1 June 2004
As of 31 May 2004, the health authorities of Yambio County have reported a total of 25 cases, including 6 deaths of Ebola haemorrhagic fever (EHF) in Yambio, Western Equatoria, south Sudan.
Enhanced surveillance, follow-up of contacts and case management are continuing. As part of the Global Outbreak Alert and Response Network (GOARN), additional staff are assisting the team with social mobilization activities in the surrounding area.
For more information about EHF, please see the WHO fact sheet


Chapter 27 Linear Regression and Broom for Tidying Models
Linear regression allows you to:
estimate the effects of predictors (independent variables) on an outcome (dependent variable), assuming that there is a linear relationship
Make predictions about future cases (patients) with their measured predictors on this continuous outcome.
Let’s look at a simple linear model to predict annual health care expenses.
library(tidyverse)
library(mlbench)
library(broom)
library(cutpointr)
##
## Attaching package: 'cutpointr'
## The following object is masked from 'package:bayestestR':
##
## auc
library(janitor)
library(easystats)
library(medicaldata)
<- data("PimaIndiansDiabetes2", package = "mlbench")
dm_data
# build model, all variables
<- glm(diabetes ~ .,
dm_mod data = PimaIndiansDiabetes2,
family = "binomial")
# output
summary(dm_mod)
##
## Call:
## glm(formula = diabetes ~ ., family = "binomial", data = PimaIndiansDiabetes2)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.7823 -0.6603 -0.3642 0.6409 2.5612
##
## Coefficients:
## Estimate Std. Error z value
## (Intercept) -10.0407392 1.2176743 -8.246
## pregnant 0.0821594 0.0554255 1.482
## glucose 0.0382695 0.0057677 6.635
## pressure -0.0014203 0.0118334 -0.120
## triceps 0.0112214 0.0170837 0.657
## insulin -0.0008253 0.0013064 -0.632
## mass 0.0705376 0.0273421 2.580
## pedigree 1.1409086 0.4274337 2.669
## age 0.0339516 0.0183817 1.847
## Pr(>|z|)
## (Intercept) < 0.0000000000000002 ***
## pregnant 0.13825
## glucose 0.0000000000324 ***
## pressure 0.90446
## triceps 0.51128
## insulin 0.52757
## mass 0.00989 **
## pedigree 0.00760 **
## age 0.06474 .
## ---
## Signif. codes:
## 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 498.10 on 391 degrees of freedom
## Residual deviance: 344.02 on 383 degrees of freedom
## (376 observations deleted due to missingness)
## AIC: 362.02
##
## Number of Fisher Scoring iterations: 5
#tidy version
tidy(dm_mod)
## # A tibble: 9 × 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) -10.0 1.22 -8.25 1.64e-16
## 2 pregnant 0.0822 0.0554 1.48 1.38e- 1
## 3 glucose 0.0383 0.00577 6.64 3.24e-11
## 4 pressure -0.00142 0.0118 -0.120 9.04e- 1
## 5 triceps 0.0112 0.0171 0.657 5.11e- 1
## 6 insulin -0.000825 0.00131 -0.632 5.28e- 1
## 7 mass 0.0705 0.0273 2.58 9.89e- 3
## 8 pedigree 1.14 0.427 2.67 7.60e- 3
## 9 age 0.0340 0.0184 1.85 6.47e- 2
# model performance
glance(dm_mod)
## # A tibble: 1 × 8
## null.de…¹ df.null logLik AIC BIC devia…² df.re…³ nobs
## <dbl> <int> <dbl> <dbl> <dbl> <dbl> <int> <int>
## 1 498. 391 -172. 362. 398. 344. 383 392
## # … with abbreviated variable names ¹null.deviance,
## # ²deviance, ³df.residual
# augment data with fitted predictions and residuals
<- augment(dm_mod) %>%
dm_data_plus mutate(pct_prob = 100 * plogis(.fitted)) %>%
relocate(diabetes, .fitted, pct_prob) %>%
arrange(-.fitted)
# select a cut point for classification
<- dm_data_plus %>%
cp cutpointr(pct_prob, diabetes,
pos_class = "pos",
method= maximize_metric,
metric = sum_sens_spec)
## Assuming the positive class has higher x values
cp
## # A tibble: 1 × 16
## direction optimal_cutpoint method sum_sens_spec
## <chr> <dbl> <chr> <dbl>
## 1 >= 28.5839 maximize_metric 1.57504
## acc sensitivity specificity AUC pos_class
## <dbl> <dbl> <dbl> <dbl> <chr>
## 1 0.772959 0.830769 0.744275 0.862361 pos
## neg_class prevalence outcome predictor data
## <fct> <dbl> <chr> <chr> <list>
## 1 neg 0.331633 diabetes pct_prob <tibble [392 × 2]>
## roc_curve boot
## <list> <lgl>
## 1 <rc_ctpnt [393 × 10]> NA
summary(cp)
## Method: maximize_metric
## Predictor: pct_prob
## Outcome: diabetes
## Direction: >=
##
## AUC n n_pos n_neg
## 0.8624 392 130 262
##
## optimal_cutpoint sum_sens_spec acc sensitivity
## 28.5839 1.575 0.773 0.8308
## specificity tp fn fp tn
## 0.7443 108 22 67 195
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean
## Overall 0.8690932 3.071251 8.953085 22.94296 33.16327
## neg 0.8690932 2.674187 6.392249 13.48437 21.10577
## pos 3.7635587 14.863216 34.854283 62.18036 57.46376
## 3rd Qu. 95% Max. SD NAs
## 53.11714 88.92870 99.46861 28.45645 0
## 28.96611 69.31582 97.91551 20.49784 0
## 80.93884 92.17256 99.46861 26.71998 0
plot(cp)
plot_metric(cp)
# classify based on cut point
<- dm_data_plus %>%
dm_data_plus mutate(pred_yes_dm =
case_when(pct_prob > cp$optimal_cutpoint ~ "pred_yes_dm",
TRUE ~ "pred_no")) %>%
relocate(pred_yes_dm, .after =pct_prob)
# check confusion matrix
%>%
dm_data_plus tabyl(diabetes, pred_yes_dm) %>%
adorn_totals("both") %>%
adorn_percentages() %>%
adorn_pct_formatting()
## diabetes pred_no pred_yes_dm Total
## neg 74.4% 25.6% 100.0%
## pos 17.7% 82.3% 100.0%
## Total 55.6% 44.4% 100.0%
#check model performance
::check_model(dm_mod, panel = FALSE) performance
## Variable `Component` is not in your data frame :/
# use panel = TRUE in Rmarkdown to get 2x3 panels for 6 plots
#
::model_performance(dm_mod) performance
## # Indices of model performance
##
## AIC | AICc | BIC | Tjur's R2 | RMSE | Sigma | Log_loss | Score_log | Score_spherical | PCP
## --------------------------------------------------------------------------------------------------------
## 362.021 | 362.492 | 397.763 | 0.364 | 0.376 | 0.948 | 0.439 | -74.015 | 0.009 | 0.718
#try a different model
<- glm(diabetes ~ glucose + mass + pedigree + age,
dm_mod2 data = PimaIndiansDiabetes2,
family = "binomial")
# build a really simple (NULL) model as a baseline
<- glm(diabetes ~ 1,
dm_mod3 data = PimaIndiansDiabetes2,
family = "binomial")
summary(dm_mod3)
##
## Call:
## glm(formula = diabetes ~ 1, family = "binomial", data = PimaIndiansDiabetes2)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.9265 -0.9265 -0.9265 1.4511 1.4511
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.62362 0.07571 -8.237 <0.0000000000000002
##
## (Intercept) ***
## ---
## Signif. codes:
## 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 993.48 on 767 degrees of freedom
## Residual deviance: 993.48 on 767 degrees of freedom
## AIC: 995.48
##
## Number of Fisher Scoring iterations: 4
# compare models
# compare_performance(dm_mod, dm_mod2, dm_mod3, rank = TRUE)
# plot(compare_performance(dm_mod, dm_mod2, dm_mod3, rank = TRUE)) + labs(subtitle = "Larger Area is Better")
# plot(compare_performance(dm_mod, dm_mod2, rank = TRUE)) + labs(subtitle = "Larger Area is Better")
#test_performance(dm_mod, dm_mod2, dm_mod3)
# save model to RDS
saveRDS(dm_mod, "dm_mod.RDS")
This is a simple web application that lets users who are not familiar with R use your model.
They can enter values for the 3 predictor variables (gender, age, BMI) for a new patient, and predict their annual health insurance expenses in dollars. This web app produces a table with the inputs, and shows the output when you click the PREDICT button. Try it out here:
Model Predictions Shiny Web App.
Enter different predictor data points, and recalculate the expenses.
You can click on the About tab for an explanation of the model, and even explore the publicly shared underlying code on GitHub (link in the About tab).
We will walk through how to build, test, and share your own models in this chapter.
27.1 Packages needed
- {tidyverse}
- {medicaldata}
- {broom}
- {easystats} you can install this one (Not on CRAN) with
install.packages("easystats", repos = "https://easystats.r-universe.dev")
- {performance}
- {insight}
- {gtsummary}
Note that the base modeling function lm() comes from the {stat} package, which loads by default when you start R.
27.2 Building a simple base model with {lm}
The simplest model is called the null model, with no predictors. This model uses the mean value of the outcome to estimate it.
In the blood_storage (prostate cancer) dataset in {medicaldata}, the mean time to recurrence is 32.92 months.
To build a simple null model, you will need two main arguments to the lm() function:
the formula
the data
The formula follows the format dependent_variable ~ independent_variables.
Note that the data argument is not the first argument, so it does not automatically play well with pipes.
You can pipe in data if you make the data argument explicit, and set it to data = .
Let’s look at a simple example:
Copy the code chunk below and run it in your RStudio Console.
::blood_storage %>%
medicaldatalm(TimeToRecurrence ~ NULL, data = .)
##
## Call:
## lm(formula = TimeToRecurrence ~ NULL, data = .)
##
## Coefficients:
## (Intercept)
## 32.92
The output tells you the Call
- the model being run, and then all the coefficients.
In the case of the NULL model, the only coefficient is the intercept.
This intercept is equal to the mean value of the outcome variable, TimeToRecurrence, in months.
With no other predictor variables, this is the best estimate available for time to recurrence.
We can also output the results as a nice tibble, using the tidy() function from the {broom} package.
::blood_storage %>%
medicaldatalm(TimeToRecurrence ~ NULL, data = .) %>%
::tidy() broom
## # A tibble: 1 × 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) 32.9 1.61 20.5 1.04e-59
This model has only one term, the intercept.
It estimates every value of time to recurrence with the mean, 32.91.
This is a pretty poor model, but is a place to start.
Let’s look at how good this model is, using another function from the {broom} package.
We can glance() our model, again output into a nice tibble.
::blood_storage %>%
medicaldatalm(TimeToRecurrence ~ NULL, data = .) %>%
::glance() broom
## # A tibble: 1 × 12
## r.squared adj.r…¹ sigma stati…² p.value df logLik AIC
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0 0 28.6 NA NA NA -1502. 3009.
## # … with 4 more variables: BIC <dbl>, deviance <dbl>,
## # df.residual <int>, nobs <int>, and abbreviated variable
## # names ¹adj.r.squared, ²statistic
The r.squared and adj.r.squared are both 0, so we are capturing none of the variation in the data with this null model. The log likelihood is -1502, and the AIC 3009, and BIC 3016 (these are both high because this is a crummy model).
AIC is Akaike’s Information Criterion, and estimates the out-of-sample prediction error and relative quality of a statistical model. A higher number indicates more information lost. Lower numbers for AIC = higher quality models.
BIC is the Bayesian Information Criterion, which like AIC, penalizes models for the number of parameters to reduce overfitting. BIC also considers the number of observations in the data, which AIC does not. Lower values of BIC are better, and BIC is generally always higher than AIC, but absolute values do not matter, only relative values when comparing models on the same dataset for the same outcome. If we improve the model (with useful predictor variables), the BIC should go down.
Let’s add some predictors: Age, TVol (tumor volume), and sGS (surgical Gleason score), and see if we do better.
::blood_storage %>%
medicaldatalm(TimeToRecurrence ~ Age + TVol + sGS, data = .) %>%
::glance() broom
## # A tibble: 1 × 12
## r.squared adj.r…¹ sigma stati…² p.value df logLik AIC
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.0130 0.00334 28.5 1.34 0.260 3 -1472. 2954.
## # … with 4 more variables: BIC <dbl>, deviance <dbl>,
## # df.residual <int>, nobs <int>, and abbreviated variable
## # names ¹adj.r.squared, ²statistic
We are now explaining some (about 0.33% = 100*the adjusted R-squared) of the variation with this predictor, and the log likelihood (-1472) got closer to zero, and the AIC (2954) and BIC (2972) were reduced, showing that this is a better model than the NULL model (though still not great).
<IP_ADDRESS> Key Takeaways
use lm() to build the model
argument: formula = outcome ~ predictor1 + predictor2 + …
argument: data = . with the pipe
tidy() from {broom} to see the model table of estimates
glance() from {broom}to see measures of model accuracy
<IP_ADDRESS> Your turn with licorice!
Pipe the licorice data into an lm() function, with a formula argument and the data = . argument. Use the outcome of pacu30min_throatPain. Use predictors like intraOp_surgerySize, treat, preOp_pain, preOp_gender, and preOp_smoking.
Then pipe the result into the function tidy() to see the model, and (separately) into the function glance() to evaluate the model quality.
Copy the code chunk below into RStudio as a start. Use tidy() to see the model table, and glance() to look at the performance of the model.
::licorice_gargle %>%
medicaldatalm(formula = pacu30min_throatPain ~ var1 + var2 + var3,
data = .)
::licorice_gargle %>%
medicaldatalm(formula = pacu30min_throatPain ~ intraOp_surgerySize +
+ preOp_pain + preOp_gender +
treat
preOp_smoking,data = .) %>%
tidy()
## # A tibble: 6 × 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) 0.376 0.365 1.03 0.304
## 2 intraOp_surgerySize 0.316 0.143 2.21 0.0283
## 3 treat -0.690 0.154 -4.48 0.0000117
## 4 preOp_pain 1.96 0.835 2.35 0.0197
## 5 preOp_gender -0.265 0.160 -1.65 0.100
## 6 preOp_smoking 0.0652 0.0956 0.682 0.496
::licorice_gargle %>%
medicaldatalm(formula = pacu30min_throatPain ~ intraOp_surgerySize +
+ preOp_pain + preOp_gender +
treat
preOp_smoking,data = .) %>%
glance()
## # A tibble: 1 × 12
## r.squared adj.r…¹ sigma stati…² p.value df logLik AIC
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.143 0.124 1.17 7.59 1.30e-6 5 -364. 741.
## # … with 4 more variables: BIC <dbl>, deviance <dbl>,
## # df.residual <int>, nobs <int>, and abbreviated variable
## # names ¹adj.r.squared, ²statistic
<IP_ADDRESS> Interpreting the Model Estimates
For the full model with 5 predictors (shown in the hidden solution above - Click the button to show it), you get a tidied tibble with 6 rows and 5 columns. These are
- The first column is the
term
- these include the (Intercept) and each of the predictors you called in the model. - the second column is the
estimate
. This is the point estimate of the effect of each predictor in the multivariable model. For theintraOp_surgerySize
predictor, this is 0.316. This means that for each unit or level increase ofintraOp_surgerySize
, which is defined on a 1-3 scale from small to large, thepacu_30min_throat pain
(on a 0-10 scale), increases by 0.316 points. So a large surgery (2 levels larger than small) will result in, on average, apacu_30min_throat pain
score 0.632 points higher than a small surgery. - similarly, the estimate for
preop_gender
is -0.265. This means that for each 1 point increase in the level ofpreop_gender
, coded as 0=male, 1=female, thepacu_30min_throat pain
score goes doen by -0.265 points. In this case, that means that the averagepacu_30min_throat pain
score in females was 0.265 points lower than in males. - the std.error column tells you about the variance of this estimate, and can help you calculate confidence intervals around the point estimated if needed.
- the statistic is the t value for the estimate, which allows you to calculate p values with a t test. Values with a large absolute value (farther from zero) imply a stronger effect. Values of the statistic > 1.96 (absolute value) correspond to a p value < 0.05.
- the p value is the significance of the estimate for that particular predictor variable. Low values (often < 0.05) are considered significant for traditional, historical reasons (it is an arbitrary cutoff).
<IP_ADDRESS> Your turn with supra!
Use the supraclavicular dataset to build a model with the outcome onset_sensory
, with predictors (independent variables) age, bmi, gender, and group.
Output the regression table with tidy() and the model measures with glance()
Copy the code chunk below into RStudio as a start
::supraclavicular %>%
medicaldatalm(formula = onset_sensory ~ var1,
data = .) %>%
glance()
::supraclavicular %>%
medicaldatalm(formula = onset_sensory ~ age + bmi + gender + group,
data = .) %>%
glance()
## # A tibble: 1 × 12
## r.squa…¹ adj.r.…² sigma stati…³ p.value df logLik AIC
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.0356 -0.00504 11.5 0.876 0.481 4 -383. 779.
## # … with 4 more variables: BIC <dbl>, deviance <dbl>,
## # df.residual <int>, nobs <int>, and abbreviated variable
## # names ¹r.squared, ²adj.r.squared, ³statistic
27.2.1 Producing manuscript-quality tables with {gtsummary}
Let’s take your model above, and rather than pipe it into tidy() or glance(), pipe it into the tbl_regression() function from the {gtsummary} package.
::supraclavicular %>%
medicaldatalm(formula = onset_sensory ~ age + bmi + gender + group,
data = .) %>%
tbl_regression()
| Characteristic | Beta | 95% CI1 | p-value |
|---|---|---|---|
| age | -0.02 | -0.20, 0.15 | 0.8 |
| bmi | 0.00 | -0.39, 0.38 | >0.9 |
| gender | 3.0 | -1.9, 7.8 | 0.2 |
| group | 2.4 | -2.2, 7.1 | 0.3 |
| 1 CI = Confidence Interval |
This produces a nice looking table, suitable for Rmarkdown documents, with output to Word or Powerpoint. You can even convert this to other formats:
to a tibble with as_tibble()
to a gt object with as_gt() then use gt formatting
to a flextable object with as_flextable() then add formatting with flextable
27.3 Is Your Model Valid?
Key assumptions of linear regression
Homogeneity of variance (homoscedasticity): The error variance should be constant
Linearity: the relationships between the predictors and the outcome variable should be linear
Independence: The errors associated with one observation are not correlated with the errors of any other observation
Normality: the errors should be normally distributed. Technically normality is necessary only for hypothesis tests to be valid.
These assumption can be checked easily with the {performance} package in the {easystats} meta-package ({tidyverse} is another meta-package of packages).
In the code chunk below, we assign the model to the object name supra_model
, and then run check_model
from the {performance} package.
<- medicaldata::supraclavicular %>%
supra_model lm(formula = onset_sensory ~ age + bmi + gender + group,
data = .)
::check_model(supra_model, panel = FALSE) performance
## Variable `Component` is not in your data frame :/
This produces a nice set of six plots in the Plots tab) with some guidance in the subtitles on how to interpret the plots.
There is a less pretty version in base R, using plot(model_name)
, which also works to produce four of these 6 plots.
You can also formally test for heteroscedasticity.
The variance of your residuals should be homogenous.
check_heteroscedasticity(supra_model)
## OK: Error variance appears to be homoscedastic (p = 0.427).
A green output that starts with OK
for check_heteroscedasticity
, indicating homoscedasticity (homgeneous residual variance), is good.
27.4 Making Predictions with Your Model
We can use the linear model to make predictions about the individual observations in our data, or in future data.
Let’s start with adding model predictions to each observation in our dataset.
This is often called the training data
as it was the data the model was trained on.
You can add predictions (fitted results) to your dataframe with the augment() function from the {broom} package.
We augment
this dataframe with the model predictions, and then relocate
them to the beginning (leftmost columns) of the tibble.
<- augment(supra_model) %>%
supra_data_plus relocate(onset_sensory, .fitted, .resid) %>%
arrange(-.fitted)
supra_data_plus
## # A tibble: 100 × 12
## onset_s…¹ .fitted .resid .rown…² age bmi gender group
## <dbl> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 19 16.5 2.46 52 18 22.1 1 2
## 2 10 16.5 -6.51 99 19 24.4 1 2
## 3 14 16.5 -2.50 24 19 25.1 1 2
## 4 4 16.5 -12.5 6 21 22.0 1 2
## 5 8 16.3 -8.30 87 28 30.4 1 2
## 6 9 16.3 -7.27 74 31 21.0 1 2
## 7 6 16.3 -10.3 19 28 39.8 1 2
## 8 39 16.2 22.8 39 31 29.6 1 2
## 9 38 16.2 21.8 48 32 24.4 1 2
## 10 3 16.2 -13.2 43 32 35.4 1 2
## # … with 90 more rows, 4 more variables: .hat <dbl>,
## # .sigma <dbl>, .cooksd <dbl>, .std.resid <dbl>, and
## # abbreviated variable names ¹onset_sensory, ².rownames
The dataframe supra_data_plus
includes a prediction of the outcome (.fitted
) for each observation.
We can compare these predictions to the outcome (onset_sensory
) and see how the residuals (.resid
) are calculated (onset_sensory minus .fitted
).
The Cook’s D variable (.cooksd
) is a measure of how large the effect on the model would be if you deleted that particular observation.
Large values for Cook’s distance sugest that these observations are outliers that pull the model in one direction (have high leverage), and indicate an influential data point.
Review any observation with a .cooksd
> 1 carefully.
27.4.1 Predictions from new data
You can also input new observations (in a data frame) to the model, and predict the outcome for these observations. First, we need to create a dataframe that matches the predictor variables for the supra_model. You might get a dataframe of new observations from a colleague. It is important that this is in the same format, with exactly the same variable names as the original data.
<- tibble(age = c(27, 38, 51),
new_data bmi = c(30.4, 34.2, 41.1),
gender = c(2, 1, 1),
group = c(2, 1, 2))
new_data
## # A tibble: 3 × 4
## age bmi gender group
## <dbl> <dbl> <dbl> <dbl>
## 1 27 30.4 2 2
## 2 38 34.2 1 1
## 3 51 41.1 1 2
To make predictions with the new data, you use the base {stats} function predict(), with arguments for the model, and the new data.
predict(object = supra_model,
newdata = new_data)
## 1 2 3
## 19.27186 13.62400 15.77457
This gives us predictions for each of the 3 rows of the new_data
dataframe, of the outcome onset_sensory
.


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Total Time:       Cook:       Level: Easy      Serves: 8 SERVINGS
A new version of the beloved Boston Cream Pie.
When two beloved desserts combine into one delicious mash-up, you never have to choose again.
Ingredients
For the cake layer
1 box vanilla cake mix, plus ingredients called for on box
For the cake layer
3 8-oz. packages cream cheese, softened
1 c. sugar
3 large eggs
1/4 c. sour cream
2 tsp. vanilla extract
FOR PUDDING LAYER
1 box instant vanilla pudding
1 1/2 c. whole milk
FOR GANACHE
2 c. chocolate chips
1 c. heavy cream
Directions
Preheat oven to 350° and grease a 9” springform pan with cooking spray.
Prepare cake mix according to package instructions and pour about half of the batter into prepared pan.
Bake until a toothpick inserted into the middle of the cake comes out clean, 23 to 25 minutes.
Let cake cool for 10 minutes.
Make cheesecake mixture: In a large bowl using a hand mixer, beat cream cheese and sugar until combined.
Add eggs and beat until combined, then stir in sour cream and vanilla.
Pour cream cheese batter onto cooled cake and bake until cheesecake is slightly jiggly in the center, 1 hour to 1 hour 10 minutes.
(Cheesecakes tend to crack if you don't use a water bath. If you don't want to use one, bake as directed. If you do want to use a water bath, double wrap the outside of the pan in aluminum foil and place in a baking dish.
Place on oven rack and pour in enough boiling water to reach halfway up the pan. Bake as directed.)
If not using a water bath: Remove pan from oven and run a knife around the inside of the pan to release the cheesecake.
Let cool 1 hour on a wire rack, then refrigerate until totally chilled, at least 4 hours and up to overnight.
Make pudding: In a medium bowl, combine pudding mix and milk. Whisk until thickened, about 3 minutes.
Set aside in fridge until ready to use.
Make ganache: Place chocolate chips in a medium, heatproof bowl and set aside. Heat heavy cream in a small saucepan over medium heat.
When bubbles begin to break the surface around the edges of the pan, turn off the heat.
Pour cream over chocolate and let sit 5 minutes before whisking until smooth.
Spread pudding on top of cheesecake in an even layer. Top with ganache.
Place in the refrigerator to set, 30 minutes to an hour. Serve.


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---
layout: talk_reveal3
title: Genomic tracking of SARS-CoV-2 evolution and spread
venue: "AACR Virtual Meeting: COVID-19 and Cancer"
author: Trevor Bedford
widescreen: true
---
{{ page.title }}
Trevor Bedford (@trvrb)
Associate Professor, Fred Hutchinson Cancer Research Center
{{ page.date | date: "%-d %b %Y" }}
{{ page.venue }}
Slides at: bedford.io/talks
Disclosure Information
AACR Virtual Meeting: COVID-19 and Cancer, Trevor Bedford
I have the following financial relationships to disclose:
- Grant/Research support from: NIH, Pew Charitable Trusts, Wellcome Trust, HHMI, BMGF
I will not discuss off label use and/or investigational use in my presentation.
Significant fog of war. Genomic approaches offer orthogonal data source to understand the pandemic.
Epidemic process
Sample some individuals
Sequence and determine phylogeny
Sequence and determine phylogeny
Jan 11: First five genomes showed that the outbreak was caused by a novel SARS-like coronavirus
Jan 19: First 12 genomes from Wuhan and Bangkok lack genetic diversity
Single introduction into the human population between Nov 15 and Dec 15 and
human-to-human epidemic spread from this point forward
Spent the week of Jan 20 alerting public health officials, and since then have
aimed to keep nextstrain.org up-to-date
Nextstrain
Project to conduct real-time genomic epidemiology and evolutionary analysis of emerging epidemics
with
Richard Neher,
James Hadfield,
Emma Hodcroft,
Thomas Sibley,
John Huddleston,
Louise Moncla,
Cassia Wagner,
Miguel Paredes,
Misja Ilcisin,
Kairsten Fay,
Jover Lee,
Allison Black,
Colin Megill,
Sidney Bell,
Barney Potter,
Charlton Callender
Nextstrain architecture
All code open source at github.com/nextstrain
Two central aims: (1) rapid and flexible phylodynamic analysis and
(2) interactive visualization
Rapid build pipeline for 3000 SARS-CoV-2 genomes (timings are for a laptop)
- Align with MAFFT (~20 min)
- Build ML tree with IQTREE (~40 min)
- Temporally resolve tree and geographic ancestry with TreeTime (~50 min)
- Total pipeline (~2 hr)
Current data flow for SARS-CoV-2
- Labs contribute directly to GISAID (now have >63k full genomes)
- Nextstrain pulls a complete dataset from GISAID every 24 hours
- This triggers an automatic rebuild on Amazon Web Services
- We manually update new lat/longs, etc...
- We push this build online to nextstrain.org and tweet the update from @nextstrain
Dec/Jan: Emergence from Wuhan in ~Nov 2019
Jan/Feb: Spread within China and seeding elsewhere
Feb/Mar: Epidemic spread within North America and Europe
Mar/Apr: Decreasing transmission with social distancing
Epidemic in the USA was introduced from China in late Jan and from Europe during Feb
Once in the US, virus spread rapidly
Single introduction at the beginning of Feb quickly shows up throughout the country
Sequencing immediately useful for epidemiological understanding, but selection and functional impacts should also be studied
Significant interest in spike mutation D614G
This mutation occurred in the initial European introduction
D614G is prevalent throughout Europe and mixed in US and Australia
D614G is increasing in frequency across states in US and Australia
D614G is increasing in frequency across states in US and Australia
The success of D614G can be explained by either:
- D614G is more transmissible and has higher $R_0$
- founder effects and epidemiological confounding
Additional evidence from Ct values of clinical specimens
Sheffield, UK
Seattle, USA
Further phylodynamic models
UK
Washington State
Moving forward
- Better methods for large datasets
- Distinguishing endogenous spread from importations
- Tying genomic epidemiology together with richer epi data to better understand local transmission
- Incorporating within-host variation to improve phylogenetic resolution
- Integrating clinical data to look for mutations that impact clinical outcomes
Acknowledgements
Genomic epi: Data producers from all over the world, GISAID and the Nextstrain team
Bedford Lab:
Alli Black,
John Huddleston,
James Hadfield,
Katie Kistler,
Louise Moncla,
Maya Lewinsohn,
Thomas Sibley,
Jover Lee,
Kairsten Fay,
Misja Ilcisin,
Cassia Wagner,
Miguel Paredes,
Nicola Müller,
Marlin Figgins,
Eli Harkins


Delivering email is easy. Having that email actually get received is freaking hard. In this era rife with spammers, if you don't jump through several hoops of verifying yourself, your messages will be automatically marked as spam during transit, and never see the light of an inbox. I didn't realize how tricky this was when I first started sending out email for Outspokes, but when our account activation and notification emails were always being delivered to the spam folder, I dug deeper and learned quite a lot. Follow the jump to save your future emails.
Before I share my personal experiences, I want to point out that the fastest and most sensible way to guarantee good email deliverability is to have someone else do it for you. Rather than setting up your own mail server and coming up with a solution that you have to maintain yourself, there are smart dedicated teams working on this problem on your behalf. The cost of entry is so low that they're all effectively free until the volume of you email you need is large enough that you should generate enough revenue to cover the cost. I'll cover a few hosted services like Sendgrid, and Postmark in a separate blog post.
Now that I've warned you of the easier path, here's the steps I took to make sure our emails were actually delivered rather than canned outright.
This is important when you're using a hosted environment. Outspokes uses Rackspace, and the initial reverse lookup of our IP address yielded a very spam-like looking domain:
67-23-23-207.static.slicehost.net
From the control panel, I changed this value to a more sane-looking 'outspokes.com'. To see what your current reverse dns entry is:
dig -x <IP_ADDRESS> # your static IP address
# other useful commands
dig outspokes.com ANY # show all DNS records for outspokes.com
dig @ns1.editdns.net outspokes.com # ask editdns for outspokes records
SPF stands for Sender Policy Framework, and is a DNS record that can be looked up to determine what servers are allowed to send email for a given hostname. For example, for Outspokes, I only wanted the server with the static IP <IP_ADDRESS> to be able to send email. If a spam server tries to send email with a 'from address' of outspokes.com from a non-verified server, then that email will be marked as spam and rightfully zapped from existence.
In your DNS, add a TXT record:
v=spf1 a mx ~all
This allow servers listed in A and MX records to send email. Some DNS services don't support adding TXT records, so your mileage may vary for getting this setup.
DKIM stands for DomainKeys, and works similar in concept to SPF. I followed a guide from the Ubuntu forums to get it setup.
aptitude install dkim-filter
generate rsa keypair, put in /etc/ssl/private/dkim/private.key
edit dns add TXT record with public key
edit /etc/dkim-filter.conf, /etc/default/dkim-filter
edit /etc/postfix/main.cf - add smtpd milter (mail filter)
/etc/init.d/dkim-filter start
/etc/init.d/postfix restart
If you've followed this guide blindly up until this point. Chances are good that you're emails are still getting spammed-out. Luckily, there are tools that help you diagnose whether all these new changes are actually making a positive effect.
One great way to test is to pretend that you're a mail client sending an email and telneting to the mail server directly. I learned this from a Slicehost tutorial.
# I ran this directly from the mail server b/c of firewall rules
telnet localhost 25
HELO localhost
MAIL FROM<EMAIL_ADDRESS>RCPT TO<EMAIL_ADDRESS>DATA
Subject: test
body text
.
QUIT
Now you can email anyone you'd like for testing, but it's much more helpful to email<EMAIL_ADDRESS>or<EMAIL_ADDRESS>These two emails will check your mail headers against your DNS records and give you a rating of how likely your emails will be marked as spam. The report will be sent to the 'MAIL FROM' field.
Now that you have a good baseline setup for your emails to be delivered, it's time to take a breather and marvel at how your messages actually make it past spam filters. But spammers are constantly improving their game, so having a baseline setup far from guarantees that your emails will be safe forever. Here are some additional tips that may mark your message as suspicious.
Send multipart email instead of only HTML email: send an HTML version that includes your pretty graphics, but also send a plain text version that gets the same point across.
Shrink size of images in message - large image implies spam.
Use a real email address instead of<EMAIL_ADDRESS>- By real address, I mean an address that can be delivered to. This could be<EMAIL_ADDRESS>or<EMAIL_ADDRESS>Quick listing of links that I read:
Like I said before, I strongly believe that the right thing to do for email deliverability is to have someone dedicated handling it. For small teams with limited resources, this means going with an external hosted SMTP service. On the other hand, if you're curious about how email works, setting up your own mail server and going through the setup for ensuring the messages get delivered can teach you the fundamentals of how email work and also a lot of practical tools to go along with it.


SGT. BILKO A film review by Edwin Jahiel Copyright 1996 Edwin Jahiel
SGT. BILKO *** 1/4 first half, ** second half. Directed by Jonathan Lynn. Written by Andy Breckman. Photography, Peter Sova. Editing, Tony Lombardo. Production design, Lawrence G. Paull. Music, Alan Silvestri. Producer, Brian Grazer. Cast: Steve Martin (Sgt. Bilko), Dan Aykroyd (Col. Hall), Phil Hartman (Major Thorn), Glenne Headly (Rita Robbins), Daryl Mitchell (Wally Holbrook), Eric Edwards (Doberman), Travis Tritt as himself, Austin Pendleton (Major Ebersole), Chris Rock (Lt. Oster). A Universal release. 92 min. Rated PG.
Confessions from the potato-couch.
Whenever a new movie is made from TV shows, I take stock of my pop fiction background and find that my familiarity with television oldies is most limited.
Coming rather late to TV ownership, I made up for this with a big appetite for movies and more movies, programs -- mostly British, mostly funny (from Monty Python to Blackadder to Mr. Bean, Yes, Minister, Keeping Up Appearances, Waiting for God), Poirot, Inspector Morse, and so on. Even higher on the list are documentaries and cultural programs, especially those dealing with history, politics, society--what could be called The Way We Were.
I marvel at the millions who remember shows with Groucho Marx, Jack Benny, Burns and Allen. Or All in the Family, Route 66, the Twilight Zone, Charlie's Angels, Dragnet, Bewitched... the list is huge. I suppose that those viewers sacrificed movie-movies for TV, and that more millions, younger folk with a vast knowledge of things well before their time, have a thing for reruns.
My knowledge of TV's Sergeant Bilko is zilch. With some sympathy, I remember Phil Silvers from films, even though these were rarely top drawer. So, I cannot argue with persons who call the Phil Silvers Show a classic--though "classic" seems to apply to most anything that was on TV years ago, just as "antique" is used for many objects just a couple of decades old.
The upshot of all this is that I came to the movie "Sgt. Bilko" without prejudices or the ability to compare. The only comparison I can make is of Steve Martin with himself, in other movies. The answer is that once again, Martin is a major talent that almost never wears thin.
The movie's Bilko is a magna cum laude operator of tricks, scams, gambling, con-games, loaded dice set-ups, all issuing in and from the Ft. Baxter motor pool that the Sergeant runs. "Pool" takes on new meanings as men and women live it up in fun and games.
Martin, silver-haired and silver-tongued, thinks, improvises and acts on his feet so lightning fast that you just got to respect him. He lines with silver the pockets of his crew. They admire, love and follow him blindly, even as he lines his own pockets with gold. His bigger share is only fair, given his leadership, imagination and abilities.
Steve Martin is a great performer who here plays a great performer, a man of (metaphorically) a thousand faces. He issues brisk orders one moment, plays humble the next. When facing officers he has a visible factor of independence from authority, shows defiance, superiority or familiarity. The brass are putty in his hands.
A juicy contrast is established when an outsider, young, straight-arrow, honest Pfc.Wally Holbrook joins the pool. Without missing a beat, Bilko, talking a mile a minute, puts the stupefied kid in the picture. Then, pointing to one of his people he adds: "He'll take your bag. Don't worry. He's bonded." If that's not a four-star line, what is?
Likewise, when the horrified Holbrook tries to wake up the men and women spread out all over the floor after what must have been a no holds barred party. As he enters Bilko's room, reveille is sounding. "What's that music?" asks the groggy Sergeant.
The performances, all of them, from big to tiny roles, are excellent. Dan Aykroyd plays beautifully second banana as Bilko's bozoid, gullible yet complicitous Colonel. Phil Hartman as Major Thorn does slow burns, all the better since they are acted with minimalism instead of the classic way made famous long ago by Edgar Kennedy.
Thorn, now on inspection, might have been Colonel or General by now, except for a mishap, years ago, unwittingly caused by the Sergeant. In a hilarious flashback we see how Bilko had bribed an Army boxer to take a dive. Mistakenly, his assistant gave the money to the opponent pugilist, so that both fighters threw the match.
Without my getting into developments or details, the gags cascade and are most comical. Not subtle, not witty, but broad as befits farce.
There's a wild switching of barracks signs and soldiers' identitities during a Thorn inspection. Then comes an investigation by military nerds who must break the codes of Bilko's computers.
There is also a subplot about Bilko and his girl (Glenne Headley, reunited with Martin after the excellent "Dirty Rotten Scoundrels") whom he stands up at their wedding time after time, but who, in an appealing (and feminist) way, has a mind and will of her own. All this is wrapped up in fine production values and Alan Silvestri's clever, humorous score.
Now for the not-so-good news. As a great 19th Century actor lay on his deathbed, he turned to his sad-faced company: "Don't feel sorry for me. Dying is easy. Comedy is hard." Farce is even harder.
It's a rare movie that can sustain its brio past a certain point. At exactly the halfway mark "Sgt. Bilko" starts to slide and get rather tedious, at least in comparison to what came before.
I noticed that, in the public, loud peals of laughter subsided after the first part. Even so, the wonderfully imaginative, enjoyable, well timed initial 45 minutes are reason enough to see the film.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Relative kinematic quantities between frame_a and frame_b are determined and provided at the output signal connector y in packed format in the order
For example, if parameters get_v_rel and get_w_rel are true and all other get_XXX parameters are false, then y contains 6 elements:
y = relative velocity y = relative angular velocity
In the following figure the animation of a RelativeSensor component is shown. The light blue coordinate system is frame_a, the dark blue coordinate system is frame_b, and the yellow arrow is the animated sensor.
If parameter resolveInFrame_a = true, then the provided relative kinematic vectors of frame_b with respect to frame_a are resolved before differentiation in frame_a. If this parameter is false, the relative kinematic vectors are resolved before differentiation in frame_b. If frame_resolve is connected to another frame, then the kinematic vector as defined above and/or its required derivatives are resolved in frame_resolve. Note, derivatives of relative kinematic quantities are always performed with respect to frame_a (resolveInFrame_a = true) or with respect to frame_b (resolveInFrame_a = false). The resulting vector is then resolved in frame_resolve, if this connector is connected.
For example, if frame_resolve is not connected and if resolveInFrame_a = false, and get_v = true, then
y = v_rel = der(r_rel)
is returned (r_rel = resolve2(frame_b.R, frame_b.r_0 - frame_a.r0)), i.e., the derivative of the relative distance from frame_a to frame_b, resolved in frame_b. If frame_resolve is connected, then
y = v_rel = resolve2(frame_resolve.R, der(r_rel))
is returned, i.e., the previous relative velocity vector is additionally resolved in frame_resolve.
Note, the cut-force and the cut-torque in frame_resolve are always zero, whether frame_resolve is connected or not.
If get_angles = true, the 3 angles to rotate frame_a into frame_b along the axes defined by parameter sequence are returned. For example, if sequence = {3,1,2} then frame_a is rotated around angles[1] along the z-axis, afterwards it is rotated around angles[2] along the x-axis, and finally it is rotated around angles[3] along the y-axis and is then identical to frame_b. The 3 angles are returned in the range
-p <= angles[i] <= p
There are two solutions for "angles[1]" in this range. Via parameter guessAngle1 (default = 0) the returned solution is selected such that |angles[1] - guessAngle1| is minimal. The relative transformation matrix between frame_a and frame_b may be in a singular configuration with respect to "sequence", i.e., there is an infinite number of angle values leading to the same relative transformation matrix. In this case, the returned solution is selected by setting angles[1] = guessAngle1. Then angles[2] and angles[3] can be uniquely determined in the above range.
Note, that parameter sequence has the restriction that only values 1,2,3 can be used and that sequence[1] ≠ sequence[2] and sequence[2] ≠ sequence[3]. Often used values are:
sequence = {1,2,3} // Cardan angle sequence = {3,1,3} // Euler angle sequence = {3,2,1} // Tait-Bryan angle sequence
Exact definition of the returned quantities (r_rel_ab, R_rel_ab, w_rel_ab are defined below the enumeration):
using the auxiliary quantities
and resolved in the following frame
| frame_resolve is | resolveInFrame_a = | vector is resolved in |
|---|---|---|
| connected | true | frame_resolve |
| connected | false | frame_resolve |
| not connected | true | frame_a |
| not connected | false | frame_b |


#include <piece.h>
◆ Piece()
◆ AddenPassant()
| void Piece::AddenPassant |
( |
Piece * |
ptr | ) |
|
|
virtual |
◆ addMove()
| void Piece::addMove |
( |
Point |
pos | ) |
|
|
virtual |
◆ clearPieces()
| void Piece::clearPieces |
( |
| ) |
|
|
static |
◆ enPassant()
| bool Piece::enPassant |
( |
Piece * |
ptr | ) |
|
|
virtual |
◆ generateMoves()
| void Piece::generateMoves |
( |
Point |
currentPos | ) |
|
|
virtual |
◆ getAvailableMoves()
| vector< Point > Piece::getAvailableMoves |
( |
| ) |
|
|
virtual |
◆ getColor()
| Color Piece::getColor |
( |
| ) |
|
|
virtual |
◆ getCount()
◆ getName()
◆ getPawnDirection()
◆ getPieces()
| vector< Piece * > Piece::getPieces |
( |
| ) |
|
|
static |
◆ getPossibleMoves()
| vector< Point > Piece::getPossibleMoves |
( |
| ) |
|
◆ inLimits()
| int Piece::inLimits |
( |
Point |
checkPos | ) |
|
◆ isMoveLegal()
| int Piece::isMoveLegal |
( |
Point |
newPos | ) |
|
|
virtual |
◆ isUnderAttack()
| bool Piece::isUnderAttack |
( |
Point |
currentPos | ) |
|
|
virtual |
◆ numberMoves()
| int Piece::numberMoves |
( |
| ) |
|
|
virtual |
◆ PieceMoved()
| void Piece::PieceMoved |
( |
| ) |
|
|
virtual |
◆ setCellArray()
| void Piece::setCellArray |
( |
Cell * |
m_cell | ) |
|
|
static |
◆ cell
◆ color
◆ moveCount
◆ movesAvailable
| vector<Point> Piece::movesAvailable |
|
protected |
◆ name
◆ pawnLocation
| vector<Piece*> Piece::pawnLocation |
|
protected |
◆ pieces
| vector< Piece * > Piece::pieces |
|
staticprotected |
The documentation for this class was generated from the following files:


Go to the source code of this file.
Defines | |
| #define | ATOMIC_SET(var, val) ((var) = (val)) |
| #define | ATOMIC_INC(var) (++(var)) |
| #define | ATOMIC_DEC(var) (--(var)) |
| #define | ATOMIC_OR(var, val) ((var) |= (val)) |
| #define | ATOMIC_EXCHANGE(var, val) ruby_atomic_exchange(&(var), (val)) |
Typedefs | |
| typedef int | rb_atomic_t |
Functions | |
| rb_atomic_t | ruby_atomic_exchange (rb_atomic_t *ptr, rb_atomic_t val) |
| #define ATOMIC_DEC | ( | var | ) | (--(var)) |
Definition at line 51 of file atomic.h.
Referenced by rb_get_next_signal().
| #define ATOMIC_EXCHANGE | ( | var, | |||
| val | ) | ruby_atomic_exchange(&(var), (val)) |
Definition at line 53 of file atomic.h.
Referenced by rb_threadptr_execute_interrupts_common().
| #define ATOMIC_INC | ( | var | ) | (++(var)) |
Definition at line 50 of file atomic.h.
Referenced by sighandler().
| typedef int rb_atomic_t |
| rb_atomic_t ruby_atomic_exchange | ( | rb_atomic_t * | ptr, | |
| rb_atomic_t | val | |||
| ) |


NetTimeTraveler takes you back to favorite memories via a website and
book randomly chosen from the past two decades. Just enter a year
between 2000 to 2020 in the above search box, and we will time travel!
(Every time you click the "Search" button, you will see a different
search result.) Are you ready?


England cricket match umpire was porn star who 'starred in Sex Around the World'
Garth Stirrat, 51, was on duty for England's T20 defeat to New Zealand in Nelson earlier this week
by Liam Prenderville, http://www.facebook.com/mirrorfootballThe umpire who was on duty for England's match with New Zealand is a former porn star.
Garth Stirrat, 51, was the fourth official for England's T20 loss in the Nelson Region earlier this week.
He has taken charge of domestic matches in New Zealand as main umpire and also women's international matches.
Stirrat is alleged to have featured in magazine porn shoots for NZX and Brass under the name Steve Parnell, when he was a his post with the New Zealand Professional Golfers Association, reports the Sun.
Steve Parnell is listed as appearing in the adult film Sex Around The World: New Zealand in 2003.
Garth Stirrat told Mirror Sport: "I have no comment to make on an issue from my very distant past and which I have no intention of relitigating."
Speaking in 2010 , New Zealand Hotel Council chairwoman Jenny Langley said: "I've spoken to Garth and he told me that he was involved in some filming over six years ago, while he was single, living in Auckland.
"I have also spoken to NZHC Board members who were previously unaware of the situation and they have accepted his resignation."
Stirrat, from Wellington, expressed his delight at being named as fourth official for England's defeat to New Zealand.
"Was proud as punch to be involved in the T20i in Nelson as 4th umpire. Wasn't a bad way to spend the birthday too," he wrote.
"Thanks for putting on weather and a sell-out crowd."
Stirrat previously played cricket in his homeland before later venturing into umpiring.
Stirrat had previously been chief executive of the New Zealand Professional Golfers’ Association but resigned in 2008.
He also held a post as head of the New Zealand Hotel Council but quit as news of his X-rated antics came to light.
On his profile on the New Zealand Professional Cricket Umpires' Association (NZPCUA) page, he explains his decision to go into umpiring.
"I’ve always said it’s the best seat in the house – if you like watching cricket and have a passion for it, you can’t get any closer to the action," he said.
Mirror Sport has also contacted NZPCUA for comment.


Plots Class
Utility class used for drawing markers.
Item Index
Methods
Properties
Methods
_clearMarkerCache
()
private
Removes unused markers from the marker cache
_createGroupMarkers
-
styles
Draws a series of markers in a single shape instance.
Parameters:
-
styles
ObjectSet of configuration properties used to create the markers.
_createMarker
-
styles
-
order
-
index
Creates a shape to be used as a marker.
Parameters:
-
styles
ObjectHash of style properties.
-
order
NumberOrder of the series.
-
index
NumberIndex within the series associated with the marker.
Returns:
_createMarkerCache
()
private
Creates a cache of markers for reuse.
_getGroupShape
-
shape
Returns the correct group shape class.
Parameters:
-
shape
Shape | StringIndicates which shape class.
Returns:
_getItemColor
-
val
-
i
Parses a color from an array.
Parameters:
-
val
Arraycollection of colors
-
i
Numberindex of the item
Returns:
_getPlotDefaults
()
protected
Gets the default values for series that use the utility. This method is used by
the class' styles
attribute's getter to get build default values.
Returns:
_getState
-
type
Returns marker state based on event type
Parameters:
-
type
Stringevent type
Returns:
_parseMarkerStyles
-
Object
Combines new styles with existing styles.
Parameters:
-
Object
Objectcontaining style properties for the marker.
Returns:
_setStyles
-
newStyles
Method used by styles
setter. Overrides base implementation.
Parameters:
-
newStyles
ObjectHash of properties to update.
Returns:
_toggleVisible
-
visible
Toggles visibility
Parameters:
-
visible
Booleanindicates visibilitye
drawPlots
()
protected
Draws the markers
getMarker
-
styles
-
order
-
index
Gets and styles a marker. If there is a marker in cache, it will use it. Otherwise it will create one.
Parameters:
-
styles
ObjectHash of style properties.
-
order
NumberOrder of the series.
-
index
NumberIndex within the series associated with the marker.
Returns:
updateMarkerState
-
type
-
i
Resizes and positions markers based on a mouse interaction.
Parameters:
-
type
Stringstate of the marker
-
i
Numberindex of the marker
Properties
_groupShapes
Unknown
private
Pre-defined group shapes.
_markerCache
Array
private
Collection of markers to be re-used on a series redraw.
_markers
Array
private
Collection of markers to be used in the series.
_plotDefaults
Object
private
Storage for default marker styles.


This inspection reports abstract methods which override abstract methods. Methods with different return types or exception declarations than the method they override are not reported by this inspection.
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ANNA KARENINA 1997 A film review by Timothy Voon Copyright 1997 Timothy Voon
Cast: Sophie Marceau, Sean Bean, Alfred Molina, Mia Kirshner, James Fox, Fiona Shaw Director: Bernard Rose Screenplay: Bernard Rose based on the novel by Leo Tolstoy
The deranged mind ramblings of a majorly depressed woman.
'God Forgive me... I love my son, but I also love the Count... I like my husband, but its the Count who really counts... My husband won't give me a divorce, damn that Russian house keeper with a great English accent for turning him against me... I need opium to dull this deep, unbearable pain within me... God forgive me... I'm lonely... I'm a fool to think the Count still loves me... Damn his mother.... Why does he get to go to the opera every night and I have to stay at home.... It's not fair! He's having an affair with Princess Georgina, I just know it... I can feel it and see it in his eyes.... Why won't they let me see my son? I need more opium.... I've cheated on my husband, what makes me think that I'm so special that the Count won't also cheat on me... Ooooh look at that pretty train on the tracks.... Reminds me of swimming when I was a kid... Gosh, I really feel like swimming right now.... God forgive meeeeeeeeee!'
Really Anna, pull yourself together. Fast trains and pretty bodies are a no no. 'Arsenic and Old Lace', now that's a more lady like way to go.
Where's the Prozac when you really need it?
On a more serious note. This movie must be commended for its outstanding costumes, sets and cinematography. Sophie Marceau doesn't carry quite the same tense, woman on edge, feel that Vivien Leigh portrayed in the 1948 version of 'Karenina'. But few can, considering the amazing parallel's between Leigh's real life and that of 'Anna Karenina'. Sean Bean is a believable Count Vronsky, and James Fox carries the tortured husband look well. Please get rid of that Russian Countess housekeeper, with an English accent suitable only for 'tea and scones' within the confines of Windsor castle. When she first spoke I broke into fits of laughter in the cinema, and I apologise for my unruly behaviour to anyone who may have witnessed it.
Be swept away by the romance of the Russian country; twirl till midnight to the tune of Tchaikovsky and Rachmaninov; waltz the evening away in palatial ballrooms draped with silks and satin; and watch the 'dying swan' performed by artists who made the term 'ballet' famous; but above all, keep a weary eye on that damn fast moving train.
Comment: One mind sweeping, breath taking, Russian operatic, Tol story. Feel Scale: 0% Danielle Steel / *TOLSTOY* / The Bible 100%
Timothy Voon e-mail<EMAIL_ADDRESS>The review above was posted to the
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THE DEVIL'S OWN A film review by Mark R. Leeper Copyright 1997 Mark R. Leeper
Capsule: An IRA terrorist comes to the United States to buy weapons and brings mortal danger to his hosts, an honest cop and his family. Alan J. Pakula directs Brad Pitt and Harrison Ford in a thoughtful action film whose strongest feature is that it does not have a pre-digested point of view on the politics of Northern Ireland. The film is a taut and not surprisingly violent thriller, but the BBC does better on a skimpy budget. Rating: high +1 (-4 to +4)
THE DEVIL'S OWN cost a reported $90-100 million to make. Ordinarily that is of little interest to me as a viewer since I pay the about the same ticket price to see it as to see a little inexpensive independent film. But it should be noted that this is not a subject matter that requires a big budget. Whatever was good in this film was done far more intelligently but on a television budget in the British made-for-TV thriller HARRY'S GAME (1982). With the exception of bigger (but not more realistic) action scenes THE DEVIL'S OWN offers little that HARRY'S GAME did not. (For all I know there may be other equally good thrillers on this subject from the BBC that just never made it to this country. But I do notice that the previous Harrison Ford IRA- related film, PATRIOT GAMES, borrowed the music of HARRY'S GAME in apparent homage.) Perhaps the real virtue of THE DEVIL'S OWN is a moderately even-handed approach to the politics. These days it seems unusual to have a major action film which covers politics and which does not make it obvious which side is right and which is wrong. Leaving audiences to wonder about which side to be on in a conflict is not generally thought to reflect well at the box-office. It is only a minor touch in THE DEVIL'S OWN that the story does not come right out and take sides. It is the most refreshing touch that it is left to the viewer to decide. Perhaps the writers, and there were at least four, felt that pushing one point of view was not really crucial to tell the story.
At a young age Frankie McGuire (to be played as an adult by Brad Pitt) saw his beloved father gunned down at the dinner table by masked Protestants. Two decades later Frankie is an IRA terrorist who has killed more than twenty people and British Intelligence is trying desperately to stop him. Frankie's next project involves stinger missiles and he has to go to New York to buy them with a large satchel of money. A sympathetic judge finds lodging for Frankie at the house of honest cop Tom O'Meara (Harrison Ford). Tom, glad to have another male in a house full of women, has no idea of who Frankie is or what he is really doing. The two men are each interested in the other's country and culture, each surprised at what he discovers. They begina friendship that the viewer knows can only end only in violence. Violence is key word and not all of it is over Northern Ireland politics. But the even-handedness in treating these conflicts even extends to partially justifying a halfworld-ish bar owner and arms dealer played by Treat Williams. This is a film of many different conflicts in which nobody is entirely right or wrong.
This admittedly is a minority opinion, but Harrison Ford is not a particularly expressive or charismatic actor. In spite of his popularity he has a limited acting range and does not project much personality beyond weariness. The same can be said of many other actors, but few of his repute. Pitt carries his role with a thick, at times impenetrable Irish accent, that is reasonably convincing. Their friendship could easily devolve into a simple father-and-son relationship, but it is more complex than that with Ford playing the innocent compared to the generally more worldly and certainly more experienced Pitt. The cinematography by Gordon Willis creates mood by matching the darkest moods of the characters with dark murky scenes in Ulster and in New York.
THE DEVIL'S OWN is more sophisticated and intelligent than most of the action films in the theaters these days, but this expensive production is still not up to what the BBC can do on a skittles and beer budget. There was little on the screen to justify the enormous cost of the film. Still, I rate THE DEVIL'S OWN a high +1 on the -4 to +4 scale.
Mark R. Leeper<EMAIL_ADDRESS>The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
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13
1 "On that day a spring will be opened for the house of David and the inhabitants of Jerusalem, for their sin and impurity.
2 Then it will be on that day,"—this is Yahweh of hosts' declaration—"that I will cut off the names of the idols from the land so that they will no longer be called to mind.  Then the father and mother who bore him will pierce him when he prophesies.
4 Then it will happen on that day that each prophet will be ashamed of his vision when he is about to prophesy. These prophets will no longer wear a hairy cloak, in order to deceive the people.
5 For each will say, 'I am not a prophet! I am a man who works the soil, for the land became my work while I was still a young man!'
6 But someone will say to him, 'What are these wounds between your arms?' and he will answer, 'I was wounded with those in my friends' house.'"
7 "Sword! Rouse yourself against my shepherd,
the man who stands close to me"—
this is Yahweh of hosts' declaration.
"Kill the shepherd,
and the flock will scatter!
For I will turn my hand against the lowly ones.
8 Then it will come about that throughout all the land"—this is Yahweh's declaration—
"that two thirds of it will be cut off! Those people will perish;
only one third will remain there.
9 I will bring that third through the fire
and refine them as silver is refined;
I will test them as gold is tested.
They will call on my name,
and I will answer them and say,
'This is my people!'
and they will say, 'Yahweh is my God!'"


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