[英]Data name won't carry through when applied to gls function as an argument inside another function
使用包nlme
,我有模型:
gls(temp.avg ~ year, data = df, method = 'ML')
这确实符合我的期望。 但是,当我在函数中使用gls
函数的数据参数更改模型来创建模型时,我的模型不再使用数据“ df”,而是仅打印“ dat”。 这是函数:
function(dat) { gls(temp.avg ~ year, data = dat, method = 'ML') }
这是我在包装器函数外部创建模型时(第一次)然后在函数内部创建模型(第二次)时查看模型时的样子[请注意“数据”行]:
Generalized least squares fit by maximum likelihood
Model: temp.avg ~ year
Data: df
Log-likelihood: -3877.052
Coefficients:
(Intercept) (year)
15.135135363 -0.008796849
Degrees of freedom: 1116 total; 1114 residual
Residual standard error: 7.807791
##########################
Generalized least squares fit by maximum likelihood
Model: temp.avg ~ year
Data: dat
Log-likelihood: -3877.052
Coefficients:
(Intercept) (year)
15.135135363 -0.008796849
Degrees of freedom: 1116 total; 1114 residual
Residual standard error: 7.807791
我真的很想不要这样做。
我怎样才能获得继承自dat vs“ dat”本身的功能?
library(nlme)
fun <- function(dat) {
res <- gls(follicles ~ sin(2*pi*Time) + cos(2*pi*Time), dat)
res$call$data <- substitute(dat)
res
}
fun(Ovary)
#Generalized least squares fit by REML
# Model: follicles ~ sin(2 * pi * Time) + cos(2 * pi * Time)
# Data: Ovary
# Log-restricted-likelihood: -898.434
#
#Coefficients:
# (Intercept) sin(2 * pi * Time) cos(2 * pi * Time)
# 12.2155822 -3.3396116 -0.8697358
#
#Degrees of freedom: 308 total; 305 residual
#Residual standard error: 4.486121
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