Construct the Model Frame of a VLM Object

This function returns a data.frame with the variables. It is applied to an object which inherits from class "vlm" (e.g., a fitted model of class "vglm").

Usage

model.framevlm(object, setupsmart = TRUE, wrapupsmart = TRUE, ...)

Arguments

object

a model object from the VGAM R package that inherits from a vector linear model (VLM), e.g., a model of class "vglm".

...

further arguments such as data, na.action, subset. See model.frame for more information on these.

setupsmart, wrapupsmart

Logical. Arguments to determine whether to use smart prediction.

Details

Since object is an object which inherits from class "vlm" (e.g., a fitted model of class "vglm"), the method will either returned the saved model frame used when fitting the model (if any, selected by argument model = TRUE) or pass the call used when fitting on to the default method.

This code implements smart prediction (see smartpred).

Value

A data.frame containing the variables used in the object plus those specified in ....

References

Chambers, J. M. (1992). Data for models. Chapter 3 of Statistical Models in S eds J. M. Chambers and T. J. Hastie, Wadsworth & Brooks/Cole.

Examples

# Illustrates smart prediction
pneumo <- transform(pneumo, let = log(exposure.time))
fit <- vglm(cbind(normal,mild, severe) ~ poly(c(scale(let)), 2),
            multinomial, pneumo, trace = TRUE, x = FALSE)
#> Iteration 1: deviance = 3.716285
#> Iteration 2: deviance = 3.499289
#> Iteration 3: deviance = 3.487318
#> Iteration 4: deviance = 3.487262
#> Iteration 5: deviance = 3.487262
class(fit)
#> [1] "vglm"
#> attr(,"package")
#> [1] "VGAM"

check1 <- head(model.frame(fit))
check1
#>   cbind(normal, mild, severe).normal cbind(normal, mild, severe).mild
#> 1                                 98                                0
#> 2                                 51                                2
#> 3                                 34                                6
#> 4                                 35                                5
#> 5                                 32                               10
#> 6                                 23                                7
#>   cbind(normal, mild, severe).severe poly(c(scale(let)), 2).1
#> 1                                  0              -0.77195016
#> 2                                  1              -0.27226249
#> 3                                  3              -0.08294405
#> 4                                  8               0.04649255
#> 5                                  9               0.15028005
#> 6                                  8               0.23692162
#>   poly(c(scale(let)), 2).2
#> 1               0.50285988
#> 2              -0.43378435
#> 3              -0.42122091
#> 4              -0.29641247
#> 5              -0.12815229
#> 6               0.05878623
check2 <- model.frame(fit, data = head(pneumo))
check2
#>   cbind(normal, mild, severe).normal cbind(normal, mild, severe).mild
#> 1                                 98                                0
#> 2                                 51                                2
#> 3                                 34                                6
#> 4                                 35                                5
#> 5                                 32                               10
#> 6                                 23                                7
#>   cbind(normal, mild, severe).severe poly(c(scale(let)), 2).1
#> 1                                  0              -0.79735249
#> 2                                  1              -0.19033917
#> 3                                  3               0.03964213
#> 4                                  8               0.19687984
#> 5                                  9               0.32295938
#> 6                                  8               0.42821031
#>   poly(c(scale(let)), 2).2
#> 1               0.43043654
#> 2              -0.57044185
#> 3              -0.41509933
#> 4              -0.13981459
#> 5               0.18011485
#> 6               0.51480438
all.equal(unlist(check1), unlist(check2))  # Should be TRUE
#> [1] "Mean relative difference: 0.5527089"

q0 <- head(predict(fit))
q1 <- head(predict(fit, newdata = pneumo))
q2 <- predict(fit, newdata = head(pneumo))
all.equal(q0, q1)  # Should be TRUE
#> [1] TRUE
all.equal(q1, q2)  # Should be TRUE
#> [1] "Mean relative difference: 0.2835235"