predict() generates predictions for models fit using glm_weightit(), ordinal_weightit(), multinom_weightit(), or coxph_weightit(). This page only details the predict() methods after using glm_weightit(), ordinal_weightit(), or multinom_weightit(). See survival::predict.coxph() for predictions when fitting Cox proportional hazards models using coxph_weightit().
Usage
# S3 method for class 'glm_weightit'
predict(object, newdata = NULL, type = "response", na.action = na.pass, ...)
# S3 method for class 'ordinal_weightit'
predict(
object,
newdata = NULL,
type = "response",
na.action = na.pass,
values = NULL,
...
)
# S3 method for class 'multinom_weightit'
predict(
object,
newdata = NULL,
type = "response",
na.action = na.pass,
values = NULL,
...
)Arguments
- object
a
glm_weightitobject.- newdata
optionally, a data frame in which to look for variables with which to predict. If omitted, the fitted values applied to the original dataset are used.
- type
the type of prediction desired. Allowable options include
"response", predictions on the scale of the original response variable (also"probs");"link", predictions on the scale of the linear predictor (also"lp");"class", the modal predicted category for ordinal and multinomial models; and"mean", the expected value of the outcome for ordinal and multinomial models. See Details for more information. The default is"response"for all models, which differs fromstats::predict.glm().- na.action
function determining what should be done with missing values in
newdata. The default is to predictNA.- ...
further arguments passed to or from other methods.
- values
when
type = "mean", the numeric values each level corresponds to. Should be supplied as a named vector with outcome levels as the names. IfNULLand the outcome levels can be converted to numeric, those will be used. See Details.
Value
A numeric vector containing the desired predictions, except for the following circumstances when an ordinal or multinomial model was fit:
when
type = "response", a numeric matrix with a row for each unit and a column for each level of the outcome with the predicted probability of the corresponding outcome in the cellswhen
type = "class", a factor with the model predicted class for each unit; for ordinal models, this will be an ordered factor.
Details
For generalized linear models other than ordinal and multinomial models, see stats::predict.glm() for more information on how predictions are computed and which arguments can be specified. Note that standard errors cannot be computed for the predictions using predict.glm_weightit().
For ordinal and multinomial models, setting type = "mean" computes the expected value of the outcome for each unit; this corresponds to the sum of the values supplied in values weighted by the predicted probability of those values. If values is omitted, predict() will attempt to convert the outcome levels to numeric values, and if this cannot be done, an error will be thrown. values should be specified as a named vector, e.g., values = c(one = 1, two = 2, three = 3), where "one", "two", and "three" are the original outcome levels and 1, 2, and 3 are the numeric values they correspond to. This method only makes sense to use if the outcome levels meaningfully correspond to numeric values.
For ordinal models, setting type = "link" (also "lp") computes the linear predictor without including the thresholds. This can be interpreted as the prediction of the latent variable underlying the ordinal response. This cannot be used with multinomial models.
See also
stats::predict.glm() for predictions from generalized linear models. glm_weightit() for the fitting function. survival::predict.coxph() for predictions from Cox proportional hazards models.
Examples
data("lalonde", package = "cobalt")
# Logistic regression model
fit1 <- glm_weightit(
re78 > 0 ~ treat * (age + educ + race + married +
re74 + re75),
data = lalonde, family = binomial, vcov = "none")
summary(predict(fit1))
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 0.2747 0.6986 0.7868 0.7671 0.8429 1.0000
# G-computation using predicted probabilities
p0 <- predict(fit1, type = "response",
newdata = transform(lalonde,
treat = 0))
p1 <- predict(fit1, type = "response",
newdata = transform(lalonde,
treat = 1))
mean(p1) - mean(p0)
#> [1] 0.0921469
# Multinomial logistic regression model
lalonde$re78_3 <- factor(findInterval(lalonde$re78,
c(0, 5e3, 1e4)),
labels = c("low", "med", "high"))
fit2 <- multinom_weightit(
re78_3 ~ treat * (age + educ + race + married +
re74 + re75),
data = lalonde, vcov = "none")
# Predicted probabilities
head(predict(fit2))
#> low med high
#> 1 0.4897563 0.1814665 0.3287772
#> 2 0.5027746 0.3133358 0.1838896
#> 3 0.5453691 0.1974158 0.2572151
#> 4 0.5809239 0.2219530 0.1971231
#> 5 0.5964825 0.2995950 0.1039225
#> 6 0.6217877 0.2666334 0.1115788
# Class assignment accuracy
mean(predict(fit2, type = "class") == lalonde$re78_3)
#> [1] 0.5635179
# G-computation using expected value of the outcome
values <- c("low" = 2500,
"med" = 7500,
"high" = 12500)
p0 <- predict(fit2, type = "mean", values = values,
newdata = transform(lalonde,
treat = 0))
p1 <- predict(fit2, type = "mean", values = values,
newdata = transform(lalonde,
treat = 1))
mean(p1) - mean(p0)
#> [1] 677.4256
# \donttest{
# Ordinal logistic regression
fit3 <- ordinal_weightit(
re78 ~ treat * (age + educ + race + married +
re74 + re75),
data = lalonde, vcov = "none")
# G-computation using expected value of the outcome;
# using original outcome values
p0 <- predict(fit3, type = "mean",
newdata = transform(lalonde,
treat = 0))
p1 <- predict(fit3, type = "mean",
newdata = transform(lalonde,
treat = 1))
mean(p1) - mean(p0)
#> [1] 943.9958
# }