xiaoruizhu / passo Goto Github PK

Prepare for release:

• git pull
• Check current CRAN check results
• Polish NEWS
• urlchecker::url_check()
• devtools::check(remote = TRUE, manual = TRUE)
• devtools::check_win_devel()
• rhub::check_for_cran()
• rhub::check(platform = 'ubuntu-rchk')
• rhub::check_with_sanitizers()
• revdepcheck::revdep_check(num_workers = 4)
• Update cran-comments.md
• git push

Submit to CRAN:

• usethis::use_version('patch')
• devtools::submit_cran()
• Approve email

Wait for CRAN...

• Accepted 🎉
• git push
• usethis::use_github_release()
• usethis::use_dev_version()
• git push

By default, we want the "residuals()" function in the PAsso package can include the features of the function "stats::residuals()". But it seems the default type of residuals generated from "PAsso::residuals()" for the "glm" class object with the "binomial" family is "deviance" residuals, rather than the surrogate residuals. This needs to be fixed.

data("ANES2016")
# Partial association between "PreVote.num" and "PID" after adjusting "income.num", "age", "edu.year"
# "PAsso" function: Only need input responses, adjustments, data
PAsso_1 <- PAsso(responses = c("PreVote.num", "PID"),
adjustments = c("income.num", "age", "edu.year"),
data = ANES2016,
uni.model = "probit",
method = c("kendall"))
diagnostic.plot(object = PAsso_1, output = "covariate", x_name = "income.num", model_id = 2)

Error in assign(x = x_name, object$data[, x_name]) : invalid first argument

Got suggestion from Hadley about this. I need to carefully read the Advanced R again. LOL

As a maintainer, I test PAsso v0.1.8 on my macOS Mojave 10.14.5 with R 3.6.3. It throws issue when install:

fatal error: 'math.h' file not found

Then, the solution will be:

install.packages("https://cran.r-project.org/bin/macosx/el-capitan/contrib/3.6/PAsso_0.1.8.tgz", repos = NULL, type = "source")

However, on my end, another error comes:

there is no package called 'gsl'

Then, use binary installation of gsl package will solve the problem.

install.packages("gsl", type = "mac.binary")

When running the example in README, if use corr function for marginal correlation, it throws into error due to missing of "adjustments".

Fix it to not require the adjustments if run "marginal" association.

phi2 <- PAsso(responses = c("PID", "selfLR", "TrumpLR", "ClinLR"), adjustments = c("age", "edu.year", "income.num"), data = ANES2016, method = "kendall", model = c("acat", "acat", "acat", "acat"))
diagnostic.plot(phi2, output = "fitted")

The residuals are supposed to be in U(0,1) scale under acat model, but the plot shows different scale.

Am I too annoying? hahaha~

a1<- residuals(fit.PID1, jitter="latent",jitter.uniform.scale="response")
a2<- residuals(fit.PID1, jitter="uniform",jitter.uniform.scale="response")
a3<- residuals(fit.PID1, jitter="latent",jitter.uniform.scale="probability")
a4<- residuals(fit.PID1, jitter="uniform",jitter.uniform.scale="probability")
hist(a1)
hist(a2)
hist(a3)
hist(a4)

a4 does not work. a1 and a3 seem to be the same thing, but what is a2???

data("ANES2016")
fit.PID1<- polr(as.factor(PID)~age+edu.year+income.num, data=ANES2016, method="probit")
fit.PID2<- vglm(as.numeric(PID)~age+edu.year+income.num, data=ANES2016, family=acat(reverse=TRUE, parallel=TRUE))
r1<- residuals(fit.PID2, jitter="uniform",jitter.uniform.scale="response", nsim=30)
r2<- residuals(fit.PID2, jitter="uniform",jitter.uniform.scale="probability", nsim=30)
attr(r1,"draws")[,1]

the use of attr() is extremely inconvenient to get a simple output. Suggest to use more traditional way, e.g., r1[,1]
5th line, r2<-... does not work even if fit.PID2 is adjacent category logit model...