[R] Fwd: Error message: object of type 'closure' is not subsettable

[Eric Berger]

You may be calling a function when you think you are referring to an array.
You can reproduce this error message as follows:

f <- function(x) {x}
f[1]

HTH,
Eric


On Mon, Apr 1, 2019 at 5:49 PM Simrit Rattan <simrit.rattan using gmail.com>
wrote:

> hey everyone :),
> Subject: Re: Error message: object of type 'closure' is not subsettable
> I am writing a package which should calculate the binary logistic
> regression.
> The function itself work perfectly, but if I want to load the function from
> my package it gives me the above mentioned error. I have tried a lot of
> things, googled and so on, but I can not figure out what to change so it
> works. I also found out the mistake lies in the first function
> (logisticRegression)
>
> logisticRegression <- function(x,y, threshold = 1e-10, maxIter = 100)
> {
>   calcPi <- function(x,betaCoef)
>   {
>     betaCoef <- as.matrix(betaCoef)
>     return(exp(x%*%betaCoef)/(1 + exp(x%*%betaCoef)))
>   }
>   #initial guess for beta (mostly we start with 0)
>   betaCoef <- rep(0, ncol(x))
>
>   #some initial value which is bigger than the threshold for the loop
>   initialValue <- 10000
>
>   #count of iteration to make sure its not an infinite loop
>   iterCount <- 0
>
>   #iteration process (loop)
>   while(initialValue > threshold) #convergence test
>   {
>     #calculates probabilities using the current estimate of beta
>     p <- as.vector(calcPi(x, betaCoef))
>
>     #calculates W which is needed for the Hessian
>     W <- diag(p*(1-p))
>
>     #calculates the gradient
>     gradient <- t(x)%*%(y-p)
>
>     #calculates the hessian
>     hessian <- -(t(x)%*%W)%*%x
>
>     #calculates beta
>     betaCoef <- betaCoef - solve(hessian) %*% gradient
>
>     #how much did we change beta?
>     initialValue <- sum((solve(hessian) %*% gradient)^2)
>
>     #to see if we reached the max # of iteration
>     iterCount <- iterCount + 1
>     if(iterCount > maxIter) {
>       stop("This is not converging")
>
>     }
>   }
>   #df
>   Totaldf <- length(y) - 1
>   Residualdf <- length(y) - length(betaCoef)
>   degreesOfFreedom <- cbind(Totaldf, Residualdf)
>   #fisher information, variance, standard error
>   fisherInformation <- -hessian
>   vCov <- solve(fisherInformation)
>   stdError <- sqrt(diag(vCov))
>
>   #wald test statistic
>   waldTestStatistic <- betaCoef/stdError
>   pValue <- 2 * pnorm(-abs(waldTestStatistic), mean = 0, sd = 1)
>
>   #loglikelihood and deviance
>   logLikelihoodFunction <-  y%*%x%*%betaCoef -
> sum(log(1+exp(x%*%betaCoef)))
>   residualDeviance <- -2*logLikelihoodFunction
>
>   #AIC
>   akaikeIC <- residualDeviance + 2*ncol(betaCoef)
>   #for R2
>   cV <- 2/nrow(x) #for C&S R2
>
>   #deviance residual
>   sign1 <- replace(y, y == 0, -1)
>   devianceResiduals <- sign1*sqrt(-2*(y*log(p) + (1-y)*log(1-p)))
>   residualQuantiles <- summary(devianceResiduals)
>
>   #fitted values
>   fittedValues <- log(p/(1-p))
>   #what to return
>   coefficients <- betaCoef
>   output <- list()
>   output$degreesF <- Residualdf
>   output$nulldf <- Totaldf
>   output$fittedValues <- fittedValues
>   output$devianceResiduals <- devianceResiduals
>   output$residualQuantiles <- residualQuantiles
>   output$coefficients <- coefficients
>   output$vCov <- vCov
>   output$coefMat <- cbind(coefficients,
>                           stdError,
>                           waldTestStatistic,
>                           pValue)
>   colnames(output$coefMat) <- c("Estimate",
>                                 "Std.Err",
>                                 "z value",
>                                 "Pr(>|z|)")
>   output$logLikelihoodFunction <- logLikelihoodFunction
>   output$residualDeviance <- residualDeviance
>   output$akaikeIC <- akaikeIC
>   output$iterCount <- iterCount -1
>   output$p <- p
>   output$cV <- cV
>   output
>
>
> }
>
> logReg <- function(formula, data) {
>   x <- model.matrix(formula, data)
>   regressant <- model.frame(formula, data)
>   y <- regressant[,1]
>   constant <- matrix(rep(1, length(y)))
>   nulllog <- logisticRegression(constant,y)
>   nullDeviance <- nulllog$residualDeviance
>   nullLogLikelihood <- nulllog$logLikelihoodFunction
>
>   output <- list()
>   output <- logisticRegression(x,y)
>   output$formula <- formula
>   output$nullDeviance <- nullDeviance
>   output$nullLogLikelihood <- nullLogLikelihood
>   output$call <- match.call()
>
>   #define the S3 class
>
>   class(output) <- "logReg"
>   print(output)
>
> }
>
> logReg.default <- function(x, ...) UseMethod("logReg")
>
> print.logReg <- function(x, ...) {
>   cat("\nCall:\n")
>   print(x$call)
>   cat("\nCoefficients:\n")
>   colnames(x$coefficients) <- ""
>   print(t(x$coefficients), digits = 5)
>   cat("\nDegrees of Freedom:",
>   paste(x$nulldf, "Total (i.e. Null);", x$degreesF, "Residual"))
>   cat("\nNull Deviance:", paste(round(x$nullDeviance, digits = 2)))
>   cat("\nResidual Deviance:", paste(round(x$residualDeviance, digits = 2)))
>   invisible(x)
> }
>
> Thanks for your help! :D
>
>
>
>
> --
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