0.120 0.070 1.718 0.086
#> .x3 0.467 0.090 5.177 0.000
#> .y1 1.891 0.444 4.256 0.000
#> .y2 7.373 1.374 5.366 0.000
#> .y3 5.067 0.952 5.324 0.000
#> .y4 3.148 0.739 4.261 0.000
#> .y5 2.351 0.480 4.895 0.000
#> .y6 4.954 0.914 5.419 0.000
#> .y7 3.431 0.713 4.814 0.000
#> .y8 3.254 0.695 4.685 0.000
#> ind60 0.448 0.087 5.173 0.000
#> .dem60 3.956 0.921 4.295 0.000
#> .dem65 0.172 0.215 0.803 0.422
# Same information but as a data frames
summary_lavaan(fit)
#> $header
#> $header$iter
#> [1] "lavaan (0.6-2) converged normally after 68 iterations\n"
#>
#> $header$n_groups
#> name_group used total
#> 1 Group 1 75 NA
#>
#> $header$estimator_details
#> estimator ml ml_scaled
#> 1 Minimum Function Test Statistic 38.12522 NA
#> 2 Degrees of freedom 35.00000 NA
#> 3 P-value (Chi-square) 0.32900 NA
#>
#>
#> $estimates
#> lhs op rhs exo est se z pvalue
#> 1 ind60 =~ x1 0 1.000 0.000 NA NA
#> 2 ind60 =~ x2 0 2.180 0.139 15.742 0.000
#> 3 ind60 =~ x3 0 1.819 0.152 11.967 0.000
#> 4 dem60 =~ y1 0 1.000 0.000 NA NA
#> 5 dem60 =~ y2 0 1.257 0.182 6.889 0.000
#> 6 dem60 =~ y3 0 1.058 0.151 6.987 0.000
#> 7 dem60 =~ y4 0 1.265 0.145 8.722 0.000
#> 8 dem65 =~ y5 0 1.000 0.000 NA NA
#> 9 dem65 =~ y6 0 1.186 0.169 7.024 0.000
#> 10 dem65 =~ y7 0 1.280 0.160 8.002 0.000
#> 11 dem65 =~ y8 0 1.266 0.158 8.007 0.000
#> 12 dem60 ~ ind60 0 1.483 0.399 3.715 0.000
#> 13 dem65 ~ ind60 0 0.572 0.221 2.586 0.010
#> 14 dem65 ~ dem60 0 0.837 0.098 8.514 0.000
#> 15 y1 ~~ y5 0 0.624 0.358 1.741 0.082
#> 16 y2 ~~ y4 0 1.313 0.702 1.871 0.061
#> 17 y2 ~~ y6 0 2.153 0.734 2.934 0.003
#> 18 y3 ~~ y7 0 0.795 0.608 1.308 0.191
#> 19 y4 ~~ y8 0 0.348 0.442 0.787 0.431
#> 20 y6 ~~ y8 0 1.356 0.568 2.386 0.017
#> 21 x1 ~~ x1 0 0.082 0.019 4.184 0.000
#> 22 x2 ~~ x2 0 0.120 0.070 1.718 0.086
#> 23 x3 ~~ x3 0 0.467 0.090 5.177 0.000
#> 24 y1 ~~ y1 0 1.891 0.444 4.256 0.000
#> 25 y2 ~~ y2 0 7.373 1.374 5.366 0.000
#> 26 y3 ~~ y3 0 5.067 0.952 5.324 0.000
#> 27 y4 ~~ y4 0 3.148 0.739 4.261 0.000
#> 28 y5 ~~ y5 0 2.351 0.480 4.895 0.000
#> 29 y6 ~~ y6 0 4.954 0.914 5.419 0.000
#> 30 y7 ~~ y7 0 3.431 0.713 4.814 0.000
#> 31 y8 ~~ y8 0 3.254 0.695 4.685 0.000
#> 32 ind60 ~~ ind60 0 0.448 0.087 5.173 0.000
#> 33 dem60 ~~ dem60 0 3.956 0.921 4.295 0.000
#> 34 dem65 ~~ dem65 0 0.172 0.215 0.803 0.422
asqm/digavaan documentation built on May 10, 2019, 8:05 a.m.
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
digavaan
The goal of digavaan is to extract information as R data structures from
a lavaan object.
Installation
Install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("asqm/digavaan")
Warning: digavaan will most likely never go to CRAN because it
uses internal functions from lavaan. This hinders the package as
unstable. However, because most of the inner functions that are used in
this package are the same used by lavaan::summary, unless there’s a
breaking change (very unlikely with such a mature package) the same
information should be stable.
Example
Using the same example from lavaan:
library(lavaan)
#> This is lavaan 0.6-2
#> lavaan is BETA software! Please report any bugs.
library(digavaan)
model <- "
# latent variables
ind60 =~ x1 + x2 + x3
dem60 =~ y1 + y2 + y3 + y4
dem65 =~ y5 + y6 + y7 + y8
# regressions
dem60 ~ ind60
dem65 ~ ind60 + dem60
# residual covariances
y1 ~~ y5
y2 ~~ y4 + y6
y3 ~~ y7
y4 ~~ y8
y6 ~~ y8"
fit <- sem(model, data=PoliticalDemocracy)
# Traditional print
summary(fit)
#> lavaan 0.6-2 ended normally after 68 iterations
#>
#> Optimization method NLMINB
#> Number of free parameters 31
#>
#> Number of observations 75
#>
#> Estimator ML
#> Model Fit Test Statistic 38.125
#> Degrees of freedom 35
#> P-value (Chi-square) 0.329
#>
#> Parameter Estimates:
#>
#> Information Expected
#> Information saturated (h1) model Structured
#> Standard Errors Standard
#>
#> Latent Variables:
#> Estimate Std.Err z-value P(>|z|)
#> ind60 =~
#> x1 1.000
#> x2 2.180 0.139 15.742 0.000
#> x3 1.819 0.152 11.967 0.000
#> dem60 =~
#> y1 1.000
#> y2 1.257 0.182 6.889 0.000
#> y3 1.058 0.151 6.987 0.000
#> y4 1.265 0.145 8.722 0.000
#> dem65 =~
#> y5 1.000
#> y6 1.186 0.169 7.024 0.000
#> y7 1.280 0.160 8.002 0.000
#> y8 1.266 0.158 8.007 0.000
#>
#> Regressions:
#> Estimate Std.Err z-value P(>|z|)
#> dem60 ~
#> ind60 1.483 0.399 3.715 0.000
#> dem65 ~
#> ind60 0.572 0.221 2.586 0.010
#> dem60 0.837 0.098 8.514 0.000
#>
#> Covariances:
#> Estimate Std.Err z-value P(>|z|)
#> .y1 ~~
#> .y5 0.624 0.358 1.741 0.082
#> .y2 ~~
#> .y4 1.313 0.702 1.871 0.061
#> .y6 2.153 0.734 2.934 0.003
#> .y3 ~~
#> .y7 0.795 0.608 1.308 0.191
#> .y4 ~~
#> .y8 0.348 0.442 0.787 0.431
#> .y6 ~~
#> .y8 1.356 0.568 2.386 0.017
#>
#> Variances:
#> Estimate Std.Err z-value P(>|z|)
#> .x1 0.082 0.019 4.184 0.000
#> .x2 0.120 0.070 1.718 0.086
#> .x3 0.467 0.090 5.177 0.000
#> .y1 1.891 0.444 4.256 0.000
#> .y2 7.373 1.374 5.366 0.000
#> .y3 5.067 0.952 5.324 0.000
#> .y4 3.148 0.739 4.261 0.000
#> .y5 2.351 0.480 4.895 0.000
#> .y6 4.954 0.914 5.419 0.000
#> .y7 3.431 0.713 4.814 0.000
#> .y8 3.254 0.695 4.685 0.000
#> ind60 0.448 0.087 5.173 0.000
#> .dem60 3.956 0.921 4.295 0.000
#> .dem65 0.172 0.215 0.803 0.422
# Same information but as a data frames
summary_lavaan(fit)
#> $header
#> $header$iter
#> [1] "lavaan (0.6-2) converged normally after 68 iterations\n"
#>
#> $header$n_groups
#> name_group used total
#> 1 Group 1 75 NA
#>
#> $header$estimator_details
#> estimator ml ml_scaled
#> 1 Minimum Function Test Statistic 38.12522 NA
#> 2 Degrees of freedom 35.00000 NA
#> 3 P-value (Chi-square) 0.32900 NA
#>
#>
#> $estimates
#> lhs op rhs exo est se z pvalue
#> 1 ind60 =~ x1 0 1.000 0.000 NA NA
#> 2 ind60 =~ x2 0 2.180 0.139 15.742 0.000
#> 3 ind60 =~ x3 0 1.819 0.152 11.967 0.000
#> 4 dem60 =~ y1 0 1.000 0.000 NA NA
#> 5 dem60 =~ y2 0 1.257 0.182 6.889 0.000
#> 6 dem60 =~ y3 0 1.058 0.151 6.987 0.000
#> 7 dem60 =~ y4 0 1.265 0.145 8.722 0.000
#> 8 dem65 =~ y5 0 1.000 0.000 NA NA
#> 9 dem65 =~ y6 0 1.186 0.169 7.024 0.000
#> 10 dem65 =~ y7 0 1.280 0.160 8.002 0.000
#> 11 dem65 =~ y8 0 1.266 0.158 8.007 0.000
#> 12 dem60 ~ ind60 0 1.483 0.399 3.715 0.000
#> 13 dem65 ~ ind60 0 0.572 0.221 2.586 0.010
#> 14 dem65 ~ dem60 0 0.837 0.098 8.514 0.000
#> 15 y1 ~~ y5 0 0.624 0.358 1.741 0.082
#> 16 y2 ~~ y4 0 1.313 0.702 1.871 0.061
#> 17 y2 ~~ y6 0 2.153 0.734 2.934 0.003
#> 18 y3 ~~ y7 0 0.795 0.608 1.308 0.191
#> 19 y4 ~~ y8 0 0.348 0.442 0.787 0.431
#> 20 y6 ~~ y8 0 1.356 0.568 2.386 0.017
#> 21 x1 ~~ x1 0 0.082 0.019 4.184 0.000
#> 22 x2 ~~ x2 0 0.120 0.070 1.718 0.086
#> 23 x3 ~~ x3 0 0.467 0.090 5.177 0.000
#> 24 y1 ~~ y1 0 1.891 0.444 4.256 0.000
#> 25 y2 ~~ y2 0 7.373 1.374 5.366 0.000
#> 26 y3 ~~ y3 0 5.067 0.952 5.324 0.000
#> 27 y4 ~~ y4 0 3.148 0.739 4.261 0.000
#> 28 y5 ~~ y5 0 2.351 0.480 4.895 0.000
#> 29 y6 ~~ y6 0 4.954 0.914 5.419 0.000
#> 30 y7 ~~ y7 0 3.431 0.713 4.814 0.000
#> 31 y8 ~~ y8 0 3.254 0.695 4.685 0.000
#> 32 ind60 ~~ ind60 0 0.448 0.087 5.173 0.000
#> 33 dem60 ~~ dem60 0 3.956 0.921 4.295 0.000
#> 34 dem65 ~~ dem65 0 0.172 0.215 0.803 0.422
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