是否有一种简单的方法可以避免R中的0除法错误.具体来说,
a <- c(1,0,2,0)
b <- c(3,2,1,0)
sum(b/a)
此代码由于除以零而产生错误.我想要一种方法来定义任何/ 0 = 0,以便这种操作仍然有效.
我试图在R(mac)中安装openNLP包并继续收到以下错误消息
> install.packages("openNLP")
trying URL 'https://rweb.crmda.ku.edu/cran/bin/macosx/mavericks/contrib/3.2/openNLP_0.2-6.tgz'
Content type 'application/x-gzip' length 32952 bytes (32 KB)
==================================================
downloaded 32 KB
The downloaded binary packages are in
/var/folders/20/gzm2zpr94pz9dghqd8l5ff1c0000gn/T//Rtmp8n5kNw/downloaded_packages
> library(openNLP)
JavaVM: requested Java version ((null)) not available. Using Java at "" instead.
JavaVM: Failed to load JVM: /bundle/Libraries/libserver.dylib
JavaVM FATAL: Failed to load the jvm library.
Error : .onLoad failed in loadNamespace() for 'openNLPdata', details:
call: .jinit()
error: JNI_GetCreatedJavaVMs returned -1
Error: package or namespace load failed for ‘openNLP’
Here is my R …我最近阅读了Google的史蒂文·斯科特（Steven Scott）的贝叶斯结构时间序列模型的bsts软件包，并希望将其与我用于各种预测任务的预测软件包中的auto.arima函数相对应。
我在几个示例上进行了尝试，并对该程序包的效率和预测点印象深刻。但是，当我查看预测方差时，我几乎总是发现bsts最终给出了比auto.arima更大的置信度范围。这是有关白噪声数据的示例代码
library("forecast")
library("data.table")
library("bsts")
truthData = data.table(target = rnorm(250))
freq = 52
ss = AddGeneralizedLocalLinearTrend(list(), truthData$target)
ss = AddSeasonal(ss, truthData$target, nseasons = freq)
tStart = proc.time()[3]
model = bsts(truthData$target, state.specification = ss, niter = 500)
print(paste("time taken: ", proc.time()[3] - tStart))
burn = SuggestBurn(0.1, model)
pred = predict(model, horizon = 2 * freq, burn = burn, quantiles = c(0.10, 0.90))
## auto arima fit
max.d = 1; max.D = 1; max.p = 3; max.q = 3; max.P = …