Fetches elements from scan objects

The fetch function is a getter function for scan objects returned from regression functions such as plm(), hplm(), bplm(), and mplm(). It allows users to extract specific elements from these objects, such as the fitted model.

Usage

fetch(object, what, ...)

Arguments

object

Object returned from a scan function.

what

Element/part to be extracted. Currently, only "model" is supported to extract the fitted regression model.

...

Further parameters passed to the function.

Value

An object of the respective regression model class.

See also

Other regression functions: bplm(), hplm(), mplm(), plm(), print.sc_ac(), print.sc_bctau(), trend()

Author

Juergen Wilbert

Examples

# plm regression
model1 <- plm(example_A24)
fetch(model1, what = "model") |> summary()
#> 
#> Call:
#> glm(formula = formula_full, family = family, data = data, na.action = na.action)
#> 
#> Coefficients:
#>             Estimate Std. Error t value Pr(>|t|)    
#> (Intercept)  258.714     18.036  14.344 1.21e-11 ***
#> year           1.857      5.002   0.371    0.715    
#> phaseB      -150.383     25.694  -5.853 1.23e-05 ***
#> interB        -1.726      5.204  -0.332    0.744    
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> (Dispersion parameter for gaussian family taken to be 700.6563)
#> 
#>     Null deviance: 111568  on 22  degrees of freedom
#> Residual deviance:  13312  on 19  degrees of freedom
#> AIC: 221.57
#> 
#> Number of Fisher Scoring iterations: 2
#> 
# Multilevel plm regression
model2 <- hplm(exampleAB_50)
fetch(model2, what = "model") |> summary()
#> Linear mixed-effects model fit by maximum likelihood
#>   Data: dat 
#>        AIC      BIC    logLik
#>   8758.802 8790.185 -4373.401
#> 
#> Random effects:
#>  Formula: ~1 | case
#>         (Intercept) Residual
#> StdDev:    9.969762 5.284501
#> 
#> Fixed effects:  values ~ 1 + mt + phaseB + interB 
#>                Value Std.Error   DF  t-value p-value
#> (Intercept) 48.39806 1.4840943 1328 32.61117       0
#> mt           0.57893 0.1156551 1328  5.00566       0
#> phaseB      14.03787 0.6548726 1328 21.43603       0
#> interB       0.90227 0.1189152 1328  7.58755       0
#>  Correlation: 
#>        (Intr) mt     phaseB
#> mt     -0.246              
#> phaseB  0.112 -0.770       
#> interB  0.239 -0.972  0.654
#> 
#> Standardized Within-Group Residuals:
#>         Min          Q1         Med          Q3         Max 
#> -3.25058631 -0.65706678  0.01381196  0.68617339  3.04005252 
#> 
#> Number of Observations: 1381
#> Number of Groups: 50 
# Bayesian plm regression
model3 <- bplm(exampleAB_50, nitt = 5000)
fetch(model3, what = "model") |> summary()
#> 
#>  Iterations = 3001:4991
#>  Thinning interval  = 10
#>  Sample size  = 200 
#> 
#>  DIC: 8574.971 
#> 
#>  G-structure:  ~case
#> 
#>      post.mean l-95% CI u-95% CI eff.samp
#> case     104.3    69.03    143.1    273.2
#> 
#>  R-structure:  ~units
#> 
#>       post.mean l-95% CI u-95% CI eff.samp
#> units     27.97    26.17    29.92      200
#> 
#>  Location effects: values ~ 1 + mt + phaseB + interB 
#> 
#>             post.mean l-95% CI u-95% CI eff.samp  pMCMC   
#> (Intercept)   48.5216  45.0742  51.4028    243.6 <0.005 **
#> mt             0.5846   0.3416   0.8156    200.0 <0.005 **
#> phaseB        14.0314  12.7356  15.1772    200.0 <0.005 **
#> interB         0.8952   0.6623   1.1684    200.0 <0.005 **
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1