Last updated on 2026-04-25 20:50:34 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.9 | 8.91 | 35.14 | 44.05 | NOTE | |
| r-devel-linux-x86_64-debian-gcc | 1.9 | 4.60 | 27.62 | 32.22 | NOTE | |
| r-devel-linux-x86_64-fedora-clang | 1.9 | 11.00 | 50.22 | 61.22 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.9 | 11.00 | 60.55 | 71.55 | OK | |
| r-patched-linux-x86_64 | 1.9 | 5.35 | 31.00 | 36.35 | OK | |
| r-release-linux-x86_64 | 1.9 | 5.59 | 29.92 | 35.51 | 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 | 8.00 | 66.00 | 74.00 | 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 | 71.00 | 84.00 | OK |
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