13 Ordered Logistic Regression - Ex: Spaking
library(tidyverse)
library(haven) # read in SPSS dataset
library(furniture) # nice table1() descriptives
library(stargazer) # display nice tables: summary & regression
library(texreg) # Convert Regression Output to LaTeX or HTML Tables
library(psych) # contains some useful functions, like headTail
library(car) # Companion to Applied Regression
library(sjPlot) # Quick plots and tables for models
library(car) # Companion to Applied Regression (a text book - includes datasets)
library(MASS) # Support Functions and Datasets
library(nnet) # Multinomial Log-Linear Models
library(pscl) # Political Science Computational Laboratory (ZIP)
13.1 Background
This dataset comes from John Hoffman’s textbook: Regression Models for Categorical, Count, and Related Variables: An Applied Approach (2004) Amazon link, 2014 edition
Chapter 4: Ordered Logistic and Probit Regression Models
Dataset: The following example uses the SPSS data set gss.sav
. The dependent variable of interest is labeled spanking
.
" The pertinent question (
sapnking
) asks “Do you strongly agree, agree, disagree, or strongly disagree that it is sometimes necessary to discipline a child with a good, hard spanking?” The possible answers are coded as1
= strongly agree,2
= agree,3
= disagree, and4
= strongly disagree. A common hypothesis is that support for corporal punishment of children decreases at higher levels of education."
13.1.1 Raw Dataset
data_gss <- haven::read_spss("https://raw.githubusercontent.com/CEHS-research/data/master/Hoffmann_datasets/gss.sav") %>%
haven::as_factor()
data_gss %>%
dplyr::select(spanking, female, nonwhite, educate, income) %>%
dplyr::filter(!is.na(spanking)) %>% # about 1/3 of participants are missing this
head()
# A tibble: 6 x 5
spanking female nonwhite educate income
<fct> <fct> <fct> <dbl> <dbl>
1 agree male white 16 12
2 agree female white 11 2
3 disagree male white 15 12
4 disagree male white 14 NA
5 agree female non-white 16 12
6 agree male white 12 NA
13.2 Exploratory Data Analysis
13.2.1 Entire Sample
data_gss %>%
furniture::table1(spanking,
na.rm = FALSE,
output = "markdown",
caption = "Hoffmann's Example 4.1 Summary of the Spanking Variable")
| Mean/Count (SD/%) | |
|---|---|
| n = 2903 | |
| spanking | |
| strongly agree | 512 (17.6%) |
| agree | 890 (30.7%) |
| disagree | 357 (12.3%) |
| strongly disagree | 164 (5.6%) |
| NA | 980 (33.8%) |
13.2.2 By Education
data_gss %>%
dplyr::group_by(forcats::fct_explicit_na(spanking)) %>%
furniture::table1("Educations, years" = educate,
"Education, factor" = factor(educate),
na.rm = FALSE,
digits = 2,
output = "markdown")
| strongly agree | agree | disagree | strongly disagree | (Missing) | |
|---|---|---|---|---|---|
| n = 512 | n = 890 | n = 357 | n = 164 | n = 980 | |
| Educations, years | |||||
| 12.64 (2.96) | 13.40 (2.84) | 14.00 (2.74) | 14.24 (3.00) | 13.32 (2.95) | |
| Education, factor | |||||
| 0 | 0 (0%) | 2 (0.2%) | 0 (0%) | 0 (0%) | 2 (0.2%) |
| 3 | 5 (1%) | 0 (0%) | 0 (0%) | 0 (0%) | 3 (0.3%) |
| 4 | 1 (0.2%) | 0 (0%) | 0 (0%) | 1 (0.6%) | 4 (0.4%) |
| 5 | 4 (0.8%) | 5 (0.6%) | 0 (0%) | 1 (0.6%) | 3 (0.3%) |
| 6 | 5 (1%) | 5 (0.6%) | 1 (0.3%) | 0 (0%) | 3 (0.3%) |
| 7 | 7 (1.4%) | 8 (0.9%) | 0 (0%) | 1 (0.6%) | 7 (0.7%) |
| 8 | 25 (4.9%) | 15 (1.7%) | 5 (1.4%) | 2 (1.2%) | 33 (3.4%) |
| 9 | 15 (2.9%) | 20 (2.2%) | 7 (2%) | 4 (2.4%) | 23 (2.3%) |
| 10 | 27 (5.3%) | 44 (4.9%) | 11 (3.1%) | 4 (2.4%) | 35 (3.6%) |
| 11 | 39 (7.6%) | 42 (4.7%) | 16 (4.5%) | 5 (3%) | 53 (5.4%) |
| 12 | 152 (29.7%) | 270 (30.3%) | 97 (27.2%) | 33 (20.1%) | 297 (30.3%) |
| 13 | 56 (10.9%) | 90 (10.1%) | 41 (11.5%) | 19 (11.6%) | 90 (9.2%) |
| 14 | 56 (10.9%) | 111 (12.5%) | 41 (11.5%) | 19 (11.6%) | 112 (11.4%) |
| 15 | 19 (3.7%) | 47 (5.3%) | 14 (3.9%) | 14 (8.5%) | 59 (6%) |
| 16 | 59 (11.5%) | 105 (11.8%) | 68 (19%) | 31 (18.9%) | 129 (13.2%) |
| 17 | 13 (2.5%) | 45 (5.1%) | 14 (3.9%) | 6 (3.7%) | 42 (4.3%) |
| 18 | 17 (3.3%) | 43 (4.8%) | 15 (4.2%) | 11 (6.7%) | 39 (4%) |
| 19 | 3 (0.6%) | 15 (1.7%) | 8 (2.2%) | 2 (1.2%) | 13 (1.3%) |
| 20 | 8 (1.6%) | 20 (2.2%) | 18 (5%) | 11 (6.7%) | 29 (3%) |
| NA | 1 (0.2%) | 3 (0.3%) | 1 (0.3%) | 0 (0%) | 4 (0.4%) |
13.2.3 Spanking by Sex
data_gss %>%
dplyr::filter(complete.cases(female, spanking)) %>%
dplyr::select(female, spanking) %>%
table() %>%
addmargins()
spanking
female strongly agree agree disagree strongly disagree Sum
male 243 388 156 56 843
female 269 502<PHONE_NUMBER>
Sum 512 890<PHONE_NUMBER>
data_gss %>%
dplyr::filter(complete.cases(female, spanking)) %>%
furniture::tableX(female, spanking,
type = "count")
spanking
female strongly agree agree disagree strongly disagree Total
male 243 388 156 56 843
female 269 502<PHONE_NUMBER>
Total 512 890<PHONE_NUMBER>
data_gss %>%
dplyr::filter(complete.cases(female, spanking)) %>%
furniture::tableX(female, spanking,
type = "row_perc")
spanking
female strongly agree agree disagree strongly disagree Total
male 28.83 46.03 18.51 6.64 100.00
female 24.91 46.48 18.61 10.00 100.00
All 26.63 46.28 18.56 8.53 100.00
data_gss %>%
dplyr::filter(complete.cases(female, spanking)) %>%
furniture::tableX(female, spanking,
type = "col_perc")
spanking
female strongly agree agree disagree strongly disagree All
male 47.46 43.60 43.70 34.15 43.84
female 52.54 56.40 56.30 65.85 56.16
Total 100.00 100.00 100.00 100.00 100.00
data_gss %>%
dplyr::filter(complete.cases(female, spanking)) %>%
furniture::tableX(female, spanking,
type = "cell_perc")
spanking
female strongly agree agree disagree strongly disagree Total
male 12.64 20.18 8.11 2.91 43.84
female 13.99 26.11 10.45 5.62 56.16
Total 26.63 46.28 18.56 8.53 100.00
data_gss %>%
dplyr::filter(complete.cases(female, spanking)) %>%
dplyr::group_by(spanking) %>%
furniture::table1(female)
--------------------------------------------------------------------
spanking
strongly agree agree disagree strongly disagree
n = 512 n = 890 n = 357 n = 164
female
male 243 (47.5%) 388 (43.6%) 156 (43.7%) 56 (34.1%)
female 269 (52.5%) 502 (56.4%) 201 (56.3%) 108 (65.9%)
--------------------------------------------------------------------
13.3 Linear Regression
Linear regression is often ill-suited to fitting a likert rating, such as agreement.
13.3.1 Visualization
data_gss_model %>%
ggplot(aes(x = educate,
y = spankingN)) +
geom_count() + # point size relative to over-plotting
geom_smooth(method = "lm") + # add linear regression line (OLS)
theme_bw() +
labs(x = "Years of Formal Education",
y = "Spanking")
13.3.2 Fit the Model
Call:
lm(formula = spankingN ~ educate, data = data_gss_model)
Residuals:
Min 1Q Median 3Q Max
-1.44768 -0.79947 -0.06955 0.66036 2.41660
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.367326 0.093670 14.597 < 2e-16 ***
educate 0.054018 0.006839 7.898 4.73e-15 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.8729 on 1916 degrees of freedom
Multiple R-squared: 0.03153, Adjusted R-squared: 0.03103
F-statistic: 62.38 on 1 and 1916 DF, p-value: 4.73e-15
# A tibble: 2 x 5
Df `Sum Sq` `Mean Sq` `F value` `Pr(>F)`
<int> <dbl> <dbl> <dbl> <dbl>
1 1 47.5 47.5 62.4 4.73e-15
2 1916 1460. 0.762 NA NA
13.3.3 Tabulate Parameters
texreg::knitreg(fit_lm,
custom.model.name = "Linear Regression",
caption = "Hoffmann's Example 4.2",
caption.above = TRUE,
single.row = TRUE,
digits = 4)
| Linear Regression | |
|---|---|
| (Intercept) | 1.3673 (0.0937)*** |
| educate | 0.0540 (0.0068)*** |
| R2 | 0.0315 |
| Adj. R2 | 0.0310 |
| Num. obs. | 1918 |
| p < 0.001; p < 0.01; p < 0.05 |
13.3.4 Model Fit and Variance Explained
# A tibble: 1 x 5
AIC BIC R2 R2_adjusted RMSE
<dbl> <dbl> <dbl> <dbl> <dbl>
1 4926. 4942. 0.0315 0.0310 0.872
# R2 for Linear Regression
R2: 0.032
adj. R2: 0.031
13.4 Ordered Logistic Regression
data_gss_model %>%
dplyr::group_by(forcats::fct_explicit_na(spanking)) %>%
furniture::table1("Sex" = female,
caption = "Hoffmann's Example 4.3 Crosstabulate DV with Sex",
na.rm = FALSE,
digits = 2,
total = TRUE,
output = "markdown")
| Total | strongly agree | agree | disagree | strongly disagree | |
|---|---|---|---|---|---|
| n = 1918 | n = 511 | n = 887 | n = 356 | n = 164 | |
| Sex | |||||
| male | 841 (43.8%) | 243 (47.6%) | 387 (43.6%) | 155 (43.5%) | 56 (34.1%) |
| female | 1077 (56.2%) | 268 (52.4%) | 500 (56.4%) | 201 (56.5%) | 108 (65.9%) |
| NA | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) |
spanking
female strongly agree agree disagree strongly disagree Total
male 243 387 155 56 841
female 268 500<PHONE_NUMBER>
Total 511 887<PHONE_NUMBER>
13.5 Proportional-odds (ordinal) Logistic Regression
This type of logisit regression model forces the predictors to have similar relationship with the outcome (slopes), but different means (intercepts). This is called the proportional odds assumption.
13.5.1 Fit Model 1: Sex
Use polr()
function in the base \(R\) MASS
package. While outcome variable (dependent variable, “Y”) may be a regular factor
, it is preferable to specify it as an ordered
factor.
Call:
MASS::polr(formula = spanking ~ female, data = data_gss_model)
Coefficients:
Value Std. Error t value
femalefemale 0.2116 0.08532 2.48
Intercepts:
Value Std. Error t value
strongly agree|agree -0.8967 0.0694 -12.9114
agree|disagree 1.1094 0.0711 15.6078
disagree|strongly disagree 2.4922 0.0958 26.0026
Residual Deviance: 4719.394
AIC: 4727.394
13.5.2 Extract Parameters
<IP_ADDRESS> Logit Scale
strongly agree|agree agree|disagree
-0.8966862 1.1093754
disagree|strongly disagree
2.4921855
femalefemale
0.2116244
2.5 % 97.5 %
0.04451894 0.37901780
<IP_ADDRESS> Odds-Ratio Scale
strongly agree|agree agree|disagree
0.4079192 3.0324638
disagree|strongly disagree
12.0876653
femalefemale
1.235684
2.5 % 97.5 %
1.045525 1.460849
<IP_ADDRESS> Predicted Probabilities
model: spanking ~ female
female effect (probability) for strongly agree
female
male female
0.289732 0.248186
female effect (probability) for agree
female
male female
0.4622807 0.4623011
female effect (probability) for disagree
female
male female
0.1715795 0.1967672
female effect (probability) for strongly disagree
female
male female
0.07640782 0.09274572
13.5.3 Tabulate parameters
texreg::knitreg(fit_polr_1,
custom.model.name = c("b (SE)"),
custom.coef.map = list("femalefemale" = "Female vs. Male",
"strongly agree|agree" = "strongly agree|agree",
"agree|disagree" = "agree|disagree",
"disagree|strongly disagree" = "disagree|strongly disagree"),
groups = list("Predictors" = 1,
"Cut Values (i.e. threasholds)" = 2:4),
caption = "Hoffmann's Example 4.4 Ordered Logistic Regression",
caption.above = TRUE,
single.row = TRUE,
digits = 4)
| b (SE) | |
|---|---|
| Predictors | |
| Female vs. Male | 0.2116 (0.0853)* |
| Cut Values (i.e. threasholds) | |
| strongly agree|agree | -0.8967 (0.0694)*** |
| agree|disagree | 1.1094 (0.0711)*** |
| disagree|strongly disagree | 2.4922 (0.0958)*** |
| AIC | 4727.3944 |
| BIC | 4749.6306 |
| Log Likelihood | -2359.6972 |
| Deviance | 4719.3944 |
| Num. obs. | 1918 |
| p < 0.001; p < 0.01; p < 0.05 |
13.5.4 Predicted Probabilities
# A tibble: 8 x 6
x response.level predicted std.error conf.low conf.high
<fct> <chr> <dbl> <dbl> <dbl> <dbl>
1 male strongly.agree 0.290 0.0694 0.263 0.319
2 female strongly.agree 0.248 0.0647 0.225 0.273
3 male agree 0.462 0.0460 0.440 0.485
4 female agree 0.462 0.0459 0.440 0.485
5 male disagree 0.172 0.0708 0.153 0.192
6 female disagree 0.197 0.0653 0.177 0.218
7 male strongly.disagree 0.0764 0.0958 0.0642 0.0908
8 female strongly.disagree 0.0927 0.0889 0.0791 0.108
# A tibble: 4 x 6
x response.level predicted std.error conf.low conf.high
<fct> <chr> <dbl> <dbl> <dbl> <dbl>
1 female strongly.agree 0.248 0.0647 0.225 0.273
2 female agree 0.462 0.0459 0.440 0.485
3 female disagree 0.197 0.0653 0.177 0.218
4 female strongly.disagree 0.0927 0.0889 0.0791 0.108
13.5.5 Plot Predicted Probabilities
ggeffects::ggeffect(model = fit_polr_1,
terms = c("female")) %>% # x-axis
data.frame() %>%
ggplot(aes(x = x,
y = predicted,
group = response.level,
color = response.level)) +
geom_errorbar(aes(ymin = conf.low,
ymax = conf.high),
width = .25) +
geom_point(size = 4) +
geom_line(aes(linetype = response.level))
ggeffects::ggeffect(model = fit_polr_1,
terms = c("female")) %>% # x-axis
data.frame() %>%
dplyr::mutate(response.level = response.level %>%
forcats::fct_reorder(predicted) %>%
forcats::fct_rev()) %>%
ggplot(aes(x = x,
y = predicted,
group = response.level,
color = response.level)) +
geom_errorbar(aes(ymin = conf.low,
ymax = conf.high),
width = .25) +
geom_point(size = 4) +
geom_line(aes(linetype = response.level)) +
theme_bw() +
labs(x = NULL,
y = "Predicted Probability",
color = "Spanking:",
shape = "Spanking:",
linetype = "Spanking:") +
theme(legend.key.width = unit(2, "cm")) +
scale_linetype_manual(values = c("solid", "longdash", "dotdash", "dotted")) +
scale_shape_manual(values = c(0, 1, 2, 8))
13.5.6 Model Fit and Variance Explained
# A tibble: 2 x 7
Model `Resid. df` `Resid. Dev` Test ` Df` `LR stat.` `Pr(Chi)`
<chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl>
1 1 1915 4726. "" NA NA NA
2 female 1914 4719. "1 vs 2" 1 6.16 0.0130
Can't extract residuals from model.
Can't calculate log-loss.
Can't calculate proper scoring rules for ordinal, multinomial or cumulative link models.
# A tibble: 1 x 3
AIC BIC R2_Nagelkerke
<dbl> <dbl> <dbl>
1 4727. 4750. 0.00351
$R2_Nagelkerke
Nagelkerke's R2
0.003506369
13.5.7 Assumptions
<IP_ADDRESS> Proportional Odds: Brant Test
The poTest
function implements tests proposed by Brant (1990) for proportional odds for logistic models fit by the polr()
function in the MASS package.
Tests for Proportional Odds
MASS::polr(formula = spanking ~ female, data = data_gss_model)
b[polr] b[>strongly agree] b[>agree] b[>disagree] Chisquare df
Overall 3.01 2
femalefemale 0.212 0.204 0.183 0.446 3.01 2
Pr(>Chisq)
Overall 0.22
femalefemale 0.22
A significant test statistics provides evidence that the parallel regression assumption has been violated!
13.5.8 Fit Model 2: Sex + Covars
fit_polr_2 <- MASS::polr(spanking ~ female + educate + polviewsN,
data = data_gss_model)
summary(fit_polr_2)
Call:
MASS::polr(formula = spanking ~ female + educate + polviewsN,
data = data_gss_model)
Coefficients:
Value Std. Error t value
femalefemale 0.2532 0.08825 2.869
educate 0.1153 0.01564 7.374
polviewsN -0.2215 0.03248 -6.818
Intercepts:
Value Std. Error t value
strongly agree|agree -0.2977 0.2671 -1.1146
agree|disagree 1.7845 0.2706 6.5935
disagree|strongly disagree 3.1926 0.2793 11.4312
Residual Deviance: 4396.504
AIC: 4408.504
(97 observations deleted due to missingness)
13.5.9 Extract Parameters
<IP_ADDRESS> Logit Scale
strongly agree|agree agree|disagree
-0.2976843 1.7844863
disagree|strongly disagree
3.1926342
femalefemale educate polviewsN
0.2532132 0.1152980 -0.2214577
2.5 % 97.5 %
femalefemale 0.08039420 0.4263963
educate 0.08472724 0.1460403
polviewsN -0.28526298 -0.1579046
<IP_ADDRESS> Odds-Ratio Scale
strongly agree|agree agree|disagree
0.7425358 5.9565193
disagree|strongly disagree
24.3524926
femalefemale educate polviewsN
1.2881578 1.1222079 0.8013498
2.5 % 97.5 %
femalefemale 1.0837142 1.5317277
educate 1.0884202 1.1572429
polviewsN 0.7518165 0.8539312
13.5.10 Tabulate parameters
texreg::knitreg(fit_polr_2,
custom.model.name = c("b (SE)"),
custom.coef.map = list("femalefemale" = "Female vs. Male",
"educate" = "Years of Education",
"polviewsN" = "Level of Polytical Views",
"strongly agree|agree" = "strongly agree|agree",
"agree|disagree" = "agree|disagree",
"disagree|strongly disagree" = "disagree|strongly disagree"),
groups = list("Predictors" = 1:3,
"Cut Values" = 4:6),
caption = "Hoffmann's Example 4.7 Ordered Logistic Regression",
caption.above = TRUE,
single.row = TRUE,
digits = 4)
| b (SE) | |
|---|---|
| Predictors | |
| Female vs. Male | 0.2532 (0.0883)** |
| Years of Education | 0.1153 (0.0156)*** |
| Level of Polytical Views | -0.2215 (0.0325)*** |
| Cut Values | |
| strongly agree|agree | -0.2977 (0.2671) |
| agree|disagree | 1.7845 (0.2706)*** |
| disagree|strongly disagree | 3.1926 (0.2793)*** |
| AIC | 4408.5038 |
| BIC | 4441.5466 |
| Log Likelihood | -2198.2519 |
| Deviance | 4396.5038 |
| Num. obs. | 1821 |
| p < 0.001; p < 0.01; p < 0.05 |
13.5.11 Predicted Probabilities
The
ggeffects
package computes estimated marginal means (predicted values) for the response, at the margin of specific values or levels from certain model terms, i.e. it generates predictions by a model by holding the non-focal variables constant and varying the focal variable(s).
ggpredict()
usespredict()
for generating predictions- factors: uses the reference level
ggeffect()
computes marginal effects by internally callingeffects::Effect()
- factors: compute a kind of “average” value, which represents the proportions of each factor’s category
ggemmeans()
usesemmeans::emmeans()
- factors: compute a kind of “average” value, which represents the proportions of each factor’s category
Use condition
to set a specific level for factors in ggemmeans()
, so factors are not averaged over their categories, but held constant at a given level.
ggeffects::ggpredict()
Adjusted for: * educate = 13.51 The grand mean value * polviewsN = 4.17 The grand mean value
## Hoffmann's Example 4.8 (continues...approximated)
ggeffects::ggpredict(model = fit_polr_2,
terms = c("female"))
# A tibble: 8 x 7
x predicted std.error conf.low conf.high response.level group
<fct> <dbl> <dbl> <dbl> <dbl> <chr> <fct>
1 male 0.283 0.253 0.193 0.393 strongly agree 1
2 male 0.477 0.253 0.357 0.600 agree 1
3 male 0.169 0.253 0.110 0.250 disagree 1
4 male 0.0718 0.253 0.0450 0.113 strongly disagree 1
5 female 0.234 0.276 0.151 0.344 strongly agree 1
6 female 0.476 0.276 0.346 0.610 agree 1
7 female 0.199 0.276 0.126 0.299 disagree 1
8 female 0.0906 0.276 0.0549 0.146 strongly disagree 1
# A tibble: 8 x 7
x predicted std.error conf.low conf.high response.level group
<fct> <dbl> <dbl> <dbl> <dbl> <chr> <fct>
1 male 0.283 0.253 0.193 0.393 strongly agree 1
2 male 0.477 0.253 0.357 0.600 agree 1
3 male 0.169 0.253 0.110 0.250 disagree 1
4 male 0.0718 0.253 0.0450 0.113 strongly disagree 1
5 female 0.234 0.276 0.151 0.344 strongly agree 1
6 female 0.476 0.276 0.346 0.610 agree 1
7 female 0.199 0.276 0.126 0.299 disagree 1
8 female 0.0906 0.276 0.0549 0.146 strongly disagree 1
13.6 Hoffmann’s Example 4.8 (continues…approximated)
ggeffects::ggpredict()
Adjusted for: * female = male The reference category * polviewsN = 4.17 The grand mean value
ggeffects::ggpredict(model = fit_polr_2,
terms = c("educate [10, 16]", # 1st = x
"female")) %>% # 2nd = group
data.frame() %>%
dplyr::filter(group == "male")
# A tibble: 8 x 7
x predicted std.error conf.low conf.high response.level group
<dbl> <dbl> <dbl> <dbl> <dbl> <chr> <fct>
1 10 0.371 0.209 0.282 0.471 strongly agree male
2 10 0.454 0.209 0.356 0.556 agree male
3 10 0.125 0.209 0.0868 0.177 disagree male
4 10 0.0491 0.209 0.0331 0.0721 strongly disagree male
5 16 0.228 0.287 0.144 0.342 strongly agree male
6 16 0.475 0.287 0.340 0.614 agree male
7 16 0.203 0.287 0.127 0.309 disagree male
8 16 0.0935 0.287 0.0555 0.153 strongly disagree male
ggeffects::ggeffect()
Adjusted for: * female computed a kind of “average” value, which represents the proportions of male/female * polviewsN = 4.17 The grand mean value
# A tibble: 8 x 6
x response.level predicted std.error conf.low conf.high
<dbl> <chr> <dbl> <dbl> <dbl> <dbl>
1 10 strongly.agree 0.339 0.0744 0.307 0.372
2 16 strongly.agree 0.204 0.0692 0.183 0.227
3 10 agree 0.465 0.0498 0.441 0.490
4 16 agree 0.469 0.0487 0.445 0.493
5 10 disagree 0.139 0.0793 0.122 0.159
6 16 disagree 0.221 0.0663 0.199 0.244
7 10 strongly.disagree 0.0561 0.105 0.0461 0.0682
8 16 strongly.disagree 0.106 0.0885 0.0908 0.124
ggeffects::ggemmeans(model = fit_polr_2,
terms = c("educate [10, 16]"),
condition = c(female = "female")) %>%
data.frame()
# A tibble: 8 x 7
x predicted std.error conf.low conf.high response.level group
<dbl> <dbl> <dbl> <dbl> <dbl> <fct> <fct>
1 10 0.314 0.0178 0.279 0.349 strongly agree 1
2 10 0.472 0.0124 0.448 0.496 agree 1
3 10 0.151 0.0107 0.130 0.172 disagree 1
4 10 0.0624 0.00641 0.0498 0.0749 strongly disagree 1
5 16 0.187 0.0124 0.162 0.211 strongly agree 1
6 16 0.461 0.0125 0.437 0.486 agree 1
7 16 0.235 0.0130 0.209 0.260 disagree 1
8 16 0.117 0.0100 0.0976 0.137 strongly disagree 1
Predictions for specific values: females with 10 or 16 years education
ggeffects::ggeffect(model = fit_polr_2,
terms = c("female", # 1st var = `x`
"educate [10, 16]")) %>% # 2nd var = `group`
data.frame()
# A tibble: 16 x 7
x group response.level predicted std.error conf.low conf.high
<fct> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
1 male 10 strongly.agree 0.371 0.0909 0.331 0.414
2 female 10 strongly.agree 0.314 0.0825 0.281 0.350
3 male 16 strongly.agree 0.228 0.0818 0.201 0.258
4 female 16 strongly.agree 0.187 0.0820 0.164 0.212
5 male 10 agree 0.454 0.0544 0.428 0.481
6 female 10 agree 0.472 0.0496 0.448 0.496
7 male 16 agree 0.475 0.0489 0.451 0.499
8 female 16 agree 0.461 0.0504 0.437 0.486
9 male 10 disagree 0.125 0.0937 0.106 0.147
10 female 10 disagree 0.151 0.0834 0.132 0.174
11 male 16 disagree 0.203 0.0756 0.180 0.228
12 female 16 disagree 0.235 0.0721 0.210 0.261
13 male 10 strongly.disagree 0.0491 0.120 0.0392 0.0613
14 female 10 strongly.disagree 0.0624 0.110 0.0509 0.0762
15 male 16 strongly.disagree 0.0935 0.101 0.0780 0.112
16 female 16 strongly.disagree 0.117 0.0968 0.0990 0.138
ggeffects::ggemmeans(model = fit_polr_2,
terms = "female",
condition = c(educate = 12,
polviewsN = 4.5))
# A tibble: 8 x 7
x predicted std.error conf.low conf.high response.level group
<fct> <dbl> <dbl> <dbl> <dbl> <fct> <fct>
1 male 0.335 0.0169 0.302 0.368 strongly agree 1
2 male 0.467 0.0124 0.442 0.491 agree 1
3 male 0.141 0.00969 0.122 0.160 disagree 1
4 male 0.0570 0.00571 0.0458 0.0682 strongly disagree 1
5 female 0.281 0.0141 0.254 0.309 strongly agree 1
6 female 0.477 0.0121 0.453 0.501 agree 1
7 female 0.169 0.0101 0.149 0.189 disagree 1
8 female 0.0723 0.00653 0.0595 0.0851 strongly disagree 1
13.6.1 Plot Predicted Probabilites
ggeffects::ggeffect(model = fit_polr_2,
terms = c("educate [10, 16]", # x-axis
"female")) %>% # lines by group
data.frame() %>%
ggplot(aes(x = x,
y = predicted,
color = group,
shape = group)) +
geom_point(size = 4) +
geom_line(aes(linetype = group)) +
facet_wrap(~ response.level)
ggeffects::ggeffect(model = fit_polr_2,
terms = c("educate [10, 16]", # x-axis
"female")) %>% # lines by group
data.frame() %>%
dplyr::filter(response.level == "strongly.agree") %>%
ggplot(aes(x = x,
y = predicted,
color = group)) +
geom_point(size = 4) +
geom_line(aes(linetype = group))
ggeffects::ggeffect(model = fit_polr_2,
terms = c("educate [10, 16]", # x-axis
"female")) %>% # lines by group
data.frame() %>%
dplyr::filter(response.level == "strongly.agree") %>%
ggplot(aes(x = x,
y = predicted,
color = group,
shape = group)) +
geom_errorbar(aes(ymin = conf.low,
ymax = conf.high),
width = .5,
position = position_dodge(width =.25)) +
geom_point(size = 4,
position = position_dodge(width =.25)) +
geom_line(aes(linetype = group),
position = position_dodge(width =.25)) +
theme_bw() +
labs(x = "Education, years",
y = "Predicted Probability for Strongly Agree",
color = NULL,
shape = NULL,
linetype = NULL)
ggeffects::ggeffect(model = fit_polr_2,
terms = c("educate [10, 16]", # x-axis
"female")) %>% # lines by group
data.frame() %>%
dplyr::mutate(group = forcats::fct_rev(group)) %>%
dplyr::filter(response.level == "strongly.agree") %>%
ggplot(aes(x = x,
y = predicted,
shape = group)) +
geom_errorbar(aes(ymin = conf.low,
ymax = conf.high),
width = .25,
position = position_dodge(.2)) +
geom_point(size = 4,
position = position_dodge(.2)) +
geom_line(aes(linetype = group),
size = 1,
position = position_dodge(.2)) +
theme_bw() +
theme(legend.position = c(1, 1),
legend.justification = c(1.1, 1.1),
legend.key.width = unit(2, "cm"),
legend.background = element_rect(color = "black")) +
scale_linetype_manual(values = c("solid", "longdash")) +
labs(x = "Years of Formal Education",
y = "Predicted Probabilit for\nResponding 'Strongly Agree'",
color = NULL,
shape = NULL,
linetype = NULL,
title = "Adjusted Predictions: Strongly Agree Spanking is Appropriate")
ggeffects::ggeffect(model = fit_polr_2,
terms = c("female")) %>% # lines by group
data.frame() %>%
dplyr::filter(response.level %in% c("strongly.agree",
"strongly.disagree")) %>%
dplyr::mutate(resonse.level = factor(response.level)) %>%
ggplot(aes(x = x,
y = predicted,
fill = resonse.level)) +
geom_col(position = position_dodge())
ggeffects::ggeffect(model = fit_polr_2,
terms = c("female")) %>% # lines by group
data.frame() %>%
dplyr::filter(response.level %in% c("strongly.agree",
"strongly.disagree")) %>%
dplyr::mutate(resonse.level = factor(response.level)) %>%
ggplot(aes(x = forcats::fct_rev(x),
y = predicted,
fill = resonse.level)) +
geom_col(position = position_dodge()) +
theme_bw() +
theme(legend.position = "bottom") +
scale_fill_manual(values = c("gray30", "gray70")) +
labs(x = NULL,
y = "Predicted Probability of Response",
fill = NULL,
title = "Attitues towareds Spanking, by Sex")
13.6.2 Model Fit and Variance Explained
fit_polr_1redeo <- MASS::polr(spanking ~ female,
data = data_gss_model %>%
dplyr::filter(complete.cases(educate, polviewsN)))
# A tibble: 2 x 7
Model `Resid. df` `Resid. Dev` Test ` Df` `LR stat.` `Pr(Chi)`
<chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl>
1 female 1817 4501. "" NA NA NA
2 female + educate~ 1815 4397. "1 vs~ 2 105. 0
Can't extract residuals from model.
Can't extract residuals from model.
# A tibble: 2 x 7
Model Type AIC BIC R2_Nagelkerke BF Performance_Score
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 fit_polr_2 polr 4409. 4442. 0.179 1.00e+ 0 1
2 fit_polr_1redeo polr 4509. 4531. 0.00384 3.26e-20 0
13.6.3 Assumptions
<IP_ADDRESS> Proportional Odds: Brant Test
The poTest
function implements tests proposed by Brant (1990) for proportional odds for logistic models fit by the polr()
function in the MASS package.
Tests for Proportional Odds
MASS::polr(formula = spanking ~ female + educate + polviewsN,
data = data_gss_model)
b[polr] b[>strongly agree] b[>agree] b[>disagree] Chisquare df
Overall 8.80 6
femalefemale 0.2532 0.2342 0.2180 0.4719 2.64 2
educate 0.1153 0.1248 0.1036 0.0927 1.17 2
polviewsN -0.2215 -0.1622 -0.2558 -0.2872 4.85 2
Pr(>Chisq)
Overall 0.185
femalefemale 0.268
educate 0.558
polviewsN 0.089 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
A significant test statistics provides evidence that the parallel regression assumption has been violated!


2.13. An Empirical Comparison of Sorting Algorithms¶
2.13.1. An Empirical Comparison of Sorting Algorithms¶
Which sorting algorithm is fastest? Asymptotic complexity analysis lets us distinguish between \(\Theta(n^2)\) and \(\Theta(n \log n)\) algorithms, but it does not help distinguish between algorithms with the same asymptotic complexity. Nor does asymptotic analysis say anything about which algorithm is best for sorting small lists. For answers to these questions, we can turn to empirical testing.
Table 2.14.1
Empirical comparison of sorting algorithms run on a 3.4 GHz Intel Pentium 4 CPU running Linux. All times shown are milliseconds.
Table 2.14.1 shows timing results for actual implementations of the sorting algorithms presented in this chapter. The algorithms compared include Insertion Sort, Bubble Sort, Selection Sort, Quicksort, and Mergesort.
For Quicksort, two versions are compared: the basic implementation and an optimized version that does not partition sublists below length nine (with Insertion Sort performed at the end).
Except for the rightmost columns, the input to each algorithm is a random array of integers. This affects the timing for some of the sorting algorithms. For example, Selection Sort is not being used to best advantage because the record size is small, so it does not get the best possible showing.
The various sorting algorithms are shown for lists of sizes 10, 100, 1000, 10,000, 100,000, and 1,000,000. The final two columns of each table show the performance for the algorithms on inputs of size 10,000 where the numbers are in ascending (sorted) and descending (reverse sorted) order, respectively. These columns demonstrate best-case performance for some algorithms and worst-case performance for others. They also show that for some algorithms, the order of input has little effect.
These figures show a number of interesting results. As expected, the \(O(n^2)\) sorts are quite poor performers for large arrays. Insertion Sort is by far the best of this group, unless the array is already reverse sorted. Optimized Quicksort is clearly the best overall algorithm for all but lists of 10 records. Even for small arrays, optimized Quicksort performs well because it does one partition step before calling Insertion Sort. In general, optimizing the various algorithms makes a noticeable improvement for larger array sizes.
Here are a few multiple choice questions that ask you to compare the sorting algorithms that we learned about in this chapter.


Overview »
Read More »
The Extractive Industries Map is an online, publicly-accessible, interactive mapping application focused on the extractive industries of oil, gas and mining. With this tool, users can map the locations of mineral, oil and gas deposits, active mines, oil and gas wells, socioeconomic indicators such as unemployment and malnutrition rates, government revenues, and donor-funded projects. The goal is to create a tool that advocates and policy-makers can use to understand governance issues in the extractive industries and advocate for solutions. The platform was piloted in in Ghana and is designed to scale up easily to accommodate other countries and datasets as the demand arises. By allowing users to create their own maps of this information, the relationships between extractive activities, socioeconomic and other governance-related data can be better explored and understood than through the sole use of tables and text, and provides a central space through which a variety of information can be accesssed.
Click here to go to the map page.
Various data sources were used to creating the extractive industries map. These individual sources and the way the data were processed to create the mapped information is described below. Wherever possible, links to the original sources are provided. As additional updates and additions are made, they will be recorded here as well.
Data Categories:
Information about the industrial mines in Ghana and the locations of mineral occurrences came from multiple sources.
The locations of industrial mines were identified using company declarations and Google Earth to locate the approximate coordinates of the mine.
The Extractive Industries Transparency Initiative (EITI) reports provided on the Ghana EITI website were the source of the following information: royalty, tax and fee payments by mining companies; reported government receipts of royalty, tax and fee payments; disbursements owed to districts by the central government from mineral royalties; reported disbursements received by districts. Ghana EITI has only released reports for the years 2004 to 2008. As the most current district boundaries are not yet available the EITI data reported for Tarkwa Municipal district and the Prestea/Huni Valley district are combined and reported together as the Wassa West district, while the data for Obuasi Municipal district is reported as Adansi West district.
Gold production data was provided in a Ghana Geological Survey Department report titled Performance of the Mining Industry in 2009. Data from 2010 on gold, bauxite and managanese production were provided by the Ghana Minerals Commission. They are included in the total production summations, but are not reported separately.
Employment data were provided by the Ghana Minerals Commission. The mapped values are the yearly averages of monthly employment levels for 2010 reported to the Minerals Commission by each mining company. National service staff, trainees, expatriate staff, and local staff were included, but contractors were excluded as some companies did not report this information. Data on indirect employment (i.e. construction workers) and induced employment (i.e. spending of staff wages on goods and services) are not available at this time.
Company names, website URLs and dates of entry into mining in Ghana were collected from individual company websites, when available.
The point locations and types of mineral deposits were provided by the Ghana Geological Survey Department.
The data on oil and gas in Ghana came from multiple sources.
Information posted on the Ghana National Petroleum Corporation (GNPC) website and the websites of individual companies provided data on the extent and location of commercial oil fields; the presence of exploration wells; the presence of production wells; oil discoveries; the boundaries of oil lease blocks; the ownership stake of the GNPC in each oil block and well; the date work was initiated in Ghana; and the financial interests of the various companies in each oil block and well.
Oil sales and oil production values were collected from GNPC official statements and news media reports. The sale value of the GNPC’s first oil lift is the total received by the Bank of Ghana received on April 8th, as reported here. The value of the sale was converted from U.S. dollars to Ghana Cedis using the April 8th average exchange rate reported by Forexpros.com. Oil sale values were not available for the Saltpond field. Oil production values for the Jubilee field were taken from Ghana Oil Watch’s report on lifting schedules and reported lift amounts, and represents the total amount lifted between the start of production in January, 2011, to the March 19th lift by Tullow Plc and E.O. Group. Oil production for the Saltpond field was taken from the GNPC’s Saltpond Field – SOPCL petroleum agreement summary posted on its website, and represents the total amount lifted between June 2003 and November 2008.
Where possible, links to petroleum agreements were compiled from the Ghana Ministry of Energy's Agreements page (under Tenders/Bids tab) or the United States Securities and Exchange Commission. Otherwise, summaries posted on the GNPC website were used.Petroleum Agreements page.
The locations of active World Bank-financed activities fall under 12 "lending" product lines. The dataset of financed activities is provided and regularly updated through the Open Data API. Out of these, so far, 1264 activities have been mapped in Ghana as of Spring 2011 under the following product lines: IBRD/IDA, Global Environment Project, Special Financing, Montreal Protocol, GEF Medium Sized Programs, Guarantees, Debt Reduction Facility, and Carbon Offsets. Locations of mapped financed activities are as of February 8, 2011.
The locations of and information about International Finance Corporation (IFC) projects specifically related to oil, gas and mining were collected from project information available at the IFC’s project portal. The approach for identifying and coding IFC activity locations followed the methodology used by Mapping for Results to geocode World Bank-financed activities.
A description of the two data sources used for development indicators in the Mapping the Extractives Industries platform are provided below, followed by descriptions of the indicators.
Demographic and Health Surveys (DHS) were used for indicators of Malnutrition, Infant Mortality and Births Attended by a Skilled Provider. The MEASURE DHS (Demographic and Health Surveys) Project is responsible for collecting and disseminating accurate, nationally representative data on health and population in developing countries. The project is implemented by Macro International, Inc. and is funded by the United States Agency for International Development (USAID) with contributions from other donors such as UNICEF, UNFPA, WHO, UNAIDS.
The Statoids website was consulted for population data. Statoids provides sub-national population data by first and second level administrative divisions for each country.
Malnutrition prevalence, weight for age (% of children under 5): Prevalence of child malnutrition is the percentage of children under age 5 whose height for age (stunting) is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's new child growth standards released in 2006. Source: Demographic and Health Surveys by Macro International. Catalog Sources: World Development Indicators.
Mortality rate, infant (per 1,000 live births): Infant mortality rate is the number of infants dying before reaching one year of age, per 1,000 live births in a given year. Source: Demographic and Health Surveys by Macro International. Catalog Sources: World Development Indicators.
Births attended by skilled health staff (% of total): Percentage of deliveries attended by personnel trained to give the necessary supervision, care, and advice to women during pregnancy, labor, and the postpartum period; to conduct deliveries on their own; and to care for newborns. Source: Demographic and Health Surveys by Macro International. Catalog Sources: World Development Indicators.
Unemployment rate (percentage): The percentage of the population in each first-order administrative unit that were not employed in the 12 months prior to the survey. Source: Demographic and Health Surveys by Macro International. Catalog Sources: World Development Indicators.
Population (count of individuals by second-order administrative unit): The estimated number of individuals living in each second-order administrative unit at the time of the 2000 Ghana Census. The administrative units used in this mapping were those that were current as of the 2000 census. Source: 2000 Ghana Census (from Statoids).
Population density (persons per square kilometer): The estimated population density reported as the number of persons per square kilometer for each second-order administrative unit. This data was calculated by dividing the population counts reported in the 2000 Ghana Census by the area of each second -order administrative unit. The area was derived from the Global Administrative Unit Layers spatial data using a Geographic Information System (see below). Sources: 2000 Ghana Census (from Statoids).
The maps displayed on the World Bank web site are for reference only. The boundaries, colors, denominations and any other information shown on these maps do not imply, on the part of the World Bank Group, any judgment on the legal status of any territory, or any endorsement or acceptance of such boundaries.
Countries: Ghana (2005)
The Global Administrative Unit Layers is an initiative implemented by FAO within the EC-FAO Food Security Programme funded by the European Commission. The GAUL aims at compiling and disseminating the most reliable spatial information on administrative units for all the countries in the world, providing a contribution to the standardization of the spatial dataset representing administrative units. The GAUL sub-national boundaries for Ghana were last updated in 2005, and do not include several changes made since then to the number and shape of second-order administrative units (districts) in the country.


Blockchain Interoperability
1.
Introduction
»
Next
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Blockchain interoperability based on Verifiable Credentials
¶
1. Introduction
2. DID Methods for organisations
2.1. ELSI DID Method
2.1.1. ELSI DID syntax
2.1.2. ELSI DID Document
2.2. SI-Chain DID Method
2.3. IBSI DID Method
3. Trust Framework: trusted registration of legal entities
3.1. Example: Trust Framework in Red T
3.2. Creating identities
4. Verifiable Credentials
4.1. Data Model
4.2. Example of Verifiable Credential
4.3. Verification of the credentials
4.3.1. Using the Universal Resolver API
4.3.2. Delegating verification to a trusted server
5. References
Quick Links
Introduction
DID Methods
Trust Framework
Credentials
References
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Example code related to use of Dygraphs
- Example 1 - Dynamically loading down-sampled data while zooming, without modifying dygraphs
- Example 2 - Similar to example 1, but with selectable range
- Example 3 - Shows workaround for handling pan interaction. Also enables auto range on y-axis.
- Example 4 - Same as example 3, but verifies works with multiple graph instances.
- Example 5 - More complex example showing multiple independent series & 2nd y-axis


Stormzy has just announced a massive Dublin gig for next September
by Ruairi Scott ByrneStormzy has just announced a huge 55-date ‘H.I.T.H. World Tour’ which includes a massive gig in Ireland next September.
With shows spanning across Europe, Asia, Australia, New Zealand, North America, UK & Ireland and Africa, the tour marks Stormzy’s biggest live offering to date.
Kicking off the tour in Dubai on February 7 and concluding in Africa in October, the tour will also see him make his solo debut at London’s O2 Arena for two nights on September 3 and 4, marking his first solo shows in the capital since his 3-day stint at Brixton Academy in May 2017.
Subsequent to London’s O2 Arena, Stormzy will grace the stages of 11 more arenas throughout the UK and Ireland, including a stop at Dublin's 3Arena on September 8.
Tickets for the Dublin gig start from €49.50 and go on sale at 9am next Friday (December 6).
Thereafter he will head to Africa to tour for the first time, notably playing a date in his homeland of Ghana, before ending the world tour with a show in Lagos, Nigeria.
The touring news comes straight off the back of a phenomenal few weeks for 26-year-old Stormzy.
Kicking-off the month announcing his hugely anticipated album ‘Heavy Is The Head’, last week the musician also announced his brand new single ‘Own It’ featuring musical heavyweights Ed Sheeran and Burna Boy which is on course become Stormzy’s 7th top 10 single in today’s Official UK Charts!


CulTourist is the perfect resource to know local customs,
slang and scams while abroad. Get trusted advice from fellow
travelers to blend in with the locals at your next
destination!
Dinner Decider is finally the answer to the age old
question, “What do you want for dinner?” Singles, couples
and families everywhere rejoice because they finally have
something else to make the decision for them!
Have a trip planned or just need to check out the weather
near you? This application is just what you need with
current weather conditions and a five day forecast for all
your new searches or saved cities.
Are you a coding genius or totally lost? Test it out with
this super fun coding quiz, but be careful a wrong answer
will lose seconds on the clock! Save your high scores and
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Does it feel like you don't have enough hours in the day? Do
you struggle to keep track of your daily tasks at work? This
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appointments throughout the day and get color coded
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We all know that our usual password is tired, we use it for
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As you already think, this seems a lot like the one-page apps that are all around. True. Everything loads when the dom is ready.
Search engine spider bots do not like that very much, but we have predicted a little something for it.
When a request comes from a spider bot, the application will have phantom to request the same page so that the ready made page html can be served back to the bot.
Quite simple as that! This is the reason that you will need to have phantom installed on your server running jsnbt.


The ExternalProgram object executes an external program when it receives an entity. Model execution is suspended until the external program terminates. The external program can be written in Python or any other language.
ExternalProgram Inputs
|
Keyword |
Description |
|
Key Inputs |
|
|
Description |
A free-form string describing the object. |
|
NextComponent |
The next object to which the processed entity is passed. |
|
ProgramFile |
The executable file for the external program. If the external program is written in Python, the program file is the executable for Python interpreter (python.exe). |
|
InputFile |
Optional input file for the external program. If the external program is written in Python, the input file is the python code file (*.py) |
|
DataSource |
A list of expressions that provide the parameters to the external program. The inputs must be provided in the order in which they are to be entered in the external program's command line. |
|
InitialValue |
The Value output prior to receiving the first entity. |
|
TimeOut |
Maximum time in milliseconds for the external program to finish executing. |
|
Options |
|
|
AttributeDefinitionList, CustomOutputList |
See Section 6.2. |
|
StateAssignment |
The state to be assigned to each entity on arrival at this object. No state is assigned if the entry is blank. |
|
Graphics |
|
|
Position, Alignment, Size, Orientation, Region, RelativeEntity, DisplayModel, Show, Movable, VisibleViews, DrawRange |
See Section 11.1. |
ExternalProgram Outputs
|
Output Name |
Description |
|
Entity and DisplayEntity |
|
|
Name, ObjectType, SimTime, Parent, Attributes, Custom Outputs, Position, Size, Orientation, Alignment, GraphicalLength, ObserverList, NextList, PreviousList |
See Section 10.1. |
|
StateEntity |
|
|
State, WorkingState, WorkingTime, StateTimes, TotalTime |
Not used. |
|
LinkedComponent |
|
|
obj, NumberAdded, NumberProcessed, NumberInProgress, ProcessingRate, ReleaseTime |
See Section 16. |
|
ExternalProgram |
|
|
Value |
An array of values returned by the external program after parsing. Returned strings are converted automatically to numbers, times, or entities, if appropriate. For example, if the external program returns a single number, its value is 'this.Value(1)'. |


Puppet Class: r_profile::puppet::master::hiera
- Inherits:
- r_profile::puppet::params
- Defined in:
- manifests/puppet/master/hiera.pp
Overview
R_profile::Puppet::Master::Hiera
Configure a custom hierarchy and setup hiera-eyaml
End users are able to customise their hierarchy completly by specifying it
in in its entirety in a file called hierarchy.txt
in
production hieradata directory within the control repository (final file
location on Puppet Master:
/etc/puppetlabs/code/environments/production/hieradata/hierarchy.txt
).
Each line in the file will be taken verbatim to form a level of the
:hierarchy:
key in specified in hiera.yaml
. Eg, a
file with the content:
"node/%{clientcert}"
"os/%{osfamily}_%{operatingsystemmajrelease}"
"common"
Would be rewritten in hiera.yaml
as:
:hierarchy:
- "node/%{::trusted.certname}"
- "os/%{osfamily}_%{operatingsystemmajrelease}"
- "common"
If the hierarchy.txt
file is not available, then a default
hierarchy sourced from params.pp
will be used instead.
hiera-eyaml
Will be automatically configured unless you have
told this profile not to, which may be necessary where there is limited
internet connectivity or http proxies in use.
38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 |
# File 'manifests/puppet/master/hiera.pp', line 38
class r_profile::puppet::master::hiera(
$eyaml = hiera('r_profile::puppet::master::hiera::eyaml', true),
$hieradir = $r_profile::puppet::params::hieradir,
) inherits r_profile::puppet::params {
if $eyaml {
$backends = [ "eyaml" ]
# Hiera module will only install eyaml if the manage_package attribute is set,
# however, setting this also installs the hiera package itself, eg completly
# breaks puppet enterprise ;-) best thing to do here is install eyaml ourselves
# and then use the hiera module to finish setting up the hierarchy and eyaml
# keys. Note that we have to do this twice - once for vendored ruby and once
# for vendored jruby. This isn't need for installations created with
# puppetizer since it does all this for you...
# we need a composite namevar to allow this to succeed:
# http://www.craigdunn.org/2016/07/composite-namevars-in-puppet/
package { "vendored ruby eyaml":
ensure => present,
name => "hiera-eyaml",
provider => puppet_gem,
}
package { "vendored jruby eyaml":
ensure => present,
name => "hiera-eyaml",
provider => puppetserver_gem,
notify => Service['pe-puppetserver'],
}
} else {
# [yaml]
$backends = undef
}
# read from /dev/null to prevent error if file is not present. This allows
# easy fallback to the default hierachy in params.pp
$hierarchy_raw = file(
"/etc/puppetlabs/code/environments/production/hieradata/hierarchy.txt",
"/dev/null"
)
if $hierarchy_raw == "" {
$hierarchy = $hierarchy_default
} else {
$hierarchy = split($hierarchy_raw, '\n')
}
class { "hiera":
hierarchy => $hierarchy,
hiera_yaml => "/etc/puppetlabs/puppet/hiera.yaml",
datadir => $hieradir,
backends => $backends,
eyaml => $eyaml,
owner => "pe-puppet",
group => "pe-puppet",
provider => "puppetserver_gem",
eyaml_extension => "yaml",
notify => Service["pe-puppetserver"],
}
}
|


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add
Cricket
| POOL 'A' |
POOL 'B' |
| PG's |
2k13 |
| 2k11 |
2k14 |
| 2k12 |
2k15 |
| 2k17 |
2k16 |
| Schedules |
Contact: Number |
| POOL 'A' |
POOL 'B' |
| 2k13 |
2k12 |
| 2k14 |
2k15 |
| 2k17 |
2k16 |
| Schedules |
Contact: Number |
Volleyball
| POOL 'A' |
POOL 'B' |
| Alvin |
$0.87 |
| Jonathan |
$7.00 |
| POOL 'A' |
POOL 'B' |
| Alvin |
$0.87 |
| Jonathan |
$7.00 |
| POOL 'A' |
POOL 'B' |
| Alvin |
$0.87 |
| Jonathan |
$7.00 |
Basketball
| POOL 'A' |
POOL 'B' |
| Alvin |
$0.87 |
| Jonathan |
$7.00 |
| POOL 'A' |
POOL 'B' |
| Alvin |
$0.87 |
| Jonathan |
$7.00 |
| POOL 'A' |
POOL 'B' |
| Alvin |
$0.87 |
| Jonathan |
$7.00 |
Football
| POOL 'A' |
POOL 'B' |
| Alvin |
$0.87 |
| Jonathan |
$7.00 |
| POOL 'A' |
POOL 'B' |
| Alvin |
$0.87 |
| Jonathan |
$7.00 |
| POOL 'A' |
POOL 'B' |
| Alvin |
$0.87 |
| Jonathan |
$7.00 |
Badminton
| POOL 'A' |
POOL 'B' |
| Alvin |
$0.87 |
| Jonathan |
$7.00 |
| POOL 'A' |
POOL 'B' |
| Alvin |
$0.87 |
| Jonathan |
$7.00 |
| POOL 'A' |
POOL 'B' |
| Alvin |
$0.87 |
| Jonathan |
$7.00 |
Throwball
| POOL 'A' |
POOL 'B' |
| Alvin |
$0.87 |
| Jonathan |
$7.00 |
| POOL 'A' |
POOL 'B' |
| Alvin |
$0.87 |
| Jonathan |
$7.00 |
| POOL 'A' |
POOL 'B' |
| Alvin |
$0.87 |
| Jonathan |
$7.00 |
Galli Cricket
| POOL 'A' |
POOL 'B' |
| Alvin |
$0.87 |
| Jonathan |
$7.00 |
| POOL 'A' |
POOL 'B' |
| Alvin |
$0.87 |
| Jonathan |
$7.00 |
| POOL 'A' |
POOL 'B' |
| Alvin |
$0.87 |
| Jonathan |
$7.00 |


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We are developing a learning platform where students and teachers can interact in much more free and meaningful ways then the ways in traditional teaching methods. The concept of a teacher and student is much more loose in the sense that anyone teaching anything is a teacher and anyone learning anything is a student. These loose meanings in teacher and student concepts allow us to create much more creative learning environments. For more info please visit our github page.


| Name: SMT. JAYASHREE | Reg No: 07-12SMHC18 |
|---|---|
| Age / Sex: 46/F | Contact No:9731623777 |
| Marital Status:ML- 30 YEARS | Date: 12/12/18 |
| Occupation: HOUSEWIFE. STUDIED UPTO PUC. | Dr. PNB |
Address: NATIVE OF CHITRADURGA, IN BLORE FROM SILK BOARD |
DIAGNOSIS- |
JOINT PAIN SINCE 6 YEARS. GOT CHIKUN GUNYA 10-12 YERS BCK SINCE THEN THER FRM 6 YRS INC. LAST YEAR AGIN GOT CHIKUN GUNYA. FEELS VERY TIRED.. USE TO STITCH ALSO. RESTLESS USE TO DO. HER TIME PASS IS THAT. < MORE 1 WEEK BEFORE MENSES. > SUMMERS.
BP SINCE PREGNANCY 1989; GOT SWELLING OF LIMBS AND BODY ALSO. FLUCTUATING TILL LAST 2 YEARS BUT FRM THEN HIGH ONLY. GIDDINESS , VERY ANGRY.
VARICOSE VEINS NO PAIN BUT UNABLE TO SIT ON THE FLOOR. MORE ON THE RIGHT LEG. SPECIALLY ON THE KNEE REGION. LIKES TO SIT ON THE FLOOR ONLY.
DARK CIRCLES SINCE DELIVERY. REDUCING FRM 3 YEARS BY ITSELF. REDUCED STITCHING. DUE TO FAMILY PBM USE TO STICTCH AND EARN MONEY. SHE LEFT HER HUSBAND AND CAME AS HE HAS NO RESPONSIBILITY AND USE TO BEAT KIDS AND ALL.
ALLERGIC COLD AND COUGH < SEASONING, DUST SMTIMES GETS ITCHING ON SKIN ALSO. 6 YRS BCK USED INHALER ALSO. (SAID AFTER ME ASKING). USE TO HAVE IT SINCE 30 YEARS AFTER DADS DEATH.
HAS 3 GIRLS. FTND.
29
26
24.
LEFT HUSBAND 15 YEARS BCK.
DAD DIED WHEN SHE WAS 30 YEARS.
IM VERY STUBBORN.
4.  (SAID AFTER ME ASKING)
6. HEADACHE MORE FROM DADS DEATH. ALWAYS THERE TEMPLES. SOMETIMES OCCIPETAL IF INCREASES MORE.
F/H:
DAD:CA OF JAW. DIED AT THE AGE OF 53 YRS. PT WAS IN 8TH STD THEN. VERY MUCH ATTACHED TO HIM.
MOM: BP, ANGIOGRAM, DM
5 SISTERS AND PT IS YOUNGEST.
ELDER SIS: THYROID DYSFUNCTION AND VARICOSE VEINS.
2ND SIS: BREATHING ISSUES
3RDSIS: OBESE.
OVERALL FAMILY HAD RECC TONSILLITIS.
PAST H/O:
TYPHOID – 7TH STD
RECC TONSILS.
PERSONAL HISTORY:
STOPPED NON VEG WHEN WENT TO KASI.
SPICY FOODS . SWEETS ALSO BUT STOPPED DUE TO DIABETES.
SWEAT NORMAL – MORE ON CHEST UNDERARMS.
SLEEP : SLEEPS IMM FR AN HOUR DEEP. AFTER THT KEEPS WAKING AND GOING BACK TO SLEEP. EARLY HOURS AGAIN DEEP SLEEP. DREAMS MORE OF TEMPLES. IF THINKS ABOUT DAD WILL GET HIM IN DREAMS AND THE NEXT DAYS WILL BE VERY GOOD. STARTLES IN SLEEP.
LIKES WARM WEATHER. LIKES SODAS AND COFFE ALSO MORE- 6-7 COFFEES A DAY.
MIND:
CLOSE TO DAD. VERY MUCH.
AFTER HOMOE:
BETTER IN JOINT PAIN
BETTER IN ENERGY.
ALLERGY SAME.
RX:
CARC1M – 3 DOSES – 5 DAYS ONCE B/BRUSH WATER.
LACH 200 – 4PILLS AFTERNOON B/FOOD.
POLYPORUS 200- DISC 4-4-4.
RUB 15 – 1 DOSE NIGHT B/FOOD.
BRYONIA 30 – 7 DOSES BOIRON – ALTERNATE DAYS B/FOOD NIGHT.
29/12/18: HEADACHE MUCH BETTER. PAIN ALOS MUCH BETTER.
RX:
CARC1M – 3 DOSES – 5 DAYS ONCE B/BRUSH WATER.
LACH 200 – 6PILLS AFTERNOON B/FOOD. ALTERNATE DAYS.
POLYPORUS 200- DISC 4-0-4.
RUB 15 – 1 DOSE NIGHT B/FOOD.
BRYONIA 30 – 7 DOSES BOIRON – ALTERNATE DAYS B/FOOD NIGHT.
PHYTOLACCA+FUCUS+AVENA+ALLIUM SAT Q – 15-15-15 DROPS A/FOOD. WATER.
19/1/19: no headache. Burning in abdomen < night when lying down, after eating within 10 min. must get up at drink buttermilk n sleep. Wt: 74.1 kg.
Rx:
Nat phos 6x 4-4-4-4
Carbo veg 3x 2-2-2-2
PHYTOLACCA+FUCUS+AVENA+ALLIUM SAT Q – 15-15-15 DROPS A/FOOD. WATER.
Rub 15 – 1 tab night b/food.
23/2/19: burning abdomen lot of vomiting and lot of burning. After eating within 15-20 min lot of burning. Headache mild. Sneezing mild. Wt: 74.3 kg.
Rx:
Robinia 200 – 4-4-4 b/food. 3 dram pills b/food.
Lach 200 – 4-0-4 b/food. Alternate days.
Polyporus 200 – disc 4-0-4
Rub 30
Digestol syp 1-0-1 teaspoon a/food. 2 glass bottles
China 200 – 4 doses weekly. b/food mng.
13/4/19: pains occasional if works a lot or journey and walks more. Shoulder pain is a bit more. No headache. Even if it comes less intense. Sneezing alos not much. Gastritis is not there. Weight loss seen. Cracks on feet more. Cuts more. And painful more. Night times more. Sleep reduced a bit. Backpain more. Stitiching started. So pain. < night moe on feet so much pain. lmp: 9th april.5-6 days if delayed otherwise 3 days it will stop.
Rx:
Polyporus 1m/rhustox 1m/bryonia1m – disc 4-0-4
Robinia 30 – 4-4-4 b/food. 3 dram pills b/food.
Calcarea carb 200 – 4-4-4-4 1st and 3rd week 1 dram pills each.
KurchiQ – 15-15-15 a/food water.
Lachesis 1m – 4 doses weekly. b brush morning.
Rub 30 – 1 tab night.
4/5/19: shoulder pain increasing.
Rx:
Lachesis 10m – 1 dose.
Rub 30 – 1 tab night b/food.
Gelsimium1m/lyco 1m – 4-4-4-4 1st and 3rd week.
Nat phos 200 – disc 4-0-4 b/food.
3/8/19: went to tpt fr week after tht getting nausea and not feeling like eating anything. Hunger is there but cant eat. Burning abdomen. Mmotion immediately.
Rx:
Ars alb 200/ipecac 200/nux vom 200- 4-4-4-4 b/food.
Thuja 200 – 4 doses – mng b/food.
Enterodin drops 25-25-25-25 a/food water lukewarm half cup.


bill-com
Businessmodel of Bill.com
Customer Segments
Bill.com has a niche market business model, with a specialized customer segment. The company targets its offerings at small and mid-sized businesses (SMBs).
Value Proposition
Bill.com offers five primary value propositions: convenience, accessibility, performance, risk reduction, and brand/status.
The company offers convenience by simplifying operations. Its solution automates a wide range of payable and receivable processes, saving clients the time needed to search for missing documents, chase after approvers, sign checks, print and mail checks, and look up information for vendors.
The company creates accessibility by offering a wide variety of options. It acts as an add-on feature to accounting software packages (rather than as a standalone option), and it enables integration with most major providers, including QuickBooks, QuickBooks Online, NetSuite, Xero, and Intacct.
The company has demonstrated strong performance through tangible results. It reduces the time spent for clients to receive, approve, and pay their bills online by 50%. When the clients’ customers pay their invoices online, the cash goes into clients’ bank accounts 2-3 times faster than previously.
The company reduces risk through high standards for financial data protection. Examples of its security practices include the following:
- Physical Protections – The company’s servers are located in a high-security facility with biometric access controls and guard and video surveillance 24 hours a day, 365 days a year.
- Network Protections – The company prevents unauthorized electronic access to servers through intrusion-detection devices and firewalls.
- Data Protections – The company protects data with the same encryption tools that banks use.
- Applicaiton Protections – The company utilizes multi-factor authentication (MFA) to prevent fraudulent access to clients‘ Bill.com accounts.
- Payments Protections – The company’s Positive Pay feature works at the bank level to assure that altered checks will not be paid.
- Process Protections – The company has a leading national accounting firm regularly conduct a SOC 1, Type 2 Audit. The company has established a powerful brand as a result of its success. It touts itself as the leading digital business payments firm offering A/P and A/R solutions. It helps more than 1,000,000 network members process more than $26 billion in payments each year. It has many prominent clients, including three of the top ten U.S. banks and 44% of the top 100 accounting firms. Lastly, Bill.com has won 44 major awards for technology innovation and leadership. These include recognition as one of the San Francisco Business Times‘ *“Most Innovative Companies“, a *PC Magazine Editor’s Choice Award, and placement on the CNBC Disruptor 50 and OnDemand Top 100 lists.
Channels
Bill.com’s main channels are its website and its direct sales team. The company promotes its offering through its social media pages and participation in summits and conferences.
Customer Relationships
Bill.com’s customer relationship is primarily of a self-service, automated nature.
Customers utilize the service through the main platform while having limited interaction with employees. The company’s website features a “Resources” section that includes self-help tools such as white papers, case studies, and webinars. The site also provides answers to frequently asked questions.
That said, there is a personal assistance component in the form of phone, e-mail, and live chat support.
Key Activities
Bill.com’s business model entails maintaining a robust cloud platform for its clients.
Key Partners
Bill.com’s main partners are the vendors whose offerings integrate with its solution in order to offer its clients automation services. They are as follows:
- Hubdoc
- Xero
- Intacct
- NetSuite
- QuickBooks for Windows
- QuickBooks Online
- Tallie
- Salesforce.com
- Earth Class Mail
- Tax1099.com The company also maintains the following types of partnerships:
Referral Partners – Bill.com invites third parties to refer its solution to their associates. Referrals that lead to a purchase result in one month’s worth of free service for the referring party and the new customer. The offer is limited up to $150 per month.
Developers – Bill.com invites developers to use its APIs to integrate their products with its solution, where they can then benefit from integrations with the numerous applications on its platform.
Key Resources
Bill.com’s main resource is its proprietary software platform, which serves over 1,000,000 clients.
It depends on its engineering employees to maintain and update the platform and its customer service staff to provide support.
Lastly, as a startup it has relied heavily on funding from outside parties, raising $159.1 million from 19 investors as of April 2015.
Cost Structure
Bill.com has a cost-driven structure, aiming to minimize expenses through significant automation and low-price value propositions.
Its biggest cost driver is likely marketing expenses, a fixed cost. Other major drivers are in the areas of product development and administration, both fixed expenses.
Revenue Streams
Bill.com has two revenue streams:
Subscription Revenues – Revenues generated from fees charged for monthly access to the company’s SaaS platform. Subscription plans are as follows:
- Essentials – Manages payables or receivables. Costs $19 per user per month.
- Team – Manages payables or receivables and accounting software integration. Costs $29 per user per month.
- Corporate – Manages payables and receivables and provides robust workflow options. Costs $49 per user per month.
-
Enterprise – Offers same features as Corporate plan as well as additional integration features. Sales staff must be contacted directly as pricing is customized. Transaction Revenues – Revenues generated from fees charged for the following transactions:
- Send or receive ACH payments - $0.49 each
- Bill.com mails client’s checks or invoices - $1.49 each
- Bill.com conducts client’s payables data entry - $0.50 each


For the upcoming school year, our focus will be on getting HospitalRun’s desktop and web React apps to version 2.0 (release build), which will be a significant improvement for current users compared to the beta build.
Some of the features we worked on last year include viewable diagnoses, inventory modules, and incident notes! For additional information about HospitalRun’s mission and implementation, visit hospitalrun.io.


|
Clause
1.0
Chatopera Language Understanding Service.
|
#include <crfsuite_api.hpp>
Public 成员函数 | |
| Tagger () | |
| virtual | ~Tagger () |
| bool | open (const std::string &name) |
| void | close () |
| StringList | labels () |
| StringList | tag (const ItemSequence &xseq) |
| void | set (const ItemSequence &xseq) |
| StringList | viterbi () |
| double | probability (const StringList &yseq) |
| double | marginal (const std::string &y, const int t) |
Protected 属性 | |
| crfsuite_model_t * | model |
| crfsuite_tagger_t * | tagger |
The tagger class. This class provides the functionality for predicting label sequences for input sequences using a model.
| chatopera::bot::crfsuite::Tagger::Tagger | ( | ) |
Construct a tagger.
|
virtual |
Destruct a tagger.
| void chatopera::bot::crfsuite::Tagger::close | ( | ) |
Close the model.
| StringList chatopera::bot::crfsuite::Tagger::labels | ( | ) |
Obtain the list of labels.
| std::invalid_argument | A model is not opened. |
| std::runtime_error | An internal error. |
| double chatopera::bot::crfsuite::Tagger::marginal | ( | const std::string & | y, |
| const int | t | ||
| ) |
Compute the marginal probability of the label.
| y | The label. |
| t | The position of the label. |
| std::invalid_argument | A model is not opened. |
| std::runtime_error | An internal error. |
| bool chatopera::bot::crfsuite::Tagger::open | ( | const std::string & | name | ) |
Open a model file.
| name | The file name of the model file. |
true
if the model file is successfully opened, false
otherwise (e.g., when the mode file is not found). | std::runtime_error | An internal error in the model. |
| double chatopera::bot::crfsuite::Tagger::probability | ( | const StringList & | yseq | ) |
Compute the probability of the label sequence.
| yseq | The label sequence. |
| std::invalid_argument | A model is not opened. |
| std::runtime_error | An internal error. |
| void chatopera::bot::crfsuite::Tagger::set | ( | const ItemSequence & | xseq | ) |
Set an item sequence. This function sets an item sequence for future calls for viterbi(), probability(), and marginal() functions.
| xseq | The item sequence to be tagged |
| std::invalid_argument | A model is not opened. |
| std::runtime_error | An internal error. |
| StringList chatopera::bot::crfsuite::Tagger::tag | ( | const ItemSequence & | xseq | ) |
Predict the label sequence for the item sequence. This function calls set() and viterbi() functions to obtain the label sequence predicted for the item sequence.
| xseq | The item sequence to be tagged. |
| std::invalid_argument | A model is not opened. |
| std::runtime_error | An internal error. |
| StringList chatopera::bot::crfsuite::Tagger::viterbi | ( | ) |
Find the Viterbi label sequence for the item sequence.
| std::invalid_argument | A model is not opened. |
| std::runtime_error | An internal error. |


Solid Technical Reports
- This version
- https://solidproject.org/TR/
- Created
- Published
- Modified
- Repository
- GitHub
- Issues
MIT License. Copyright © 2019–2021 W3C Solid Community Group.
Abstract
Hi! We’re the Solid Community Group (CG) of the W3C. The purpose of this document is to help readers orient themselves with the activities of the Solid CG.
The Solid CG’s technical reports (TR) include specifications, use cases and requirements, best practices and guidelines, primers and notes about the Solid ecosystem.
Work Items
The aims of the Solid project are in line with those of the Web itself: empowerment towards an equitable, informed and interconnected society
. Solid adds to existing Web standards to realise a space where individuals can maintain their autonomy, control their data and privacy, and choose applications and services to fulfil their needs.
The TRs incorporated have been reviewed following the Solid Process. However, the information in these documents may be subject to change, therefore please see each document’s publication status and versions for further details. You are invited to contribute any feedback, comments, or questions you might have.
| Work Item | Repository | Current Stage | Next Stage | Target | Expected Completion |
|---|---|---|---|---|---|
| Solid Ecosystem | https://github.com/solid/specification | Editor’s Draft | N/A | N/A | N/A |
| Solid Protocol | https://github.com/solid/specification | Editor’s Draft | Published Draft | Community Report | |
| Solid OIDC | https://github.com/solid/authentication-panel | Editor’s Draft | Ecosystem Proposal | Community Report | |
| Web Access Control | https://github.com/solid/web-access-control-spec/ | Unofficial Draft | Editor’s Draft | Community Report | |
| Solid Application Interoperability | https://github.com/solid/data-interoperability-panel | Editor’s Draft | Ecosystem Proposal | Community Report | |
| Shape Trees | https://github.com/shapetrees/specification | Editor’s Draft | Ecosystem Proposal | Community Report | |
| Solid OIDC Primer | https://github.com/solid/authentication-panel | Editor’s Draft | Ecosystem Proposal | Community Report | |
| Authorization Use Cases and Requirements | https://github.com/solid/authorization-panel | Unofficial Draft | Editor’s Draft | Community Report | TBD |
| Authorization Use Cases Survey | https://github.com/solid/authorization-panel | Unofficial Draft | Editor’s Draft | Community Report | TBD |
| Solid Application Interoperability Primer | https://github.com/solid/data-interoperability-panel | Editor’s Draft | Ecosystem Proposal | Community Report | |
| "Expected Completion" date refers to the publication status of "Target". Completion date for "Next Stage" is roughly midway point. |
The Test Suites support the Technical Reports:
| Work Item | Repository | Current Stage | Next Stage | Target | Expected Completion |
|---|---|---|---|---|---|
| Test Suite | https://github.com/solid/test-suite | TBD | TBD | TBD |
Participate
It’s easy to join the CG if you’d like to contribute to the Solid project. Please note that in order to join, you’ll need to request a W3C account if you don’t already have one.
We publish a list of current participants on our W3C page.
The Solid CG conducts all technical work in public, mainly in various repositories of the Solid organisation but also in text chat, periodic teleconferences and face-to-face meetings.
Code of Conduct
Please note that all work and communication within the Solid CG is covered by the Solid Code of Conduct as well as the Positive Work Environment at W3C: Code of Ethics and Professional Conduct.


Batch operations combines the ideas of parameters
and running other specifications. It allows you to run a series of specifications, with
the parameters coming from:
files in a directory
lines in a text file
For example, this can be used to perform the same series of operations on all
files in a directory:
resize image file
convert image file to grayscale
convert image file to a different format
move converted image file
To add a batch operation, add a step to your specification and
select Batch from the Category dropdown. There should only be one
step listed.
The configuration user interface for batch operation allows you to:
add/remove and reorder other specifications
select a directory or file for the batch parameters
Once you have added other specifications, when you edit one of the
specifications, additional context menu items are available:
Batch Parameter
[Directory] name of file including extension eg MyImage.png
[File] line of text from file eg c:\Documents\MyDocuments\MyImage.png
Batch Parameter [Directory]
[Directory] fully qualified path to directory not including trailing backslash eg c:\Documents\MyDocuments
Batch Parameter [FileName]
[Directory] name of file not including extension eg MyImage


--- title: "Code of Conduct" layout: splash classes: - landing - dark-theme ---
Map Camp should be a safe environment for all and we will do everything we can to make sure that it is.
All attendees, speakers, sponsors and volunteers at our conference are required to agree with the following code of conduct. Organisers will enforce this code throughout the event. We expect cooperation from all participants to help ensure a safe environment for everybody.
For general assistance, please join us on Slack or direct message via Twitter - Simon Wardley @swardley or Jane Kingston @JaneKingston.
Our conference is dedicated to providing a harassment-free conference experience for everyone, regardless of gender, gender identity and expression, age, sexual orientation, disability, physical appearance, body size, race, ethnicity, religion (or lack thereof), or technology choices. We do not tolerate harassment of conference participants in any form. Sexual language and imagery is not appropriate for any conference venue, including talks, workshops, parties, Twitter and other online media. Conference participants violating these rules may be sanctioned or expelled from the conference without a refund or compensation at the discretion of the conference organisers.

Participants asked to stop any harassing behaviour are expected to comply immediately.
Sponsors are also subject to the anti-harassment policy. In particular, sponsors should not use sexualised images, activities, or other material. Booth staff (including volunteers) should not use sexualised clothing/uniforms/costumes, or otherwise create a sexualised environment.
If a participant engages in harassing behaviour, the conference organisers may take any action they deem appropriate, including warning the offender or expulsion from the conference with no refund.
If you are being harassed, notice that someone else is being harassed, or have any other concerns, please contact a member of conference staff immediately. Conference staff can be identified as they'll be wearing branded clothing and/or badges.
Conference staff will be happy to help participants contact hotel/venue security or local law enforcement, provide escorts, or otherwise assist those experiencing harassment to feel safe for the duration of the conference. We value your attendance.
We expect participants to follow these rules at conference and workshop venues and conference-related social events.


Convenience wrapper that uses the 'rmarkdown' package to render
small snippets of code to target formats that include both code and output.
The goal is to encourage the sharing of small, reproducible, and runnable
examples on code-oriented websites, such as <https://stackoverflow.com> and
<https://github.com>, or in email. The user's clipboard is the default source
of input code and the default target for rendered output. 'reprex' also
extracts clean, runnable R code from various common formats, such as
copy/paste from an R session.
| Version: |
0.2.1 |
| Depends: |
R (≥ 3.1) |
| Imports: |
callr (≥ 2.0.0), clipr (≥ 0.4.0), fs, rlang, rmarkdown, tools, utils, whisker, withr |
| Suggests: |
covr, devtools, fortunes, knitr, miniUI, rprojroot, rstudioapi, shiny, styler (≥ 1.0.2), testthat (≥ 2.0.0) |
| Published: |
2018-09-16 |
| Author: |
Jennifer Bryan [aut, cre],
Jim Hester [aut],
David Robinson [aut],
Hadley Wickham [aut],
RStudio [cph, fnd] |
| Maintainer: |
Jennifer Bryan <jenny at rstudio.com> |
| BugReports: |
https://github.com/tidyverse/reprex/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://reprex.tidyverse.org,
https://github.com/tidyverse/reprex#readme |
| NeedsCompilation: |
no |
| SystemRequirements: |
pandoc (>= 1.12.3) - http://pandoc.org |
| Materials: |
README NEWS |
| CRAN checks: |
reprex results |


{{ extend 'layout.html'}} {{ block moreincludes }} {{end}}
Sorry you are having trouble. I want to hear about problems you are having but before you create a bug report please Check our FAQ
If you have a question that isn't answered in the FAQ there are a number of places to get help from the Runestone Community. Each of our books has a great community of teachers who are willing to help new people get started. Check This List of community resources!
If you are a new instructor there is a very helpful playlist of videos on YouTube. In the summer of 2020 we did a 4 day virtual workshop for 200+ instructors. I can almost guarantee that one of these videos will answer your questions about running a class on Runestone.OK, You don't have a question and are still running into some problem. Its time to create an issue on our Github Issue Tracker I regret that I am no longer able to respond to problems sent to me in email. Runestone has just grown too big for that. Filing issues on Github creates a public record that others can learn from and help with. Your own problem may already be on Github, if so, you can add more evidence to the record that will help me (or someone else) get to the bottom of the problem more quickly.
Telling me that X does not work doesn’t really help. The truth is runestone.academy gets over 300,000 page views a day from people around the world. So most of the time when someone says “X does not work” it does in fact work fine for most people. This does not make it your fault, it just means I need more information to try to make sense of what is going on.  Imagine if a student sent you the following email:
Hey, You know problem 2 for the homework next week. My program just doesn't work. I'm pretty sure I've done everything right. Can you tell me how to fix it? Thanks, Student
You wouldn't be able to help this student very much, in fact you probably wouldn't have any idea what program that student was even asking about without spending your own valuable time figuring it out. Sadly, It is a pretty good representation of thousands of bug reports I've received. So please, take a few seconds and read or re-read Filing an effective bug report. Thanks.
---------------- Traceback -------------- {{=ticket}}{{ pass }}


Jane Doe
Ikoyi to Bariga
Number of seats: 4
₦350
Jane Doe
Ikoyi to Bariga
Number of seats: 4
₦350
Jane Doe
Ikoyi to Bariga
Number of seats: 4
₦350
Jane Doe
Ikoyi to Bariga
Number of seats: 4
₦350
Jane Doe
Ikoyi to Bariga
Number of seats: 4
₦350
Jane Doe
Ikoyi to Bariga
Number of seats: 4


Basic Example¶
This example just defines the root folder and a job called job1.
jobs/__init__.py¶
from jenni.models import RootFolderBase
class Folder(RootFolderBase):
pass
jobs/job1.py¶
from jenni.models import GroovyJobBase
class Job(GroovyJobBase):
def __init__(self):
super().__init__(description="A very simple job!", trigger_cron_spec="H 8 * * *")
self.code(f"echo 'hello from {self.name}';")
self.include("job1.groovy")
jobs/job1.groovy¶
echo "Hello from job1.groovy"


It should not last long! – Antonio Conte advises Inter players on how to have sex
by FSA EditorInter have made a good start under Antonio Conte, who admitted he has given advise to his players on how to have sex during the season.
Antonio Conte has revealed he advises his Inter players on how to have sex during the football season in order to maximise their on-field performance.
The 50-year-old, who won the Premier League title with Chelsea in 2017 and three Serie A titles with Juventus, took over as Inter boss in May.
Inter have made an impressive start to the campaign, winning 10 of their opening 12 league matches to leave themselves a point adrift of leaders Juventus.
A part of their success could be down to the bizarre off-field demands of Conte, the Italian coach admitting he urges his players not to over-exert themselves in the bedroom.
“During the season, I advise my players to have sex for short periods and with the minimum of effort,” he told L’Equipe.
“And preferably with their wives, because if with others, well, that needs extra action.”
Inter are back in action on Saturday after a two-week break with a trip to Torino, who ended a six-game winless run last time out.
Conte confirmed at Friday’s pre-match news conference that Stefano Sensi, Roberto Gagliardini and Kwadwo Asamoah are still injured, but Danilo D’Ambrosio is back in contention.
“When there is a break it is difficult to prepare for games,” he told reporters. “Yesterday I welcomed back all the players who were away with their national teams.
“Sensi, Gagliardini and Asamoah are unavailable. Sensi missed last month and has had a few problems. We are trying to get him back to his best condition so that he can help the group when back.
“D’Ambrosio has recovered and must now get back to his best. He is a very versatile player, a joker in the pack that can be used in the centre and out wide. It’s very important to have players like him to count on.”
Lukaku is expected to lead the line for Inter in Turin and Conte is happy with the Belgium international, despite him firing blanks in back-to-back games before the two-week break.
“He is very important to us, as is Lautaro [Martinez],” Conte added. “Both players are doing very well – there is little else more to say.
“They are both doing great work for the team and I am very happy. I hope they can continue in the same way.”


Version 1 - dropdown menu with reaction to input
Version 2 - limit by class/link name (radio-buttons + dropdown)
Select link and class:
Version 3 - composite version (link - direction - class)


Vegan parents accused of starving 18-month-old son to death on raw food diet
by Jackie SaloTwo vegan parents from Florida are accused of starving their 18-month-old son to death by feeding him only raw fruits and vegetables, officials said.
Sheila O’Leary, 35, and Ryan O’Leary, 30, face charges of manslaughter and child neglect in the death last month of their son — who weighed just 17 pounds, the Florida Myers News-Press reported.
Police said the mom called 911 on Sept. 27 to report that the boy was cold and not breathing, while her husband attempted to resuscitate him at their Cape Coral home.
When authorities arrived, the malnourished child was pronounced dead, according to the report.
The mom told officers that the family was vegan and her four kids were on a raw food diet that consisted of mangoes, rambutans, bananas and avocados.
She told authorities that before the toddler’s death, he had not eaten solid food in a week but was being breastfed. She said she believed that was due to teething.
Both parents turned themselves in to police shortly after the medical examiner determined on Nov. 6 that the boy died from malnutrition, the Florida Myers News-Press reported.
They were booked into Lee County jail, where their bond was set at $250,000. The couple is scheduled to be arraigned Dec. 9.


Searches records in the grid.
search(field, value)
| field |
string, name of the search field |
| value |
string, value of the search field |
OR
search(searches, [logic])
| searches |
array, array of search objects |
| logic |
string, defines search logic AND/OR (optional) |
Returns undefined.
Description
This method will generate .searchData array of objects, apply the search and refresh the grid.
If the data source is local, it will call .localSearch method. If the data source is remote
it will submit search data to the server side to apply the search.
If the first argument field is string then it is the name of the
search field or 'all' to search all applicable fields (search fields where data type matches). The second argument
value is the search string.
If the first argument is an array and second optional argument is a string (AND or OR), then the method will assume that it is the array
of search objects. This way you can submit search on multiple fields at the same time.
The search object has the following structure:
If you have a grid defined in the following way:
You can do following searches:


Ghost of the Railroad Engineer:
How did you come to be here? I have nothing to tell you.
(You must be above level 39 and have cleared the Beyond the Sea of Stars quest in order to perform this quest.)


Upload artboards to Google Slides with ease. For a hassel free presentation making.
Sign in with Google


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Let everyone know what you're passionate about by adding it to your profile
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🏊♀ Swimming
🏄♀ Surfing
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🏌♀ Golf
🤾♀ Handball
🤺 Fencing
⛹♀ Basketball
🤸♀ Calisthenics
🤼♀ Wrestling
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🥏 Ultimate Frisbee
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⚾ Baseball
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💃 Dancing
🫁 Cardio
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Well, today I'm going to tell you a nice joke about my first trying with programming and writing code.
On 2018, I started my first trip with programming and codes , It was very silly trip. I started with
Unity Software to make games. I worked for long time , hours days months . And I did the impossible
to make it success. And finally , I finished and released it on Google Play Console for Android,
I paid more than 100$ to make it success . And during two years my income from this game is 2$.
It was very bad experience with programming , but I did not stop there , I have learned more about programming .
And now, I'm a member in a scholarship for programming and design WebPages in full stack at LTUC .


secv_guis.bimask_app package¶
Submodules¶
secv_guis.bimask_app.dialogs module¶
This module contains definitions for different kinds of dialogs and related components that are specific for this application.
-
class
secv_guis.bimask_app.dialogs.
AboutDialog
[source]¶ Bases:
secv_guis.dialogs.InfoDialog
Info dialog showing about section
-
staticMetaObject
= <PySide2.QtCore.QMetaObject object>¶
-
-
class
secv_guis.bimask_app.dialogs.
InstructionsDialog
[source]¶ Bases:
secv_guis.dialogs.InfoDialog
Info dialog showing instructions
-
staticMetaObject
= <PySide2.QtCore.QMetaObject object>¶
-
-
class
secv_guis.bimask_app.dialogs.
KeymapsDialog
(mappings, parent=None)[source]¶ Bases:
secv_guis.dialogs.FlexibleDialog
Info dialog showing keymap list
-
staticMetaObject
= <PySide2.QtCore.QMetaObject object>¶
-
-
class
secv_guis.bimask_app.dialogs.
SaveWarningDialog
[source]¶ Bases:
secv_guis.dialogs.InfoDialog
A dialog to be prompted when trying to delete unsaved changes. Usage example:
self.dialog = SaveWarningDialog() user_wants_to_remove = bool(self.dialog.exec_()) ...
-
staticMetaObject
= <PySide2.QtCore.QMetaObject object>¶
-
-
class
secv_guis.bimask_app.dialogs.
SavedInfoDialog
(save_dict, timeout_ms=500)[source]¶ Bases:
secv_guis.dialogs.InfoDialog
Informative dialog telling about saved paths.
-
staticMetaObject
= <PySide2.QtCore.QMetaObject object>¶
-
-
class
secv_guis.bimask_app.dialogs.
SavedStateTracker
[source]¶ Bases:
object
Create one of these every time a new state is loaded, call
edited
when the state has been changed, andsaved
when saved.The
saved
function will optionally show an informative dialog.Then call
delete
when the state is intended to be deleted. The method makes sure that unsaved changes are only deleted with user’s confirmation.Note for developers:
State machines with callbacks are a classic recipe for spaghetti alla callback inferno. Here we didn’t provide a structured way to handle GUI state, so we are applying this as high in the API as possible. It works NOW, but consider restructuring it if it gets in the way.
secv_guis.bimask_app.main_window module¶
This module contains the logic and widgets pertaining to the main window of the bimask app: An app that allows displaying an image, editing a mask on it and also displaying/editing a preannotation mask.
It can be used to efficiently annotate large images with pixel precision. Check instructions.txt for more details.
-
class
secv_guis.bimask_app.main_window.
CrackAnnotPaintForm
(main_window, brushes, max_brush_size=100, parent=None, thresh_min=0, thresh_max=1, thresh_num_steps=100)[source]¶ Bases:
secv_guis.base_widgets.MaskPaintForm
A
MaskPaintForm
that holds a reference to the app’s main window and connects its callbacks with the main window’s corresponding components.Setter
-
staticMetaObject
= <PySide2.QtCore.QMetaObject object>¶
-
class
secv_guis.bimask_app.main_window.
FileLists
(parent=None, img_extensions=['.png', '.jpg', '.jpeg'], mask_extensions=None, preannot_extensions=None)[source]¶ Bases:
PySide2.QtWidgets.QWidget
A cluster of 3 file lists: one for images, one for masks and one for preannotations.
-
staticMetaObject
= <PySide2.QtCore.QMetaObject object>¶
-
-
class
secv_guis.bimask_app.main_window.
IntegratedDisplayView
(main_window, scale_percent=15)[source]¶ Bases:
secv_guis.masked_scene.DisplayView
This class implements the main component of the main window: it features a view of the image and the masks, together with a set of operations that can be done on them (painting, updating…), and the callback mechanisms to trigger those operations.
-
change_preannot_pval
(keep_p_value, discard_p_value=0.5)[source]¶ Updates the preannot->mask threshold.
-
clickdrag_action
(x, y)[source]¶ Paint to the currently selected mask, with the currently selected brush type, at the given position. The given
x, y
position is in ‘scene coordinates’, i.e. the position from a mouse event has to be translated as follows:xpos, ypos = self.mapToScene(event.pos()).toTuple() self.clickdrag_action(xpos, ypos)
-
mask_from_path
(mask_path, rgba)[source]¶ Parameters: - mask_path – Path to an image containing a binary mask, where zero pixels are considered false and non-zero true.
- rgba – Color of the loaded mask
Loads a binary mask into the scene as an RGBA-colored mask.
-
new_image
(img_path, initial_mask_color=(219, 54, 148, 150), initial_preannot_color=(102, 214, 123, 100))[source]¶ If successful, removes all elements from the scene and the undo stack, and loads a fresh image and masks. If there are unsaved changes, a dialog asking for confirmation will pop up.
Returns: True if the action completed successfully, False if the user decides to abort.
-
on_left_release
(event)[source]¶ If there is an open macro command, closes it and adds it to the undo stack
-
on_move
(event, has_left, has_mid, has_right, this_pos, last_pos)[source]¶ Callback implementation, calls
clickdrag_action
if moving while pressing left.
-
preannot_from_path
(preannot_path, rgba, keep_p_value=0.05, discard_p_value=0.5, normalize=False)[source]¶ This method is prototype-ish: It loads an
.npz
file with and ‘entropy’ field, expected to have a numpy float matrix with same shape as the image.
-
staticMetaObject
= <PySide2.QtCore.QMetaObject object>¶
-
-
class
secv_guis.bimask_app.main_window.
IntegratedSaveForm
(main_window, default_path=None, save_dialog_timeout_ms=1000)[source]¶ Bases:
secv_guis.base_widgets.SaveForm
A
SaveForm
that implements this app’s logic, namely, it features 2 masks, one for annot and one for preannot, and saves them as B&W png.-
save_bool_arr_as_img
(arr, outpath, overwrite_existing=False)[source]¶ Output: RGB PNG image where false is black (0, 0, 0) and true is white (255, 255, 255).
-
save_masks
(states, suffixes, overwrite)[source]¶ Overriden method that we don’t call directly. See
SaveForm
for interface details.
-
staticMetaObject
= <PySide2.QtCore.QMetaObject object>¶
-
-
class
secv_guis.bimask_app.main_window.
MainWindow
(parent=None, initial_mask_color=(255, 54, 76, 150), initial_preannot_color=(102, 214, 123, 100), max_brush_size=200)[source]¶ Bases:
PySide2.QtWidgets.QMainWindow
This is the central widget for the bimask application. It is a composition of all the used elements, together with the logic that binds them.
-
DISCARD_P_VALUE
= 0.5¶
-
ERASER_TXT
= 'Eraser'¶
-
MASKED_PAINTER_TXT
= 'Masked painter'¶
-
PAINTER_TXT
= 'Painter'¶
-
POINT_LIST_TXT
= 'Points'¶
-
THRESH_MAX
= 1e-09¶
-
THRESH_MIN
= 1e-07¶
-
THRESH_NUM_STEPS
= 400¶
-
keymaps
()[source]¶ Returns: A dictionary in the form name: QtGui.QKeySequence
, where theDefine this GUI’s specific key mappings. Note that this method can be overriden to return a different mapping, but the
name``s have to remain identical, in order to be recognized by ``_add_keymaps
.
-
staticMetaObject
= <PySide2.QtCore.QMetaObject object>¶
-
-
secv_guis.bimask_app.main_window.
exp_lambda_estimator
(elts)[source]¶ Parameters: elts – A collection of elements (can also be numpy). Given a set of elements, assumed to be exponentially distributed, returns the unbiased estimator for the lambda parameter of the exp distribution.
-
secv_guis.bimask_app.main_window.
exp_threshold
(keep_p_value, lmbd)[source]¶ Parameters: - keep_p_value – Scalar in range
(0, 1]
- elts – collection of values, assumed to be sampled from an exponential distribution.
Returns: A threshold
t
, so that the integral forexp(lambda)
from t to infinity equalskeep_p_value
.This function assumes that the given
elts
have been sampled from an exponential distribution. Then inferes lambda, using the unbiased ML estimator (see https://en.wikipedia.org/wiki/Exponential_distribution) and returns the thresholdt
that fulfillskeep_p_value
for the distribution abovet
.- keep_p_value – Scalar in range
-
secv_guis.bimask_app.main_window.
pmap_to_mask
(pmap, keep_highest_pval=0.05, discard_lowest_pval=0.5)[source]¶ This method performs the following steps:
- Assuming that pmap values are exponentially distributed, extracts the
- unbiased lambda parameter and the cumulative distribution.
- Applies threshold to the given low/high p-values
This method uses the standard connected component extraction mechanism in Python, i.e.
scipy.ndimage.measurements.label
andskimage.measure.regionprops
.Parameters: - pmap – A float array of shape
h, w
. - keep_highest_pval – The p-value designing the amount of top ‘pmap’ scores to be surely kept.
- discard_lowest_pval – The p-value designing the amount of bottom ‘pmap’ scores to be surely discarded.
Returns: The output mask