I am trying to calculate bootstrap confidence intervals.
Here is my code.
library(boot)
nboot <- 10000 # Number of simulations
alpha <- .01 # alpha level
n <- 1000 # sample size
bootThetaQuantile <- function(x,i) {
quantile(x[i], probs=.5)
}
raw <- rnorm(n,0, 1) # raw data
( theta.boot.median <- boot(raw, bootThetaQuantile, R=nboot) )
boot.ci(theta.boot.median, conf=(1-alpha)) #this causes no error
boot.ci(theta.boot.median, conf=(1-alpha), type = "percent") #this causes an error
The error message reads "Error in ci.out[[4L]] : subscript out of bounds". I am very confused by this because I am not sure why the call to boot.ci will cause an error when the previous line caused no error.
That is because you have to use type = 'perc'.
boot.ci(theta.boot.median, conf=(1-alpha), type = "perc")
Related
I am trying to use BayesARIMAX to model and predict us gdp (you can find the data here: https://fred.stlouisfed.org/series/GDP).I followed the example (https://cran.r-project.org/web/packages/BayesARIMAX/BayesARIMAX.pdf) to build my model. I didnt have any major issue to build the model(used error handling to overcome Getting chol.default error when using BayesARIMAX in R issue). However could not get the prediction of the model. I tried to look for solution and there is no example of predicting the model that is build using BayesARIMAX. Every time that I run the "predict" I get the following error:
"Error in eval(expr, p) : object 'X' not found"
Here is my code.
library(xts)
library(zoo)
library(tseries)
library(tidyverse)
library(fpp2)
gdp <- read.csv("GDP.csv", head = T)
date.q <- as.Date(gdp[, 1], "%Y-%m-%d")
gdp <- xts(gdp[,2],date.q)
train.row <- 248
number.row <- dim(merge.data)[1]
gdp.train <- gdp[1:train.row]
gdp.test <- gdp[(train.row+1):number.row]
date.test <- date.q[(train.row+1):number.row]
library(BayesARIMAX)
#wrote this function to handle randomly procuded error due to MCMC simulation
test_function <- function(a,b,P=1,Q=1,D=1,error_count = 0)
{
tryCatch(
{
model = BayesARIMAX(Y=a,X = b,p=P,q=Q,d=D)
return(model)
},
error = function(cond)
{
error_count=error_count+1
if (error_count <40)
{
test_function(a,b,P,Q,D,error_count = error_count)
}
else
{
print(paste("Model doesnt converge for ARIMA(",P,D,Q,")"))
print(cond)
}
}
)
}
set.seed(1)
x = rnorm(length(gdp.train),4,1)
bayes_arima_model <- test_function(a = gdp.train,b=x,P = 3,D = 2,Q = 2)
bayes_arima_pred <- xts(predict(bayes_arima_model[[1]],newxreg = x[1:3])$pred,date.test)
and here is the error code
Error in eval(expr, p) : object 'X' not found
Here is how I resolve the issue after reading through the BayesARIMAX code (https://rdrr.io/cran/BayesARIMAX/src/R/BayesianARIMAX.R) . I basically created the variable "X" and passed it to predict function to get the result. You just have to set the length of X variable equal to number of prediction.
here is the solution code for prediction.
X <- c(1:3)
bayes_arima_pred <- xts(predict(bayes_arima_model[[1]],newxreg = X[1:3])$pred,date.test)
which gave me the following results.
bayes_arima_pred
[,1]
2009-01-01 14462.24
2009-04-01 14459.73
2009-07-01 14457.23
I've been working on the creation of a training model in R for MS Azure. When I initially set up the model it all worked fine. Now it's continuously returning the below:
{"error":{"code":"LibraryExecutionError","message":"Module execution encountered an internal library error.","details":[{"code":"FailedToEvaluateRScript","target":"Score Model (RPackage)","message":"The following error occurred during evaluation of R script: R_tryEval: return error: Error in png(file = \"3e25ea05d5bc49d683f4471ff40780bcrViz%03d.png\", bg = \"transparent\") : \n too many open devices\n"}]}}
I haven't changed anything, and have looked around online only to find references to other issues. My code is as follows:
Trainer R Script
# Modify Datatype, factor Level, Replace NA to 0
x <- dataset
for (i in seq_along(x)) {
if (class(x[[i]]) == "character") {
#Convert Type
x[[i]] <- type.convert(x[[i]])
#Apply Levels
# levels(x[[i]]) <- levels(cols_modeled[, names(x)[i]]) # linked with levels in model
}
if (is.numeric(x[[i]]) && is.na(x[[i]]) ){
#print("*** Updating NA to 0")
x[[i]] <- 0
}
}
df1 <- x
rm(x)
set.seed(1234)
model <- svm(Paid ~ ., data= df1, type= "C")
Scorer R Script
library(e1071)
scores <- data.frame( predicted_result = predict(model, dataset))
Has anyone come across this before?
I am trying to run supervised LDA on a set of annotated tweets. I keep getting this error msg:
Error in structure(.Call("collapsedGibbsSampler", documents, as.integer(K), : This should not have happened (nan).
How do I fix it?
It only happens when I set logistic = T.
Code below. s is a sample of the data.
corpus1 <- lexicalize(tweets[s], lower=TRUE)
to.keep <- corpus1$vocab[word.counts(corpus1$documents, corpus1$vocab) >= 1]
documents <- lexicalize(tweets[s], lower=TRUE, vocab=to.keep)
params <- sample(c(-1, 1), num.topics, replace=TRUE)
result <- slda.em(documents=documents,
K=num.topics,
vocab=to.keep,
num.e.iterations=10,
num.m.iterations=4,
alpha=1.0, eta=0.1,
annotations = as.integer(annotations[s]),
params,
variance=0.25,
lambda=1.0,
logistic=T)
I'm trying to run this code, and I'm using mhadaptive package, but the problem is that when I run these code without writing metropolis_hastings (that is one part of mhadaptive package) error does not occur, but when I add mhadaptive package the error occur. What should I do?
li_F1<-function(pars,data) #defining first function
{
a01<-pars[1] #defining parameters
a11<-pars[2]
epsilon<<-pars[3]
b11<-pars[4]
a02<-pars[5]
a12<-pars[6]
b12<-pars[7]
h<-pars[8]
h[[i]]<-list() #I want my output is be listed in the h
h[[1]]<-0.32082184 #My first value of h is known and other values should calculate by formula
for(i in 2:nrow(F_2_))
{
h[[i]]<- ((a01+a11*(h[[i-1]])*(epsilon^2)*(h[[i-1]])*b11)+(F1[,2])*((a02+a12*(h[[i-1]])*(epsilon^2)+(h[[i-1]])*b12)))
pred<- h[[i]]
}
log_likelihood<-sum(dnorm(prod(h[i]),pred,sd = 1 ,log = TRUE))
return(h[i])
prior<- prior_reg(pars)
return(log_likelihood + prior)
options(digits = 22)
}
prior_reg<-function(pars) #defining another function
{
epsilon<<-pars[3] #error
prior_epsilon<-pt(0.95,5,lower.tail = TRUE,log.p = FALSE)
return(prior_epsilon)
}
F1<-as.matrix(F_2_) #defining my importing data and simulatunig data with them
x<-F1[,1]
y<-F1[,2]
d<-cbind(x,y)
#using mhadaptive package
mcmc_r<-Metro_Hastings(li_func = li_F1,pars=c(10,15,10,10,10,15),par_names=c('a01','a02','a11','a12','b11','b12'),data=d)
By running this code this error occur.
Error in h[[i]] <- list() : replacement has length zero
I'll so much appreciate who help me.
I'm trying to use cor.ci to obtain polychoric correlations with significance tests, but it keeps giving me an error message. Here is the code:
install.packages("Hmisc")
library(Hmisc)
mydata <- spss.get("S-IAT for R.sav", use.value.labels=TRUE)
install.packages('psych')
library(psych)
poly.example <- cor.ci(mydata(nvar = 10,n = 100)$items,n.iter = 10,poly = TRUE)
poly.example
print(corr.test(poly.example$rho), short=FALSE)
Here is the error message it gives:
> library(psych)
> poly.example <- cor.ci(mydata(nvar = 10,n = 100)$items,n.iter = 10,poly = TRUE)
Error in cor.ci(mydata(nvar = 10, n = 100)$items, n.iter = 10, poly = TRUE) :
could not find function "mydata"
> poly.example
Error: object 'poly.example' not found
> print(corr.test(poly.example$rho), short=FALSE)
Error in is.data.frame(x) : object 'poly.example' not found
How can I make it recognize mydata and/or select certain variables from this dataset for the analysis? I got the above code from here:
Polychoric correlation matrix with significance in R
Thanks!
You have several problems.
1) As previously commented upon, you are treating mydata as a function, but you need to treat it as a data.frame. Thus the call should be
poly.example <- cor.ci(mydata,n.iter = 10,poly = TRUE)
If you are trying to just get the first 100 cases and the first 10 variables, then
poly.example <- cor.ci(mydata[1:10,1:100],n.iter = 10,poly = TRUE)
2) Then, you do not want to run corr.test on the resulting correlation matrix. corr.test should be run on the data.
print(corr.test(mydata[1:10,1:100],short=FALSE)
Note that corr.test is testing the Pearson correlation.