predictvglm.RdPredicted values based on a vector generalized linear model (VGLM) object.
predictvglm(object, newdata = NULL,
type = c("link", "response", "terms"),
se.fit = FALSE, deriv = 0, dispersion = NULL,
untransform = FALSE,
type.fitted = NULL, percentiles = NULL, ...)Object of class inheriting from "vlm",
e.g., vglm.
An optional data frame in which to look for variables with which to predict. If omitted, the fitted linear predictors are used.
The value of this argument can be abbreviated. The type of prediction required. The default is the first one, meaning on the scale of the linear predictors. This should be a \(n \times M\) matrix.
The alternative "response" is on the scale of the
response variable, and depending on the family function,
this may or may not be the mean.
Often this is the fitted value, e.g.,
fitted(vglmObject)
(see fittedvlm).
Note that the response is output from the @linkinv slot,
where the eta argument is the \(n \times M\) matrix
of linear predictors.
The "terms" option returns a matrix giving the
fitted values of each term in the model formula on the
linear predictor scale.
The terms have been centered.
logical: return standard errors?
Non-negative integer. Currently this must be zero. Later, this may be implemented for general values.
Dispersion parameter. This may be inputted at this stage, but the default is to use the dispersion parameter of the fitted model.
Some VGAM family functions have an argument by
the same name. If so, then one can obtain fitted values
by setting type = "response" and
choosing a value of type.fitted from what's
available.
If type.fitted = "quantiles" is available then
the percentiles argument can be used to specify
what quantile values are requested.
Used only if type.fitted = "quantiles" is
available and is selected.
Logical. Reverses any parameter link function.
This argument only works if
type = "link", se.fit = FALSE, deriv = 0.
Setting untransform = TRUE does not work for
all VGAM family functions; only ones where there
is a one-to-one correspondence between a simple link function
and a simple parameter might work.
Arguments passed into predictvlm.
Obtains predictions and optionally estimates
standard errors of those predictions from a
fitted vglm object.
By default,
each row of the matrix returned can be written
as \(\eta_i^T\), comprising of \(M\)
components or linear predictors.
If there are any offsets, these
are included.
This code implements smart prediction
(see smartpred).
If se.fit = FALSE, a vector or matrix
of predictions.
If se.fit = TRUE, a list with components
Predictions
Estimated standard errors
Degrees of freedom
The square root of the dispersion parameter (but these are being phased out in the package)
Yee, T. W. (2015). Vector Generalized Linear and Additive Models: With an Implementation in R. New York, USA: Springer.
Yee, T. W. and Hastie, T. J. (2003). Reduced-rank vector generalized linear models. Statistical Modelling, 3, 15–41.
Setting se.fit = TRUE and
type = "response"
will generate an error.
The arguments type.fitted
and percentiles
are provided in this function to give more
convenience than
modifying the extra slot directly.
This function may change in the future.
# Illustrates smart prediction
pneumo <- transform(pneumo, let = log(exposure.time))
fit <- vglm(cbind(normal, mild, severe) ~ poly(c(scale(let)), 2),
propodds, pneumo, trace = TRUE, x.arg = FALSE)
#> Iteration 1: deviance = 4.162527
#> Iteration 2: deviance = 3.951629
#> Iteration 3: deviance = 3.946558
#> Iteration 4: deviance = 3.94655
#> Iteration 5: deviance = 3.94655
class(fit)
#> [1] "vglm"
#> attr(,"package")
#> [1] "VGAM"
(q0 <- head(predict(fit)))
#> logitlink(P[Y>=2]) logitlink(P[Y>=3])
#> 1 -6.6420717 -7.540193
#> 2 -2.7470610 -3.645182
#> 3 -1.6175447 -2.515666
#> 4 -0.9547998 -1.852921
#> 5 -0.4876278 -1.385749
#> 6 -0.1414232 -1.039544
(q1 <- predict(fit, newdata = head(pneumo)))
#> logitlink(P[Y>=2]) logitlink(P[Y>=3])
#> 1 -5.773141107 -6.6712621
#> 2 -2.059406372 -2.9575274
#> 3 -1.017291252 -1.9154122
#> 4 -0.420223629 -1.3183446
#> 5 -0.009188218 -0.9073092
#> 6 0.287785773 -0.6103352
(q2 <- predict(fit, newdata = head(pneumo)))
#> logitlink(P[Y>=2]) logitlink(P[Y>=3])
#> 1 -5.773141107 -6.6712621
#> 2 -2.059406372 -2.9575274
#> 3 -1.017291252 -1.9154122
#> 4 -0.420223629 -1.3183446
#> 5 -0.009188218 -0.9073092
#> 6 0.287785773 -0.6103352
all.equal(q0, q1) # Should be TRUE
#> [1] "Mean relative difference: 0.2354654"
all.equal(q1, q2) # Should be TRUE
#> [1] TRUE
head(predict(fit))
#> logitlink(P[Y>=2]) logitlink(P[Y>=3])
#> 1 -6.6420717 -7.540193
#> 2 -2.7470610 -3.645182
#> 3 -1.6175447 -2.515666
#> 4 -0.9547998 -1.852921
#> 5 -0.4876278 -1.385749
#> 6 -0.1414232 -1.039544
head(predict(fit, untransform = TRUE))
#> P[Y>=2] P[Y>=3]
#> 1 0.001302623 0.0005310131
#> 2 0.060252851 0.0254519377
#> 3 0.165543769 0.0747672299
#> 4 0.277920572 0.1355303291
#> 5 0.380452564 0.2000873101
#> 6 0.464703016 0.2612379561
p0 <- head(predict(fit, type = "response"))
p1 <- head(predict(fit, type = "response", newdata = pneumo))
p2 <- head(predict(fit, type = "response", newdata = pneumo))
p3 <- head(fitted(fit))
all.equal(p0, p1) # Should be TRUE
#> [1] TRUE
all.equal(p1, p2) # Should be TRUE
#> [1] TRUE
all.equal(p2, p3) # Should be TRUE
#> [1] TRUE
predict(fit, type = "terms", se = TRUE)
#> $fitted.values
#> poly(c(scale(let)), 2):1 poly(c(scale(let)), 2):2
#> 1 -5.1276963 -5.1276963
#> 2 -1.2326855 -1.2326855
#> 3 -0.1031692 -0.1031692
#> 4 0.5595757 0.5595757
#> 5 1.0267477 1.0267477
#> 6 1.3729523 1.3729523
#> 7 1.6576137 1.6576137
#> 8 1.8466615 1.8466615
#> attr(,"vterm.assign")
#> attr(,"vterm.assign")$`poly(c(scale(let)), 2)`
#> [1] 1 2
#>
#>
#> $se.fit
#> poly(c(scale(let)), 2):1 poly(c(scale(let)), 2):2
#> 1 1.7242629 1.7242629
#> 2 0.2327739 0.2327739
#> 3 0.3311413 0.3311413
#> 4 0.3768132 0.3768132
#> 5 0.3653147 0.3653147
#> 6 0.3317412 0.3317412
#> 7 0.3044709 0.3044709
#> 8 0.3113166 0.3113166
#> attr(,"vterm.assign")
#> attr(,"vterm.assign")$`poly(c(scale(let)), 2)`
#> [1] 1 2
#>
#>
#> $df
#> [1] 12
#>
#> $sigma
#> [1] 1
#>