I want to run dontrun part of examples within example function. Tried both run.dontrun=TRUE and run.dontrun = FALSE options but getting the same output. Any thoughts.
install.packages("eda4treeR")
With run.dontrun=TRUE option
library(eda4treeR)
example(
topic = "Exam8.2"
, package = "eda4treeR"
, lib.loc = NULL
, character.only = c(TRUE, FALSE)[2]
, give.lines = c(TRUE, FALSE)[2]
, local = c(TRUE, FALSE)[2]
, type = c("console", "html")[2]
, echo = c(TRUE, FALSE)[1]
, verbose = getOption("verbose")
, setRNG = c(TRUE, FALSE)[1]
, ask = getOption("example.ask")
, prompt.prefix = NULL
, run.dontrun = c(TRUE, FALSE)[1]
, run.donttest = c(TRUE, FALSE)[2]
)
### ** Examples
library(car)
library(dae)
library(dplyr)
library(emmeans)
library(ggplot2)
library(lmerTest)
library(magrittr)
library(predictmeans)
library(supernova)
data(DataExam8.2)
# Pg.
fm8.2 <-
lmer(
formula = dbhmean ~ Repl + Column + Contcompf + Contcompf:Standard +
(1|Repl:Row ) + (1|Repl:Column ) + (1|Contcompv:Clone)
, data = DataExam8.2
)
fixed-effect model matrix is rank deficient so dropping 5 columns / coefficients
## Not run:
##D varcomp(fm8.2)
## End(Not run)
anova(fm8.2)
Missing cells for: Contcompf0:Standard0, Contcompf1:StandardUG323, Contcompf1:StandardU6, Contcompf1:StandardPN14, Contcompf1:StandardSSOseed.
Interpret type III hypotheses with care.
Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value
Repl 3.2720 0.8180 4 26.467 2.0489
Column 3.1018 0.6204 5 19.545 1.5539
Contcompf 5.3203 5.3203 1 54.905 13.3265
Contcompf:Standard 20.6587 6.8862 3 207.152 17.2488
Pr(>F)
Repl 0.1162606
Column 0.2194719
Contcompf 0.0005845 ***
Contcompf:Standard 0.0000000004896 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Anova(fm8.2, type = "II", test.statistic = "Chisq")
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: dbhmean
Chisq Df Pr(>Chisq)
Repl 8.1957 4 0.08467 .
Column 7.7694 5 0.16941
Contcompf 4.6841 1 0.03044 *
Contcompf:Standard 51.7463 3 3.392e-11 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
predictmeans(model = fm8.2, modelterm = "Repl")
Warning in Kmatrix(model, modelterm, covariate, prtnum = prtnum):
Missing treatments' combination appeared, predicted means maybe
misleading!
Warning in Kmatrix(model, modelterm): Missing treatments' combination
appeared, predicted means maybe misleading!
$`Predicted Means`
Repl
1 2 3 4 5
7.8926 8.2070 8.3429 8.4604 8.5464
$`Standard Error of Means`
Repl
1 2 3 4 5
0.33123 0.33126 0.32992 0.32992 0.32992
$`Standard Error of Differences`
Max.SED Min.SED Aveg.SED
0.2239675 0.2167320 0.2196681
$LSD
Max.LSD Min.LSD Aveg.LSD
0.44792 0.43345 0.43932
attr(,"Significant level")
[1] 0.05
attr(,"Degree of freedom")
[1] 60.56
$mean_table
Repl Mean SE Df LL(95%) UL(95%)
1 1 7.8926 0.33123 60.55892 7.2302 8.5551
2 2 8.2070 0.33126 60.55892 7.5445 8.8695
3 3 8.3429 0.32992 60.55892 7.6831 9.0027
4 4 8.4604 0.32992 60.55892 7.8006 9.1202
5 5 8.5464 0.32992 60.55892 7.8866 9.2062
predictmeans(model = fm8.2, modelterm = "Column")
Warning in Kmatrix(model, modelterm, covariate, prtnum = prtnum):
Missing treatments' combination appeared, predicted means maybe
misleading!
Warning in Kmatrix(model, modelterm, covariate, prtnum = prtnum):
Missing treatments' combination appeared, predicted means maybe
misleading!
$`Predicted Means`
Column
1 2 3 4 5 6
8.2214 8.4708 8.3779 7.9721 7.8166 8.7141
$`Standard Error of Means`
Column
1 2 3 4 5 6
0.31662 0.39168 0.39315 0.26648 0.26646 0.31653
$`Standard Error of Differences`
Max.SED Min.SED Aveg.SED
0.2714760 0.2102583 0.2373610
$LSD
Max.LSD Min.LSD Aveg.LSD
0.54413 0.42143 0.47575
attr(,"Significant level")
[1] 0.05
attr(,"Degree of freedom")
[1] 54.65
$mean_table
Column Mean SE Df LL(95%) UL(95%)
1 1 8.2214 0.31662 54.64679 7.5868 8.8561
2 2 8.4708 0.39168 54.64679 7.6857 9.2558
3 3 8.3779 0.39315 54.64679 7.5900 9.1659
4 4 7.9721 0.26648 54.64679 7.4380 8.5063
5 5 7.8166 0.26646 54.64679 7.2825 8.3507
6 6 8.7141 0.31653 54.64679 8.0797 9.3486
library(emmeans)
emmeans(object = fm8.2, specs = ~Contcompf|Standard)
NOTE: A nesting structure was detected in the fitted model:
Standard %in% Contcompf
Contcompf = 1, Standard = 0:
emmean SE df lower.CL upper.CL
8.91 0.117 65.9 8.67 9.14
Contcompf = 0, Standard = UG323:
emmean SE df lower.CL upper.CL
8.97 0.770 55.6 7.43 10.51
Contcompf = 0, Standard = U6:
emmean SE df lower.CL upper.CL
6.55 0.770 55.5 5.01 8.10
Contcompf = 0, Standard = PN14:
emmean SE df lower.CL upper.CL
7.70 0.771 55.8 6.16 9.25
Contcompf = 0, Standard = SSOseed:
emmean SE df lower.CL upper.CL
6.08 0.770 55.5 4.54 7.63
Results are averaged over the levels of: Repl, Column
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
With run.dontrun=FALSE option
example(
topic = "Exam8.2"
, package = "eda4treeR"
, lib.loc = NULL
, character.only = c(TRUE, FALSE)[2]
, give.lines = c(TRUE, FALSE)[2]
, local = c(TRUE, FALSE)[2]
, type = c("console", "html")[2]
, echo = c(TRUE, FALSE)[1]
, verbose = getOption("verbose")
, setRNG = c(TRUE, FALSE)[1]
, ask = getOption("example.ask")
, prompt.prefix = NULL
, run.dontrun = c(TRUE, FALSE)[2]
, run.donttest = c(TRUE, FALSE)[2]
)
### ** Examples
library(car)
library(dae)
library(dplyr)
library(emmeans)
library(ggplot2)
library(lmerTest)
library(magrittr)
library(predictmeans)
library(supernova)
data(DataExam8.2)
# Pg.
fm8.2 <-
lmer(
formula = dbhmean ~ Repl + Column + Contcompf + Contcompf:Standard +
(1|Repl:Row ) + (1|Repl:Column ) + (1|Contcompv:Clone)
, data = DataExam8.2
)
fixed-effect model matrix is rank deficient so dropping 5 columns / coefficients
## Not run:
##D varcomp(fm8.2)
## End(Not run)
anova(fm8.2)
Missing cells for: Contcompf0:Standard0, Contcompf1:StandardUG323, Contcompf1:StandardU6, Contcompf1:StandardPN14, Contcompf1:StandardSSOseed.
Interpret type III hypotheses with care.
Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value
Repl 3.2720 0.8180 4 26.467 2.0489
Column 3.1018 0.6204 5 19.545 1.5539
Contcompf 5.3203 5.3203 1 54.905 13.3265
Contcompf:Standard 20.6587 6.8862 3 207.152 17.2488
Pr(>F)
Repl 0.1162606
Column 0.2194719
Contcompf 0.0005845 ***
Contcompf:Standard 0.0000000004896 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Anova(fm8.2, type = "II", test.statistic = "Chisq")
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: dbhmean
Chisq Df Pr(>Chisq)
Repl 8.1957 4 0.08467 .
Column 7.7694 5 0.16941
Contcompf 4.6841 1 0.03044 *
Contcompf:Standard 51.7463 3 3.392e-11 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
predictmeans(model = fm8.2, modelterm = "Repl")
Warning in Kmatrix(model, modelterm, covariate, prtnum = prtnum):
Missing treatments' combination appeared, predicted means maybe
misleading!
Warning in Kmatrix(model, modelterm): Missing treatments' combination
appeared, predicted means maybe misleading!
$`Predicted Means`
Repl
1 2 3 4 5
7.8926 8.2070 8.3429 8.4604 8.5464
$`Standard Error of Means`
Repl
1 2 3 4 5
0.33123 0.33126 0.32992 0.32992 0.32992
$`Standard Error of Differences`
Max.SED Min.SED Aveg.SED
0.2239675 0.2167320 0.2196681
$LSD
Max.LSD Min.LSD Aveg.LSD
0.44792 0.43345 0.43932
attr(,"Significant level")
[1] 0.05
attr(,"Degree of freedom")
[1] 60.56
$mean_table
Repl Mean SE Df LL(95%) UL(95%)
1 1 7.8926 0.33123 60.55892 7.2302 8.5551
2 2 8.2070 0.33126 60.55892 7.5445 8.8695
3 3 8.3429 0.32992 60.55892 7.6831 9.0027
4 4 8.4604 0.32992 60.55892 7.8006 9.1202
5 5 8.5464 0.32992 60.55892 7.8866 9.2062
predictmeans(model = fm8.2, modelterm = "Column")
Warning in Kmatrix(model, modelterm, covariate, prtnum = prtnum):
Missing treatments' combination appeared, predicted means maybe
misleading!
Warning in Kmatrix(model, modelterm, covariate, prtnum = prtnum):
Missing treatments' combination appeared, predicted means maybe
misleading!
$`Predicted Means`
Column
1 2 3 4 5 6
8.2214 8.4708 8.3779 7.9721 7.8166 8.7141
$`Standard Error of Means`
Column
1 2 3 4 5 6
0.31662 0.39168 0.39315 0.26648 0.26646 0.31653
$`Standard Error of Differences`
Max.SED Min.SED Aveg.SED
0.2714760 0.2102583 0.2373610
$LSD
Max.LSD Min.LSD Aveg.LSD
0.54413 0.42143 0.47575
attr(,"Significant level")
[1] 0.05
attr(,"Degree of freedom")
[1] 54.65
$mean_table
Column Mean SE Df LL(95%) UL(95%)
1 1 8.2214 0.31662 54.64679 7.5868 8.8561
2 2 8.4708 0.39168 54.64679 7.6857 9.2558
3 3 8.3779 0.39315 54.64679 7.5900 9.1659
4 4 7.9721 0.26648 54.64679 7.4380 8.5063
5 5 7.8166 0.26646 54.64679 7.2825 8.3507
6 6 8.7141 0.31653 54.64679 8.0797 9.3486
library(emmeans)
emmeans(object = fm8.2, specs = ~Contcompf|Standard)
NOTE: A nesting structure was detected in the fitted model:
Standard %in% Contcompf
Contcompf = 1, Standard = 0:
emmean SE df lower.CL upper.CL
8.91 0.117 65.9 8.67 9.14
Contcompf = 0, Standard = UG323:
emmean SE df lower.CL upper.CL
8.97 0.770 55.6 7.43 10.51
Contcompf = 0, Standard = U6:
emmean SE df lower.CL upper.CL
6.55 0.770 55.5 5.01 8.10
Contcompf = 0, Standard = PN14:
emmean SE df lower.CL upper.CL
7.70 0.771 55.8 6.16 9.25
Contcompf = 0, Standard = SSOseed:
emmean SE df lower.CL upper.CL
6.08 0.770 55.5 4.54 7.63
Results are averaged over the levels of: Repl, Column
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
TL;DR tools::example2html was rewritten to satisfy your expectations, and browse_example is offered. Examples will be run and converted to HTML on the fly.
I can reproduce your problem with:
library(eda4treeR)
example(
topic = "Exam8.2",
package = "eda4treeR",
run.dontrun = c(TRUE, FALSE)[1],
type = c("console", "html")[2]
)
I discovered that the problem you faced only exists if you use the type="html", and for type="console" it works perfectly fine.
I investigate it further and investigate the internals of the utils::example.
For the HTML type output the function accesses the R DB with HTML items like demo/examples/docs for each package. This DB is built with a default setup so the run.dontrun does not impact results. For example, I can access the mice::mice example with http://127.0.0.1:24851/library/mice/Example/mice; you should be able to get the port with port <- tools::startDynamicHelp(NA).
The R HTML DB is built from static content (Rd files) generated when any package is installed.
You can access the Rd BD with pkgRdDB <- tools::Rd_db("eda4treeR").
Later each Rd if converted to HTML (only once) with proper tools function like tools:::example2html("Exam8.1.1", "eda4treeR"). It can be surprising that the base tools package uses knitr dependency in its internals.
The possible solution requires the update of the tools::example2html function. The run.dontrun argument has to be added and htmltools::browsable is needed to print it.
Then you are not printing already rendered files from DB but generating the examples from scratch.
example2html2 <- function (topic, package, run.dontrun = FALSE, Rhome = "", env = NULL)
{
enhancedHTML <- tools:::config_val_to_logical(Sys.getenv("_R_HELP_ENABLE_ENHANCED_HTML_",
"TRUE"))
if (!enhancedHTML || !requireNamespace("knitr", quietly = TRUE)) {
utils::example(topic, package = package, character.only = TRUE,
ask = FALSE, run.dontrun = run.dontrun)
tools:::.code2html_payload_console("example", topic, package,
enhancedHTML = enhancedHTML, Rhome = Rhome)
}
else {
ecode <- utils::example(topic, package = package, character.only = TRUE,
give.lines = TRUE, run.dontrun = run.dontrun)
hlines <- grep("^###[ ][^*]", ecode)
wskip <- which(diff(hlines) != 1)
if (length(wskip))
hlines <- hlines[seq_len(wskip[1])]
if (length(hlines)) {
header.info <- as.list(read.dcf(textConnection(substring(ecode[hlines],
5)))[1, , drop = TRUE])
ecode <- ecode[-hlines]
}
else header.info <- NULL
tools:::.code2html_payload_browser("example", ecode, topic, package,
Rhome = Rhome, header.info = header.info, env = env)
}
}
browse_example <- function(topic, package, run.dontrun = FALSE) {
stopifnot(package %in% rownames(installed.packages()))
stopifnot(is.character(topic))
stopifnot(is.logical(run.dontrun))
ee <- example2html2(topic, package, run.dontrun = run.dontrun)
htmltools::browsable(htmltools::HTML(ee$payload))
}
browse_example("Exam8.1.1", "eda4treeR", run.dontrun = FALSE)
browse_example("Exam8.1.1", "eda4treeR", run.dontrun = TRUE)