我在与R和论坛打交道方面还比较陌生，所以请原谅我一两个错误。我要执行的操作如下：我想为每个14个变量生成6000个观察值。
这是我到目前为止所做的。我已经用适当的长度初始化了每个变量的向量：
#Market1
price_1 <- vector(mode = "numeric", length = 6002)
demandChartist_1 <- vector(mode = "numeric", length = 6000)
demandFundamentalist_1 <- vector(mode = "numeric", length = 6001)
percentFT_Wf_1 <- vector(mode = "numeric", length = 6001)
percentCT_Wc_1 <- vector(mode = "numeric", length = 6001)
fitnessTradingFundamentalist_Af_1 <- vector(mode = "numeric", length = 6001)
fitnessTradingChartist_Ac_1 <- vector(mode = "numeric", length = 6001)
#Market2
price_2 <- vector(mode = "numeric", length = 6002)
demandChartist_2 <- vector(mode = "numeric", length = 6000)
demandFundamentalist_2 <- vector(mode = "numeric", length = 6001)
percentFT_Wf_2 <- vector(mode = "numeric", length = 6001)
percentCT_Wc_2 <- vector(mode = "numeric", length = 6001)
fitnessTradingFundamentalist_Af_2 <- vector(mode = "numeric", length = 6001)
fitnessTradingChartist_Ac_2 <- vector(mode = "numeric", length = 6001)
percentNoTrading <- vector(mode = "numeric", length = 6001)
T <- 1:6000
下一步是设置起始值。
# set the first 4 values for price equal to 0, 1 whatever otherwise we can't compute the previous periods
price_1[1:4] <- 0
price_2[1:4] <- 0
fitnessTradingChartist_Ac_1[1:3] <- 0
fitnessTradingFundamentalist_Af_1[1:3] <- 0
fitnessTradingChartist_Ac_2[1:3] <- 0
fitnessTradingFundamentalist_Af_2[1:3] <- 0
a <- 1
b <- 0.05
c <- 0.05
d <- 0.975
e <- 300
F1 <- 0
F2 <- 0
我设法建立了For循环，以便所有向量都填充有随机生成的值。 （请参见代码）由于这些是随机生成的值，因此，如果我可以多次执行循环并为每个变量创建一个数据框，则该模型的准确性将大大提高，其中第一次运行的6000个观测值存储在第1列中第二次运行的6000个观测值存储在第2列等中，我最终可以计算出每个周期的平均值。
for (i in 4:6002) {
# [i-2] weil fitness tradingchartist der jetzigen Periode Demand der Periode t-2 also 2 erfordert.
demandChartist_1[i-2] =
b * (price_1[i-2] - price_1[i-3]) + rnorm(1, mean=0, sd=0.05)
# [i-2] weil fitness tradingFundamentalist der jetzigen Periode Demand der Periode t-2 also 2 erfordert.
demandChartist_2[i-2] =
b * (price_2[i-2] - price_2[i-3]) + rnorm(1, mean=0, sd=0.05)
demandFundamentalist_1[i-2] =
c * (F1 - price_1[i-2]) + rnorm(1, mean=0, sd=0.01)
demandFundamentalist_2[i-2] =
c * (F2 - price_2[i-2]) + rnorm(1, mean=0, sd=0.01)
fitnessTradingChartist_Ac_1[i] =
(exp(price_1[i]) - exp(price_1[i-1])) * demandChartist_1[i-2] +
d * fitnessTradingChartist_Ac_1[i-1]
fitnessTradingChartist_Ac_2[i] =
(exp(price_2[i]) - exp(price_2[i-1])) * demandChartist_2[i-2] +
d * fitnessTradingChartist_Ac_2[i-1]
fitnessTradingFundamentalist_Af_1[i] =
(exp(price_1[i]) - exp(price_1[i-1])) * demandFundamentalist_1[i-2] +
d * fitnessTradingChartist_Ac_1[i-1]
fitnessTradingFundamentalist_Af_2[i] =
(exp(price_2[i]) - exp(price_2[i-1])) * demandFundamentalist_2[i-2] +
d * fitnessTradingChartist_Ac_2[i-1]
percentCT_Wc_1[i] =
exp(e * fitnessTradingChartist_Ac_1[i]) /
(exp(e * fitnessTradingChartist_Ac_1[i]) +
exp(e * fitnessTradingFundamentalist_Af_1[i]) +
exp(e * fitnessTradingChartist_Ac_2[i]) +
exp(e * fitnessTradingFundamentalist_Af_2[i]) +
exp(0)
)
percentCT_Wc_2[i] =
exp(e * fitnessTradingChartist_Ac_2[i]) /
(exp(e * fitnessTradingChartist_Ac_1[i]) +
exp(e * fitnessTradingFundamentalist_Af_1[i]) +
exp(e * fitnessTradingChartist_Ac_2[i]) +
exp(e * fitnessTradingFundamentalist_Af_2[i]) +
exp(0)
)
percentFT_Wf_1[i] =
exp(e * fitnessTradingFundamentalist_Af_1[i]) /
(exp(e * fitnessTradingChartist_Ac_1[i]) +
exp(e * fitnessTradingFundamentalist_Af_1[i]) +
exp(e * fitnessTradingChartist_Ac_2[i]) +
exp(e * fitnessTradingFundamentalist_Af_2[i]) +
exp(0)
)
percentFT_Wf_2[i] =
exp(e * fitnessTradingFundamentalist_Af_2[i]) /
(exp(e * fitnessTradingChartist_Ac_1[i]) +
exp(e * fitnessTradingFundamentalist_Af_1[i]) +
exp(e * fitnessTradingChartist_Ac_2[i]) +
exp(e * fitnessTradingFundamentalist_Af_2[i]) +
exp(0)
)
percentNoTrading[i] =
1- percentCT_Wc_1[i] - percentFT_Wf_1[i] - percentCT_Wc_2[i] - percentFT_Wf_2[i]
price_1[i] =
price_1[i-1] +
a * ((percentCT_Wc_1[i-1] * demandChartist_1[i-1] +
percentFT_Wf_1[i-1] * demandFundamentalist_1[i-1]
)
) +
rnorm(1, mean=0, sd=0.01)
price_2[i] =
price_2[i-1] +
a * ((percentCT_Wc_2[i-1] * demandChartist_2[i-1] +
percentFT_Wf_2[i-1] * demandFundamentalist_2[i-1]
)
) +
rnorm(1, mean=0, sd=0.01)
}
有人知道如何做到这一点吗？我将不胜感激！ 干杯
答案 0 :(得分：0)
您的代码并非绝对不容易跟踪。
在这里，我要尝试改变的两点是要获得具有50列和6000个观测值的14个数据框。
# definition of dataframe
#Market1
price_1 <- matrix(ncol = 50, nrow =6002, 0L)
demandChartist_1 <- matrix(ncol = 50, nrow =6002, 0L)
demandFundamentalist_1 <- matrix(ncol = 50, nrow =6002, 0L)
percentFT_Wf_1 <- matrix(ncol = 50, nrow =6002, 0L)
percentCT_Wc_1 <- matrix(ncol = 50, nrow =6002, 0L)
fitnessTradingFundamentalist_Af_1 <- matrix(ncol = 50, nrow =6002, 0L)
fitnessTradingChartist_Ac_1 <- matrix(ncol = 50, nrow =6002, 0L)
#Market2
price_2 <- matrix(ncol = 50, nrow =6002, 0L)
demandChartist_2 <- matrix(ncol = 50, nrow =6002, 0L)
demandFundamentalist_2 <- matrix(ncol = 50, nrow =6002, 0L)
percentFT_Wf_2 <- matrix(ncol = 50, nrow =6002, 0L)
percentCT_Wc_2 <- matrix(ncol = 50, nrow =6002, 0L)
fitnessTradingFundamentalist_Af_2 <- matrix(ncol = 50, nrow =6002, 0L)
fitnessTradingChartist_Ac_2 <- matrix(ncol = 50, nrow =6002, 0L)
percentNoTrading <- matrix(ncol = 50, nrow =6002, 0L)
然后，您定义第一个值：
#set the first 4 values for price equal to 0, 1 whatever otherwise we cant compute the previous periods
price_1[1:4,] <- 0
price_2[1:4,] <- 0
fitnessTradingChartist_Ac_1[1:3,] <- 0
fitnessTradingFundamentalist_Af_1[1:3,] <- 0
fitnessTradingChartist_Ac_2[1:3,] <- 0
fitnessTradingFundamentalist_Af_2[1:3,] <- 0
a <- 1
b <- 0.05
c <- 0.05
d <- 0.975
e <- 300
F1 <- 0
F2 <- 0
最后，您的大部分代码包括第二个列循环
for(j in 1:50)
{
for (i in 4:6002) {
# [i-2] weil fitness tradingchartist der jetzigen Periode Demand der Periode t-2 also 2 erfordert.
demandChartist_1[i-2,j] =
b * (price_1[i-2,j] - price_1[i-3,j]) + rnorm(1, mean=0, sd=0.05)
# [i-2] weil fitness tradingFundamentalist der jetzigen Periode Demand der Periode t-2 also 2 erfordert.
demandChartist_2[i-2,j] = b * (price_2[i-2,j] - price_2[i-3,j]) + rnorm(1, mean=0, sd=0.05)
print("1 OK")
demandFundamentalist_1[i-2,j] = c * (F1 - price_1[i-2,j]) + rnorm(1, mean=0, sd=0.01)
demandFundamentalist_2[i-2,j] = c * (F2 - price_2[i-2,j]) + rnorm(1, mean=0, sd=0.01)
fitnessTradingChartist_Ac_1[i,j] = (exp(price_1[i,j]) - exp(price_1[i-1,j])) * demandChartist_1[i-2,j] +
d * fitnessTradingChartist_Ac_1[i-1,j]
fitnessTradingChartist_Ac_2[i,j] = (exp(price_2[i,j]) - exp(price_2[i-1,j])) * demandChartist_2[i-2,j] +
d * fitnessTradingChartist_Ac_2[i-1,j]
fitnessTradingFundamentalist_Af_1[i,j] = (exp(price_1[i,j]) - exp(price_1[i-1,j])) * demandFundamentalist_1[i-2,j] +
d * fitnessTradingChartist_Ac_1[i-1,j]
fitnessTradingFundamentalist_Af_2[i,j] = (exp(price_2[i,j]) - exp(price_2[i-1,j])) * demandFundamentalist_2[i-2,j] +
d * fitnessTradingChartist_Ac_2[i-1,j]
percentCT_Wc_1[i,j] = exp(e * fitnessTradingChartist_Ac_1[i,j]) /
(exp(e * fitnessTradingChartist_Ac_1[i,j]) +
exp(e * fitnessTradingFundamentalist_Af_1[i,j]) +
exp(e * fitnessTradingChartist_Ac_2[i,j]) +
exp(e * fitnessTradingFundamentalist_Af_2[i,j]) +
exp(0)
)
percentCT_Wc_2[i,j] =
exp(e * fitnessTradingChartist_Ac_2[i,j]) /
(exp(e * fitnessTradingChartist_Ac_1[i,j]) +
exp(e * fitnessTradingFundamentalist_Af_1[i,j]) +
exp(e * fitnessTradingChartist_Ac_2[i,j]) +
exp(e * fitnessTradingFundamentalist_Af_2[i,j]) +
exp(0)
)
percentFT_Wf_1[i,j] =
exp(e * fitnessTradingFundamentalist_Af_1[i,j]) /
(exp(e * fitnessTradingChartist_Ac_1[i,j]) +
exp(e * fitnessTradingFundamentalist_Af_1[i,j]) +
exp(e * fitnessTradingChartist_Ac_2[i,j]) +
exp(e * fitnessTradingFundamentalist_Af_2[i,j]) +
exp(0)
)
percentFT_Wf_2[i,j] =
exp(e * fitnessTradingFundamentalist_Af_2[i,j]) /
(exp(e * fitnessTradingChartist_Ac_1[i,j]) +
exp(e * fitnessTradingFundamentalist_Af_1[i,j]) +
exp(e * fitnessTradingChartist_Ac_2[i,j]) +
exp(e * fitnessTradingFundamentalist_Af_2[i,j]) +
exp(0)
)
percentNoTrading[i,j] =
1- percentCT_Wc_1[i,j] - percentFT_Wf_1[i,j] - percentCT_Wc_2[i,j] - percentFT_Wf_2[i,j]
price_1[i,j] =
price_1[i-1,j] +
a * ((percentCT_Wc_1[i-1,j] * demandChartist_1[i-1,j] +
percentFT_Wf_1[i-1,j] * demandFundamentalist_1[i-1,j]
)
) +
rnorm(1, mean=0, sd=0.01)
price_2[i,j] =
price_2[i-1,j] +
a * ((percentCT_Wc_2[i-1,j] * demandChartist_2[i-1,j] +
percentFT_Wf_2[i-1,j] * demandFundamentalist_2[i-1,j]
)
) +
rnorm(1, mean=0, sd=0.01)
}
}
希望这是您想要的。也许有更简单的方法，但是代码的结构首先需要一些优化。