Chapter 41 Frequency table analysis
Packages for this chapter:
41.1 College plans
5199 male high school seniors in Wisconsin38 were classified by socio-economic status (low, lower-middle, upper-middle, high), by the degree that their parents encouraged them in their education (low or high), and whether or not they had plans to go to college (yes or no). How, if at all, are these categorical variables related? The data can be found at link.
Read in the data and check that you have a column for each variable and a column of frequencies.
Fit a log-linear model containing all possible interactions. You don’t need to examine it yet.
Find out which terms (interactions) could be removed. Do you think removing any of them is a good idea?
Remove anything you can, and fit again. Hint:
update
.Continue to examine what can be removed, and if reasonable, remove it, until you need to stop. Which terms are left in your final model?
Make two-way tables of any remaining two-way interactions, and describe any relationships that you see.
41.2 Predicting voting
1257 British voters were classified according to their social class, age (categorized), sex and the political party they voted for (Labour or Conservative). Which, if any, of these factors influences the party that someone votes for? The data are in link, one voter per line.
Read in the data and display (some of) the data frame.
There is no frequency column here, because each row of the data frame only represents one voter. Count up the frequencies for each combo of the categorical variables, and save it (this is the data frame that we will use for the analysis). Display the first few rows of the result. Do you now have something that you need?
Fit a log-linear model with the appropriate interaction (as a starting point).
Refine your model by taking out suitable non-significant terms, in multiple steps. What model do you finish with?
If we think of the party someone votes for as the final outcome (that depends on all the other things), what does our final model say that someone’s vote depends on?
Obtain sub-tables that explain how
vote
depends on any of the things it’s related to.
41.3 Brand M laundry detergent
A survey was carried out comparing respondents’ preferences of a laundry detergent M compared to a mystery brand X. For each respondent, the researchers recorded the temperature of the laundry load (low or high), whether or not they previously used brand M (yes or no), and the softness of the water used for the laundry load(hard, medium or soft). The aim of the survey was to find out what was associated with the respondents preferring brand M. The data are in http://ritsokiguess.site/datafiles/brand_m.csv.
Read in and display (some of) the data. Explain briefly how the data is laid out appropriately to fit a log-linear model.
Using backward elimination, build a suitable log-linear model for the associations between the variables. (Do not use
step
; do the elimination yourself).What is associated with the brand a respondent prefers? By obtaining suitable frequency tables, describe the nature of these associations.
My solutions follow:
41.4 College plans
5199 male high school seniors in Wisconsin39 were classified by socio-economic status (low, lower-middle, upper-middle, high), by the degree that their parents encouraged them in their education (low or high), and whether or not they had plans to go to college (yes or no). How, if at all, are these categorical variables related? The data can be found at link.
- Read in the data and check that you have a column for each variable and a column of frequencies.
Solution
Delimited by one space:
##
## ── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## cols(
## social.stratum = col_character(),
## encouragement = col_character(),
## college.plans = col_character(),
## frequency = col_double()
## )
## # A tibble: 16 x 4
## social.stratum encouragement college.plans frequency
## <chr> <chr> <chr> <dbl>
## 1 lower low no 749
## 2 lower low yes 35
## 3 lower high no 233
## 4 lower high yes 133
## 5 lowermiddle low no 627
## 6 lowermiddle low yes 38
## 7 lowermiddle high no 330
## 8 lowermiddle low no 303
## 9 uppermiddle low no 627
## 10 uppermiddle low yes 38
## 11 uppermiddle high no 374
## 12 uppermiddle high yes 467
## 13 higher low no 153
## 14 higher low yes 26
## 15 higher high no 266
## 16 higher high yes 800
As promised. We only have 16 observations, because we have all possible combinations of categorical variable combinations, 4 social strata, times 2 levels of encouragement, times 2 levels of college plans.
Each line of the data file summarizes a number of students, not just one. For example, the first line says that 749 students were in the lower social stratum, received low encouragement and have no college plans. If we sum up the frequencies, we should get 5199 because there were that many students altogether:
## # A tibble: 1 x 1
## tot
## <dbl>
## 1 5199
\(\blacksquare\)
- Fit a log-linear model containing all possible interactions. You don’t need to examine it yet.
Solution
wisc.1 <- glm(frequency ~ social.stratum * encouragement * college.plans,
data = wisc, family = "poisson"
)
\(\blacksquare\)
- Find out which terms (interactions) could be removed. Do you think removing any of them is a good idea?
Solution
This is drop1
. If you forget the test=
, you won’t
get any P-values:
## Single term deletions
##
## Model:
## frequency ~ social.stratum * encouragement * college.plans
## Df Deviance AIC LRT Pr(>Chi)
## <none> 115.28 259.52
## social.stratum:encouragement:college.plans 2 118.98 259.22 3.697 0.1575
This P-value is not small, so the three-way interaction can be removed.
\(\blacksquare\)
- Remove anything you can, and fit again. Hint:
update
.
Solution
In this kind of modelling, it’s easier to describe what changes should be made to get from one model to another, rather than writing out the whole thing from scratch again. Anyway, the three-way interaction can come out:
\(\blacksquare\)
- Continue to examine what can be removed, and if reasonable, remove it, until you need to stop. Which terms are left in your final model?
Solution
Start with drop1
:
## Single term deletions
##
## Model:
## frequency ~ social.stratum + encouragement + college.plans +
## social.stratum:encouragement + social.stratum:college.plans +
## encouragement:college.plans
## Df Deviance AIC LRT Pr(>Chi)
## <none> 118.98 259.22
## social.stratum:encouragement 3 379.18 513.42 260.20 < 2.2e-16 ***
## social.stratum:college.plans 3 331.86 466.10 212.88 < 2.2e-16 ***
## encouragement:college.plans 1 1024.69 1162.94 905.72 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
These are all strongly significant, so they have to stay. There is nothing else we can remove. All the two-way interactions have to stay in the model.
\(\blacksquare\)
- Make two-way tables of any remaining two-way interactions, and describe any relationships that you see.
Solution
We have three two-way tables to make.
My first one is social stratum by parental encouragement. Neither of these is really a response, but I thought that social stratum would influence parental encouragement rather than the other way around, hence:
## encouragement
## social.stratum high low
## higher 0.8562249 0.1437751
## lower 0.3182609 0.6817391
## lowermiddle 0.2542373 0.7457627
## uppermiddle 0.5584329 0.4415671
This says that there tends to be more parental encouragement, the higher the social stratum. Next, this:
## college.plans
## social.stratum no yes
## higher 0.33654618 0.66345382
## lower 0.85391304 0.14608696
## lowermiddle 0.97072419 0.02927581
## uppermiddle 0.66467463 0.33532537
In this one (and the next), college.plans
is the response, in
columns, so we want to have the rows adding up to 1.
The higher the social stratum, the more likely is a male high school senior to have plans to go to college. (The social stratum is not in order, so you’ll have to jump from the second row to the third to the fourth to the first to assess this. Lower and lower middle are not in order, but the others are.)
Finally, this:
## college.plans
## encouragement no yes
## high 0.4621590 0.5378410
## low 0.9472265 0.0527735
And here you see an enormous effect of parental encouragement on college plans: if it is low, the high-school senior is very unlikely to be considering college.
Nothing, in all honesty, that is very surprising here. But the two-way interactions are easier to interpret than a three-way one would have been.
Here, we think of college plans as being a response, and this analysis has shown that whether or not a student has plans to go to college depends separately on the socio-economic status and the level of parental encouragement (rather than on the combination of both, as would have been the case had the three-way interaction been significant).
\(\blacksquare\)
41.5 Predicting voting
1257 British voters were classified according to their social class, age (categorized), sex and the political party they voted for (Labour or Conservative). Which, if any, of these factors influences the party that someone votes for? The data are in link, one voter per line.
- Read in the data and display (some of) the data frame.
Solution
Space-delimited:
##
## ── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## cols(
## id = col_double(),
## class = col_character(),
## age = col_character(),
## sex = col_character(),
## vote = col_character()
## )
## # A tibble: 1,257 x 5
## id class age sex vote
## <dbl> <chr> <chr> <chr> <chr>
## 1 1 upper middle >75 male conservative
## 2 2 upper middle >75 male conservative
## 3 3 upper middle >75 male conservative
## 4 4 upper middle >75 male conservative
## 5 5 upper middle >75 female conservative
## 6 6 upper middle >75 female conservative
## 7 7 upper middle >75 female conservative
## 8 8 upper middle >75 female conservative
## 9 9 upper middle >75 female conservative
## 10 10 upper middle >75 female conservative
## # … with 1,247 more rows
I gave it a “disposable” name, since we make the “real” data set shortly.
\(\blacksquare\)
- There is no frequency column here, because each row of the data frame only represents one voter. Count up the frequencies for each combo of the categorical variables, and save it (this is the data frame that we will use for the analysis). Display the first few rows of the result. Do you now have something that you need?
Solution
I changed my mind about how to do this from last year. Using
count
is alarmingly more direct than the method I had before:
## # A tibble: 58 x 5
## class age sex vote n
## <chr> <chr> <chr> <chr> <int>
## 1 lower middle <26 female conservative 13
## 2 lower middle <26 female labour 7
## 3 lower middle <26 male conservative 9
## 4 lower middle <26 male labour 9
## 5 lower middle >75 female conservative 9
## 6 lower middle >75 female labour 2
## 7 lower middle >75 male conservative 8
## 8 lower middle >75 male labour 4
## 9 lower middle 26-40 female conservative 17
## 10 lower middle 26-40 female labour 13
## # … with 48 more rows
Exactly the right thing now: note the new column n
with
frequencies in it. (Without a column of frequencies we can’t fit a
log-linear model.) There are now only 58 combinations of the four
categorical variables, as opposed to 1247 rows in the original data
set (with, inevitably, a lot of repeats).
\(\blacksquare\)
- Fit a log-linear model with the appropriate interaction (as a starting point).
Solution
\(\blacksquare\)
- Refine your model by taking out suitable non-significant terms, in multiple steps. What model do you finish with?

## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
class:sex:vote
:
## Single term deletions
##
## Model:
## n ~ class + age + sex + vote + class:age + class:sex + age:sex +
## class:vote + age:vote + sex:vote + class:age:vote + age:sex:vote
## Df Deviance AIC LRT Pr(>Chi)
## <none> 12.794 360.29
## class:sex 2 13.477 356.97 0.6830 0.71070
## class:age:vote 7 26.698 360.19 13.9036 0.05292 .
## age:sex:vote 4 21.211 360.71 8.4172 0.07744 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
class:sex
:
## Single term deletions
##
## Model:
## n ~ class + age + sex + vote + class:age + age:sex + class:vote +
## age:vote + sex:vote + class:age:vote + age:sex:vote
## Df Deviance AIC LRT Pr(>Chi)
## <none> 13.477 356.97
## class:age:vote 7 27.633 357.13 14.1555 0.04848 *
## age:sex:vote 4 22.081 357.57 8.6037 0.07181 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
I don’t like having three-way interactions, so I’m going to yank
age:sex:vote
now, even though its P-value is smallish:
## Single term deletions
##
## Model:
## n ~ class + age + sex + vote + class:age + age:sex + class:vote +
## age:vote + sex:vote + class:age:vote
## Df Deviance AIC LRT Pr(>Chi)
## <none> 22.081 357.57
## age:sex 4 22.918 350.41 0.8372 0.9333914
## sex:vote 1 33.018 366.51 10.9376 0.0009423 ***
## class:age:vote 7 36.236 357.73 14.1555 0.0484843 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
The age-sex interaction can go, but we must be near the end now:
## Single term deletions
##
## Model:
## n ~ class + age + sex + vote + class:age + class:vote + age:vote +
## sex:vote + class:age:vote
## Df Deviance AIC LRT Pr(>Chi)
## <none> 22.918 350.41
## sex:vote 1 33.808 359.30 10.890 0.0009667 ***
## class:age:vote 7 37.073 350.57 14.155 0.0484843 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
And that’s it. The age and sex main effects are not included in the list of droppable things because both variables are part of higher-order interactions that are still in the model.
If you want to, you can look at the summary
of your final model:
##
## Call:
## glm(formula = n ~ class + age + sex + vote + class:age + class:vote +
## age:vote + sex:vote + class:age:vote, family = "poisson",
## data = votes)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.82417 -0.39708 -0.00015 0.41445 1.41435
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.50737 0.21613 11.601 < 2e-16 ***
## classupper middle -0.45199 0.34188 -1.322 0.186151
## classworking 0.37469 0.27696 1.353 0.176088
## age>75 -0.25783 0.32292 -0.798 0.424622
## age26-40 0.34294 0.27877 1.230 0.218619
## age41-50 0.93431 0.25162 3.713 0.000205 ***
## age51-75 0.89794 0.25293 3.550 0.000385 ***
## sexmale -0.23242 0.08016 -2.900 0.003737 **
## votelabour -0.50081 0.33324 -1.503 0.132882
## classupper middle:age>75 0.25783 0.49713 0.519 0.604013
## classworking:age>75 0.01097 0.41896 0.026 0.979113
## classupper middle:age26-40 0.82466 0.41396 1.992 0.046358 *
## classworking:age26-40 0.41083 0.35167 1.168 0.242713
## classupper middle:age41-50 0.37788 0.39239 0.963 0.335542
## classworking:age41-50 -0.28917 0.33310 -0.868 0.385327
## classupper middle:age51-75 0.43329 0.39277 1.103 0.269954
## classworking:age51-75 0.10223 0.32668 0.313 0.754325
## classupper middle:votelabour -0.12338 0.53898 -0.229 0.818936
## classworking:votelabour 1.05741 0.39259 2.693 0.007073 **
## age>75:votelabour -0.72300 0.57745 -1.252 0.210547
## age26-40:votelabour 0.21667 0.41944 0.517 0.605452
## age41-50:votelabour -0.93431 0.43395 -2.153 0.031315 *
## age51-75:votelabour -0.62601 0.41724 -1.500 0.133526
## sexmale:votelabour 0.37323 0.11334 3.293 0.000992 ***
## classupper middle:age>75:votelabour NA NA NA NA
## classworking:age>75:votelabour -0.29039 0.68720 -0.423 0.672607
## classupper middle:age26-40:votelabour -0.53698 0.65445 -0.821 0.411931
## classworking:age26-40:votelabour -0.28479 0.49429 -0.576 0.564516
## classupper middle:age41-50:votelabour -0.01015 0.68338 -0.015 0.988147
## classworking:age41-50:votelabour 1.06121 0.50772 2.090 0.036603 *
## classupper middle:age51-75:votelabour -0.06924 0.65903 -0.105 0.916328
## classworking:age51-75:votelabour 0.16608 0.49036 0.339 0.734853
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 797.594 on 57 degrees of freedom
## Residual deviance: 22.918 on 27 degrees of freedom
## AIC: 350.41
##
## Number of Fisher Scoring iterations: 4
These tend to be rather unwieldy, and we’ll see a better way of understanding the results below, but you can look for the very significant results, bearing in mind that the first category is the baseline, for example, more of the males in the survey voted Labour (than Conservative).
\(\blacksquare\)
- If we think of the party someone votes for as the final outcome (that depends on all the other things), what does our final model say that someone’s vote depends on?
Solution
Find out which of the surviving terms are interactions with
vote
. Here, there are two things, that vote
depends on separately:
sex
The
age
-class
interaction.
\(\blacksquare\)
- Obtain sub-tables that explain how
vote
depends on any of the things it’s related to.
Solution
This is xtabs
again.  This is a small effect, but I guess the large number of voters made it big enough to be significant.
I took it this way around because vote
is the outcome, and
therefore I want to address things
like “if a voter is female, how likely are they to vote Labour”,
rather than conditioning the other
way around (which would
be “if a voter voted Labour, how likely are they to be female”,
which doesn’t make nearly so much sense).
Then the tricky one:
## , , class = lower middle
##
## age
## vote <26 >75 26-40 41-50 51-75
## conservative 22 17 31 56 54
## labour 16 6 28 16 21
##
## , , class = upper middle
##
## age
## vote <26 >75 26-40 41-50 51-75
## conservative 14 14 45 52 53
## labour 9 0 21 13 17
##
## , , class = working
##
## age
## vote <26 >75 26-40 41-50 51-75
## conservative 32 25 68 61 87
## labour 67 19 133 145 115
Doing it this way has produced different subtables for each
class
. This is actually OK, because we can
say “if a voter was of lower middle class” and then talk about the relationship
between age and vote, as if we were looking at a simple effect:
If a voter was of lower-middle-class, they strongly favour voting Conservative in all age groups except for
<26
and 26–40.If a voter was of upper-middle-class, they even more strongly favour voting Conservative in all age groups except for “under 26” and maybe 26–40.
If a voter was of Working class, they strongly favour voting Labour, except in the “over 75” age group (and maybe 51–75 as well).
If the anomalous age group(s) had been the same one every time, there
would no longer have been an interaction between age and class in
their effect on vote
. But the anomalous age groups were
different for each class (“different pattern”), and that explains
why there was a vote:age:class
interaction: " the way someone votes depends on the combination of age and social class".
For prop.table
in three dimensions, as we have here, we have to be a little
more careful about what to make add up to 1. For example, to make the
social classes each add up to 1, which is the third dimension:
## , , class = lower middle
##
## age
## vote <26 >75 26-40 41-50 51-75
## conservative 0.08239700 0.06367041 0.11610487 0.20973783 0.20224719
## labour 0.05992509 0.02247191 0.10486891 0.05992509 0.07865169
##
## , , class = upper middle
##
## age
## vote <26 >75 26-40 41-50 51-75
## conservative 0.05882353 0.05882353 0.18907563 0.21848739 0.22268908
## labour 0.03781513 0.00000000 0.08823529 0.05462185 0.07142857
##
## , , class = working
##
## age
## vote <26 >75 26-40 41-50 51-75
## conservative 0.04255319 0.03324468 0.09042553 0.08111702 0.11569149
## labour 0.08909574 0.02526596 0.17686170 0.19281915 0.15292553
What happened here is that each of the three subtables adds up to 1,
so that we have a “joint distribution” in each table. We can put
two variables into prop.table
, and see what happens then:
## , , class = lower middle
##
## age
## vote <26 >75 26-40 41-50 51-75
## conservative 0.5789474 0.7391304 0.5254237 0.7777778 0.7200000
## labour 0.4210526 0.2608696 0.4745763 0.2222222 0.2800000
##
## , , class = upper middle
##
## age
## vote <26 >75 26-40 41-50 51-75
## conservative 0.6086957 1.0000000 0.6818182 0.8000000 0.7571429
## labour 0.3913043 0.0000000 0.3181818 0.2000000 0.2428571
##
## , , class = working
##
## age
## vote <26 >75 26-40 41-50 51-75
## conservative 0.3232323 0.5681818 0.3383085 0.2961165 0.4306931
## labour 0.6767677 0.4318182 0.6616915 0.7038835 0.5693069
This is making each class
-age
combination add up to
1, so that we can clearly see what fraction of voters voted for each
party in each case.40
In the first two subtables, the two youngest
subgroups are clearly different from the others, with a smaller
proportion of people voting Conservative rather than Labour than for
the older subgroups. If that same pattern persisted for the third
subtable, with the two youngest age groups being different from the
three older ones, then we would have an age by vote interaction rather
than the age by class by vote interaction that we actually have. So
the third class
group should be different. It is: it seems
that the first three age groups are different from the other
two, with ages 41–50 being more inclined to vote Labour, like the
younger groups. That’s where the interaction came from.
The Labour Party in the UK is like the NDP here, in that it has strong ties with “working people”, trades unions in particular. The Conservatives are like the Conservatives here (indeed, the nickname “Tories” comes from the UK; the Conservatives there were officially known as the Tories many years ago). Many people are lifelong voters for their party, and would never think of voting for the “other side”, in the same way that many Americans vote either Democrat or Republican without thinking about it too much. Our parliamentary system comes from the UK system (vote for a candidate in a riding, the leader of the party with the most elected candidates becomes Prime Minister), and a “landslide” victory often comes from persuading enough of the voters open to persuasion to switch sides. In the UK, as here, the parties’ share of the popular vote doesn’t change all that much from election to election, even though the number of seats in Parliament might change quite a lot.
\(\blacksquare\)
41.6 Brand M laundry detergent
A survey was carried out comparing respondents’ preferences of a laundry detergent M compared to a mystery brand X. For each respondent, the researchers recorded the temperature of the laundry load (low or high), whether or not they previously used brand M (yes or no), and the softness of the water used for the laundry load(hard, medium or soft). The aim of the survey was to find out what was associated with the respondents preferring brand M. The data are in http://ritsokiguess.site/datafiles/brand_m.csv.
- Read in and display (some of) the data. Explain briefly how the data is laid out appropriately to fit a log-linear model.
Solution
The reading-in is entirely familiar:
##
## ── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## cols(
## softness = col_character(),
## m_user = col_character(),
## temperature = col_character(),
## prefer = col_character(),
## frequency = col_double()
## )
## # A tibble: 24 x 5
## softness m_user temperature prefer frequency
## <chr> <chr> <chr> <chr> <dbl>
## 1 hard no low x 68
## 2 hard no low m 42
## 3 hard no high x 42
## 4 hard no high m 30
## 5 hard yes low x 37
## 6 hard yes low m 52
## 7 hard yes high x 24
## 8 hard yes high m 43
## 9 medium no low x 66
## 10 medium no low m 50
## # … with 14 more rows
This is good because we have each “observation” (frequency, here) in one row, or, said differently, we have a column of frequencies and each of the factors in a column of its own. (See the Extra for the kind of layout we might have had to deal with.)
Extra: as you might expect, this is very much not how the data came to me. It was originally in a textbook, laid out like this:
This is a common layout for frequency data, because it saves a lot of space. Multiple header rows are hard to deal with, though, so I combined the three column variables into one with a layout like this (aligned in columns):
softness no_low_x no_low_m no_high_x no_high_m yes_low_x yes_low_m yes_high_x yes_high_m hard 68 42 42 30 37 52 24 43 medium 66 50 33 23 47 55 23 47 soft 63 53 29 27 57 49 19 29
Let’s read this in and then think about what to do with it:
##
## ── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## cols(
## softness = col_character(),
## no_low_x = col_double(),
## no_low_m = col_double(),
## no_high_x = col_double(),
## no_high_m = col_double(),
## yes_low_x = col_double(),
## yes_low_m = col_double(),
## yes_high_x = col_double(),
## yes_high_m = col_double()
## )
## # A tibble: 3 x 9
## softness no_low_x no_low_m no_high_x no_high_m yes_low_x yes_low_m yes_high_x yes_high_m
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 hard 68 42 42 30 37 52 24 43
## 2 medium 66 50 33 23 47 55 23 47
## 3 soft 63 53 29 27 57 49 19 29
We want to get all those frequencies into one column, which suggest some kind of pivot_longer.
There are two ways to go about this. One is to try a regular pivot_longer
and see what happens. I had to think for a moment about what to call the column that ended up as combo
:
## # A tibble: 24 x 3
## softness combo frequency
## <chr> <chr> <dbl>
## 1 hard no_low_x 68
## 2 hard no_low_m 42
## 3 hard no_high_x 42
## 4 hard no_high_m 30
## 5 hard yes_low_x 37
## 6 hard yes_low_m 52
## 7 hard yes_high_x 24
## 8 hard yes_high_m 43
## 9 medium no_low_x 66
## 10 medium no_low_m 50
## # … with 14 more rows
This is the right kind of shape, but those things in the column combo
are three variables all smooshed together: respectively, previous user (of brand M), temperature, preference (you can tell by the values, which are all different). These can be split up with separate
, thus:
prefs0 %>% pivot_longer(-softness, names_to = "combo", values_to = "frequency") %>%
separate(combo, into = c("prev_user", "temperature", "preference"), sep = "_")
## # A tibble: 24 x 5
## softness prev_user temperature preference frequency
## <chr> <chr> <chr> <chr> <dbl>
## 1 hard no low x 68
## 2 hard no low m 42
## 3 hard no high x 42
## 4 hard no high m 30
## 5 hard yes low x 37
## 6 hard yes low m 52
## 7 hard yes high x 24
## 8 hard yes high m 43
## 9 medium no low x 66
## 10 medium no low m 50
## # … with 14 more rows
That works, but the combination of pivot_longer
and separate
is a common one, and so there is an “advanced” version of pivot_longer
that does it all at once. The idea is that you enter three columns into names_to
and then use names_sep
to say what they’re separated by:
prefs0 %>%
pivot_longer(-softness,
names_to = c("m_user", "temperature", "prefer"),
names_sep = "_", values_to = "frequency")
## # A tibble: 24 x 5
## softness m_user temperature prefer frequency
## <chr> <chr> <chr> <chr> <dbl>
## 1 hard no low x 68
## 2 hard no low m 42
## 3 hard no high x 42
## 4 hard no high m 30
## 5 hard yes low x 37
## 6 hard yes low m 52
## 7 hard yes high x 24
## 8 hard yes high m 43
## 9 medium no low x 66
## 10 medium no low m 50
## # … with 14 more rows
This data frame is what I saved for you.
\(\blacksquare\)
- Using backward elimination, build a suitable log-linear model for the associations between the variables. (Do not use
step
; do the elimination yourself).
Solution
The first step is to fit a model containing all the interactions between the factors, using frequency
as the response, and then to use drop1
with test="Chisq"
to see what can come out. Don’t forget the family = "poisson"
, since that’s what drives the modelling. I tnink it’s easiest to number these models, since there might be a lot of them:
prefs.1 <- glm(frequency~softness*m_user*temperature*prefer, family = "poisson", data=prefs)
drop1(prefs.1, test = "Chisq")
## Single term deletions
##
## Model:
## frequency ~ softness * m_user * temperature * prefer
## Df Deviance AIC LRT Pr(>Chi)
## <none> 0.00000 180.41
## softness:m_user:temperature:prefer 2 0.73732 177.15 0.73732 0.6917
To our relief, the four-way interaction is not significant and can be removed. (I was not looking forward to the prospect of interpreting that!)
Now write an update
line that removes that four-way interaction from your model, as shown below, and copy-paste your drop1
line from above, changing the number of your model to the one coming out of update
. Copy-paste the complicated interaction from the drop1
output:
## Single term deletions
##
## Model:
## frequency ~ softness + m_user + temperature + prefer + softness:m_user +
## softness:temperature + m_user:temperature + softness:prefer +
## m_user:prefer + temperature:prefer + softness:m_user:temperature +
## softness:m_user:prefer + softness:temperature:prefer + m_user:temperature:prefer
## Df Deviance AIC LRT Pr(>Chi)
## <none> 0.7373 177.15
## softness:m_user:temperature 2 2.1146 174.53 1.3773 0.5022
## softness:m_user:prefer 2 5.3086 177.72 4.5713 0.1017
## softness:temperature:prefer 2 0.8991 173.31 0.1618 0.9223
## m_user:temperature:prefer 1 2.9594 177.37 2.2220 0.1361
There are now four three-way interactions that could be removed. You might suspect that they are all going to go eventually, but as in regression, we take them one at a time, starting with the one that has the highest P-value (just in case, for example, the P-value of the second one goes under 0.05 when we remove the others). The easiest way to do the coding is a vigorous amount of copying and pasting. Copy-paste your last code chunk:
Change the interaction in the update
to the one you want to remove (from the drop1
table), which is softness:temperature:prefer
(you can copy-paste that too), and then increase all three of the model numbers by 1:
## Single term deletions
##
## Model:
## frequency ~ softness + m_user + temperature + prefer + softness:m_user +
## softness:temperature + m_user:temperature + softness:prefer +
## m_user:prefer + temperature:prefer + softness:m_user:temperature +
## softness:m_user:prefer + m_user:temperature:prefer
## Df Deviance AIC LRT Pr(>Chi)
## <none> 0.8991 173.31
## softness:m_user:temperature 2 2.2506 170.67 1.3514 0.5088
## softness:m_user:prefer 2 5.4952 173.91 4.5961 0.1005
## m_user:temperature:prefer 1 3.1148 173.53 2.2157 0.1366
Then, as they say, rinse and repeat. This one takes a while, but each step is just like the others.
drop softness:m_user:temperature
## Single term deletions
##
## Model:
## frequency ~ softness + m_user + temperature + prefer + softness:m_user +
## softness:temperature + m_user:temperature + softness:prefer +
## m_user:prefer + temperature:prefer + softness:m_user:prefer +
## m_user:temperature:prefer
## Df Deviance AIC LRT Pr(>Chi)
## <none> 2.2506 170.67
## softness:temperature 2 7.8155 172.23 5.5649 0.06189 .
## softness:m_user:prefer 2 7.0585 171.47 4.8080 0.09036 .
## m_user:temperature:prefer 1 4.5184 170.93 2.2678 0.13209
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
drop m_user:temperature:prefer
## Single term deletions
##
## Model:
## frequency ~ softness + m_user + temperature + prefer + softness:m_user +
## softness:temperature + m_user:temperature + softness:prefer +
## m_user:prefer + temperature:prefer + softness:m_user:prefer
## Df Deviance AIC LRT Pr(>Chi)
## <none> 4.5184 170.93
## softness:temperature 2 10.6035 173.02 6.0851 0.04771 *
## m_user:temperature 1 5.2477 169.66 0.7293 0.39311
## temperature:prefer 1 8.2467 172.66 3.7283 0.05350 .
## softness:m_user:prefer 2 9.8462 172.26 5.3278 0.06967 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
drop m_user:temperature
## Single term deletions
##
## Model:
## frequency ~ softness + m_user + temperature + prefer + softness:m_user +
## softness:temperature + softness:prefer + m_user:prefer +
## temperature:prefer + softness:m_user:prefer
## Df Deviance AIC LRT Pr(>Chi)
## <none> 5.2477 169.66
## softness:temperature 2 11.2951 171.71 6.0474 0.04862 *
## temperature:prefer 1 9.5576 171.97 4.3099 0.03789 *
## softness:m_user:prefer 2 10.5860 171.00 5.3383 0.06931 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
drop softness:m_user:prefer
## Single term deletions
##
## Model:
## frequency ~ softness + m_user + temperature + prefer + softness:m_user +
## softness:temperature + softness:prefer + m_user:prefer +
## temperature:prefer
## Df Deviance AIC LRT Pr(>Chi)
## <none> 10.586 171.00
## softness:m_user 2 11.543 167.96 0.9569 0.61974
## softness:temperature 2 16.633 173.05 6.0474 0.04862 *
## softness:prefer 2 10.798 167.21 0.2120 0.89942
## m_user:prefer 1 31.049 189.46 20.4633 6.079e-06 ***
## temperature:prefer 1 14.896 173.31 4.3099 0.03789 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
More two-ways.
drop softness:prefer
## Single term deletions
##
## Model:
## frequency ~ softness + m_user + temperature + prefer + softness:m_user +
## softness:temperature + m_user:prefer + temperature:prefer
## Df Deviance AIC LRT Pr(>Chi)
## <none> 10.798 167.21
## softness:m_user 2 11.886 164.30 1.0885 0.58027
## softness:temperature 2 16.910 169.33 6.1125 0.04706 *
## m_user:prefer 1 31.393 185.81 20.5949 5.675e-06 ***
## temperature:prefer 1 15.173 169.59 4.3750 0.03647 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
One more?
drop softness:m_user
## Single term deletions
##
## Model:
## frequency ~ softness + m_user + temperature + prefer + softness:temperature +
## m_user:prefer + temperature:prefer
## Df Deviance AIC LRT Pr(>Chi)
## <none> 11.886 164.30
## softness:temperature 2 17.986 166.40 6.0991 0.04738 *
## m_user:prefer 1 32.468 182.88 20.5815 5.715e-06 ***
## temperature:prefer 1 16.248 166.66 4.3616 0.03676 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
And finally we are done! There are three two-way interactions left, which shouldn’t be too hard to interpret. That’s in the next part.
\(\blacksquare\)
- What is associated with the brand a respondent prefers? By obtaining suitable frequency tables, describe the nature of these associations.
Solution
To see what is associated with brand preference, look for significant associations with prefer
. There are two of them, one with m_user
, and, separately, one with temperature. This means that a respondent’s brand preference depends on whether or not they previously used brand M, and also on what temperature the laundry was washed at.
To investigate these, use xtabs
to get a frequency table (easiest), and if necessary use prop.table
to get row or column proportions as appropriate. The first thing in the model formula in xtabs
is the frequency column; the first thing after the squiggle is rows and the second thing is columns. (If there happened to be a third thing, it would be “layers” in the table.) You might find that the table of frequencies is enough to interpret what is going on, but if not, use prop.table
to get a table of proportions. I talk about that in a moment.
## prefer
## m_user m x
## no 225 301
## yes 275 207
prefer
is the response (outcome), which I put in the columns, so I look along the rows to see what is going on. Out of the people who were previous users of Brand M (second row), slightly more of them preferred Brand M; out of the people who were not Brand M users (first row), somewhat more of them preferred Brand X.
This was not hard to see, because the opposite frequencies were bigger each time. But you might find it easier to compute percentages and compare those. In my table, the response was rows, so the right percentages to compute are row ones. That is done like this. 1 is rows, 2 is columns:
## prefer
## m_user m x
## no 0.4277567 0.5722433
## yes 0.5705394 0.4294606
Out of the people who were previous Brand M users, 57% preferred Brand M; out of the people who were not previous Brand M users, only 43% of them preferred Brand M.
Advertisers use terms like “brand familiarity” to capture ideas like this: more people prefer Brand M in the survey if they have used it before. Not altogether surprising.
On to the effects of temperature on preference:
## prefer
## temperature m x
## high 199 170
## low 301 338
## prefer
## temperature m x
## high 0.5392954 0.4607046
## low 0.4710485 0.5289515
Out of the people who used a high-temperature wash, 54% of them preferred brand M, but out of the people who used a low-temperature wash, only 47% of them preferred brand M.

Those are really the two effects of interest, since they are the ones associated with brand preference. But there was one more association that was significant: between temperature and softness. The softness in this case was of the water used to do the laundry (and not, for example, the softness of the clothes after they come out of the dryer). That goes like this:
## softness
## temperature hard medium soft
## high 139 126 104
## low 199 218 222
## softness
## temperature hard medium soft
## high 0.4112426 0.3662791 0.3190184
## low 0.5887574 0.6337209 0.6809816
I decided to condition on the softness of the water, since that cannot be controlled by the person doing the laundry (the water just is hard or soft, depending on where you live and where your water comes from).
In each case, a majority of the washes were done at low temperature, but the softer the water, the bigger that majority was. Once again, the effect is not all that big, because the association was only just significant.
\(\blacksquare\)


MdCheckboxModule
A material design checkbox component. Supports all of the functionality of an HTML5 checkbox, and exposes a similar API. A MdCheckbox can be either checked, unchecked, indeterminate, or disabled. Note that all additional accessibility attributes are taken care of by the component, so there is no need to provide them yourself. However, if you want to omit a label and still have the checkbox be accessible, you may supply an [aria-label] input. See: https://www.google.com/design/spec/components/selection-controls.html
| Name | Description |
|---|---|
@Input('aria-label')
ariaLabel
|
Attached to the aria-label attribute of the host element. In most cases, arial-labelledby will take precedence so this may be omitted. |
@Input('aria-labelledby')
ariaLabelledby
|
Users can specify the `aria-labelledby` attribute which will be forwarded to the input element |
@Input() id
|
A unique id for the checkbox. If one is not supplied, it is auto-generated. |
@Input() disableRipple
|
Whether the ripple effect for this checkbox is disabled. |
inputId
|
ID of the native input element inside `<md-checkbox>` |
@Input() required
|
Whether the checkbox is required. |
@Input() align
|
Whether or not the checkbox should appear before or after the label. |
@Input() labelPosition
|
Whether the label should appear after or before the checkbox. Defaults to 'after' |
@Input() disabled
|
Whether the checkbox is disabled. |
@Input() name
|
Name value will be applied to the input element if present |
@Output() change
|
Event emitted when the checkbox's `checked` value changes. |
@Input() checked
|
Whether the checkbox is checked. Note that setting `checked` will immediately set `indeterminate` to false. |
@Input() indeterminate
|
Whether the checkbox is indeterminate. This is also known as "mixed" mode and can be used to represent a checkbox with three states, e.g. a checkbox that represents a nested list of checkable items. Note that whenever `checked` is set, indeterminate is immediately set to false. This differs from the web platform in that indeterminate state on native checkboxes is only remove when the user manually checks the checkbox (rather than setting the `checked` property programmatically). However, we feel that this behavior is more accommodating to the way consumers would envision using this component. |
@Input() color
|
The color of the button. Can be `primary`, `accent`, or `warn`. |
| toggle | |
|---|---|
| Toggles the `checked` state of the checkbox. |
| focus | |
|---|---|
| Focuses the checkbox. |
Change event object emitted by MdCheckbox.
| Name | Description |
|---|---|
source
|
|
checked
|


-
.NET Anywhere initial commit
-
Web Assembly 1.0 released
-
Steve Sanderson discovers .NET Anywhere
-
Steve Sanderson Blazor initial commit
-
ASP.NET Core team hold an internal hackathon at Microsoft, working on Blazor
-
Steve Sanderson shows Blazor off at NDC Oslo
-
.NET Anywhere is abandoned
-
Public Preview Announced
-
I hosted an Internal hackathon/lab at work using Blazor
-
ASP.NET Community Standup showing off Blazor public preview
-
The .NET Core Podcast - Episode 5: Blazor with Ed Charbeneau
-
ASP.NET Community Standup: The Blazor Show
-
ASP.NET Community Standup - Post NDC Blazor Show


We got bitcoin!
- redefine vertical e-commerce
- iterate cross-media vortals
- expedite value-added bandwidth
- deliver viral methodologies
- extend cross-platform initiatives
- monetize efficient e-business
- iterate proactive methodologies
- utilize bleeding-edge experiences


SourceBans++
Long Term Support
SourceBans++ has been supported since 2015! With the original starting back in 2009!
Huge Community
Over 2000 Servers run SourceBans++
Cross-Game Support
SourceBans++ runs on variety of games that use the Source Engine, from Alien Swarm to CS:GO


Cloud Vision V1 API - Class Google::Cloud::Vision::V1::BatchOperationMetadata (v0.6.4)
Reference documentation and code samples for the Cloud Vision V1 API class Google::Cloud::Vision::V1::BatchOperationMetadata.
Metadata for the batch operations such as the current state.
This is included in the metadata
field of the Operation
returned by the
GetOperation
call of the google::longrunning::Operations
service.
Inherits
- Object
Extended By
- Google::Protobuf::MessageExts::ClassMethods
Includes
- Google::Protobuf::MessageExts
Methods
#end_time
def end_time() -> ::Google::Protobuf::Timestamp
Returns
- (::Google::Protobuf::Timestamp) — The time when the batch request is finished and google.longrunning.Operation.done is set to true.
#end_time=
def end_time=(value) -> ::Google::Protobuf::Timestamp
Parameter
- value (::Google::Protobuf::Timestamp) — The time when the batch request is finished and google.longrunning.Operation.done is set to true.
Returns
- (::Google::Protobuf::Timestamp) — The time when the batch request is finished and google.longrunning.Operation.done is set to true.
#state
def state() -> ::Google::Cloud::Vision::V1::BatchOperationMetadata::State
Returns
- (::Google::Cloud::Vision::V1::BatchOperationMetadata::State) — The current state of the batch operation.
#state=
def state=(value) -> ::Google::Cloud::Vision::V1::BatchOperationMetadata::State
Parameter
- value (::Google::Cloud::Vision::V1::BatchOperationMetadata::State) — The current state of the batch operation.
Returns
- (::Google::Cloud::Vision::V1::BatchOperationMetadata::State) — The current state of the batch operation.
#submit_time
def submit_time() -> ::Google::Protobuf::Timestamp
Returns
- (::Google::Protobuf::Timestamp) — The time when the batch request was submitted to the server.
#submit_time=
def submit_time=(value) -> ::Google::Protobuf::Timestamp
Parameter
- value (::Google::Protobuf::Timestamp) — The time when the batch request was submitted to the server.
Returns
- (::Google::Protobuf::Timestamp) — The time when the batch request was submitted to the server.


Pedestran
Pretend you are Invisible: Don’t assume a driver sees you and behave accordingly
Face Traffic: It’s easier to see, and react to oncoming cars. And cars will see you more clearly
Make Room: If traffic gets heavy, or the road is narrow, be prepared to move onto the sidewalk
Be Seen: Wear highly visible, bright coloured clothing when its after sunset, use hand held light so you can see wherever you’re going, and drivers can see you.
Unplug Your Ears: Avoid using ipods or wearing head phones – you need to be able to hear approaching vehicles. If you do use it, run with very low volume.
Beware of High Risk Drivers: Stay clear of potential problem areas like entrance to parking lots, bars & restaurants where there may be heavy traffic
Mind Your Manners: At a stop sign or light, wait for the driver to wave you through- then acknowledge with your own polite wave. That acknowledgement will make the driver feel more inclined to do it again.
Motorists
Speed should be adjusted according to the road condition
Drive at a safe speed and always observe the speed limit
Always wear your seat belt and ensure that your passengers do the same
Do not drink and drive
Follow other vehicle at a safe distance
Do not use a mobile phone while driving
Overtake with care
Things Needed in the Vehicle
Depending on the manufacturer and model of a car, a spare tire is typically located in the trunk, tail-mounted, or attached to the undercarriage. Ensure that you have a viable spare tire and that it is fully inflated based on the recommended PSI. The PSI information can usually be found on the sticker inside the driver’s side door or in the owner’s manual. Also make sure that the clamp screw, spare tire housing (or lock) is secure but easily accessible. Sometimes rust or corrosion may build-up making it difficult to access your spare tire. WD-40
Tip: Check your spare tire pressure as frequently as you would check your regular tires. This is especially important before a long trip.Review your vehicle owner's manual to ensure your car jack and all accessories are accounted for and in working order. Similar to the spare tire housing, clamp screw, or lock, this item may develop rust or corrosion over time. Perform maintenance according to the vehicle owner's manual recommendations.
Keep a strong, high-capacity tow strap in your car in case you get stuck in mud or snow. A typical rope will not suffice in most situations. Tow straps are also known as recovery straps, and come at various ratings based on vehicle weight. You should find one that is sturdy, preferably with double-stitched webbing, and that meets or exceeds your vehicle's weight (be sure to include passenger weight and luggage when you assess capacity rating). Most come in 20' and 30' lengths. A quality tow strap should come with forged steel safety hooks that have retaining clips.
A flashlight with new batteries is essential especially when you need to replace a tire or look under the engine hood at night.
The tire tool (lug wrench) is typically located with your vehicle's tire jack.
There are fire extinguishers that are specifically made for car fires. You will need a fire extinguisher that handles oil, gasoline and electrical fires. One that is getting great reviews right now is the First Alert Auto Fire Extinguisheron Amazon and can be purchased for under $20. There are also a variety of products that handle both marine and car fires if you are seeking a multi-purpose extinguisher you can transfer from one vehicle to another. If budget is not an issue and you own a high-end car, classic car, or just want a cleaner discharge that won't ruin your vehicle, then I would recommend looking into products such as a HalGuard Clean Agent Fire Extinguisher. These products are effective at putting out fires and leave less mess and prevent damage. As an added bonus, they come in chrome if you want to dress-up your car.
You want to be seen at night by other motorists if your car breaks down to avoid a potentially fatal situation. As previously mentioned on the common items list, a flashlight is essential. However, a flashlight will not provide enough of a light signature at night to warn other motorists to pay attention or slow-down. You need markers that are highly reflective like reflective emergency triangles and/or flares well behind your vehicle at appropriate distance to give an early warning. Most important for your safety, you need a reflective vest so that you can be easily seen. All first responders such as police officers and paramedics utilize reflective clothing or vests on the road for a good reason -- because reflective clothing save lives. I would recommend a yellow reflective safety vest that meets ANSI/ISEA standards such as the Neiko High Visibility Neon Yellow Vest.
Other considerations: Emergency reflective triangles are recommended for placement behind your vehicle. Just make sure they have a substantially weighted base. Too often light weight triangles get blown off the road by high winds or from large trucks passing by.


PLEASE FIND HIM
Scots hillwalker missing overnight on coldest night of the year as cops launch frantic search
by Blair MeikleA SCOTS hillwalker has been missing overnight on the coldest night of the year after vanishing yesterday.
Paul Murphy, 51, was last seen leaving his home in Stenhousemuir, Falkirk, at around 7am yesterday, as he set off to go hillwalking.
He left in his car which has since been found at the Cairngorms Ski Centre in Aviemore.
He has not been seen or heard from since, and concerns are growing for his safety amid freezing temperatures.
We told how last night was recorded as the coldest night of the season so far, with -8.6C recorded in the Highlands.
Paul is described as 6ft, of stocky build with short black hair and facial hair.
He was wearing a dark coloured hillwalking jacket, trousers and boots and was carrying a hillwalking rucksack.
Paul was driving a beige Nissan X trail motor vehicle with registration SM62 BUF which has been traced to the Cairngorms Ski Centre in Aviemore.
Cops are desperately trying to trace Paul and are appealing to the public for information.
Sergeant Alyson Bolton of Forth Valley police said: “This is very out of character for Paul who has never gone missing before and his family want to make sure he is ok.
“We believe he may have travelled to the North East area and urge anyone who may have seen Paul to contact us. Similarly, if Paul is reading this, let us know you are safe and well.”
Anyone with information should contact police on 101 quoting incident number 4053 of Sunday, 17 November, 2019.
We told earlier how a Scots mum has frantically pleaded with her teenage daughter to come home after she vanished two days ago.
Shekira Horner, 17, was last seen heading towards Kilmarnock train station at around 5.30pm on Saturday.
Since then, her phone has been switched off and friends and family have not seen or heard from her.
Meanwhile, a body has been pulled from a river in the Highlands during a search for a missing man.
Calum MacKenzie disappeared from his home in Salvesen Crescent in Alness, Ross and Cromarty, on Thursday November 7.


PDX Social History Guide is a free app and website featuring stories about people and places vital to the city of Portland's cultural heritage. Portland has been home to many ethnic groups and cultures — including Native American, African American, Chinese, Japanese, Greeks and Jews, among others.
Because of urban development, some of these communities have little physical remnants of their neighborhoods. Consequently, many visiting Portland for the first time — and even those that have lived here many years — are unaware of the city's history.
PDX Social History Guide brings these stories to the surface through interviews, mapping and smart phone technology, as well as a corresponding website.
Know Your City is a nonprofit organization that connects people to place. To fulfill our mission, we organize tours, lectures, publications and youth programs. Our programs engage the public through experiential learning, creative placemaking, policy-making and culture.
For content questions<EMAIL_ADDRESS>For technical questions<EMAIL_ADDRESS>To volunteer<EMAIL_ADDRESS>Send us an email.
We're seeking donations to continue the project, and volunteers to help us continue the research, editing, and testing process. We are specifically looking for people with audio editing and research experience. To get involved, send a brief note of inquiry to ask@knowyourcity.org


{% extends 'emails/email_base.html' %} {% load i18n %} {% autoescape off %} {% block email_content %}
{% blocktrans with site=site_name trimmed %} You have been sent this email because you (or someone pretending to be you) tried to set up a new account at {{ site }} using this email address. {% endblocktrans %}
{% trans "If this was not you, you may safely ignore this email" %}.
{% blocktrans trimmed %} If you wish to reactivate your closed account at this email address, please visit the following page: {% endblocktrans %}
{% endblock %} {% block email_footer %} {% blocktrans with site=site_name trimmed %} You received this email because you were previously a registered user at {{ site }}. {% endblocktrans %} {% endblock %} {% endautoescape %}


Welcome to WebDomus! This is your perfect Go-To destination for software service(s) such as Website Development, UI/UX Designing,Mobile Application Development and much more!
We are WebDomus, we estabilished as a startup with an aim to provide software service(s) such as Website Development, UI/UX Designing,Android Development,Digital Marketing and various other services for our clients on a contractual basis. We understand that not everyone will come to us as a client and some people may need to learn these skills as well. So, we also provide courses at a very affordable rate so that if anyone who wants to learn these skills can also learn from us.
Our mission is to assist our clients in presenting a professional front to the marketplace. Presentation is everything and we believe this at WebDomus.Our clients need to showcase themselves in a professional manner - and this is why we offer Website Development, UI/UX Designing,etc.Our mission is to help with your professionalism through the services that we offer.
The Vision of our company is to become the most trusted name in the realm of IT by offering unmatched services, solutions and support for the growth of our precious clients. Our imagination is to set into actions through unique programs with a focus on successful accomplishment of all the projects in stipulated timeframe while building a long-term relationship with our clients.
What brings together in building an organization that has a unique culture is our value system. Every Mind is driven by class, the acronym for our core values of Caring, Learning, Achieving, Sharing, and Social Responsibility. Everything we are, everything we do, and everything we believe in revolves around our class values and the distinct culture that we have built.
These are the members of our team, they work day and night to achieve the mission and vision of our startup. They strive hard to statisfy the expections of the clients.
- STEVE JOBS -
Are you running a business for a long time but still haven’t been to the online route? Do you want to take your business to its next stage of success? Do you want to create a website right from scratch or revamp your existing one? Whatever field or industry you want your website to be built in, with our qualified web developers at WebDomus you will find web solutions that go beyond your expectations in terms of performance and usability.
Our expertise in User Experience Design (UX) and User Interface Design (UI) has enabled us to simplify and enhance the interaction between our clients target customers and their digital presence. Whether it is on a website, mobile application, email campaign, social media campaign, or even an Ad banner, our designs offer a unique digital experience. Our UX has enabled our clients to create compelling digital media presences, spurring customer loyalty and improving
If you’re looking to leverage mobile technology for your business, our best app developers are at your service. We provide iOS and Android mobile application development services so you can reach your customers on their
Creativity is the act of turning new and imaginative ideas into reality. Creativity is characterised by the ability to perceive the world in new ways, to find hidden patterns, to make connections between seemingly unrelated phenomena, and to generate solutions. Creativity involves two processes: thinking, then producing.
A strategy is all about integrating organizational activities and utilizing and allocating the scarce resources within the organizational environment so as to meet the present objectives. While planning a strategy it is essential to consider that decisions are not taken in a vaccum and that any act taken by a firm is likely to be met by a reaction from those affected, competitors, customers, employees or suppliers.
A well-designed website can help you form a good impression on your prospective customers. It can also help you nurture your leads and get more conversions.But, more importantly, it provides good user experience and helps your website visitors access and navigate your website with ease.
We will be more than happy to serve our clients according to their requirements. Our clients get best-in-market services from WebDomus and also we get a chance to channelize our skills in a right way and satisfying client's requirements. So, it is a win-win situation both for us and the client.
I am really impressed by the quality of services I received from WebDomus. They were right on schedule, charged reasonable prices, were professional and courteous in dealings, and delivered items well before time. I have got a good e-commerce site for my products. My revenue has increased because of WebDomus and I will definitely use their services again.
I can't say enough about the excellent work that WebDomus has done on our website. They took a below-average website and transformed it into an appealing and informative website. It was an absolute pleasure to work with them. The designer listened to my thoughts and suggestions and far surpassed my expectations. There were minor problems along the way but these were sorted out quickly and efficiently. We are looking forward to working with them on future projects.  You would be extremely unwise to overlook such a dedicated and professional team.
Have any queries? Our dedicated team of support are there for you 24X7!


YOUR TODOS of {{notebook}} Notebook
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Actions
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Edit
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Showing 1 of 5
TODO
*
Notebooks
UTILITIES
Create a TODO
Utilities
Custom Utilities:
Colors
Borders
Animations
Other


7z - A file archiver with highest compression ratio
7z [adeltux], [-], [SWITCH], <ARCHIVE_NAME>, <ARGUMENTS>...
7-Zip is a file archiver with the highest compression ratio. The program supports 7z (that implements LZMA compression algorithm), LZMA2, XZ, ZIP, Zip64, CAB, RAR (if the non-free p7zip-rar package is installed), ARJ, GZIP, BZIP2, TAR, CPIO, RPM, ISO, most filesystem images and DEB formats. Compression ratio in the new 7z format is 30-50% better than ratio in ZIP format.
7z uses plugins to handle archives.
a Add
d Delete
e Extract
l List
t Test
u Update
x eXtract with full paths
-ai[r[-|0]]{@listfile|!wildcard} Include archives
-ax[r[-|0]]{@listfile|!wildcard} eXclude archives
-bd Disable percentage indicator
-i[r[-|0]]{@listfile|!wildcard} Include filenames
-l don't store symlinks; store the files/directories they point to (CAUTION : the scanning stage can never end because of recursive symlinks like 'ln -s .. ldir')
-m{Parameters} Set Compression Method (see /usr/share/doc/p7zip-full/DOCS/MANUAL/switches/method.htm for a list of methods)
-mhe=on|off 7z format only : enables or disables archive header encryption (Default : off)
-o{Directory} Set Output directory
-p{Password} Set Password
-r[-|0] Recurse subdirectories (CAUTION: this flag does not do what you think, avoid using it)
-sfx[{name}] Create SFX archive
-si Read data from StdIn (eg: tar cf - directory | 7z a -si directory.tar.7z)
-so Write data to StdOut (eg: % echo foo | 7z a dummy -tgzip -si -so > /dev/null)
-slt Sets technical mode for l (list) command
-t{Type} Type of archive (7z, zip, gzip, bzip2 or tar. 7z format is default)
-v{Size}[b|k|m|g] Create volumes
-u[-][p#][q#][r#][x#][y#][z#][!newArchiveName] Update options
-w[path] Set Working directory
-x[r[-|0]]]{@listfile|!wildcard} Exclude filenames
-y Assume Yes on all queries
7-Zip returns the following exit codes:
Normal (no errors or warnings detected)
Warning (Non fatal error(s)). For example, some files cannot be read during compressing. So they were not compressed
Fatal error
Bad command line parameters
Not enough memory for operation
User stopped the process with control-C (or similar)
DO NOT USE the 7-zip format for backup purpose on Linux/Unix because : - 7-zip does not store the owner/group of the file.
On Linux/Unix, in order to backup directories you must use tar : - to backup a directory : tar cf - directory | 7za a -si directory.tar.7z - to restore your backup : 7za x -so directory.tar.7z | tar xf -
If you want to send files and directories (not the owner of file) to others Unix/MacOS/Windows users, you can use the 7-zip format.
example : 7za a directory.7z directory
Do not use "-r" because this flag does not do what you think.
Do not use directory/* because of ".*" files (example : "directory/*" does not match "directory/.profile")
7z a -t7z -m0=lzma -mx=9 -mfb=64 -md=32m -ms=on archive.7z dir1 adds all files from directory "dir1" to archive archive.7z using "ultra settings"
-t7z 7z archive
-m0=lzma lzma method
-mx=9 level of compression = 9 (Ultra)
-mfb=64 number of fast bytes for LZMA = 64
-md=32m dictionary size = 32 megabytes
7z a -sfx archive.exe dir1 add all files from directory "dir1" to SFX archive archive.exe (Remark : SFX archive MUST end with ".exe")
7z a -mhe=on -pmy_password archive.7z a_directory add all files from directory "a_directory" to the archive "archive.7z" (with data and header archive encryption on)
7za(1), 7zr(1), bzip2(1), gzip(1), zip(1)
/usr/share/doc/p7zip-full/DOCS/MANUAL/index.htm
Written for Debian by Mohammed Adnene Trojette.


Request items at the best prices,from any store in the world
save money by connecting with travelers who will buy and bring you item.
Meet your traveler,receive your item enjoy!
Users can validate their different social profiles simultaneous showing friends and common network with other users. Also they can verify their phone number and ID credentials to be in the community.
All payments are SSL-encrypted and secure. A internal coding system eliminates the need for subsequent approvals and confirmations, users only pay when the package arrives its destination.
We have a review system, allowing you to check your sender/traveler profile or history and see if he's trustworthy. To increase the ratings credibility they are only allowed when a task is completed.
All communication can be made within tooshe so it can be a secure and fast process, without having to disclose personal emails. This way users also can track and access to historic talks.
His story started when he was talking with his friend about possibility of receiving his documents from Iran...
For Ellie and her husband , starting a day without nutella means a big failute at the begining.So she ordered it...


Arnold Keto
arnoldketo
Java
Ping Pong will count up to your number from one with the following exceptions:


Psalms 74:3
Lift up thy feet unto the perpetual desolations; even all that the enemy hath done wickedly in the sanctuary.


This Honi issue revolved around the evolution and zeitgeist of characters in the Game Era. The inspiration for the work came from the subject matter of form and body in games and the interesting concepts of free-thinking and autonomy associated with the concept of humanoid, the uncanny.
These sketches were created with black ink on paper and inspired by the beauty in human faces and features. From Roman-esque noses to wonderful eyes and lips, I wanted to present beauty in organic hues and silky shapes.
The issue can be found here.


|
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void | Get (string url, Tile tile) |
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void | Cancel (string url) |
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void | JobTerminationEvent (object job, JobEventArgs e) |
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void | PauseAll () |
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void | UnpauseAll () |
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static TileDownloader | Instance [get] |
| |
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int | MaxSimultaneousDownloads [get, set] |
| |
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int | MaxCacheSize [get, set] |
| |
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void | EnsureDownloader () |
| |
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void | OnApplicationQuit () |
| |
|
void | Start () |
| |
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void | Update () |
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void | DownloadNextTile () |
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void | OnDestroy () |
| |
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void | KillAll () |
| |
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void | DeleteCachedTile (TileEntry t) |
| |
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void | SaveTiles () |
| |
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void | LoadTiles () |
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static bool | tileURLMatchPredicate (TileEntry entry) |
| |
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List< TileEntry > | tilesToLoad = new List<TileEntry>() |
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List< TileEntry > | tilesLoading = new List<TileEntry>() |
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List< TileEntry > | tiles = new List<TileEntry>() |
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string | tilePath = Application.temporaryCachePath |
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int | maxSimultaneousDownloads = 2 |
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int | maxCacheSize = 20000000 |
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int | cacheSize = 0 |
| |
The documentation for this class was generated from the following file:
- /Users/jonathan/Documents/UnitySlippyMap/Assets/UnitySlippyMap/Helpers/TileDownloader.cs


I would like to thank
You can fork this repository.
If you have comments, questions and/or suggestions email me.
If you find this tool useful and want to give it back, you can pay me a salad.
Thank you and be happy without doing harm 🌱— Martin


CSM
Menu
Home
Our Projects
Béta HVC-Site
Credits
Our Projects
CrossPoint - Kerstwandeling App (Discontinued)
Website
GitHub
ArScape - AR Puzzle game
Install
GitHub
EnglishGame
Install
BitBucket


PPROT width
Define the end index for state list
Version numbers to be used in creating version strings for printing or programmatic testing against version numbers
--------------------------- MACROS INDEX --------------------------------------- Define the start index for state list
Overwrite get_type_name for a parameterizable test
Define the VPI interface and set it to uvm_config_db
SVAUNIT_VERSION_STRING print as "SVAUNIT - M.m"
Define the assertion callback for DISABLE. The index value is specified by VPI.
Define the assertion callback for ENABLE. The index value is specified by VPI.
Define the assertion callback for FAILURE. The index value is specified by VPI.
Define the assertion callback for KILL. The index value is specified by VPI.
Define the assertion callback for RESET. The index value is specified by VPI.
---------------------------- Assertion callback types --------------------------------- Define the assertion callback for START. The index value is specified by VPI.
Define the assertion callback for STEP FAILURE. The index value is specified by VPI.
Define the assertion callback for STEP SUCCESS. The index value is specified by VPI.
Define the assertion callback for SUCCESS. The index value is specified by VPI.
Define assertion control constant for DIASBLE. The index value is specified by VPI.
Define assertion control constant for DIASBLE STEP. The index value is specified by VPI.
Define assertion control constant for ENABLE. The index value is specified by VPI.
Define assertion control constant for ENABLE STEP. The index value is specified by VPI.
Define assertion control constant for KILL. The index value is specified by VPI.
------------------------------- Assertion control -------------------------------- Define assertion control constant for RESET. The index value is specified by VPI.
Define assertion control constant for SYSTEM END. The index value is specified by VPI.
Define assertion control constant for SYSTEM OFF. The index value is specified by VPI.
Define assertion control constant for SYSTEM ON. The index value is specified by VPI.
------------------------------- Assertion system control -------------------------------- Define assertion control constant for SYSTEM RESET. The index value is specified by VPI.
Add test for a test suite - a test suite must instantiate, create and start tests or sequences
Verify if the expression is FALSE - the test will fail if the expression is FALSE
Arguments:
a_expression : the expression to be checked
a_error_msg : user error message to be printed if the check fails
Verify if a given SVA exists - the test will fail if SVA does not exists
Arguments:
a_sva_name : assertion name or path to be found in SVA list
a_error_msg : user error message to be printed if the check fails
Verify if a given SVA is enabled - the test will fail if SVA is enable
Arguments:
a_sva_name : assertion name or path to be found in SVA list
a_error_msg : user error message to be printed if the check fails
Verify if a given SVA finished - the test will fail if the assertion finished
Arguments:
a_sva_name : assertion name or path to be found in SVA list
a_error_msg : user error message to be printed if the check fails
Verify if a given SVA didn't finished - the test will fail if the assertion didn't finished
Arguments:
a_sva_name : assertion name or path to be found in SVA list
a_error_msg : user error message to be printed if the check fails
Verify if a given SVA didn't started - the test will fail if the assertion didn't started
Arguments:
a_sva_name : assertion name or path to be found in SVA list
a_error_msg : user error message to be printed if the check fails
Verify if a given SVA didn't succeeded (the assertion should have failed), the test will fail if the assertion didn't succeeded
Arguments:
a_sva_name : assertion name or path to be found in SVA list
a_error_msg : user error message to be printed if the check fails
Verify if a given SVA didn't finished but the first state is START, the test will fail if the assertion didn't finished but the first state is START
Arguments:
a_sva_name : assertion name or path to be found in SVA list
a_error_msg : user error message to be printed if the check fails
Verify if a given SVA succeeded - the test will fail if SVA succeeded
Arguments:
a_sva_name : assertion name or path to be found in SVA list
a_error_msg : user error message to be printed if the check fails
Verify if the expression is FALSE - the test will pass if the expression is FALSE
Arguments:
a_expression : the expression to be checked
a_error_msg : user error message to be printed if the check fails
Verify if a given SVA exists - the test will pass if SVA does not exists
Arguments:
a_sva_name : assertion name or path to be found in SVA list
a_error_msg : user error message to be printed if the check fails
Verify if a given SVA is enabled - the test will pass if SVA is enable
Arguments:
a_sva_name : assertion name or path to be found in SVA list
a_error_msg : user error message to be printed if the check fails
Verify if a given SVA finished - the test will pass if the assertion finished
Arguments:
a_sva_name : assertion name or path to be found in SVA list
a_error_msg : user error message to be printed if the check fails
Verify if a given SVA didn't finished - the test will pass if the assertion didn't finished
Arguments:
a_sva_name : assertion name or path to be found in SVA list
a_error_msg : user error message to be printed if the check fails
Verify if a given SVA didn't started - the test will pass if the assertion didn't started
Arguments:
a_sva_name : assertion name or path to be found in SVA list
a_error_msg : user error message to be printed if the check fails
Verify if a given SVA didn't succeeded (the assertion should have failed), the test will pass if the assertion didn't succeeded
Arguments:
a_sva_name : assertion name or path to be found in SVA list
a_error_msg : user error message to be printed if the check fails
Verify if a given SVA didn't finished but the first state is START, the test will pass if the assertion didn't finished but the first state is START
Arguments:
a_sva_name : assertion name or path to be found in SVA list
a_error_msg : user error message to be printed if the check fails
Verify if a given SVA succeeded - the test will pass if SVA succeeded
Arguments:
a_sva_name : assertion name to be found in SVA list
a_error_msg : user error message to be printed if the check fails


Tasks
Make a function
linearSearch
that finds a number n in an unsorted array of numbers.
The function
linearSearch
takes 2 arguments:
list
as the elements to search through and
item
, the element to search for.
Notes
Linear search goes through the elements one by one until it finds the searched element.
Linear search complexity is O(n)


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Who am I?
Sergi Almacellas Abellana
- (Python ♥) Programmer
- Open source enthusiast
- Tryton commiter since Nov 2013
Release process
- New Release every 6 months
- x.0 will be LTS releases
- LTS Releases will be supported for 5 years
- 5.0: 1st October 2018
- 5.0: Supported until October 2023
User Interface
Active Widget
User Interface
Many2One fields in editable list
Desktop client
Upgrade notification
Web Client
Editable Tree View improved
Web Client
- Format numeric fields
- Label and count position on lists
- Spell Checking
- Skip button of widgets for fast tabbing
- Sum feature on list
- Many2One as link on tree view
Parties
- Erase wizard for GDPR
- Add name to contact mechanism
- Contact mechanism usage flag
Accounting
- Allow chart template override
- Warning when creating a second chart of accounts
- Open detail on Income Statement
- Allow multiple tax codes per tax
- Tax report on cash basis
Spanish Chart of Accounts
- Removed not needed intermediary accounts
- Tax code to mimic AEAT models (111/115/303)
- Removed too specific taxes
Invoicing
- Optional description, product name is used on report
- Store reconciliation date instead of Boolean field
- Raise an error when overpaying an invoice
Payment
- Cron tasks to post clearing move after journal
- Manage stripe dispute statues
Statement
- Add a button to reconcile from statement
- Import OFX file format
Stock
- Show product quantity per template
- Add list of product quantities per location
- Skip inventory step in supplier shipment
Sales & Purchases
- Optional description, product name is used on report
- Wizard to modify header
- Invoice Paid method on purchase requests or drop shipments
Sale Reporting
Purchase
- Add link to product supplier on purchase line
- Warning when confirm a purchase linked to a request of different warehouse
Purchase Request Quotation
- Initiated by Employee
- Approval from a special group
- Using the purchase workflow


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Missing Stories We're collecting short stories / anecdotes / trivia about each show. If you've got something to share send the editors a message.


We got platforms!
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- extend customized e-business
- transition mission-critical initiatives
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- iterate bleeding-edge e-markets
- empower impactful platforms


{% assign citation = site.data.citations | where_exp: "citation", "citation.id == include.lookup or citation.title contains include.lookup" | first | default: include %}
" | remove: "


from .poutine import Poutine
[docs]class BlockPoutine(Poutine):
"""
This Poutine selectively hides pyro primitive sites from the outside world.
For example, suppose the stochastic function fn has two sample sites "a" and "b".
Then any poutine outside of BlockPoutine(fn, hide=["a"])
will not be applied to site "a" and will only see site "b":
>>> fn_inner = TracePoutine(fn)
>>> fn_outer = TracePoutine(BlockPoutine(TracePoutine(fn), hide=["a"]))
>>> trace_inner = fn_inner.get_trace()
>>> trace_outer = fn_outer.get_trace()
>>> "a" in trace_inner
True
>>> "a" in trace_outer
False
>>> "b" in trace_inner
True
>>> "b" in trace_outer
True
BlockPoutine has a flexible interface that allows users
to specify in several different ways
which sites should be hidden or exposed.
See the constructor for details.
"""
def __init__(self, fn,
hide_all=True, expose_all=False,
hide=None, expose=None,
hide_types=None, expose_types=None):
"""
:param bool hide_all: hide all sites
:param bool expose_all: expose all sites normally
:param list hide: list of site names to hide, rest will be exposed normally
:param list expose: list of site names to expose, rest will be hidden
:param list hide_types: list of site types to hide, rest will be exposed normally
:param list expose_types: list of site types to expose normally, rest will be hidden
Constructor for blocking poutine
Default behavior: block everything (hide_all == True)
A site is hidden if at least one of the following holds:
1. msg["name"] in hide
2. msg["type"] in hide_types
3. msg["name"] not in expose and msg["type"] not in expose_types
4. hide_all == True
"""
super(BlockPoutine, self).__init__(fn)
# first, some sanity checks:
# hide_all and expose_all intersect?
assert (hide_all is False and expose_all is False) or \
(hide_all != expose_all), "cannot hide and expose a site"
# hide and expose intersect?
if hide is None:
hide = []
else:
hide_all = False
if expose is None:
expose = []
assert set(hide).isdisjoint(set(expose)), \
"cannot hide and expose a site"
# hide_types and expose_types intersect?
if hide_types is None:
hide_types = []
if expose_types is None:
expose_types = []
assert set(hide_types).isdisjoint(set(expose_types)), \
"cannot hide and expose a site type"
# now set stuff
self.hide_all = hide_all
self.expose_all = expose_all
self.hide = hide
self.expose = expose
self.hide_types = hide_types
self.expose_types = expose_types
def _block_up(self, msg):
"""
:param msg: current message at a trace site, after all execution finished.
:returns: boolean decision to hide or expose site.
A site is hidden if at least one of the following holds:
1. msg["name"] in self.hide
2. msg["type"] in self.hide_types
3. msg["name"] not in self.expose and msg["type"] not in self.expose_types
4. self.hide_all == True
"""
# handle observes
if msg["type"] == "sample" and msg["is_observed"]:
msg_type = "observe"
else:
msg_type = msg["type"]
# decision rule for hiding:
if (msg["name"] in self.hide) or \
(msg_type in self.hide_types) or \
((msg["name"] not in self.expose) and
(msg_type not in self.expose_types) and self.hide_all): # noqa: E129
return True
# otherwise expose
else:
return False
def _pyro_sample(self, msg):
"""
:param msg: current message at a trace site
:returns: a sample from the stochastic function at the site.
Default sample behavior with a side effect.
Applies self._block_up to decide whether to hide the site.
"""
ret = super(BlockPoutine, self)._pyro_sample(msg)
msg["stop"] = self._block_up(msg)
return ret
def _pyro_param(self, msg):
"""
:param msg: current message at a trace site
:returns: the result of querying the parameter store.
Default param behavior with a side effect.
Applies self._block_up to decide whether to hide the site.
"""
ret = super(BlockPoutine, self)._pyro_param(msg)
msg["stop"] = self._block_up(msg)
return ret


Oppia’s Learner Feedback Program focuses on understanding students’ expectations, behaviors, and needs. Local volunteers conduct studies with students by working through lessons and gathering feedback. This feedback allows us to create content that is fun and easily digestible.This feedback is critical in helping us better align our lessons with the core needs and goals of students. It also enables us to help future students more effectively.
If you are passionate about education and want to make a difference in your local community, sign up to be a Learner Feedback Program volunteer and help us create the best platform for students around the world!
Oppia’s Learner Feedback Program focuses on understanding students’ expectations, behaviors, and needs. Local volunteers conduct studies with students by working through lessons and gathering feedback. This feedback allows us to create content that is fun and easily digestible.This feedback is critical in helping us better align our lessons with the core needs and goals of students. It also enables us to help future students more effectively.
If you are passionate about education and want to make a difference in your local community, sign up to be a Learner Feedback Program volunteer and help us create the best platform for students around the world!


When you run a build, a stack analysis is run that will provide insights around your technology stack.