search for: calucate

In some cases, incorrect results are produced by the code below intended to calculate the number of days between 2 dates. The year in question was a leap year. Note the results for 2004-04-04 and 2004-04-05 are the same! They should be 37 and 38 respectively. > as.integer(as.POSIXct("2004-04-02") - as.POSIXct("2004-02-27")) [1] 35 >

...pam" "nonspam" "spam" "spam" [etc...] # error matrix >(nn1.test.tab<-table(spam.test$type, nn1.pr.test, dnn=c('Actual', 'Predicted'))) Predicted Actual nonspam spam nonspam 778 43 spam 63 496 # Calucate overall error percentage ~ 7.68% >(nn1.test.perf <- 100 * (nn1.test.tab[2] + nn1.test.tab[3]) / sum(nn1.test.tab)) [1] 7.68116 ## Example 2 - code based on rattles log script # train artifical neural network >nn2<-nnet(as.numeric(type)-1~., data=spam.train, size=3, decay=0.1, maxit=10...

...like the sentence below can reduce many variable, How can I understand it? what is significant correlation at 5% level, what is the criterion? P value?or what? "Independent variables whose correlation with the response variable was not significant at 5% level were removed" how can I calucate the correlation between them? thank you! -- View this message in context: http://n4.nabble.com/How-can-I-understand-this-sentence-and-express-it-by-means-of-Mathematical-approach-tp1584036p1584036.html Sent from the R help mailing list archive at Nabble.com.

...ad of a filled area. I have however failed so far. Could anyone give me a few tips? There is basically two things to solve. 1. Covert histogram to a barplot 2. Convert polygon to line. Here is the code so far with comments. weights$Weight is the individual weight observations. Best regards. # calucate the right breakpoints breakpoints <- seq(min(weights), max(weights), by=binwidth) #scale density dens <- density(reference) dens$y <- dens$y * (length(weights$Weight)*binwidth) #graph it hist(weights$Weight, freq=TRUE, breaks=breakpoints, xlab=xlabel, ylab="No of Births", main...

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...explain the whole flow of the 'Letor' ranking. Structure of the Xapian::Letor class methods: Following Five methods basically prepare the statistical information needed to generate the values of desired features. tf() idf() doc_len() coll_tf() coll_len() Following six methods will calucate the particular feature values with the help of above statistical information. Here char ch plays a roll to tell the method that this features value is to be calculated for which documents part. For example [ ch = 't' says calculate feature 1 for title only , 'b' - body only , 'w...

Hi I have recently been analyzing birthweight data from a clinic. The data has obvious defects in that there is digit preference on certain weights making them overrepresented. This shows as spikes in the histogram on certain well rounded weights like 2, 2.5, 3, etc. I would like to show this to government officials but can't figure out how I should present the finding in an easy to

...n to the original return series, calculate the volatility (?t) and then just calculate the VaR with VaR_t=sigma_t*q_alpha where q_alpha is the quantile of the fitted distribution or do they fit the distribution to the standardized returns (xi_t=r_t/sigma_t), calculate the volatility and then just calucate the VaR with VaR_t=sigma_t*q_alpha where q_alpha is now the quantile of the fitted distribution which was fitted using the standardized residuals? Another question is: Did they set the mean of the return to zero? My main point is, how to fit a sophisticated distribution to financial data using a...