Power to detect mediation and other problems
September 2, 2016
I was reminded of this old R Club post recently by Rose, and decided to flesh it out a bit. The first part is about power to detect mediation in the standard 3 variable model people use. The second part examines how model misspecification gives rise to significant statistical tests of mediation when there is actually no mediation going on. Since publishing a paper (in press now, but written in ~2013) in which longitudinal data was subjected to a test of mediation, I’ve become increasingly skeptical of these kinds of analyses (including my own).
If you haven’t read this article by John Bullock and colleagues, you really, really should. The tl;dr is that mediation is always a claim about a chain of causation, and since causation itself is very difficult to pin down, mediation is doubly so. It’s a great read, and a necessary starting place if you’re serious about pursuing research on mediating processes.
Bullock, J. G., Green, D. P., & Ha, S. E. (2010). Yes, but what’s the mechanism? (don’t expect an easy answer). Journal of Personality and Social Psychology, 98(4), 550–558. http://doi.org/10.1037/a0018933
You’ll also probably want to check out docs for
Power
We’ll start as usual by loading packages – uncomment the install.pacakges
lines if you don’t have them.
#install.packages('lavaan')
library(lavaan)
#install.packages('semPlot')
library(semPlot)
#install.packages('simsem')
library(simsem)
#install.packages('dplyr')
library(dplyr)
#install.packages('ggplot2')
library(ggplot2)
library(knitr)
In the next step, we set the effect sizes for which we want to compute power, and generate a bunch of lavaan
model syntax that will subsequently let us simulate data based on these effect sizes.
The variable and path names corresond roughly to this diagram (I switched between using Z and M for the mediator variable).
effectSizes <- c(.1, .3, .5)
modelEffects <- expand.grid(effectSizes, effectSizes, effectSizes)
names(modelEffects) <- c('a', 'b', 'c')
# generate data (genModel) and then test the model (testModel)
# if we want 80% power than in 80% of simulations we should find an effect
models <- modelEffects %>%
rowwise() %>%
do({
genModel <- paste0('# direct effect
Y ~ ', .$c, '*X
# mediator
M ~ ', .$a, '*X
Y ~ ', .$b, '*M
X ~~ 1*X
Y ~~ 1*Y
M ~~ 1*M
')
testModel <-'# direct effect
Y ~ c*X
# mediator
M ~ a*X
Y ~ b*M
# indirect effect (a*b)
ab := a*b
# total effect
total := c + (a*b)
'
data.frame(a=.$a, b=.$b, c=.$c,
gen=genModel, test=testModel, stringsAsFactors=F)
})
The testModel
above is the model we’ll estimate. Notice that it’s equivalent in structure to that which is generating the data. We’re not worrying about model misspecification at this point.
We now have a data structure, in models
, representing all combinations of the effect sizes, and we can run each of them at different sample sizes using the simsem
package. Like most simulations, it’s probably a good idea to save the output so you don’t have to slog through the simulations again. You should set REDOSIMS=T
below to run them the first time. Using 8 threads on a fairly modern machine takes about 10 minutes.
The steps below are roughly:
- Within each level combination of
a
,b
, andc
effect sizes, - for every sample size from 50 to 1500:
- generate simulated data and estimate a model on those data.
REDOSIMS=F
if(REDOSIMS){
allModelPowerSim <- models %>%
rowwise() %>%
do({
manySims <- sim(NULL, model=.$test[1], n=50:1500, generate=.$gen[1],
lavaanfun='sem', multicore=T) # Enable multicore on your personal computer
data_frame(a=.$a, b=.$b, c=.$c, powersims=list(manySims))
})
#saving the above
saveRDS(allModelPowerSim, 'power_simulations.RDS')
} else {
#loading the above
allModelPowerSim <- readRDS('power_simulations.RDS')
}
Next, we’ll estimate the $\text{power}(\text{sample size})$ function to detect a significant a*b path for each of our levels of the effect sizes for the 3 paths.
powerData <- allModelPowerSim %>% rowwise() %>%
do({
aSimPower <- as.data.frame(getPower(.$powersims,
nVal=seq(50, 1500, 5),
powerParam='ab'))
data_frame(a=.$a, b=.$b, c=.$c,
alab=paste0('a=',.$a),
blab=paste0('b=',.$b),
clab=paste0('c=',.$c),
N=aSimPower[,1],
ab=aSimPower[,2])
})
print(powerData)
## Source: local data frame [7,857 x 8]
## Groups: <by row>
##
## # A tibble: 7,857 × 8
## a b c alab blab clab N ab
## * <dbl> <dbl> <dbl> <chr> <chr> <chr> <dbl> <dbl>
## 1 0.1 0.1 0.1 a=0.1 b=0.1 c=0.1 50 0.03226034
## 2 0.1 0.1 0.1 a=0.1 b=0.1 c=0.1 55 0.03284883
## 3 0.1 0.1 0.1 a=0.1 b=0.1 c=0.1 60 0.03344768
## 4 0.1 0.1 0.1 a=0.1 b=0.1 c=0.1 65 0.03405707
## 5 0.1 0.1 0.1 a=0.1 b=0.1 c=0.1 70 0.03467715
## 6 0.1 0.1 0.1 a=0.1 b=0.1 c=0.1 75 0.03530812
## 7 0.1 0.1 0.1 a=0.1 b=0.1 c=0.1 80 0.03595014
## 8 0.1 0.1 0.1 a=0.1 b=0.1 c=0.1 85 0.03660339
## 9 0.1 0.1 0.1 a=0.1 b=0.1 c=0.1 90 0.03726805
## 10 0.1 0.1 0.1 a=0.1 b=0.1 c=0.1 95 0.03794431
## # ... with 7,847 more rows
This produces a 7,857-row table (er, tibble
). Sounds like something better to plot:
ggplot(powerData, aes(x=N, y=ab))+
geom_line(aes(color=clab),alpha=.7, size=1)+
facet_grid(alab ~ blab, as.table=F)+
geom_hline(yintercept=.8,color='red',alpha=.5)+
labs(x='sample size', y='power to detect a*b', color='direct effect')
And we can get the sample size for 80% power to detect an effect at any of our levels. As an example, let’s take the first row from allModelPowerSims
:
allModelPowerSim[1,]
## # A tibble: 1 × 4
## a b c powersims
## <dbl> <dbl> <dbl> <list>
## 1 0.1 0.1 0.1 <S4: SimResult>
aPowerTable <- getPower(allModelPowerSim$powersims[[1]])
#?findPower
findPower(aPowerTable,
iv="N",
power=.8)
## c a b Y~~Y M~~M ab total
## 809 837 858 Inf Inf 1332 687
So, for a small effect for paths a and b, you need 1,332 participants for 80% power (and if you want to know why you should think about small effects, read this).
When I first presented this in R club, I left the below as an exercise:
Go ahead and make a data table of the power for finding the true mediated effect, ab, at each effect size level using ‘do’, below.
abPower80 <- allModelPowerSim %>%
group_by(a, b, c) %>%
do({
aPowerTable <- getPower()
theNfor80percPower <- findPower()
data.frame()
})
The c path doesn’t have much of an effect, so I left it out. Hopefully, you got something like this:
| a | b | c | N_for_ab |
|---|---|---|---|
| 0.1 | 0.1 | 0.3 | 1297 |
| 0.1 | 0.3 | 0.3 | 842 |
| 0.1 | 0.5 | 0.3 | 786 |
| 0.3 | 0.1 | 0.3 | 858 |
| 0.3 | 0.3 | 0.3 | 151 |
| 0.3 | 0.5 | 0.3 | 102 |
| 0.5 | 0.1 | 0.3 | 862 |
| 0.5 | 0.3 | 0.3 | 110 |
| 0.5 | 0.5 | 0.3 | 51 |
Misspecification
As I mentioned in the preamble, statistical mediation relies on a certain model of causal processes. What happens if the true process that gives rise to your observed data doesn’t conform to the simple mediation model? Using lavaan
and simsem
, it’s easy to generate data from one model and analyze it (repeatedly) with another. Mediation, being causal, necessitates the use of longitudinal data. According to Todd Little, two waves is enough assuming you have the model right[citation needed].
So our data generating model will use 3 variables measured twice: X, Y, and Z – the independent, dependent, and mediator respectively. These three variables will be highly stable (test-retest between the two waves of r=.7), and moderately correlated with each other at r=.3. Importantly, we’re going to generate data in which there is absolutely no causal effect between the three variables.
generatingModel <- '
y2 ~ .7*y1
x2 ~ .7*x1
z2 ~ .7*z1
y1 ~~ .3*x1 + .3*z1
x1 ~~ .3*z1
y2 ~~ .3*x2 + .3*z2
x2 ~~ .3*z2
y2 ~ 0*x1 + 0*z1
x2 ~ 0*y1 + 0*z1
z2 ~ 0*y1 + 0*x1
y1 ~~ 1*y1
x1 ~~ 1*x1
z1 ~~ 1*z1
y2 ~~ 1*y2
x2 ~~ 1*x2
z2 ~~ 1*z2
'
We can check out a few misspecifications. First, we’ll test the best case scenario short of testing the true model. In mediationModelControlzT1
we do the usual longitudinal best-practice of regressing our wave 2 dependent variable on its wave 1 measurement (y2 ~ y1 + c*x1 + b*z2
). In the regression of our wave 2 mediator on the independent variable, we can also include the mediator’s wave 1 measurement (z2 ~ a*x1 + z1
). Conceptually, we’ve made sure to account for stability within each variable over time – most simple models of mediation don’t do this.
In the two other misspecified models, we additionally leave out the measurement of the mediator at wave 1, and then also leave out the measurement of the dependent variable at wave 1.
mediationModelControlzT1 <- '# direct effect
y2 ~ y1 + c*x1 + b*z2
z2 ~ a*x1 + z1
# indirect effect (a*b)
ab := a*b
# total effect
total := c + (a*b)
'
mediationModelNoControlzT1 <- '# direct effect
y2 ~ y1 + c*x1 + b*z2
z2 ~ a*x1
# indirect effect (a*b)
ab := a*b
# total effect
total := c + (a*b)
'
mediationModelNoControlzOryT1 <- '# direct effect
y2 ~ c*x1 + b*z2
z2 ~ a*x1
# indirect effect (a*b)
ab := a*b
# total effect
total := c + (a*b)
'
Before running a buch of simulations, I’ll make sure the models are as expected by generating a single set, and fitting the above.
someData <- simulateData(model=generatingModel, sample.nobs=250, empirical=T)
fit.DGM <- sem(generatingModel, someData, fixed.x=F)
fit.CtrlT1.yz <- sem(mediationModelControlzT1, someData)
fit.noCtrlT1.z <- sem(mediationModelNoControlzT1, someData)
fit.noCtrlT1.yz <- sem(mediationModelNoControlzOryT1, someData)
First, the data generating model:
summary(fit.DGM)
## ** WARNING ** lavaan (0.5-20) model has NOT been fitted
## ** WARNING ** Estimates below are simply the starting values
##
## Number of observations 250
##
## Estimator ML
## Minimum Function Test Statistic NA
## Degrees of freedom NA
## P-value NA
##
## Parameter Estimates:
##
## Information Expected
## Standard Errors Standard
##
## Regressions:
## Estimate Std.Err Z-value P(>|z|)
## y2 ~
## y1 0.700
## x2 ~
## x1 0.700
## z2 ~
## z1 0.700
## y2 ~
## x1 0.000
## z1 0.000
## x2 ~
## y1 0.000
## z1 0.000
## z2 ~
## y1 0.000
## x1 0.000
##
## Covariances:
## Estimate Std.Err Z-value P(>|z|)
## y1 ~~
## x1 0.300
## z1 0.300
## x1 ~~
## z1 0.300
## y2 ~~
## x2 0.300
## z2 0.300
## x2 ~~
## z2 0.300
##
## Variances:
## Estimate Std.Err Z-value P(>|z|)
## y1 1.000
## x1 1.000
## z1 1.000
## y2 1.000
## x2 1.000
## z2 1.000
semPaths(fit.DGM, what='est', rotation=2, exoCov=F, exoVar=F)
Next, our best shot, controlling for the mediator and dependent variable at wave 1.
summary(fit.CtrlT1.yz)
## lavaan (0.5-20) converged normally after 12 iterations
##
## Number of observations 250
##
## Estimator ML
## Minimum Function Test Statistic 7.234
## Degrees of freedom 2
## P-value (Chi-square) 0.027
##
## Parameter Estimates:
##
## Information Expected
## Standard Errors Standard
##
## Regressions:
## Estimate Std.Err Z-value P(>|z|)
## y2 ~
## y1 0.666 0.065 10.292 0.000
## x1 (c) -0.034 0.065 -0.527 0.598
## z2 (b) 0.211 0.051 4.110 0.000
## z2 ~
## x1 (a) 0.000 0.066 0.000 1.000
## z1 0.700 0.066 10.558 0.000
##
## Variances:
## Estimate Std.Err Z-value P(>|z|)
## y2 0.937 0.084 11.180 0.000
## z2 1.000 0.089 11.180 0.000
##
## Defined Parameters:
## Estimate Std.Err Z-value P(>|z|)
## ab 0.000 0.014 0.000 1.000
## total -0.034 0.066 -0.515 0.607
semPaths(fit.CtrlT1.yz, what='est', rotation=2, exoCov=F, exoVar=F)
Now, progressively leaving things out….
summary(fit.noCtrlT1.z)
## lavaan (0.5-20) converged normally after 12 iterations
##
## Number of observations 250
##
## Estimator ML
## Minimum Function Test Statistic 4.140
## Degrees of freedom 1
## P-value (Chi-square) 0.042
##
## Parameter Estimates:
##
## Information Expected
## Standard Errors Standard
##
## Regressions:
## Estimate Std.Err Z-value P(>|z|)
## y2 ~
## y1 0.666 0.064 10.378 0.000
## x1 (c) -0.034 0.065 -0.524 0.600
## z2 (b) 0.211 0.051 4.144 0.000
## z2 ~
## x1 (a) 0.210 0.076 2.761 0.006
##
## Variances:
## Estimate Std.Err Z-value P(>|z|)
## y2 0.937 0.084 11.180 0.000
## z2 1.446 0.129 11.180 0.000
##
## Defined Parameters:
## Estimate Std.Err Z-value P(>|z|)
## ab 0.044 0.019 2.298 0.022
## total 0.010 0.066 0.155 0.877
semPaths(fit.noCtrlT1.z, what='est', rotation=2, exoCov=F, exoVar=F)
summary(fit.noCtrlT1.yz)
## lavaan (0.5-20) converged normally after 13 iterations
##
## Number of observations 250
##
## Estimator ML
## Minimum Function Test Statistic 0.000
## Degrees of freedom 0
## Minimum Function Value 0.0000000000000
##
## Parameter Estimates:
##
## Information Expected
## Standard Errors Standard
##
## Regressions:
## Estimate Std.Err Z-value P(>|z|)
## y2 ~
## x1 (c) 0.151 0.074 2.043 0.041
## z2 (b) 0.279 0.061 4.588 0.000
## z2 ~
## x1 (a) 0.210 0.076 2.761 0.006
##
## Variances:
## Estimate Std.Err Z-value P(>|z|)
## y2 1.334 0.119 11.180 0.000
## z2 1.446 0.129 11.180 0.000
##
## Defined Parameters:
## Estimate Std.Err Z-value P(>|z|)
## ab 0.059 0.025 2.366 0.018
## total 0.210 0.076 2.761 0.006
semPaths(fit.noCtrlT1.yz, what='est', rotation=2, exoCov=F, exoVar=F)
You can see that we’re getting a hint that the ab
path might be getting bigger as we further misspecify the model. To find out for sure, let’s run the simulations. Our outcome of interest will be the measure of power to detect a significant ab
path – the mediated effect. Usually power is a good thing, but if you have power to detect something that’s not there, it’s an indication that your model is reliably giving you the wrong answer.
Again, if you want to redo these simulations, you can set REDOSIMS=T
.
REDOSIMS=F
if(REDOSIMS){
sim.CtrlT1.yz <- simsem::sim(nRep=1000,
model=mediationModelControlzT1,
n=250,
generate=generatingModel,
lavaanfun="sem",
std.lv=F,
multicore=T)
sim.noCtrlT1.z <- simsem::sim(nRep=1000,
model=mediationModelNoControlzT1,
n=250,
generate=generatingModel,
lavaanfun="sem",
std.lv=F,
multicore=T)
sim.noCtrlT1.yz <- simsem::sim(nRep=1000,
model=mediationModelNoControlzOryT1,
n=250,
generate=generatingModel,
lavaanfun="sem",
std.lv=F,
multicore=T)
saveRDS(object=sim.CtrlT1.yz, file='sim_CtrlT1_yz.RDS')
saveRDS(object=sim.noCtrlT1.z, file='sim_noCtrlT1_z.RDS')
saveRDS(object=sim.noCtrlT1.yz, file='sim_noCtrlT1_yz.RDS')
} else {
sim.CtrlT1.yz <- readRDS(file='sim_CtrlT1_yz.RDS')
sim.noCtrlT1.z <- readRDS(file='sim_noCtrlT1_z.RDS')
sim.noCtrlT1.yz <- readRDS(file='sim_noCtrlT1_yz.RDS')
}
kable(summaryParam(sim.CtrlT1.yz), digits=2)
| Estimate Average | Estimate SD | Average SE | Power (Not equal 0) | Std Est | Std Est SD | Std Ave SE | |
|---|---|---|---|---|---|---|---|
| y2~y1 | 0.67 | 0.07 | 0.06 | 1.00 | 0.55 | 0.05 | 0.04 |
| c | -0.04 | 0.07 | 0.06 | 0.08 | -0.03 | 0.05 | 0.05 |
| b | 0.21 | 0.05 | 0.05 | 0.98 | 0.21 | 0.05 | 0.05 |
| a | 0.00 | 0.07 | 0.07 | 0.06 | 0.00 | 0.05 | 0.05 |
| z2~z1 | 0.70 | 0.07 | 0.07 | 1.00 | 0.57 | 0.05 | 0.04 |
| y2~~y2 | 0.92 | 0.08 | 0.08 | 1.00 | 0.62 | 0.05 | 0.04 |
| z2~~z2 | 0.99 | 0.09 | 0.09 | 1.00 | 0.67 | 0.05 | 0.04 |
| ab | 0.00 | 0.01 | 0.01 | 0.03 | 0.00 | 0.01 | 0.01 |
| total | -0.04 | 0.07 | 0.07 | 0.09 | -0.03 | 0.06 | 0.05 |
kable(summaryParam(sim.noCtrlT1.z), digits=2)
| Estimate Average | Estimate SD | Average SE | Power (Not equal 0) | Std Est | Std Est SD | Std Ave SE | |
|---|---|---|---|---|---|---|---|
| y2~y1 | 0.67 | 0.07 | 0.06 | 1.00 | 0.56 | 0.05 | 0.04 |
| c | -0.04 | 0.07 | 0.06 | 0.08 | -0.03 | 0.05 | 0.05 |
| b | 0.21 | 0.05 | 0.05 | 0.99 | 0.21 | 0.05 | 0.05 |
| a | 0.21 | 0.08 | 0.08 | 0.78 | 0.17 | 0.06 | 0.06 |
| y2~~y2 | 0.92 | 0.08 | 0.08 | 1.00 | 0.64 | 0.05 | 0.04 |
| z2~~z2 | 1.44 | 0.13 | 0.13 | 1.00 | 0.97 | 0.02 | 0.02 |
| ab | 0.04 | 0.02 | 0.02 | 0.64 | 0.04 | 0.02 | 0.02 |
| total | 0.01 | 0.07 | 0.07 | 0.06 | 0.01 | 0.06 | 0.05 |
kable(summaryParam(sim.noCtrlT1.yz), digits=2)
| Estimate Average | Estimate SD | Average SE | Power (Not equal 0) | Std Est | Std Est SD | Std Ave SE | |
|---|---|---|---|---|---|---|---|
| c | 0.15 | 0.08 | 0.07 | 0.52 | 0.12 | 0.06 | 0.06 |
| b | 0.28 | 0.06 | 0.06 | 1.00 | 0.28 | 0.06 | 0.06 |
| a | 0.21 | 0.08 | 0.08 | 0.78 | 0.17 | 0.06 | 0.06 |
| y2~~y2 | 1.32 | 0.12 | 0.12 | 1.00 | 0.89 | 0.04 | 0.04 |
| z2~~z2 | 1.44 | 0.13 | 0.13 | 1.00 | 0.97 | 0.02 | 0.02 |
| ab | 0.06 | 0.03 | 0.03 | 0.69 | 0.05 | 0.02 | 0.02 |
| total | 0.21 | 0.08 | 0.08 | 0.78 | 0.17 | 0.06 | 0.06 |
Looking across those ab
lines, you see that we’re not too bad off if we control for our wave 1 measurements. However, if we don’t do that, we end up with ~64-69% power to detect a significant mediation. To me, this warrants extreme caution.


Constructor
new Router(routes, rootopt, hash)
Router constructor
Parameters:
| Name | Type | Attributes | Default | Description |
|---|---|---|---|---|
routes |
Array.<Route> |
[
|
Routes collection |
|
root |
string |
<optional> |
Router root segment |
|
hash |
string |
''
|
Router hash to be added after root and before route path |
Members
resolving
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routing
Indicates if router is currently routing
Methods
registerRoutes(routes)
Registers routes collection in router
Parameters:
| Name | Type | Description |
|---|---|---|
routes |
Route | Array.<Route> | Routes collection |


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Diabetes cases soar, 1 in 11 adults affected: Doctors
PARIS: More than 460 million people - 1 in 11 adults - now suffer from diabetes, largely brought on by an over-rich lifestyle short on exercise, the International Diabetes Federation (IDF) said Thursday (Nov 14).
Releasing its latest Diabetes Atlas, the IDF said the current number of 463 million sufferers would jump to 578 million by 2030 and to 700 million by 2045, posing huge challenges for treatment and management of a disease that is already a top-10 killer worldwide.
READ: Indonesians quitting 'rice addiction' over diabetes fears
The IDF said the report shows an increase of 38 million cases over its previous survey in 2017 and noted that "more than half (50.1 per cent) of adult sufferers (are) undiagnosed."
People with diabetes have excessively high levels of blood sugar from food.
As a result they are more likely to suffer debilitating complications such as heart attacks, strokes and other serious medical conditions, leading to reduced life expectancy and potentially huge healthcare bills.
Most sufferers have Type 2 diabetes, which is linked to obesity and other lifestyle factors, and emerges in adults and increasingly in children.
Type 1 diabetes, which cannot be prevented, is caused when the pancreas does not produce enough insulin, the hormone that regulates blood sugar levels.
IDF head Nam H Cho said in a statement on the occasion of World Diabetes Day that the disease "is a serious global health issue that has a huge socio-economic impact that cannot be ignored".
READ: WHO launches initiative to boost insulin access for diabetics
"The rising prevalence of diabetes is a real cause for concern, especially when we consider the high number of people who remain undiagnosed," Cho said.
The IDF estimates the diabetes health cost in 2019 at US$760 billion.
The World Health Organization announced Wednesday the launch of an initiative to cut prices and increase insulin access as the number of sufferers increases.


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element specifies the structure and requirements for the pattern to be
matched to structures in the database.
<query-body>
Attributes None.
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one or more | Describes a vertex in the query; matches an object in the data |
edge |
zero or more | Describes an edge in the query; matches a link in the data |
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zero or more | Specifies a subquery within the current query |
constraint |
zero or one | Specifies a constraint between query elements |
add-link |
zero or more | Specifies new links to be added to the database |
Content Model
(vertex, (edge | vertex)*, subquery*, constraint?, add-link*)
<query-body> <vertex name="A">see vertex
</vertex> <edge name="Y">see edge
</edge> <subquery>see subquery
</subquery> <constraint>see constraint
</constraint> <add-link>see add-link
</add-link> </query-body>


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The web is a very amazing thing. It is made up of billions of pages with so many different types of information. When someone goes on www.facebook.com, their computer browser sends a HTTP Request to a server. The server finds the correct page and then relays it back through the chain onto the browser and in front of the person.
Tags and Elements
The HTML documents are created using elements. We tell the web browsers the type of elements by using HTML tags.
Here are a list of some of the more commonly used tags.
Anchor- You can create an anchor tag to link a webpage
Break- A void tag. You can create an inline page break.
Emphasis-This tag is used to create italics.
Image- This tag is used if you want to use an image.
Paragraph- Use this when you want to create an invisible box around a group of text.
Computers only work given a series of very rigorous instructions. If they aren't followed to the letter then you won't get the results you are looking for.
HTML elements are either inline or block. Block elements form an "invisible box" around the content inside of them. Inline elements have no width or height and are just there.
HTML elements are either inline or block. Block elements form an "invisible box" around the content inside of them. Inline elements have no width or height and are just there. You can find the developer tools option located under the hamburger menu in the toolbar. Select more tools and then developer tool. You can see the HTML running in realtime!
 You can have HTML elements inside of other HTML elements. You can have boxes that are within boxes and so forth. The DOM or Document Object Model is the tree like structure that translates the structure.
You can see the indentations on the left side of the code. The further indented the code is the more nested it is within the box.
Text editors are a great way to code for HTML. Several different kinds exist, but for the purposes of this class we use Scratchpad, Codepen and Sublime Text. They range in difficulty of use going from the easiest(Scratchpad) to the hardest(Sublime Text)
HTML is the structure in which a webpage is written. CSS or a Cascading Style Sheet is the style of the webpage. Javascript is also used to create the interactive components of the page.
As mentioned previously, CSS is a Cascading Style sheet. CSS is code written to control the style of the HTML elements.
Three Ways to Include CSS
1.) Write CSS in the head of your HTML 2.) Link your HTML to a separate CSS File 3.) Write your style inline with your HTML
It is best not to repeat code. Not only is it harder to read, it can also cause errors in the program which the programmer has to find.


The following can be used to configure the appearance and behavior:
Define the dataset to display in the chart.
Define the value a full ring represents.
Define the number of pixels between each ring.
Define the width of each ring in pixels.
Define the color of the track. This can be a CSS color value, the name of a color from the color set
or a ThemeColor
object.
Determine if the segment should highlight on hover.
Determine if the tooltip should appear on hover.
Determine the position of the tooltip.
Define the duration of the ring entrance animation.
Emits when an item is clicked. The event will contain the data of the clicked item.
Define the name of this item in the dataset.
Define the value of this item in the dataset.
Define the color of this item in the dataset. This can be a CSS color value, the name of a color from the color set
or a ThemeColor
object.
Define some custom data for this item. This could be used to display additional information in the tooltip.
Custom Tooltips
To customize the content in the nested donut chart tooltip, add an ng-template
as a child of the chart and identify it as #tooltip
.
The following values are available in the template context:
The name of the item.
The value of the item.
The color of the item. This will be returned as a CSS color value.
The custom data specified for the item.


The main menu is part of the navSide aside block. It also has a mod-compact for simpler menus. See how it works here
navSide
mod-compact


{% extends "layout.html" -%} {% block title -%}{{ title -}} {% endblock -%} {% block pages %} {% endblock %} {% block content %}
Hi! I'm a 23 year old computer engineer that loves everything related with them. I'm a native of the Basque Country, exactly from the town of Lekeitio, in Biscay. Thus, I am a native speaker of both Basque and Spanish. I can also speak English fluently on an advanced level.
I'm particularly interested in its interaction with society, in the forms of artificial intelligence, computational science, data mining, and so on. I also like the classics, though: programming, networking, software engineering, computer architecture, Operative Systems and Information security.
I am a self-educated person: I like studying by myself (it's hard but highly rewarding), looking at some technologies or knowledge that have either not seen on class or being key to pursue my career.
I don't like the "workaholic" philosophy but I get work done when it needs to be done, responsible but with no high pressure. Being stressed worsens the quality of the work.
If you want to navigate through the page, the Résumé and Contact pages are self-explanatory. The Tech page shows the technologies that I'm mostly knowledgeable about, and the Projects page shows some work I've done for college or in my spare time.


1. Try GET endpoint to get consent information and GET endpoint to check current status of the consent.
2. Try GET endpoint to get authorisation information and GET endpoint to check current status of the authorisation.


You got here because the search.html page is enabled but TIPUE_SEARCH is not enabled.
Please visit the Eevee documentation for information on configuring the search functionality.
{% extends "base.html" %}
{% block title %}Search{%endblock%}
{% block content %}
{% if 'tipue_search' in PLUGINS %}
{% if 'assets' in PLUGINS %}
{% include 'partials/_minify_tipue.html' with context %}
{% else %}
{% endif %}
Search results
You got here because the search.html page is enabled but TIPUE_SEARCH is not enabled.
Please visit the Eevee documentation for information on configuring the search functionality.


Across: 4. As part of notifying... ? (3) 7. Boundary Commission essentially turned, with leaders of delegation eventually leaving 5 newly enraged (8) 8. Engaged in street violence in prelude to Iranian revolution (6) 10. Beat poet... real crazy, dropping ecstasy everywhere (6) 11. Oddly, canniest one left out (as it emerges) (7) 12. One established by 20 5 26 could be a test (4 5) 14. Nasty slur after last of 5 plays 5 game (5) 16. Beginnings of entente as French world farewell British gunners who opposed the 5 12 (5 2 6) 19,23dn,2dn. Dail leader keenly taking a little look, then getting badly rogered as P.M. (5 5 6) 21. Too choppy at sea in reproduction (9) 24. Confuses counsel, bragging about housing (7) 26. see 20 down 27. A sorry arrangement that Catholics might cling to (6) 28. They open out for new arrival (8) 29. Executed sound horse? (3) Down: 1. Conflict's rage dies out (8) 2. see 19 across 3. Weight of daily grind taking terrible tolls in pit (9) 4. see 25 5. see 20 6. Burmese lure Mohammedans back, offering what the 5 sought (4 4) 9. Praise next Olympic hosts (5) 13. Use some opiate & ether for dentist's business (5) 15. Patriot in disturbance which split the country (9) 17. Amazing diorama including northern highway (4 4) 18. Cut off in Vietnam, put at ease (8) 20,5,26ac.  Verbal promises of money for those in the republic (6) 23. see 19 across 25,4. A Finnegan is stirred when GAA kicked out of 5 party (4 4)


Chest CT images of a 44-year-old woman. The RT-PCR test was positive. a Axial thin-section chest CT images show minimal ground-glass opacities (GGO) in the subpleural distribution. b, c Images show progressive GGO, mixed GGO and consolidation in the bilateral lung. d Images show thinned GGO, indicating absorption in the bilateral lung. e Images show complete absorption and a normal lung


bruch module¶
Bruch class¶
Bruch class exists only to fulfill the task. The actual implementation is in Fraction class. Bruch inherits from Fraction, thus it has all of the members of Fraction. See Members of Fraction class.
-
class
bruch.
Bruch
(zaehler, nenner)[source]¶ Bases:
fraction.Fraction
A derivative of the Fraction-class without other implementations. For UnitTests see FractionTests.


It is possible to pass come compiler options directly to the code compiler (e.g. Microsoft C# compiler csc.exe) directly from the script code:
options - compiler options as per csc.exe compiler command line interface.
| static string code = @"using System; public class Script { public static void Print(string greeting) { #if USE_TIMESTAMP Console.Write(DateTime.Now.ToString() + "":""); #endif Console.WriteLine(greeting); } }" ... //executing code with USE_TIMESTAMP defined Print = CSScript.LoadCode("//css_co /define:USE_TIMESTAMP;\n" + code) .GetStaticMethod(); |


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|
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| PREV NEXT | FRAMES NO FRAMES |
| Packages that use Provider | |
|---|---|
| com.google.inject | Google Guice (pronounced "juice") is an ultra-lightweight dependency injection framework. |
| com.google.inject.assistedinject | Extension for combining factory interfaces with injection; this extension requires guice-jndi-2.0.jar . |
| com.google.inject.binder | Interfaces which make up Binder 's
expression language. |
| com.google.inject.jndi | JNDI integration; this extension requires guice-jndi-2.0.jar . |
| com.google.inject.spi | Guice service provider interface |
| com.google.inject.spring | Spring integration; this extension requires guice-spring-2.0.jar . |
| com.google.inject.util | Helper methods for working with Guice. |
| Uses of Provider in com.google.inject |
|---|
| Methods in com.google.inject that return Provider | ||
|---|---|---|
Provider<T> |
Binding.getProvider()
Returns the scoped provider guice uses to fulfill requests for this binding. |
|
protected
|
PrivateModule.getProvider(Class<T> type)
|
|
|
Injector.getProvider(Class<T> type)
Returns the provider used to obtain instances for the given type. |
|
|
Binder.getProvider(Class<T> type)
Returns the provider used to obtain instances for the given injection type. |
|
protected
|
AbstractModule.getProvider(Class<T> type)
|
|
protected
|
PrivateModule.getProvider(Key<T> key)
|
|
|
Injector.getProvider(Key<T> key)
Returns the provider used to obtain instances for the given injection key. |
|
|
Binder.getProvider(Key<T> key)
Returns the provider used to obtain instances for the given injection key. |
|
protected
|
AbstractModule.getProvider(Key<T> key)
|
|
|
Scope.scope(Key<T> key,
Provider<T> unscoped)
Scopes a provider. |
| Methods in com.google.inject with parameters of type Provider | ||
|---|---|---|
|
Scope.scope(Key<T> key,
Provider<T> unscoped)
Scopes a provider. |
| Uses of Provider in com.google.inject.assistedinject |
|---|
| Classes in com.google.inject.assistedinject that implement Provider | |
|---|---|
class |
FactoryProvider<F>
Provides a factory that combines the caller's arguments with injector-supplied values to construct objects. |
| Methods in com.google.inject.assistedinject that return Provider | ||
|---|---|---|
static
|
FactoryProvider.newFactory(Class<F> factoryType,
Class<?> implementationType)
|
|
static
|
FactoryProvider.newFactory(TypeLiteral<F> factoryType,
TypeLiteral<?> implementationType)
|
| Uses of Provider in com.google.inject.binder |
|---|
| Methods in com.google.inject.binder with parameters of type Provider | |
|---|---|
ScopedBindingBuilder |
LinkedBindingBuilder.toProvider(Provider<? extends T> provider)
See the EDSL examples at Binder . |
| Method parameters in com.google.inject.binder with type arguments of type Provider | |
|---|---|
ScopedBindingBuilder |
LinkedBindingBuilder.toProvider(Class<? extends Provider<? extends T>> providerType)
See the EDSL examples at Binder . |
ScopedBindingBuilder |
LinkedBindingBuilder.toProvider(Key<? extends Provider<? extends T>> providerKey)
See the EDSL examples at Binder . |
| Uses of Provider in com.google.inject.jndi |
|---|
| Methods in com.google.inject.jndi that return Provider | ||
|---|---|---|
static
|
JndiIntegration.fromJndi(Class<T> type,
String name)
Creates a provider which looks up objects in JNDI using the given name. |
| Uses of Provider in com.google.inject.spi |
|---|
| Classes in com.google.inject.spi with type parameters of type Provider | |
|---|---|
interface |
ProviderBinding<T extends Provider<?>>
A binding to a Provider that delegates to the binding for the provided type. |
| Subinterfaces of Provider in com.google.inject.spi | |
|---|---|
interface |
ProviderWithDependencies<T>
A provider with dependencies on other injected types. |
| Methods in com.google.inject.spi that return Provider | ||
|---|---|---|
Provider<T> |
ProviderLookup.getDelegate()
Returns the delegate provider, or null if it has not yet been initialized. |
|
Provider<T> |
ProviderLookup.getProvider()
Returns the looked up provider. |
|
|
TypeEncounter.getProvider(Class<T> type)
Returns the provider used to obtain instances for the given injection type. |
|
|
TypeEncounter.getProvider(Key<T> key)
Returns the provider used to obtain instances for the given injection key. |
|
Provider<? extends T> |
ProviderInstanceBinding.getProviderInstance()
Returns the user-supplied, unscoped provider. |
| Methods in com.google.inject.spi that return types with arguments of type Provider | |
|---|---|
Key<? extends Provider<? extends T>> |
ProviderKeyBinding.getProviderKey()
Returns the key used to resolve the provider's binding. |
| Methods in com.google.inject.spi with parameters of type Provider | |
|---|---|
void |
ProviderLookup.initializeDelegate(Provider<T> delegate)
Sets the actual provider. |
| Uses of Provider in com.google.inject.spring |
|---|
| Methods in com.google.inject.spring that return Provider | ||
|---|---|---|
static
|
SpringIntegration.fromSpring(Class<T> type,
String name)
Creates a provider which looks up objects from Spring using the given name. |
| Uses of Provider in com.google.inject.util |
|---|
| Methods in com.google.inject.util that return Provider | ||
|---|---|---|
static
|
Providers.of(T instance)
Returns a provider which always provides instance . |
|
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| PREV NEXT | FRAMES NO FRAMES |


Code for this codelab is available from github.com/googlecodelabs/webrtc-web.
This is a work in progress. If you find a mistake or have a suggestion, please file an issue.
Thanks!


Help us clean the environment
Share helps you fetch a ride from people around you in an instant.
Join the community & post your ride for anywhere you travel.
Connect & travel with a peer who offers your request.
Share your rides with others to earn rewards and credibility.


Window control data. More...
Data Fields | |
| struct win_attributes | attributes |
| Window attributes. | |
| struct win_window * | parent |
| Parent window. | |
| struct win_window * | prev_sibling |
| Previous sibling in parent's linked list. | |
| struct win_window * | next_sibling |
| Next sibling in parent's linked list. | |
| struct win_window * | top_child |
| Top child of this window, start of list. | |
| bool | is_mapped |
| True if this window should be visible if parent is. |
Window control data.
In addition to the attribute struct defined public, it also includes the required members for it to be a part of the parent's linked list of children, and also the mapping status. If mapped, the window will be visible if the parent is visible.
Definition at line 64 of file win.c.
Window attributes.
Definition at line 66 of file win.c.
Referenced by win_draw(), win_draw_child(), win_draw_contents(), win_handle_event(), win_handle_pointer_event(), and win_translate_area_to_parent().
True if this window should be visible if parent is.
Definition at line 78 of file win.c.
Referenced by win_draw(), win_draw_contents(), win_handle_pointer_event(), and win_is_visible().
| struct win_window* win_window::next_sibling |
Next sibling in parent's linked list.
Definition at line 73 of file win.c.
Referenced by win_add_top_child(), win_destroy_children(), win_handle_pointer_event(), and win_unlink().
| struct win_window* win_window::parent |
Parent window.
Definition at line 69 of file win.c.
Referenced by win_add_top_child(), win_draw(), win_draw_parent(), win_is_visible(), win_process_command_event(), win_process_keyboard_event(), and win_unlink().
| struct win_window* win_window::prev_sibling |
Previous sibling in parent's linked list.
Definition at line 71 of file win.c.
Referenced by win_add_top_child(), win_draw(), win_draw_contents(), and win_unlink().
| struct win_window* win_window::top_child |
Top child of this window, start of list.
Definition at line 75 of file win.c.
Referenced by win_add_top_child(), win_destroy_children(), win_draw(), win_draw_contents(), win_handle_pointer_event(), and win_unlink().


Sireta travel offers travelers a deep explorer local culture in Guinee, like the detailed history and story behind local characters, language, craft and religion. Our website focused on highlighting exciting,unique and memorable experiences in Guinee.We provide courses to travelers to study culture,folklore, language, and religion. The travellers also have opportunities to design and make stuff by themselves, like spell their name in local characters then put it on a t-shirt, cup , hand made craft etc. We provide crafted packages for customers who don't have time on researching, all information should be easily accessible from our site. At the beginning stage, the Sireta culture trip africa concentrates provide service in the Guinee, then it will be scaled to the west of Africa.
Introduction
I worked on this concept from start to end that is from design to prototype
What did I do on this project?
Basic research done around travel and booking websites. Competition analysis and user pain in these websites
And:
- Sketched the screens ideas on paper
- Crafted the UX solution
- Created high fidelity UI design, prototypes.
Timeline: 2 months
Result: In developing
Final Deliverables
- Product sketches, annotated wireframes/layouts and user flows
- High-fidelity prototype
Target Market
Potential users of our product are travelers who are looking for verified, unique and interesting things to in Africa. Typical users will be looking for suggestions and don’t have time to spend on research.Being able to save and share trips and itinerary with their travel partners is important.
User Research
Starting my research with surveymonkey.com and involve.me, I gathered useful information like age of travelers, what they want to do when traveling, what’s the equipment they prefer to use when they plan to travel( pad, desktop, mobile ), prefer classic sightseeing activities or unique trips.
Question “ age of travelers ” will effect design system like font size, color and contrast , question “what’s the equipment they prefer to use when they plan to travel ” indicate the design for mobile first or desktop first, the result of question”prefer classic sightseeing activities or unique art trip” will affect the fate of our product.
Survey Questions:(gathered 172 responses)
- What’s your age range?
- What does your typical day look like?
- what’s your employment status?
- Do you like to travel?If yes, Where are you most interested in traveling to next?
- Would you mind sharing with us the kinds of experiences and activities you like to do when traveling?
- What kind of equipment you use often when you plan your travel?
- How likely are you to plan your next trip in Guinee? 100%,80%,60%,50%,40%......
- If you plan to go to Guinee, which one do you like the most? classic sightseeing activities, unique trip
- How do you prefer sharing your travel experiences and memories?
- How do you use your mobile phone for travel?
Response: 20-25(40%), 25-35(35%),35-45(5%), 45-60(2%), over 60 (18%)
Response: 60% busy
Response:20% students, 21% retired, other full time employee
Response: 81% responses like travel, 63% choose africa.
Response:93% like to share
Response:57% choose desktop, 26% choose pad, other mobile
Response: 70% choose 60%
Response:40% choose classic sightseeing activity, 35% choose unique trip, other not sure
Response: 83% choose social network
Response: 95% take photo, 60% use mobile App
Overall the survey gave me useful answers that support us to continue our project.
Personas
Pain
- Managing itinerary
- Info is out of date
- Price conversions are confusing
- Always find more interesting things after come back
Design
Suggestion from survey
Website and apps feature must haves:
- Access to local info, maps and external links
- Offline access to itinerary and maps
User flow
Site map
Low fidelity
Moodboard
I designed and performed three navigation tests with Usability Hub, I got following:
Final UI
Home
Choose Destination
Trip Market
Choose trip by experience(hikes, music,craft etc)
Trip detail
Result & takeaways
Working in an early-stage startup was steep learning curve. It taught me a lot about being lean and knowing how to focus your energy and efforts.
Takeaways
- Focus on building an MVP
- Focus on pain of users
- Don't waste too much time on detail for MVP
Just focus on the features that can deliver the highest value for users
The most important reason for you design this product


Symbol Index for "Subject Contact"
Symbol Index for diagram
"Subject Contact"
classifier (Association)
contact (Association)
Contact Method (Class)
Identifier In Domain (Class)
Location (Class)
location for (Association)
Preferred Contact For Subject (Class)
SI/Uniqueness (Generalization Node)
Subject Contact (Class)
Subject Identifier (Class)
Subject Name (Class)


{% extends 'base.html' %}{% import 'macros.html' as macro%} {% block styles%} {{ super() }} {% endblock %}{% block content %}
Large
Medium
Small


Home
Services
Products
About Us
Contact
Fitness
WEBCAM TEST
Your browser does not support this feature. Please try Chrome, Firefox, or Edge.
© Untitled. All rights reserved. | Design:
HTML5 UP


One day this website will be way cooler. For now I'll tell you about myself. I work for an airline and travel all the time. My goal is to have a job I can do anywhere in the world and CodeFellows is helping me.


Cloudant Query Tutorial
Step 4 - Try it yourself
Create a query yourself and press
Query
to execute it:
Query
{ "selector": { "year" : { "$lt": 2012 } }, "limit":10 }
Response


|
Slate
1.0.1
|
Class for representation in 3D. More...
#include <primitive.h>
Public Member Functions | |
| view3D (vertex3D **a, int b) | |
| constructor. More... | |
| view3D (vertex3D **a, edge3D *b, int c) | |
| constructor. More... | |
| view3D () | |
| constructor. More... | |
| void | drawView3D (void) |
| constructor. More... | |
| void | drawWireframe (void) |
| constructor. More... | |
| void | translate3D (double x, double y, double z) |
| constructor. More... | |
| void | scaling3D (double x, double y, double z) |
| constructor. More... | |
| void | reflection3D (int b) |
| constructor. More... | |
| void | rotation3D (double a, int b) |
| constructor. More... | |
Public Attributes | |
| vertex3D ** | v |
| edge3D * | e |
| edges in a 3D view More... | |
| Face ** | f |
| faces in a 3D view More... | |
| plane * | p |
| planes in a 3D view More... | |
| int | n |
| int | nv |
| n for number of edges and nv for number of vertices More... | |
| int | nf |
Class for representation in 3D.
|
inline |
constructor.
|
inline |
constructor.
| void view3D::drawView3D | ( | void | ) |
constructor.
| void view3D::drawWireframe | ( | void | ) |
constructor.
| void view3D::reflection3D | ( | int | b | ) |
constructor.
| void view3D::rotation3D | ( | double | a, |
| int | b | ||
| ) |
constructor.
| void view3D::scaling3D | ( | double | x, |
| double | y, | ||
| double | z | ||
| ) |
constructor.
| void view3D::translate3D | ( | double | x, |
| double | y, | ||
| double | z | ||
| ) |
constructor.
| edge3D* view3D::e |
edges in a 3D view
| Face** view3D::f |
faces in a 3D view
| int view3D::n |
| int view3D::nf |
n of faces
| int view3D::nv |
n for number of edges and nv for number of vertices
| plane* view3D::p |
planes in a 3D view
| vertex3D** view3D::v |


Projects - RIT Computer Science Community
{% endblock %}
{% block desc %}
Check out a few of the CSC's featured projects, made by officers and members!
{% endblock %}
{% block content %}
{% if success %}
{% for repo in repos %}
{% repoInfo repo %}
{% endfor %}
{% else %}


--- layout: post title: "Git On Your Web Server: A Security Reminder" date: 2011-08-31 22:53:21 +0000 permalink: /2011/08/31/git-on-your-web-server-a-security-reminder/ category: Security tags: - Drupal - Git - Security - Nginx redirect_from: - /blog/git-on-your-web-server-a-security-reminder/ ---
Earlier this month, I wrote about managing a Drupal site with git. What I neglected to remember, of course, is this places a full copy of your git repository within your web server's document root. This has the potential to expose any data in your git repository -- a malicious attacker could (depending on your configuration) clone the entire repository, thus exposing source code, configuration files, database dumps, and other sensitive data.
Adam Baldwin did an interesting study on exposed repositories, and shows that the problem is widespread, even among very large-scale websites. He also offers points on how to protect your git repositories, but the Nginx directions didn't exactly work for me. No matter what I tweaked, I couldn't get "deny all;" to actually deny anyone! I ended up using "return 403;" and that worked quite well. You could even return 404 if you wanted to hide the repository entirely. For completeness, here's my configuration for protecting git:
location ~ /\.git { return 403; }


This is yet another whats-my-ip-address service. Soooo, here is your IP address:
{{ IP }}
Here you have some examples how you can use this service.
$ curl -L {{ DOMAIN }}/json } "ip": {{ IP }} }
$ curl -L {{ DOMAIN }} {{ IP }}
$ curl -L {{ DOMAIN }} {{ IP }} $ wget -qO- {{ DOMAIN }} {{ IP }} $ http --follow -b {{ DOMAIN }} {{ IP }}


About us
We Have Free Templates for Everyone
Our website templates are created with inspiration, checked for quality and originality and meticulously sliced and coded. What's more, they're absolutely free! You can do a lot with them. You can modify them. You can use them to design websites for clients, so long as you agree with the terms of Use. You can even remove all our links if you want to.
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Looking for more templates? Just browse through all our Free Website Templates and find what you're looking for. But if you don't find any website template you can use, you can try our Free Web Design service and tell us all about it. Maybe you're looking for something different, something special. And we love the challenge of doing something different and something special.
Be Part of Our Community
If you're experiencing issues and concerns about this website template, join the discussion on our forum and meet other people in the community who share the same interests with you.
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Version 12 Website Template details, discussion and updates for this Her Designs Web Template. Website Template design by Free Website Templates. Please feel free to remove some or all the text and links of this page and replace it with your own About content.


subject
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Reports variables, methods, and classes with dollar signs (
$
) in their names. While such names are legal Java, their use outside of generated java code is strongly discouraged.
Example:
class SalaryIn${}
Rename quick-fix is suggested only in the editor.


Issue 3159: Show numeric character reference of MathML
Steps to reproduce
- Open Firebug
- Switch to the HTML panel
- Inspect the first minus of the formula above
-
Click the item Show Entities As Symbols from the HTML panel options menu
⇒ The minus should be displayed as −
.
-
Click the item Show Entities As Names from the HTML panel options menu
⇒ The minus should be displayed as −
.
-
Click the item Show Entities As Unicode from the HTML panel options menu
⇒ The minus should be displayed as −
.
Expected result
-
There should be three different options to display special characters to
display them as symbols, using their names or in Unicode format.