1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
|
params <-
list(EVAL = FALSE)
## ----set-options, echo = FALSE------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
message = FALSE,
dev = "png",
fig.width = 7,
fig.height = 3.5,
warning = FALSE,
eval = TRUE
# eval = if (isTRUE(exists("params"))) params$EVAL else FALSE
)
options(width = 800, tibble.width = Inf)
if (!requireNamespace("sjmisc", quietly = TRUE) ||
!requireNamespace("splines", quietly = TRUE) ||
!requireNamespace("ggeffects", quietly = TRUE) ||
!requireNamespace("effects", quietly = TRUE) ||
!requireNamespace("ggplot2", quietly = TRUE) ||
!requireNamespace("haven", quietly = TRUE) ||
!requireNamespace("sjlabelled", quietly = TRUE)) {
knitr::opts_chunk$set(eval = FALSE)
}
## -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
library(sjPlot)
library(ggplot2)
data(efc)
theme_set(theme_sjplot())
fit <- lm(barthtot ~ c12hour + neg_c_7 + c161sex + c172code, data = efc)
plot_model(fit, type = "pred", terms = "c12hour")
## -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
plot_model(fit, type = "pred", terms = c("c12hour", "c172code"))
## -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
plot_model(fit, type = "pred", terms = c("c12hour", "c172code", "c161sex"))
## -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
plot_model(fit, type = "pred", terms = c("c12hour [30, 50, 80]", "c172code [1,3]"))
## -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
data(mtcars)
mpg_model <- lm(mpg ~ log(hp), data = mtcars)
# x-values and predictions based on the log(hp)-values
plot_model(mpg_model, type = "pred", terms = "hp")
# x-values and predictions based on hp-values from 50 to 150
plot_model(mpg_model, type = "pred", terms = "hp [50:150]")
## -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# x-values and predictions based on exponentiated hp-values
plot_model(mpg_model, type = "pred", terms = "hp [exp]")
## -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
library(splines)
data(women)
fm1 <- lm(weight ~ bs(height, df = 5), data = women)
plot_model(fm1, type = "pred", terms = "height")
## -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
data(efc)
efc$c172code <- sjlabelled::as_factor(efc$c172code)
fit <- lm(neg_c_7 ~ c12hour + c172code, data = efc)
# reference category is used for "c172code", i.e. c172code
# used the first level as value for predictions
plot_model(fit, type = "pred", terms = "c12hour")
## -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# proportion is used for "c172code", i.e. it is set to
# mean(sjlabelled::as_numeric(efc$c172code), na.rm = T),
# which is about 1.9715
plot_model(fit, type = "eff", terms = "c12hour")
|
