Build a data.frame for prediction, where one variable varies and all
other variables are set to the reference value (median for continuous
variables).
predDF(object, ...)
# S3 method for JointAI
predDF(object, vars, length = 100L, ...)
# S3 method for formula
predDF(object, data, vars, length = 100L, ...)
# S3 method for list
predDF(object, data, vars, length = 100L, idvar = NULL, ...)object inheriting from class 'JointAI'
optional specification of the values used for some (or all) of the
variables given in vars
name of variable that should be varying
number of values used in the sequence when vars is
continuous
a data.frame containing the original data
(more details below)
optional name of an ID variable
# fit a JointAI model
mod <- lm_imp(y ~ C1 + C2 + M2, data = wideDF, n.iter = 100)
# generate a data frame with varying "C2" and reference values for all other
# variables in the model
newDF <- predDF(mod, vars = ~ C2)
head(newDF)
#> y C1 C2 M2
#> 1 -3.183137 1.434298 -0.9226220 1
#> 2 -3.183137 1.434298 -0.9046136 1
#> 3 -3.183137 1.434298 -0.8866052 1
#> 4 -3.183137 1.434298 -0.8685968 1
#> 5 -3.183137 1.434298 -0.8505885 1
#> 6 -3.183137 1.434298 -0.8325801 1
newDF2 <- predDF(mod, vars = ~ C2 + M2,
C2 = seq(-0.5, 0.5, 0.25),
M2 = levels(wideDF$M2)[2:3])
newDF2
#> y C1 C2 M2
#> 1 -3.183137 1.434298 -0.50 2
#> 2 -3.183137 1.434298 -0.25 2
#> 3 -3.183137 1.434298 0.00 2
#> 4 -3.183137 1.434298 0.25 2
#> 5 -3.183137 1.434298 0.50 2
#> 6 -3.183137 1.434298 -0.50 3
#> 7 -3.183137 1.434298 -0.25 3
#> 8 -3.183137 1.434298 0.00 3
#> 9 -3.183137 1.434298 0.25 3
#> 10 -3.183137 1.434298 0.50 3