Compute the predicted values based on the models stored in an 'lm_list`-class object.
Usage
# S3 method for lm_list
predict(object, x = NULL, y = NULL, m = NULL, newdata, ...)Arguments
- object
An 'lm_list'-class object.
- x
The variable name at the start of a path.
- y
The variable name at the end of a path.
- m
Optional. The mediator(s) from
xtoy. A numeric vector of the names of the mediators. The path goes from the first element to the last element. For example, ifm = c("m1", "m2"), then the path isx -> m1 -> m2 -> y.- newdata
Required. A data frame of the new data. It must be a data frame.
- ...
Additional arguments. Ignored.
Value
A numeric vector of the predicted values, with length equal to the number of rows of user-supplied data.
Details
An lm_list-class object is
a list of lm-class objects, this
function is similar to the
stats::predict() method of lm()
but it works on a system defined by a
list of regression models.
This is an advanced helper used by some functions in this package. Exported for advanced users.
Examples
data(data_serial_parallel)
lm_m11 <- lm(m11 ~ x + c1 + c2, data_serial_parallel)
lm_m12 <- lm(m12 ~ m11 + x + c1 + c2, data_serial_parallel)
lm_m2 <- lm(m2 ~ x + c1 + c2, data_serial_parallel)
lm_y <- lm(y ~ m11 + m12 + m2 + x + c1 + c2, data_serial_parallel)
# Join them to form a lm_list-class object
lm_serial_parallel <- lm2list(lm_m11, lm_m12, lm_m2, lm_y)
lm_serial_parallel
#>
#> The models:
#> m11 ~ x + c1 + c2
#> m12 ~ m11 + x + c1 + c2
#> m2 ~ x + c1 + c2
#> y ~ m11 + m12 + m2 + x + c1 + c2
#>
summary(lm_serial_parallel)
#>
#>
#> Model:
#> m11 ~ x + c1 + c2
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 11.4546 1.1258 10.17 < 2e-16 ***
#> x 0.8001 0.0953 8.39 4.2e-13 ***
#> c1 0.0855 0.1020 0.84 0.404
#> c2 -0.2444 0.1002 -2.44 0.017 *
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> R-square = 0.459. Adjusted R-square = 0.442. F(3, 96) = 27.148, p < .001
#>
#> Model:
#> m12 ~ m11 + x + c1 + c2
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 9.8742 1.5048 6.56 2.8e-09 ***
#> m11 0.4652 0.0946 4.92 3.7e-06 ***
#> x 0.1146 0.1164 0.98 0.3274
#> c1 0.1934 0.0949 2.04 0.0444 *
#> c2 -0.2848 0.0957 -2.97 0.0037 **
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> R-square = 0.469. Adjusted R-square = 0.446. F(4, 95) = 20.963, p < .001
#>
#> Model:
#> m2 ~ x + c1 + c2
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 2.354 1.236 1.91 0.06 .
#> x 0.435 0.105 4.15 7.1e-05 ***
#> c1 0.178 0.112 1.59 0.12
#> c2 -0.167 0.110 -1.52 0.13
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> R-square = 0.196. Adjusted R-square = 0.171. F(3, 96) = 7.812, p < .001
#>
#> Model:
#> y ~ m11 + m12 + m2 + x + c1 + c2
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) -1.791908 4.613263 -0.39 0.69859
#> m11 0.203249 0.266930 0.76 0.44832
#> m12 0.519112 0.255389 2.03 0.04494 *
#> m2 0.838632 0.217639 3.85 0.00021 ***
#> x 0.071421 0.317264 0.23 0.82238
#> c1 -0.000114 0.244934 0.00 0.99963
#> c2 -0.069787 0.253231 -0.28 0.78348
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> R-square = 0.315. Adjusted R-square = 0.271. F(6, 93) = 7.133, p < .001
newdat <- data_serial_parallel[3:5, ]
predict(lm_serial_parallel,
x = "x",
y = "y",
m = "m2",
newdata = newdat)
#> [1] 16.24671 19.60951 16.58806