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  • what is the best way to track unique visitors?

    - by dnkira
    hello what i want is to make user counter as true as possible. exluding bots, and clever users as much as possible. as 4 what i know, it can be done in several ways: ip (trouble with dinamic ones and proxy's) cookies (with session id maybe, but can be deleted or browser can be changed) flash cookies (not all users have it) any other ways? and what is the best?

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  • What is the relationship between a R.V N(0,1) and others continuous random variables

    - by calejero
    Hello. I have a question. I need to kwon what is the relationship between a Random Variable with Normal distribuation (N(0,1)) and others continuous random variables. Can you write to me an example? Thank You Hola a todos. Necesito saber qué relación existe entre una variable aleatoria Normal (0,1) y cualquier variable aleatoria continua. Además, me vendría bien un ejemplo. Gracias por vuestra ayuda

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  • Dimension Reduction in Categorical Data with missing values

    - by user227290
    I have a regression model in which the dependent variable is continuous but ninety percent of the independent variables are categorical(both ordered and unordered) and around thirty percent of the records have missing values(to make matters worse they are missing randomly without any pattern, that is, more that forty five percent of the data hava at least one missing value). There is no a priori theory to choose the specification of the model so one of the key tasks is dimension reduction before running the regression. While I am aware of several methods for dimension reduction for continuous variables I am not aware of a similar statical literature for categorical data (except, perhaps, as a part of correspondence analysis which is basically a variation of principal component analysis on frequency table). Let me also add that the dataset is of moderate size 500000 observations with 200 variables. I have two questions. Is there a good statistical reference out there for dimension reduction for categorical data along with robust imputation (I think the first issue is imputation and then dimension reduction)? This is linked to implementation of above problem. I have used R extensively earlier and tend to use transcan and impute function heavily for continuous variables and use a variation of tree method to impute categorical values. I have a working knowledge of Python so if something is nice out there for this purpose then I will use it. Any implementation pointers in python or R will be of great help. Thank you.

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  • Determining the chances of an event occurring when it hasn't occurred yet

    - by sanity
    A user visits my website at time t, and they may or may not click on a particular link I care about, if they do I record the fact that they clicked the link, and also the duration since t that they clicked it, call this d. I need an algorithm that allows me to create a class like this: class ClickProbabilityEstimate { public void reportImpression(long id); public void reportClick(long id); public double estimateClickProbability(long id); } Every impression gets a unique id, and this is used when reporting a click to indicate which impression the click belongs to. I need an algorithm that will return a probability, based on how much time has past since an impression was reported, that the impression will receive a click, based on how long previous clicks required. Clearly one would expect that this probability will decrease over time if there is still no click. If necessary, we can set an upper-bound, beyond which we consider the click probability to be 0 (eg. if its been an hour since the impression occurred, we can be pretty sure there won't be a click). The algorithm should be both space and time efficient, and hopefully make as few assumptions as possible, while being elegant. Ease of implementation would also be nice. Any ideas?

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  • What is the best way to store categorical references in SQL tables?

    - by jlafay
    I'm wanting to store a wide array of categorical data in MySQL database tables. Let's say that for instance I want to to information on "widgets" and want to categorize attributes in certain ways, i.e. shape category. For instance, the widgets could be classified as: round, square, triangular, spherical, etc. Should these categories be stored within a table to reference them best from an application? Another possibility, I would imagine, would be to add a column to widgets that contained a shape column that contained a tiny int. That way my application could search shapes by that and then use a coordinating enum type that would map the shape int meanings. Which would be best? Or is there another solution that I'm not thinking of yet?

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  • Computing, storing, and retrieving values to and from an N-Dimensional matrix

    - by Adam S
    This question is probably quite different from what you are used to reading here - I hope it can provide a fun challenge. Essentially I have an algorithm that uses 5(or more) variables to compute a single value, called outcome. Now I have to implement this algorithm on an embedded device which has no memory limitations, but has very harsh processing constraints. Because of this, I would like to run a calculation engine which computes outcome for, say, 20 different values of each variable and stores this information in a file. You may think of this as a 5(or more)-dimensional matrix or 5(or more)-dimensional array, each dimension being 20 entries long. In any modern language, filling this array is as simple as having 5(or more) nested for loops. The tricky part is that I need to dump these values into a file that can then be placed onto the embedded device so that the device can use it as a lookup table. The questions now, are: What format(s) might be acceptable for storing the data? What programs (MATLAB, C#, etc) might be best suited to compute the data? C# must be used to import the data on the device - is this possible given your answer to #1?

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  • Given a document select a relevant snippet.

    - by BCS
    When I ask a question here, the tool tips for the question returned by the auto search given the first little bit of the question, but a decent percentage of them don't give any text that is any more useful for understanding the question than the title. Does anyone have an idea about how to make a filter to trim out useless bits of a question? My first idea is to trim any leading sentences that contain only words in some list (for instance, stop words, plus words from the title, plus words from the SO corpus that have very weak correlation with tags, that is that are equally likely to occur in any question regardless of it's tags)

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  • Probability Question

    - by Juddling
    if i pick 10 numbers from a possible 80. what is the probablity for someone else picking the same 10 numbers as me? or just 4 numbers? or no numbers? i think it's no matches: 10/80 one match: 10*9/80*79 so the formula would be (10!/matches!)/(80!/matches!) is this right? i've only just started doing this at A-level and i need it for a game script i'm making.

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  • Measuring the limit of a point on a smooth.spline in R

    - by Subtle Array
    I'm not sure if that's the right terminology. I've entered some data into R, and I've put a smoothingSpline through it using the following command. smoothingSpline = smooth.spline(year, rate, spar=0.35) plot(x,y) lines(smoothingSpline) Now I'd like to measure some limits (or where the curve is at a given y point), and maybe to some predictive analysis on points that extend beyond the graph. Are there commands in R for doing this?

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  • Howto plot two cumulative frequency graph together

    - by neversaint
    I have data that looks like this: #val Freq1 Freq2 0.000 178 202 0.001 4611 5300 0.002 99 112 0.003 26 30 0.004 17 20 0.005 15 20 0.006 11 14 0.007 11 13 0.008 13 13 ...many more lines.. Full data can be found here: http://dpaste.com/173536/plain/ What I intend to do is to have a cumulative graph with "val" as x-axis with "Freq1" & "Freq2" as y-axis, plot together in 1 graph. I have this code. But it creates two plots instead of 1. dat <- read.table("stat.txt",header=F); val<-dat$V1 freq1<-dat$V2 freq2<-dat$V3 valf1<-rep(val,freq1) valf2<-rep(val,freq2) valfreq1table<- table(valf1) valfreq2table<- table(valf2) cumfreq1=c(0,cumsum(valfreq1table)) cumfreq2=c(0,cumsum(valfreq2table)) plot(cumfreq1, ylab="CumFreq",xlab="Loglik Ratio") lines(cumfreq1) plot(cumfreq2, ylab="CumFreq",xlab="Loglik Ratio") lines(cumfreq2) What's the right way to approach this?

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  • Usage Tracking for Windows desktop applications ...

    - by sdaas
    Hi, I am looking for some frameworks that can be used to collect usage information for Windows desktop application and analyze it. For example, I would like to be able to answer questions like (a) how many times do people use this application in a day (b) which are their favorite menu items, etc. I looked briefly at Google Analytics and Omniture SiteCatalyst but they seem to work only on web applications. Thanks SD

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  • Proper use of use of "cor" function in R

    - by order
    I am interested to know what a proper x (vector matrix or data frame) input looks like. I am currently using the function in two different sorts of matrices. However, I am not sure how R would interpret my data the way I intend. I will explain the types of matrix by example. Type 1 Gene1 Gene2 Gene3 sample1 sample2 Type 2 Sample1 Sample2 Sample3 gene 1 gene 2 gene 3 Are either of these formats valid x parameters? I input both of types of matrices and get some results, but without knowing whether or not this a proper use the function, these are just random numbers. Thank you for your time. I apologize that this isn't more interesting.

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  • R: Given a set of random numbers drawn from a continuous univariate distribution, find the distribut

    - by knorv
    Given a set of real numbers drawn from a unknown continuous univariate distribution (let's say is is one of beta, Cauchy, chi-square, exponential, F, gamma, Laplace, log-normal, normal, Pareto, Student's t, uniform and Weibull).. x <- c(15.771062,14.741310,9.081269,11.276436,11.534672,17.980860,13.550017,13.853336,11.262280,11.049087,14.752701,4.481159,11.680758,11.451909,10.001488,11.106817,7.999088,10.591574,8.141551,12.401899,11.215275,13.358770,8.388508,11.875838,3.137448,8.675275,17.381322,12.362328,10.987731,7.600881,14.360674,5.443649,16.024247,11.247233,9.549301,9.709091,13.642511,10.892652,11.760685,11.717966,11.373979,10.543105,10.230631,9.918293,10.565087,8.891209,10.021141,9.152660,10.384917,8.739189,5.554605,8.575793,12.016232,10.862214,4.938752,14.046626,5.279255,11.907347,8.621476,7.933702,10.799049,8.567466,9.914821,7.483575,11.098477,8.033768,10.954300,8.031797,14.288100,9.813787,5.883826,7.829455,9.462013,9.176897,10.153627,4.922607,6.818439,9.480758,8.166601,12.017158,13.279630,14.464876,13.319124,12.331335,3.194438,9.866487,11.337083,8.958164,8.241395,4.289313,5.508243,4.737891,7.577698,9.626720,16.558392,10.309173,11.740863,8.761573,7.099866,10.032640) .. is there some easy way in R to programmatically and automatically find the most likely distribution and the estimated distribution parameters?

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  • How to calculate cointegrations of two lists?

    - by Damiano
    Hello everybody! Thank you in advance for your help! I have two lists with some stocks prices, example: a = [10.23, 11.65, 12.36, 12.96] b = [5.23, 6.10, 8.3, 4.98] I can calculate the correlation of these two lists, with: import scipy.stats scipy.stats.pearsonr(a, b)[0] But, I didn't found a method to calculate the co-integration of two lists. Could you give me some advices? Thank you very much!

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  • How can I neatly clean my R workspace while preserving certain objects?

    - by briandk
    Suppose I'm messing about with some data by binding vectors together, as I'm wont to do on a lazy sunday afternoon. x <- rnorm(25, mean = 65, sd = 10) y <- rnorm(25, mean = 75, sd = 7) z <- 1:25 dd <- data.frame(mscore = x, vscore = y, caseid = z) I've now got my new dataframe dd, which is wonderful. But there's also still the detritus from my prior slicings and dicings: > ls() [1] "dd" "x" "y" "z" What's a simple way to clean up my workspace if I no longer need my "source" columns, but I want to keep the dataframe? That is, now that I'm done manipulating data I'd like to just have dd and none of the smaller variables that might inadvertently mask further analysis: > ls() [1] "dd" I feel like the solution must be of the form rm(ls[ -(dd) ]) or something, but I can't quite figure out how to say "please clean up everything BUT the following objects."

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  • Which reporting tool would you recommend?

    - by grady
    Hi, I have some reporting functionality in my app and I want to improve it a bit. Its only SQL in a XML file which is read by some parser. There can be some params for that SQL and when its parsed and the params are injected, it is executed against my DB (SQL Server). I want to improve that a bit so that the results look better and are more flexible. This are the most important points I need to have: subtotals layout that can change dynamically according to settings in DB (like logo, slogan) possibility to use the same report template for several customers (same fields, but different logos,colors, slogans etc.) should run from an ASP.NET application It should be as dynamic as possible. I know of Crystal Reports and the Microsoft Reporting Tool. Are there any others that might be of interest and are my above points possible at all? Thanks for some ideas and hints :-)...

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  • Pie chart of *nix shell use [closed]

    - by hayk.mart
    I've encountered a situation where it would be very helpful to know the breakdown of shell use by percentage. For example, I'm looking for something like bash: X%, sh: Y%, csh, tcsh, zsh, ksh, dash, etc.. Obviously, I know there are several complications - multiple shells, the definition of "use", uncertainty and so forth, but I would like to see an informed answer derived from actual data and based on some stated metric, even if the result could be horribly wrong. Bonus if there is historical data demonstrating a shift in preferences.

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  • In R, How to add Row for Information:

    - by Moe
    Hi, I'm trying to add a Row to my data.frame to spit out the average of the column. This is my data frame: Weight Response 1 Control 59 0.0 2 Treatment 90 0.8 3 Treatment 47 0.1 4 Treamment 106 0.1 5 Control 85 0.7 6 Treatment 73 0.6 7 Control 61 0.2 I'd like it to become: Weight Response 1 Control 59 0.0 2 Treatment 90 0.8 3 Treatment 47 0.1 4 Treamment 106 0.1 5 Control 85 0.7 6 Treatment 73 0.6 7 Control 61 0.2 8 AVERAGES 74 0.3 Thanks!

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  • Histrogram matching - image processing - c/c++

    - by Raj
    Hello I have two histograms. int Hist1[10] = {1,4,3,5,2,5,4,6,3,2}; int Hist1[10] = {1,4,3,15,12,15,4,6,3,2}; Hist1's distribution is of type multi-modal; Hist2's distribution is of type uni-modal with single prominent peak. My questions are Is there any way that i could determine the type of distribution programmatically? How to quantify whether these two histograms are similar/dissimilar? Thanks

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  • Geometric Mean: is there a built-in?

    - by doug
    i tried to find a built-in for geometric mean but couldn't. (Obviously a built-in isn't going to save me any time while working in the shell, nor do i suspect there's any difference in accuracy; for scripts i try to use built-ins as often as possible, where the (cumulative) performance gain is often noticeable. In case there isn't one (which i doubt is the case) here's mine. gm_mean = function(a){prod(a)^(1/length(a))}

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  • Programmatically determine the relative "popularities" of a list of items (books, songs, movies, etc

    - by Horace Loeb
    Given a list of (say) songs, what's the best way to determine their relative "popularity"? My first thought is to use Google Trends. This list of songs: Subterranean Homesick Blues Empire State of Mind California Gurls produces the following Google Trends report: (to find out what's popular now, I restricted the report to the last 30 days) Empire State of Mind is marginally more popular than California Gurls, and Subterranean Homesick Blues is far less popular than either. So this works pretty well, but what happens when your list is 100 or 1000 songs long? Google Trends only allows you to compare 5 terms at once, so absent a huge round-robin, what's the right approach? Another option is to just do a Google Search for each song and see which has the most results, but this doesn't really measure the same thing

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