Question

我正在运行具有各种初始值的模型，并且我试图按行将值（3个估算器）附加到循环中的数据帧。我将值分配给循环中的估算器，但我无法调用它们来生成数据帧。

我的代码：f是估算的模型。三个参数：模型中的alpha，rho和lambda。我想输出这3个值。

library("maxLik")

f <- function(param) {
    alpha <- param[1]
    rho <- param[2]
    lambda <- param[3]
    u <- 0.5 * (dataset$v_50_1)^alpha - 0.5 * lambda * (dataset$v_50_2)^alpha
    p <- 1/(1 + exp(-rho * u))
    logl <- sum(dataset$gamble * log(p) + (1 - dataset$gamble) * log(1 - p))
}


df <- data.frame(alpha = numeric(), rho = numeric(), lambda = numeric())

for (j in 1:20) {
    tryCatch({
        ml <- maxLik(f, start = c(alpha = runif(1, 0, 2), rho = runif(1, 0, 4), lambda = runif(1, 
            0, 10)), method = "NM")

        alpha[j] <- ml$estimate[1]
        rho[j] <- ml$estimate[2]
        lambda[j] <- ml$estimate[3]

    }, error = function(e) {NA})
}
output <- data.frame(alpha, rho, lambda)

发生

错误：

 Error in data.frame(alpha, rho, lambda) : object 'alpha' not found

预期输出

 alpha    rho    lambda
  0.4      1       2      # estimators append by row. 
  0.6      1.1     3      # each row has estimators that are estimated 
  0.7      1.5     4      # by one set of initial values, there are 20 
                          # rows, as the estimation loops for 20 times.

Answer 1

我正在运行一个例子，通过改变函数f

library("maxLik")

t <- rexp(100, 2)
loglik <- function(theta) log(theta) - theta*t


df <- data.frame(alpha = numeric(), rho = numeric(), lambda = numeric())

for (j in 1:20){
  tryCatch({
    ml <- maxLik(loglik, start = c(alpha = runif(1, 0, 2), rho = runif(1, 0, 4), 
                              lambda = runif(1, 0, 10)), method = "NM")

    df <- rbind(df, data.frame(alpha = ml$estimate[1], 
                               rho = ml$estimate[2], 
                               lambda = ml$estimate[3]))
    # I tried to append values for each column         
  }, error = function(e) {NA})}

> row.names(df) <- NULL
> head(df)
     alpha      rho   lambda
1 2.368739 2.322220 2.007375
2 2.367607 2.322328 2.007093
3 2.368324 2.322105 2.007597
4 2.368515 2.322072 2.007334
5 2.368269 2.322071 2.007142
6 2.367998 2.322438 2.007391

通过循环中的dataframe行追加值

1 个答案: