解析(text = paste(“〜”,paste(nVal,collapse =“/”)))中出现错误:<text>:2:0:R中的运行 nlme包时输入意外结束
Getting Error in parse(text = paste("~", paste(nVal, collapse = "/"))) : <text>:2:0: unexpected end of input when running nlme package in R
我正在尝试使用 nlme
包将第二类分布的广义 beta 拟合到模拟健康成本数据。
运行 测试数据集上的以下代码:
打包安装(如果需要)
install.packages("withr", dependencies = T)
library(withr)
with_makevars(c(PKG_CFLAGS ="-std=gnu99"),
install.packages("cubature"), assignment="+=")
install.packages("GB2", dependencies = T)
install.packages("nlme", dependencies = T)
# load packages
library(cubature)
library(GB2)
library(nlme)
# Binary independent variables
age <- rbinom(n=1000, size=1, prob=.3)
sex <- rbinom(n=1000, size=1, prob=.5)
trmt <- rbinom(n=1000, size=1, prob=.5)
# GB2 parameter equations
shape1 <- exp(rnorm(n=1000, mean=.1 + age/100 - sex/10 + trmt/10, sd=.3))
scale <- exp(rnorm(n=1000, mean=7 + age/50 + sex - trmt, sd=.5))
shape2 <- exp(rnorm(n=1000, mean=1.5 + age/100 + sex/10 - trmt/10, sd=.3))
shape3 <- exp(rnorm(n=1000, mean=.5 + age/100 - sex/10 - trmt/10, sd=.3))
# Outcome
y <- rgb2(1000, shape1, scale, shape2, shape3)
# Create test dataset
df <- data.frame(cbind(y,age,sex,trmt,shape1,scale,shape2,shape3))
# Fit GB2 distribution to data
gb2_fit <- nlme(y ~ scale*beta(shape2 + 1/shape1, shape3 - 1/shape1)/beta(shape2, shape3),
# data = list(y=df_gb2_test[,1]),
data = df,
fixed = list(shape1 ~ age + sex + trmt,
scale ~ age + sex + trmt,
shape2 ~ age + sex + trmt,
shape3 ~ age + sex + trmt),
start = list(fixed = c(shape1 = 1.00, scale = 100, shape2 = 1.00, shape3 = 1.00)))
我收到错误:
Error in parse(text = paste("~", paste(nVal, collapse = "/"))) :
<text>:2:0: unexpected end of input
1: ~
^
知道我做错了什么吗?我似乎正确使用波浪号运算符。
早些时候也发生了一个错误:
y <- rgb2(1, shape1, scale, shape2, shape3)
Error in rgb2(1, shape1, scale, shape2, shape3) :
could not find function "rgb2"
您可能需要为此加载所需的包:
https://www.rdocumentation.org/packages/gamlss.dist/versions/5.3-2/topics/GB2
它似乎在 library(gamlss.dist)
我认为 nlme
并没有按照您的想法去做。它做非线性最小二乘混合模型;即,假设响应是高斯分布的,并且假设是随机效应(也许您将其与 SAS PROC NLMIXED
混淆了,哪个更通用?
library(bbmle)
## we need a version of the density function that takes a 'log' argument
dgb2B <- function(..., log=FALSE) {
r <- GB2::dgb2(...)
if (!log) r else log(r)
}
## don't include shape1, scale shape2, shape3 in the data, that confuses things
df2 <- df[,c("y","age","sex", "trmt")]
## fit homogeneous model
m1 <- mle2(y ~ dgb2B(shape1, scale, shape2, shape3),
method="Nelder-Mead",
trace=TRUE,
data=df2,
start = list(shape1 = 1.00, scale = 100, shape2 = 1.00, shape3 = 1.00))
## allow parameters to vary by group
mle2(y ~ dgb2B(shape1, scale, shape2, shape3),
## parameters need to be in the same order!
parameters=list(shape1 ~ age + sex + trmt,
scale ~ age + sex + trmt,
shape2 ~ age + sex + trmt,
shape3 ~ age + sex + trmt),
method="Nelder-Mead",
control=list(maxit=10000,
## set parameter scales equal to magnitude
## of starting values; each top-level parameter
## has 4 associated values (intercept, + 3 cov effects)
parscale=rep(abs(coef(m1)), each=4)),
trace=TRUE,
data=df2,
start = as.list(coef(m1))
)
值得一提的是,对于这个例子,您可以通过将八个单独的模型拟合到所有年龄×性别×治疗组来实现相同的目标(但我可以理解您的实际应用可能更复杂,即您可能只希望参数的子集在不同组之间变化,或者可能希望允许参数根据连续协变量变化。
如果您要尝试更难的问题,您可能希望在对数尺度上调整参数。
我正在尝试使用 nlme
包将第二类分布的广义 beta 拟合到模拟健康成本数据。
运行 测试数据集上的以下代码:
打包安装(如果需要)
install.packages("withr", dependencies = T)
library(withr)
with_makevars(c(PKG_CFLAGS ="-std=gnu99"),
install.packages("cubature"), assignment="+=")
install.packages("GB2", dependencies = T)
install.packages("nlme", dependencies = T)
# load packages
library(cubature)
library(GB2)
library(nlme)
# Binary independent variables
age <- rbinom(n=1000, size=1, prob=.3)
sex <- rbinom(n=1000, size=1, prob=.5)
trmt <- rbinom(n=1000, size=1, prob=.5)
# GB2 parameter equations
shape1 <- exp(rnorm(n=1000, mean=.1 + age/100 - sex/10 + trmt/10, sd=.3))
scale <- exp(rnorm(n=1000, mean=7 + age/50 + sex - trmt, sd=.5))
shape2 <- exp(rnorm(n=1000, mean=1.5 + age/100 + sex/10 - trmt/10, sd=.3))
shape3 <- exp(rnorm(n=1000, mean=.5 + age/100 - sex/10 - trmt/10, sd=.3))
# Outcome
y <- rgb2(1000, shape1, scale, shape2, shape3)
# Create test dataset
df <- data.frame(cbind(y,age,sex,trmt,shape1,scale,shape2,shape3))
# Fit GB2 distribution to data
gb2_fit <- nlme(y ~ scale*beta(shape2 + 1/shape1, shape3 - 1/shape1)/beta(shape2, shape3),
# data = list(y=df_gb2_test[,1]),
data = df,
fixed = list(shape1 ~ age + sex + trmt,
scale ~ age + sex + trmt,
shape2 ~ age + sex + trmt,
shape3 ~ age + sex + trmt),
start = list(fixed = c(shape1 = 1.00, scale = 100, shape2 = 1.00, shape3 = 1.00)))
我收到错误:
Error in parse(text = paste("~", paste(nVal, collapse = "/"))) :
<text>:2:0: unexpected end of input
1: ~
^
知道我做错了什么吗?我似乎正确使用波浪号运算符。
早些时候也发生了一个错误:
y <- rgb2(1, shape1, scale, shape2, shape3)
Error in rgb2(1, shape1, scale, shape2, shape3) :
could not find function "rgb2"
您可能需要为此加载所需的包:
https://www.rdocumentation.org/packages/gamlss.dist/versions/5.3-2/topics/GB2
它似乎在 library(gamlss.dist)
我认为 nlme
并没有按照您的想法去做。它做非线性最小二乘混合模型;即,假设响应是高斯分布的,并且假设是随机效应(也许您将其与 SAS PROC NLMIXED
混淆了,哪个更通用?
library(bbmle)
## we need a version of the density function that takes a 'log' argument
dgb2B <- function(..., log=FALSE) {
r <- GB2::dgb2(...)
if (!log) r else log(r)
}
## don't include shape1, scale shape2, shape3 in the data, that confuses things
df2 <- df[,c("y","age","sex", "trmt")]
## fit homogeneous model
m1 <- mle2(y ~ dgb2B(shape1, scale, shape2, shape3),
method="Nelder-Mead",
trace=TRUE,
data=df2,
start = list(shape1 = 1.00, scale = 100, shape2 = 1.00, shape3 = 1.00))
## allow parameters to vary by group
mle2(y ~ dgb2B(shape1, scale, shape2, shape3),
## parameters need to be in the same order!
parameters=list(shape1 ~ age + sex + trmt,
scale ~ age + sex + trmt,
shape2 ~ age + sex + trmt,
shape3 ~ age + sex + trmt),
method="Nelder-Mead",
control=list(maxit=10000,
## set parameter scales equal to magnitude
## of starting values; each top-level parameter
## has 4 associated values (intercept, + 3 cov effects)
parscale=rep(abs(coef(m1)), each=4)),
trace=TRUE,
data=df2,
start = as.list(coef(m1))
)
值得一提的是,对于这个例子,您可以通过将八个单独的模型拟合到所有年龄×性别×治疗组来实现相同的目标(但我可以理解您的实际应用可能更复杂,即您可能只希望参数的子集在不同组之间变化,或者可能希望允许参数根据连续协变量变化。
如果您要尝试更难的问题,您可能希望在对数尺度上调整参数。