Skip to contents

Record the Minimization History

record_history.Rd

Record the minimization history when a model is fitted by lavaan::lavaan() or its wrappers (e.g., lavaan::sem() or lavaan::cfa()).

Usage

record_history(fit)

# S3 method for fit_history
plot(x, params, last_n = -1, orientation = c("horizontal", "vertical"), ...)

# S3 method for fit_history
print(x, n_iterations = 10, digits = 3, ...)

Arguments

fit

An output of lavaan::lavaan() or its wrappers (e.g., lavaan::cfa() and lavaan::sem()).

x

A fit_history class object, the output of record_history().

params

The names of parameters to be plotted. Must be the names of one or more columns in x.

last_n

The lass n iterations to be plotted. Default is -1, plotting all iterations.

orientation

The orientation of the plot. Either "horizontal" (the default) or "vertical".

...

Optional arguments. To be passed to the print method of data frame.

n_iterations

The number of iterations to print. Default is 10, printing the first 10 iterations (or all iterations, if the number of iterations is less than 10).

digits

The number of digits to be displayed. Default is 3.

Value

A fit_history-class object with a plot method.

Details

It records the minimization history when a model is fitted by lavaan::lavaan() or its wrappers (e.g., lavaan::sem() or lavaan::cfa()). The recorded history can then be plotted or displayed, for visualizing and understanding how the estimates of free parameters is found.

It will refit the model by the update method of lavaan::lavaan, setting se = "none" and test = "standard" because they have no impact effect on the minimization process.

Functions

  • plot(fit_history): The plot method for the output of record_history().

  • print(fit_history): The print method for the output of record_history().

Author

Shu Fai Cheung https://orcid.org/0000-0002-9871-9448

Examples


# Adapted from the example for CFA in lavaan::cfa().
# Using only two of the factors
library(lavaan)
HS.model <-
'
visual  =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
'
fit <- cfa(HS.model, data = HolzingerSwineford1939)
# Refit the model with the history recorded
fit_h <- record_history(fit)
fit_h
#> Original call:
#> lavaan::lavaan(model = HS.model, data = HolzingerSwineford1939, 
#>     model.type = "cfa", int.ov.free = TRUE, int.lv.free = FALSE, 
#>     auto.fix.first = TRUE, auto.fix.single = TRUE, auto.var = TRUE, 
#>     auto.cov.lv.x = TRUE, auto.cov.y = TRUE, auto.th = TRUE, 
#>     auto.delta = TRUE, auto.efa = TRUE)
#> The number of iterations: 29 
#> The minimization history for the first 10 iterations:
#>    iteration     f visual=~x2 visual=~x3 textual=~x5 textual=~x6 x1~~x1 x2~~x2
#> 1          0 1.483      0.778      1.107       1.133       0.924  0.679  0.691
#> 2          1 0.349      0.791      1.126       1.161       0.950  0.746  0.767
#> 3          2 0.319      0.821      1.061       1.156       0.986  0.781  0.943
#> 4          3 0.184      0.791      0.943       1.115       0.940  0.799  0.995
#> 5          4 0.171      0.707      0.787       1.008       0.970  0.790  1.055
#> 6          5 0.061      0.668      0.768       1.115       0.876  0.767  1.062
#> 7          6 0.046      0.660      0.867       1.143       0.927  0.713  1.061
#> 8          7 0.045      0.653      0.853       1.125       0.943  0.668  1.075
#> 9          8 0.043      0.663      0.843       1.118       0.952  0.655  1.077
#> 10         9 0.042      0.660      0.830       1.128       0.930  0.651  1.081
#>    x3~~x3 x4~~x4 x5~~x5 x6~~x6 visual~~visual textual~~textual visual~~textual
#> 1   0.637  0.675  0.830  0.598          0.050            0.050           0.000
#> 2   0.702  0.727  0.871  0.658          0.481            0.781           0.505
#> 3   0.799  0.599  0.783  0.504          0.916            1.082          -0.285
#> 4   0.752  0.408  0.600  0.298          0.901            1.072          -0.162
#> 5   0.682  0.233  0.298  0.311          0.803            1.008           0.143
#> 6   0.674  0.415  0.439  0.410          0.717            0.944           0.307
#> 7   0.768  0.389  0.464  0.368          0.700            0.938           0.335
#> 8   0.788  0.387  0.451  0.348          0.662            0.931           0.393
#> 9   0.797  0.381  0.455  0.343          0.675            0.952           0.350
#> 10  0.801  0.372  0.447  0.352          0.680            0.951           0.351
plot(fit_h, params = c("visual=~x2", "visual=~x3",
                       "visual~~textual"),
            last_n = 10)

plot(fit_h, params = c("visual=~x2", "visual=~x3",
                       "visual~~textual"),
            last_n = 10,
            orientation = "vertical")