Predict method for lm_robust object
Arguments
- object
an object of class 'lm_robust'
- newdata
a data frame in which to look for variables with which to predict
- se.fit
logical. Whether standard errors are required, default = FALSE
- interval
type of interval calculation. Can be abbreviated, default = none
- alpha
numeric denoting the test size for confidence intervals
- na.action
function determining what should be done with missing values in newdata. The default is to predict NA.
- pred.var
the variance(s) for future observations to be assumed for prediction intervals.
- weights
variance weights for prediction. This can be a numeric vector or a bare (unquoted) name of the weights variable in the supplied newdata.
- ...
other arguments, unused
Details
Produces predicted values, obtained by evaluating the regression
function in the frame newdata for fits from lm_robust and
lm_lin. If the logical se.fit is TRUE, standard errors of the
predictions are calculated. Setting intervals specifies computation of
confidence or prediction (tolerance) intervals at the specified level,
sometimes referred to as narrow vs. wide intervals.
The equation used for the standard error of a prediction given a row of data \(x\) is:
\(\sqrt(x \Sigma x')\),
where \(\Sigma\) is the estimated variance-covariance matrix from
lm_robust.
The prediction intervals are for a single observation at each case in
newdata with error variance(s) pred.var. The the default is to assume
that future observations have the same error variance as those used for
fitting, which is gotten from the fit lm_robust object. If
weights is supplied, the inverse of this is used as a scale factor. If the
fit was weighted, the default is to assume constant prediction variance,
with a warning.
Examples
# Set seed
set.seed(42)
# Simulate data
n <- 10
dat <- data.frame(y = rnorm(n), x = rnorm(n))
# Fit lm
lm_out <- lm_robust(y ~ x, data = dat)
# Get predicted fits
fits <- predict(lm_out, newdata = dat)
# With standard errors and confidence intervals
fits <- predict(lm_out, newdata = dat, se.fit = TRUE, interval = "confidence")
# Use new data as well
new_dat <- data.frame(x = runif(n, 5, 8))
predict(lm_out, newdata = new_dat)
#> 1 2 3 4 5 6 7
#> -0.6633382 -0.6957332 -0.4661705 -1.0056478 -0.6934163 -0.9964482 -0.9562101
#> 8 9 10
#> -0.8134173 -1.0041648 -0.8012341
# You can also supply custom variance weights for prediction intervals
new_dat$w <- runif(n)
predict(lm_out, newdata = new_dat, weights = w, interval = "prediction")
#> $fit
#> fit lwr upr
#> [1,] -0.6633382 -5.162176 3.835499
#> [2,] -0.6957332 -5.207346 3.815879
#> [3,] -0.4661705 -4.414089 3.481748
#> [4,] -1.0056478 -5.520872 3.509577
#> [5,] -0.6934163 -10.799452 9.412619
#> [6,] -0.9964482 -5.514685 3.521789
#> [7,] -0.9562101 -5.438922 3.526502
#> [8,] -0.8134173 -6.558348 4.931514
#> [9,] -1.0041648 -6.438104 4.429774
#> [10,] -0.8012341 -5.137823 3.535355
#>
# Works for 'lm_lin' models as well
dat$z <- sample(1:3, size = nrow(dat), replace = TRUE)
lmlin_out1 <- lm_lin(y ~ z, covariates = ~ x, data = dat)
predict(lmlin_out1, newdata = dat, interval = "prediction")
#> $fit
#> fit lwr upr
#> [1,] 1.370958447 NaN NaN
#> [2,] -0.186937884 NaN NaN
#> [3,] 0.239186411 NaN NaN
#> [4,] 0.110488863 NaN NaN
#> [5,] 0.093623919 NaN NaN
#> [6,] 0.004437459 NaN NaN
#> [7,] 1.511521997 NaN NaN
#> [8,] 0.386146556 NaN NaN
#> [9,] 2.018423714 NaN NaN
#> [10,] -0.074881809 NaN NaN
#>
# Predictions from Lin models are equivalent with and without an intercept
# and for multi-level treatments entered as numeric or factor variables
lmlin_out2 <- lm_lin(y ~ z - 1, covariates = ~ x, data = dat)
lmlin_out3 <- lm_lin(y ~ factor(z), covariates = ~ x, data = dat)
lmlin_out4 <- lm_lin(y ~ factor(z) - 1, covariates = ~ x, data = dat)
predict(lmlin_out2, newdata = dat, interval = "prediction")
#> $fit
#> fit lwr upr
#> [1,] 1.370958447 0.3615444 2.380372
#> [2,] -0.186937884 -1.4647357 1.090860
#> [3,] 0.239186411 -1.0086955 1.487068
#> [4,] 0.110488863 -0.9949579 1.215936
#> [5,] 0.093623919 -1.0022995 1.189547
#> [6,] 0.004437459 -1.0812175 1.090092
#> [7,] 1.511521997 0.5021080 2.520936
#> [8,] 0.386146556 -1.1366369 1.908930
#> [9,] 2.018423714 1.0090097 3.027838
#> [10,] -0.074881809 -1.2079017 1.058138
#>
predict(lmlin_out3, newdata = dat, interval = "prediction")
#> $fit
#> fit lwr upr
#> [1,] 1.370958447 NaN NaN
#> [2,] -0.186937884 NaN NaN
#> [3,] 0.239186411 NaN NaN
#> [4,] 0.110488863 NaN NaN
#> [5,] 0.093623919 NaN NaN
#> [6,] 0.004437459 NaN NaN
#> [7,] 1.511521997 NaN NaN
#> [8,] 0.386146556 NaN NaN
#> [9,] 2.018423714 NaN NaN
#> [10,] -0.074881809 NaN NaN
#>
predict(lmlin_out4, newdata = dat, interval = "prediction")
#> $fit
#> fit lwr upr
#> [1,] 1.370958447 0.3615444 2.380372
#> [2,] -0.186937884 -1.4647357 1.090860
#> [3,] 0.239186411 -1.0086955 1.487068
#> [4,] 0.110488863 -0.9949579 1.215936
#> [5,] 0.093623919 -1.0022995 1.189547
#> [6,] 0.004437459 -1.0812175 1.090092
#> [7,] 1.511521997 0.5021080 2.520936
#> [8,] 0.386146556 -1.1366369 1.908930
#> [9,] 2.018423714 1.0090097 3.027838
#> [10,] -0.074881809 -1.2079017 1.058138
#>
# In Lin models, predict will stop with an error message if new
# treatment levels are supplied in the new data
new_dat$z <- sample(0:3, size = nrow(new_dat), replace = TRUE)
# predict(lmlin_out, newdata = new_dat)