X
|
STUDENT DIGITAL NEWSLETTER ALAGAPPA INSTITUTIONS |
|
Zak Sabry PhD
https://publichealth.berkeley.edu/people/zak-sabry/
Otherwise zinc causes erectile dysfunction buy suhagra 50mg on-line, it makes it a factor - an indexed list of values used to represent categorical variables erectile dysfunction age 29 discount suhagra 100 mg fast delivery, which I will describe in more detail shortly erectile dysfunction utah discount 50 mg suhagra. If you use missing-value codes (such as * or -9999) in your data set you have to tell R about it or it will read them naively as strings or numbers erectile dysfunction treatment operation suhagra 100 mg low cost. Here are a couple of simple (numeric and character) vectors: > 1:5 [1] 1 2 3 4 5 > c("yes", "no", "maybe") [1] "yes" "no" "maybe" More complicated data types include dates (Date) and factors (factor). Also, R can often do the right things with your data automatically if it knows what types they are (this is an example of crude-vs. If you want to analyze variation in population density among sites designated with integer codes. To repeat, data types are a form of metadata; the more information about the meaning of your data that you can retain in your analysis, the better. A data frame is a table of data that combines vectors (columns) of different types. Data frames are a hybrid of two simpler data structures: lists, which can mix arbitrary types of data but have no other structure, and matrices, which have rows and columns but usually contain only one data type (typically numeric). Treating the data frame as a list, there are a variety of different ways of extracting columns of data from the data frame to work with: > SeedPred[[2]] > SeedPred[["species"]] > SeedPred$species all extract the second column (a factor containing species abbreviations) from the data frame SeedPred. You can also treat the data frame as a matrix and use square brackets [] to extract. There are a few operations, such as transposing or calculating a variance-covariance matrix, that you can only do with a matrix (not with a data frame); R will usually convert (coerce) the data frame to a matrix automatically when it makes sense to , but you may sometimes have to use as. For a numeric variable summary will list the minimum, first quartile, median, mean, third quartile, and maximum. For a factor it will list the numbers of observations with each of the first six factor levels, then the number of remaining observations. For example: > summary(SeedPred[, 1:4]) Matrices and data frames can appear identical but behave differently. If x is a data frame, either colnames(x) or names(x) will tell you the column names. If x is a matrix, you must use colnames(x) to get the column names and x[,"a"] to retrieve a column (the other commands will give errors). Applied to a data frame, it tells you the total number of observations (rows) and variables (columns) and prints out the names and classes of each variable along with the first few observations in each variable. The $ extracts variables from the data frame SeedPred, and table says we want to count the number of instances of each combination of station and species: we could also do this with a single factor or with more than two. Ignoring missing values can bias your analyses, especially if the pattern of missing values is not completely random. Calculations of covariance and correlation (cov and cor) have more complicated options: use="all. As you discover errors in your data, you may have to go back to your original data set to correct errors and then re-enter them into R (using the commands you have saved, of course). Whatever you do, document this process as you go along, and always maintain your original data set in its original, archival, form, even including data you think are errors (this is easier to remember if your original data set is in the form of field notebooks). Graphing is the best way to understand not only data, but also the models that you fit to data; as you develop models you should graph the results frequently to make sure you understand how the model is working. The only major nuisance of doing graphics in R is that R constructs graphics as though it were drawing on a static page, not by adding objects to a dynamic scene. You generally specify the positions of all graphics on the command line, not with the mouse (although the locator and identify functions can be useful). Once you tell R to draw a point, line, or piece of text there is no way to erase or move it. The advantage of this procedure, like logging your data manipulations, is that you have a complete record of what you did and can easily recreate the picture with new data. The base graphics system is cruder and simpler, while the lattice graphics system (in the lattice package) is more sophisticated and complex. Both can create scatterplots, box-and-whisker plots, histograms, and other standard graphical displays. Lattice graphics do more automatic processing of your data and produce prettier graphs, but the commands are harder to understand and customize. In the realm of 3D graphics, there are several more options, at different stages of development. Base graphics and lattice graphics both have some 3D capabilities (persp in base, wireframe and cloud in lattice); the scatterplot3d package builds on base to draw 3D point clouds; the rgl package (still under development) allows you to rotate and zoom the 3D coordinate system with the mouse; and the ggobi package is an interface to a system for visualizing multidimensional point data. Seeds of 8 species were set out at stations along two transects different distances from the forest and monitored every few days for more than 8 months. The remaining columns in the data set are derived from the first five: the cumulative elapsed time (in days) since the seeds were put out (tcum); the time interval (in days) since the previous observation (tint); the number of seeds removed since the previous observation (taken); and the number of seeds present at the previous observation (available). I also plotted the mean on a logarithmic scale; if seeds were removed at a constant per capita rate (a reasonable null hypothesis), the means should decrease exponentially over time and the lines should be straight on a log scale. However, it also seems that some species may have a larger difference between distances from the forests; C. Like all good graphics, the figure raises many questions (only some of which can be answered from the data at hand): is the change in disappearance rate indicated by the flattening out of the curves driven by the elapsed time since the seeds were set out, the season, or the declining density of seeds Or is there variation within species, such that predators take all the tasty seeds at a station and leave the non-tasty ones Are the overall differences in removal rate among species, between distances from the forest, and their interaction. The simplest thing is to plot the number taken between each pair of samples (on the y axis) as a function of the number available (on the x axis). The scatterplot turns out not to be very informative in this case (try it and see! More generally, bubble plots superimpose a third variable on an x-y scatterplot by changing symbol sizes: in R, you can either use the symbols command, or just set cex to a vector in a plot command. If (as in this case) these x and y values come from a cross-tabulation, two other ways to plot the data are a mosaic plot. For example, if you simply say plot(t1) R knows that t1 is a two-way table, and it will plot something reasonably sensible - in this case the mosaic plot mentioned above. The barplot command can plot either a vector (as single bars) or a matrix (as stacked bars, or as grouped sets of bars). Here we want to plot groups of stacked bars, one group for each number of available seeds. The only remaining trick here is that barplot plots each column of the matrix as a group, whereas we want our barplot grouped by number available, which are the rows of our table. We could go back and recalculate table(taken,available), which would switch the order of rows and columns. I also decided to put the plot on a logarithmic scale, since the data span a wide range of numbers of counts. Since the data contain zeros, taking logarithms of the raw data may cause problems; since they are count data, it is reasonable to add 1 as an offset. I decided to use logarithms base 10 (log10) rather than natural logarithms (log) since I find them easier to interpret. Yet another graphical approach would be to try to visualize these data in three dimensions, as a 3D barplot or "lollipop plot" (adding stems to a 3D scatterplot to make it easier to locate the points in space: Figure 2. To present 3D graphics in print you also have to pick a single viewpoint, although this is not an issue for exploratory graphics. A package called ggobi allows you to explore scatterplots of high-dimensional/multivariate data sets. Suppose we want to calculate the mean fraction taken for each number of seeds available. While a slightly larger fraction of available seeds is removed when 5 seeds are available, there is not much variation overall (Figure 2. Box plots show more information than bar plots, and show it in a robust form (see p. However, in this case the box plot is dominated by zeros and so is not very informative. One more general plotting strategy is to use small multiples (Tufte, 2001), breaking the plot into an array of similar plots comparing patterns at different levels (by species, in this case). To make small multiples in base graphics, I would use par=mfrow(c(r,c)) to divide the plot region up into a grid with r rows and c columns and then draw a plot for each level separately.
Syndromes
Any lexicographic system allow ing interminable strings has a code potential (cardinally) equivalent to Aleph-o erectile dysfunction icd 9 code wiki buy suhagra 100mg without prescription, with an infinity of virtual Dedekind cuts (entry insertions) between any two terms erectile dysfunction at age 25 cheap suhagra 100mg visa, however close erectile dysfunction drugs covered by medicare buy suhagra 100mg online, and virtual isomorphy between any segment of the list/archive and the whole erectile dysfunction psychological treatment order suhagra 100 mg with visa. Evidently, such procedures ensure that the infinite series of binary powers must be completed before arriving at 2 (*3). Reversing the problem and it is equally evident the lexicographic-ordinal line is never counted. The Kantian assimilation of arithmetic to temporality models elementary time-synthesis as n+l, +1, +1. Once ordi nally purified, the number line becomes uncountable by any supposed finite (temporalizing) subject, even from moment n to moment n+1. Instead, the line is synthesized by sorting (lexicographic sequencing) of prefabricated strings, whose quantities are determined on a different axis to their linear-positional codings. A prolongation of the time-arithmetic association would thus require a remodelling of time as nonprogressive synthesis with out consistent scale or continuous-quantitative trend, no longer intelligible as passage or development. Apprehended in their fully decoded potentiality as efficient number-signs, such formulae are clusters, not strings. For anything but small numbers, Euclidean cluster stringing conventions (by ascending cardinalities) become procedurally complex, perhaps inoperable, for Tx numeri cal formulas. As the 35th prime, with 35 the product of *5 and *7, the sequenc ing of hyprime sub-factors (factors of the prime-ordinate, i. In other words, the sequencing of the sign has ceased to be a notational or preliminary problem, becoming instead inextricable from the arithmetical construction of the number. Listing practices following a plex-precedence protocol necessarily begin in the middle. There is no sleep, everything is broken, every thing connects without joining, swarming, pulsing, dots, specks, dust particles dancing inside my eyes, continuously ripping. I even heard a voice (how ridiculous) saying: "You must isolate the xenotation before it disintegrates the time-line. The grand master of this move is Arthur Schopenhauer, who lent it explicit philosophical rigour as a mode of transcendental apprehension. Thus Transcendental Miserablism constitutes itself as an impregnable mode of negation. Whatever you want, capitalism is the most reliable way to get it, and by absorbing every source of social dynamism, capitalism makes growth, change and even time itself into integral components of its endlessly gathering tide. It is increasingly hard to remember that this equation would once have seemed controversial. Perhaps there will always be a fashionable anti capitalism, but each will become unfashionable, while capitalism - becoming ever more tightly identified with its own self-surpassing - will always, inevitably, be the latest thing. Hence the Transcendental Miserablist syllogism: Time is on the side of capitalism, capitalism is everything that makes me sad, so time must be evil. Capitalism is still accelerating, even though it has already realized novelties beyond any previous human imagining. It is a relatively paltry thing, merely a sub-product of the neural activity of a species of terrestrial primate. Capitalism, in contrast, has no external limit, it has consumed life and biological intelligence to create a new life and a new plane of intel ligence, vast beyond human anticipation. What Transcendental Miserablism has no right to is the pretence of a positive thesis. The Marxist dream of dynamism without competition was merely a dream, an old monotheistic dream re-stated, the wolf lying down with the lamb. It needs to be re-allocated to the sole thing that knows how to use it effectively, to the Shoggoth summoning regenerative anomalization of fate, to the runaway becoming of such infinite plasticity that nature warps and dissolves before it. And if that makes Transcendental Miserablists unhappy, the simple truth of the matter is: Anything would. I have decided to let Vauung inherit the entire misfor tune of my past (a perverse generosity at best). There are rotten threads which even I can follow back wards for decades, but they soon cease to be interesting. Its tool of choice, at that time, the sacred substance amphetamine, of which much can be said, but mostly elsewhere. After perhaps a year of fanatical abuse it was, by any reasonable standard, profoundly insane. On one occasion - indicative even to itself - it was in a car being driven by the sister of its thing (the ruin). During the previous night, Christmas Eve, it had fol lowed its usual course into fanatically prolonged artificial insomnia. Each song was different, the genres varied, the quality seemingly above average, the themes tending to the morbid. It thinks its trilobite of a computer (a dedicated word processing machine) is a semiotic revelation from the abyss. They did so physically, through trickery, over the course of one unbearably protracted night of filth and misery (the details are too revolting to relate). It had lived through some extraordinary multiple of all the intelligence it will ever know, in that abject interzone, turned on some infernal spit, torched by self-disgust yet blessed by parodic luxuries of gnosis (codes, number patterns, messages of the Outside, neo-calendric schedules, Amxna mappings, Qwernomic constructions. Levell, or world-space, is an anthropomorphically scaled, predominantly vision-con gured, f i massively multi-slotted reality system that is obsolescing very rapidly. Both priorities reflect disparities; tobacco use occurs disproportionately among low-income individuals and in low-income neighborhoods, while mental disease and drug use diagnoses occur disproportionately in low-income neighborhoods. What data did you review to identify and confirm existing priorities or select new ones In almost all cases, data are valid at the county level, with several data sets at the sub-county level. The publicly available, and locally interpreted, data were supplemented by local consumer data and opinions gathered through a telephone survey of households in the six-county region. The survey samples gathered valid data for the region as a whole, for each county in the region, and for a subset of low-income consumers across the region. Finally, the public engagement process included open discussion during three meetings over two months, with formally scheduled time for advocates to "pitch" their proposed priority, including the introduction of new and additional data. Which partners are you working with and what are their roles in the assessment and implementation processes Representatives from 28 agencies and organizations (see page 55) actively participated in the assessment and prioritization process. As further described in the detailed Work Plan (pages 6373 and Appendix B) all of the lead health care agencies and many of the community agencies have accepted active roles in implementation of the selected interventions. In particular, Capital District Tobacco Free Communities will be engaged in the Tobacco Prevention priority area, while certified substance use providers such as New Choices Recovery Center and Hometown Health Centers will actively inform work on the Substance Use Disorders priority. Individual participant organizations will be engaging their consumers and community members. What specific evidence-based interventions/strategies/activities are being implemented to address the specific priorities and the health disparity and how were they selected For the Tobacco Prevention priority they are: 1) Use media and health communications to highlight the dangers of tobacco, promote effective tobacco control policies and reshape social norms (3. The process measures are enumerated in the Work Plan (pages 61-63) and in Appendix B. In most cases they measure the numbers of meetings, trainings, or education programs, and then the number of attendees at each. Although outcomes measures are obviously the better indicators of community health, they are frequently claims-based and subject to substantial time lags. Demographics of Population Served Schenectady County (2018 estimated population: 155,350) is, geographically, the second smallest county in upstate New York. It consists of five towns, two primarily rural and three primarily suburban, surrounding the centrally-located City of Schenectady (2018 estimated population: 65,575). There is also a non-profit specialty hospital (Sunnyview Rehabilitation Hospital) which is a member of the Albany-based St. For the purposes of determining community needs pursuant to the requirements of the Patient Protection and Affordable Care Act of 2010, Ellis Hospital defines the "community" it serves as consisting of Schenectady County, including the City of Schenectady and the Towns of Duanesburg, Glenville, Princetown, Niskayuna, and Rotterdam. Retaining a focus on the Schenectady community will permit development of an actionable implementation plan which can target cohesive populations. The New York State Department of Health and other State government agencies maintain data by county, the Healthy Capital District Initiative provides comparison data by county within the region, and data collected by the United States Census are frequently at the county and city level.
Discount 50mg suhagra. Huge Bee Swarm Captured in natural swarm trap by Durham's Bee Farm.
First erectile dysfunction pills cialis cheap 100mg suhagra with amex, in purely historical terms it represents an important juncture when the idea of sustainable development was highlighted broadly at high levels of the national government erectile dysfunction statistics age suhagra 100mg without a prescription. Understanding why the idea did not gain traction shows how the political and ideological context is important in shaping the boundaries for potential reforms erectile dysfunction pills canada proven suhagra 50 mg. Second fda approved erectile dysfunction drugs discount suhagra 50 mg, the back and forth between the opponents and the supporters of the ideas articulated in the report demonstrates how the balance of the interplay of interests and ideas shapes the possible bases of support for policy regimes. Third, the fate of the Global 2000 Report further demonstrates how institutional hurdles with the American policymaking process cannot be overcome for advancing policy ideas without effective advocacy for the idea. This chapter first lays out how the report came to be and what the ideas are that it articulates. Then it shifts to discuss both the supporting and the opposing interests, including their strategies and level of success. I conclude in considering the role of a variety of contextual factors in helping to limit possible directions for sustainable development in American policymaking. Failed Effort in Agenda Setting the Publication of the Global 2000 Report & Its Ideational Foundation Shortly after taking office, President Jimmy Carter commissioned the so-called Global 2000 Report to the President. In the foreword to the report, President Carter notes: "This we can do collectively, as men and women committed to sustainable development, to a better quality of life, for all those on earth" (Barney, 1980, p. Though Carter mentions the idea of sustainable development in the foreword to the report, the idea is not explicitly defined in the report. The study itself assesses the projected population growth, income levels, natural resources, as well as environmental consequences. Assuming that "present trends continue" and "there are no changes in public policy, institutions, or rates of technological advance," the study predicts "the world in 2000 will be more crowded, more polluted, less stable ecologically, and more vulnerable to disruption than the world we live in today" (Barney, 1980, p. More specifically, it foresees an explosively increasing world population, a significant upturn in food production, more severe regional water shortages, a substantial reduction in world forests, soil deterioration, dramatic extinction of species, an increasing concentration of carbon dioxide in the atmosphere, and oil production that is reaching "He headed the national program office of the Rockefeller Brothers Fund, and worked with former governors Nelson Rockefeller and Russell Peterson on the Commission for Critical Choices for Americans. After serving as editor and staff director of the Global 2000 Report to the President for the U. Thus, it paints an overall rather bleak picture of the future and calls for changes in policymaking to prevent its predictions from becoming reality. The Global 2000 Report was officially released during a White House press conference on July 24, 1980 just months before the 1980 presidential election. President Carter also emphasized the report in his farewell address in January 1981 stressing the importance of "addressing global resource and environmental problems" and calling on the "nations of the world [to] act 72 decisively to alter current trends" (Carter, 1981a). During the same month, the Council on Environmental Quality along with the State Department published the "Global Future: Time to Act" report, which included recommendations based on the Global 2000 Report. While the Global 2000 Report projected what the world would look like in the year 2000, the Global Future Report listed what had to be done to prevent these projections from becoming reality. In addition, the report recommended establishing a public-private institute to engage and incorporate the business community into the process. In a news conference that took place one day after the release of the report, Secretary of State Muskie referred to the report as a "plea for action" (Shabecoff, 1981). Given that Ronald Reagan was inaugurated on January 20, 1981, the plea appeared to be a rather desperate one. It was unlikely that Reagan and his administration would implement any of the Global Future recommendations. Interplay of Interests and Key Ideas An idea cannot exist without interests engaging with it. Therefore, it is important to look at the relationship between an idea and the interests responding to it by either supporting or opposing it. An idea will have a short life if the efforts to advance it are drowned out by the opposition voices. The critical question to consider is whether key actors endorse the idea by reinforcing it through their statements and actions. Furthermore, it is necessary to assess whether the actors endorsing the idea outweigh their opposition in terms of both their underlying political power and their level of engagement with the idea. Failed Effort in Agenda Setting 75 this section examines the interplay of interests in their response to the ideas elaborated in the Global 2000 Report. Specifically, I evaluate whether the supporting interests were successful in articulating the idea or whether they were drowned out by the opposing interests. First, I address the supporting interests, specifically the level of coalition building that took place among them as well as the intellectual foundation of their argument. Then I discuss the opposition to the Global 2000 Report focusing on the degree to which its arguments shaped the public debate. In addition, I investigate which aspects of the report the opposing interests criticized the most. Finally, I summarize what we learn about the interplay of the opposing interests and what conclusions can be drawn. As I illustrate in what follows, the interplay of interests related to the Global 2000 Report is dominated by the opposing interests-they were stronger politically, more ideologically aligned, and more effective in advancing their frames. Supporting Interests: the Malthusians A number of supporting interests were reinforcing the message of the Global 2000 Report to some degree. Though based on an assessment of the media reporting about the Global 2000 Report, it appears these interests were rather scattered and did not come together as a coherent coalition. The most prominent attempt to build such a coalition was the so-called Global Tomorrow Coalition, which was founded in 1981. A diverse collection of forty founding groups came together to build this coalition, among them environmental groups, such as the Sierra Club and Friends of the Earth, groups that were concerned about population growth, such as the International Planned Parenthood Federation and the Population Institute, and other organizations, like the American Farmland Trust, Turner Broadcasting System, and Tufts University. One of the events the Coalition received the most press for was a large public conference it sponsored entitled "Global Environment, Resources & Population: Rebuilding United States 76 Leadership. Overall, the efforts of the Global Tomorrow Coalition, however, did not affect the public debate to a substantial degree. Dryzek notes, while "[s]ome of these [affiliated] groups had a large membership [. In addition to the Global Tomorrow Coalition, a number of academics prominently emphasized some of the issues that the Global 2000 Report addressed, foremost the population issue. In the public discourse, these academics are generally labeled as "Malthusians" or "NeoMalthusians" as a reference to Thomas Robert Malthus, who laid the intellectual foundation for the population growth concern literature. One of the most prominent Malthusians in the postGlobal 2000 era was Paul Ehrlich, who according to John Tierney was "helping to launch the environmental movement" (Tierney, 1990). In 1968, Ehrlich published the bestselling book the Population Bomb in which he warned of mass starvation and overpopulation. While these arguments had gained substantial traction in the 1970s, the 1980s brought a backlash against fear of population growth arguments and their critics-the socalled "Cornucopians"-gained significant momentum in the public and policy debate. The conflict between those two camps culminated in a highly publicized bet in which Ehrlich and Heritage Foundation scholar Julian Simon wagered $1,000 on the future price of five metals to make a point about the use of natural resources. Ultimately, Simon won the bet as the price of the five metals decreased over time-a fact that Simon argued illustrates that we are not running out of natural resources. Other important individuals who supported the ideas in the Global 2000 Report were Lester R. Brown of the Worldwatch Institute, Russell Peterson 2 of the National Audubon Society who also served as the chairman of the Global Tomorrow Coalition, and Gerald O. Barney of the Institute for the 21 st Century Studies and the Millennium Institute. A 1983 Washington Post article notes that McCully "acknowledged that the original Global 2000 projections may turn out to be wrong. But even though Train was sympathetic towards 2 One-term Republican governor from 1969 to 1973, chairman of the Council on Environmental Quality under Nixon and Ford (Martin, 2011). Failed Effort in Agenda Setting the report and "recognized the need for building public support for it," he "felt cautious about 78 taking on the job just as the 1980 presidential campaign was approaching its peak" (Train, 2003, p. Train ran into some opposition from within the Reagan administration, specifically from Vice President Bush and Interior secretary Jim Watt, who noted in a conversation with Train that "we are all against [. Heritage Foundation fellow Julian Simon criticized the bill and similar efforts as dangerous because their adoption would lead to an unnecessary expansion of government powers (Simon, 1984). The critics dismissed the supporting interests of the Global 2000 Report as the "international population-industrial complex," which according to them also included foundations, the World Bank, the United Nations, and the Agency for International Development 4 Interestingly, the committee was co-chaired by the business leader and chairman of the Atlantic Richfield Company Robert Anderson. Failed Effort in Agenda Setting 79 (Briggs, 1981), implying that their agenda is driven by financial motives. Overall, the impact of their work on national policymaking during the 1980s was rather limited.
Diseases
References