CRAN Package Check Results for Package pan

Last updated on 2026-04-30 11:57:01 CEST.

Flavor	Version	Tinstall	Tcheck	Ttotal	Status	Flags
r-devel-linux-x86_64-debian-clang	1.9	7.40	36.04	43.44	NOTE
r-devel-linux-x86_64-debian-gcc	1.9	4.15	28.46	32.61	NOTE
r-devel-linux-x86_64-fedora-clang	1.9	11.00	52.96	63.96	OK
r-devel-linux-x86_64-fedora-gcc	1.9	11.00	60.55	71.55	OK
r-devel-windows-x86_64	1.9	14.00	66.00	80.00	OK
r-patched-linux-x86_64	1.9	5.09	31.49	36.58	OK
r-release-linux-x86_64	1.9	5.15	30.93	36.08	OK
r-release-macos-arm64	1.9	2.00	17.00	19.00	OK
r-release-macos-x86_64	1.9	8.00	59.00	67.00	ERROR
r-release-windows-x86_64	1.9	10.00	67.00	77.00	OK
r-oldrel-macos-arm64	1.9				OK
r-oldrel-macos-x86_64	1.9	6.00	84.00	90.00	OK
r-oldrel-windows-x86_64	1.9	13.00	70.00	83.00	OK

Check Details

Version: 1.9
Check: CRAN incoming feasibility
Result: NOTE Maintainer: ‘Jing hua Zhao <jinghuazhao@hotmail.com>’ No Authors@R field in DESCRIPTION. Please add one, modifying Authors@R: c(person(given = c("Original", "by", "Joseph", "L."), family = "Schafer", role = "aut"), person(given = c("Jing", "hua"), family = "Zhao", role = "cre", email = "jinghuazhao@hotmail.com")) as necessary. Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc

Version: 1.9
Check: examples
Result: ERROR Running examples in ‘pan-Ex.R’ failed The error most likely occurred in: > ### Name: pan > ### Title: Imputation of multivariate panel or cluster data > ### Aliases: pan > ### Keywords: models > > ### ** Examples > > ######################################################################## > # This example is somewhat atypical because the data consist of a > # single response variable (change in heart rate) measured repeatedly; > # most uses of pan() will involve r > 1 response variables. If we had > # r response variables rather than one, the only difference would be > # that the vector y below would become a matrix with r columns, one > # for each response variable. The dimensions of Sigma (the residual > # covariance matrix for the response) and Psi (the covariance matrix > # for the random effects) would also change to (r x r) and (r*q x r*q), > # respectively, where q is the number of random coefficients in the > # model (in this case q=1 because we have only random intercepts). The > # new dimensions for Sigma and Psi will be reflected in the prior > # distribution, as Dinv and Binv become (r x r) and (r*q x r*q). > # > # The pred matrix has the same number of rows as y, the number of > # subject-occasions. Each column of Xi and Zi must be represented in > # pred. Because Zi is merely the first column of Xi, we do not need to > # enter that column twice. So pred is simply the matrix Xi, stacked > # upon itself nine times. > # > data(marijuana) > attach(marijuana) > pred <- with(marijuana,cbind(int,dummy1,dummy2,dummy3,dummy4,dummy5)) > # > # Now we must tell pan that all six columns of pred are to be used in > # Xi, but only the first column of pred appears in Zi. > # > xcol <- 1:6 > zcol <- 1 > ######################################################################## > # The model specification is now complete. The only task that remains > # is to specify the prior distributions for the covariance matrices > # Sigma and Psi. > # > # Recall that the dimension of Sigma is (r x r) where r > # is the number of response variables (in this case, r=1). The prior > # distribution for Sigma is inverted Wishart with hyperparameters a > # (scalar) and Binv (r x r), where a is the imaginary degrees of freedom > # and Binv/a is the prior guesstimate of Sigma. The value of a must be > # greater than or equal to r. The "least informative" prior possible > # would have a=r, so here we will take a=1. As a prior guesstimate of > # Sigma we will use the (r x r) identity matrix, so Binv = 1*1 = 1. > # > # By similar reasoning we choose the prior distribution for Psi. The > # dimension of Psi is (r*q x r*q) where q is the number of random > # effects in the model (i.e. the length of zcol, which in this case is > # one). The hyperparameters for Psi are c and Dinv, where c is the > # imaginary degrees of freedom (which must be greater than or equal to > # r*q) and Dinv/c is the prior guesstimate of Psi. We will take c=1 > # and Dinv=1*1 = 1. > # > # The prior is specified as a list with four components named a, Binv, > # c, and Dinv, respectively. > # > prior <- list(a=1,Binv=1,c=1,Dinv=1) > ######################################################################## > # Now we are ready to run pan(). Let's assume that the pan function > # and the object code have already been loaded into R. First we > # do a preliminary run of 1000 iterations. > # > result <- pan(y,subj,pred,xcol,zcol,prior,seed=13579,iter=1000) > # > # Check the convergence behavior by making time-series plots and acfs > # for the model parameters. Variances will be plotted on a log > # scale. We'll assume that a graphics device has already been opened. > # > plot(1:1000,log(result$sigma[1,1,]),type="l") > acf(log(result$sigma[1,1,])) > plot(1:1000,log(result$psi[1,1,]),type="l") > acf(log(result$psi[1,1,])) > par(mfrow=c(3,2)) > for(i in 1:6) plot(1:1000,result$beta[i,1,],type="l") > for(i in 1:6) acf(result$beta[i,1,]) > # > # This example appears to converge very rapidly; the only appreciable > # autocorrelations are found in Psi, and even those die down by lag > # 10. With a sample this small we can afford to be cautious, so let's > # impute the missing data m=10 times taking 100 steps between > # imputations. We'll use the current simulated value of y as the first > # imputation, then restart the chain where we left off to produce > # the second through the tenth. > # > y1 <- result$y > result <- pan(y,subj,pred,xcol,zcol,prior,seed=9565,iter=100,start=result$last) > y2 <- result$y > result <- pan(y,subj,pred,xcol,zcol,prior,seed=6047,iter=100,start=result$last) > y3 <- result$y > result <- pan(y,subj,pred,xcol,zcol,prior,seed=3955,iter=100,start=result$last) > y4 <- result$y > result <- pan(y,subj,pred,xcol,zcol,prior,seed=4761,iter=100,start=result$last) > y5 <- result$y > result <- pan(y,subj,pred,xcol,zcol,prior,seed=9188,iter=100,start=result$last) > y6 <- result$y > result <- pan(y,subj,pred,xcol,zcol,prior,seed=9029,iter=100,start=result$last) > y7 <- result$y > result <- pan(y,subj,pred,xcol,zcol,prior,seed=4343,iter=100,start=result$last) > y8 <- result$y > result <- pan(y,subj,pred,xcol,zcol,prior,seed=2372,iter=100,start=result$last) > y9 <- result$y > result <- pan(y,subj,pred,xcol,zcol,prior,seed=7081,iter=100,start=result$last) > y10 <- result$y > ######################################################################## > # Now we combine the imputation results according to mitools > ######################################################################## > # First, we build data frames from above, > d1 <- data.frame(y=y1,subj,pred) > d2 <- data.frame(y=y2,subj,pred) > d3 <- data.frame(y=y3,subj,pred) > d4 <- data.frame(y=y4,subj,pred) > d5 <- data.frame(y=y5,subj,pred) > d6 <- data.frame(y=y6,subj,pred) > d7 <- data.frame(y=y7,subj,pred) > d8 <- data.frame(y=y8,subj,pred) > d9 <- data.frame(y=y9,subj,pred) > d10 <- data.frame(y=y10,subj,pred) > # Second, we establish a S3 object as needed for the function MIcombine > # nevertheless we start with an ordinary least squares regression > require(mitools) Loading required package: mitools Warning in library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE, : there is no package called ‘mitools’ > d <- imputationList(list(d1,d2,d3,d4,d5,d6,d7,d8,d9,d10)) Error in imputationList(list(d1, d2, d3, d4, d5, d6, d7, d8, d9, d10)) : could not find function "imputationList" Execution halted Flavor: r-release-macos-x86_64