Update Nested Models in Hierarchical Regression.

Wrapper function to ensure the same observations are used for each updated model as were used in the first model.

Usage

update_nested(object, formula., ..., evaluate = TRUE)

Arguments

object

model object to update

formula.

updated model formula

...

further parameters passed to the fitting function

evaluate

whether to evaluate the model. One of: TRUE or FALSE

Value

lm model

Details

Convenience wrapper function to ensure the same observations are used for each updated model as were used in the first model, to ensure comparability of models.

See also

https://stackoverflow.com/a/37341927

https://stackoverflow.com/a/37416336

https://stackoverflow.com/a/47195348

Other multipleRegression: lmCombine(), plot2WayInteraction(), ppPlot(), semPlotInteraction()

Examples

# Prepare Data
data("mtcars")

dat <- mtcars

# Create some missing values in mtcars
dat[1, "wt"] <- NA
dat[5, "cyl"] <- NA
dat[7, "hp"] <- NA

m1 <- lm(mpg ~ wt + cyl + hp, data = dat)
m2 <- update_nested(m1, . ~ . - wt)  # Remove wt
m3 <- update_nested(m1, . ~ . - cyl) # Remove cyl
m4 <- update_nested(m1, . ~ . - wt - cyl) # Remove wt and cyl
m5 <- update_nested(m1, . ~ . - wt - cyl - hp) # Remove all three variables
# (i.e., model with intercept only)

anova(m1, m2, m3, m4, m5)
#> Analysis of Variance Table
#> 
#> Model 1: mpg ~ wt + cyl + hp
#> Model 2: mpg ~ cyl + hp
#> Model 3: mpg ~ wt + hp
#> Model 4: mpg ~ hp
#> Model 5: mpg ~ 1
#>   Res.Df     RSS Df Sum of Sq      F    Pr(>F)    
#> 1     25  169.38                                  
#> 2     26  280.28 -1   -110.90 16.369 0.0004404 ***
#> 3     26  186.04  0     94.24                     
#> 4     27  443.80 -1   -257.76 38.045 1.892e-06 ***
#> 5     28 1088.40 -1   -644.60 95.141 5.287e-10 ***
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1