[英]lapply with multiple lists and functions for regression models
I'd like to run four multilevel models (using lmer) simultaneously using lapply.我想使用 lapply 同时运行四个多级模型(使用 lmer)。
A simple example using lm() with one dependent variable and a list of independent variables would be:使用带有一个因变量和一系列自变量的 lm() 的简单示例是:
data(mtcars)
varlist <- names(mtcars)[3:6]
models <- lapply(varlist, function(x) {
lm(substitute(mpg ~ i, list(i = as.name(x))), data = mtcars)
})
How can I expand this to run four lmer() models, each having a different dependent variable and a different list of independent variables?如何扩展它以运行四个 lmer() 模型,每个模型都有不同的因变量和不同的自变量列表? The two levels would remain the same for all four models.对于所有四个模型,这两个级别将保持不变。 Four (bogus) example models would be:四个(虚假)示例模型将是:
data(mtcars)
library(lme4)
model1 <- lmer(mpg ~ cyl + disp + hp + (1 | am) + (1 | vs), data = mtcars)
model2 <- lmer(cyl ~ mpg + disp + qsec + (1 | am) + (1 | vs), data = mtcars)
model3 <- lmer(disp ~ mpg + cyl + carb + (1 | am) + (1 | vs), data = mtcars)
model4 <- lmer(qsec ~ mpg + cyl + drat + (1 | am) + (1 | vs), data = mtcars)
Any ideas?有任何想法吗?
We can have a list
of dependent (or vector
) and independent variables and pass that into Map
to create the formula
and apply the lmer
.我们可以有一个因变量(或vector
)和自变量的list
,并将其传递给Map
以创建formula
并应用lmer
。 The unit element of a list
would be the vector
here for independent variables and the single element for dependent variable. list
的单位元素将是这里的自变量vector
和因变量的单个元素。
library(lme4)
indep_var_list <- list(c("cyl", "disp", "hp"),
c("mpg", "disp", "qsec"),
c("mpg", "cyl", "carb"),
c("mpg", "cyl", "drat"))
dep_vars <- c("mpg", "cyl", "disp", "qsec")
out <- Map(function(x, y) {
fmla <- as.formula(paste(y, "~ ", paste(x, collapse= " + ") ,
" + (1 | am) + (1 | vs)"))
model <- lmer(fmla, data = mtcars)
model
}, indep_var_list, dep_vars)
-output -输出
[1]]
Linear mixed model fit by REML ['lmerMod']
Formula: mpg ~ cyl + disp + hp + (1 | am) + (1 | vs)
Data: mtcars
REML criterion at convergence: 169.5913
Random effects:
Groups Name Std.Dev.
am (Intercept) 2.209
vs (Intercept) 0.000
Residual 2.831
Number of obs: 32, groups: am, 2; vs, 2
Fixed Effects:
(Intercept) cyl disp hp
32.55270 -0.90447 -0.00972 -0.02971
convergence code 0; 0 optimizer warnings; 1 lme4 warnings
[[2]]
Linear mixed model fit by REML ['lmerMod']
Formula: cyl ~ mpg + disp + qsec + (1 | am) + (1 | vs)
Data: mtcars
REML criterion at convergence: 78.0586
Random effects:
Groups Name Std.Dev.
am (Intercept) 0.5773
vs (Intercept) 0.4491
Residual 0.5743
Number of obs: 32, groups: am, 2; vs, 2
Fixed Effects:
(Intercept) mpg disp qsec
10.592032 -0.045832 0.006052 -0.279176
[[3]]
Linear mixed model fit by REML ['lmerMod']
Formula: disp ~ mpg + cyl + carb + (1 | am) + (1 | vs)
Data: mtcars
REML criterion at convergence: 316.1521
Random effects:
Groups Name Std.Dev.
am (Intercept) 0.00
vs (Intercept) 0.00
Residual 49.83
Number of obs: 32, groups: am, 2; vs, 2
Fixed Effects:
(Intercept) mpg cyl carb
112.57 -7.15 47.90 -12.30
convergence code 0; 0 optimizer warnings; 1 lme4 warnings
[[4]]
Linear mixed model fit by REML ['lmerMod']
Formula: qsec ~ mpg + cyl + drat + (1 | am) + (1 | vs)
Data: mtcars
REML criterion at convergence: 92.9165
Random effects:
Groups Name Std.Dev.
am (Intercept) 1.4979
vs (Intercept) 0.6131
Residual 0.9008
Number of obs: 32, groups: am, 2; vs, 2
Fixed Effects:
(Intercept) mpg cyl drat
24.5519 0.0288 -0.7956 -0.6974
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