similar to: dismo help

Hey, Does anybody know of a good Geocode library for Ruby, similar to Perl''s Geocode::Postal, that i could use in my rail app? I need to do some "within 5 miles" calculations of zipcodes and i dont have a nice ruby (or gem) utility to do it. any help would be appreciated. thanks adam _______________________________________________ Rails mailing list

Dear R-helpers and Dr. Hijmans, I can not invoke maxent() in GNU Linux. Could you give me any directions on that? There is maxent.jar in version 3.3.3e in the right folder. ######################################## R: > jar <- paste(system.file(package="dismo"), "/java/maxent.jar", sep='') > jar [1] "/ebio/abt6/jmao/rpacks/dismo/java/maxent.jar"

I have a 10-column XLS file, with 2 date fields. As far as I can tell, they were configured identically in Excel 2010. One of these fields resembles "39406.577662037", whilst in Excel, it is shown as "2007-11-20 13:42:20". Applying as.Date() with the default format doesn't do it. Any ideas as to what format this is? Many thanks! -- H -- Sent from my mobile device Envoyait

Estimados erreros, Estoy intentando entender como calcula el paquete dismo ( https://cran.r-project.org/web/packages/dismo/index.html) un coeficiente de variación. Os pongo un ejemplo: tmin <- c(10,12,14,16,18,20,22,21,19,17,15,12) # temperatura mínima media mensual de un año tmax <- tmin + 5 # temperatura máxima media mensual de un año prec <- c(0,2,10,30,80,160,80,20,40,60,20,0)

I have a bunch of clean timeseries data obtained from a sensor and I'd like to apply a Kalman Filter to it to smoothe it out. Through a few days of Googling, reading papers, implementing such a filter in various languages, I finally realised that it may be built into R. So I did a "??kalman" at the R prompt and found that it is indeed there. However, the help page is a tad bare,

A sample of the data I have is: > head(sensor) logged_on accx accy accz compassx compassy compassz gyrox gyroy gyroz 1 1326561428000 -0.4602 0.8346 0.0936 0.145508 -0.350586 0.259766 59.617390 28.521740 59.617390 2 1326561428050 -0.4212 1.0452 0.1326 0.219727 -0.321289 0.241211 88.695656 27.478260 88.695656 3 1326561428100 -0.2496 1.3416 0.2886 0.214844 -0.326172

Hola, en la misma definici?n de la funci?n: # P15. Precipitation Seasonality(Coefficient of Variation) # the "1 +" is to avoid strange CVs for areas where mean rainfaill is < 1) p[,15] <- apply(prec+1, 1, cv) Un saludo, Jorge On Martes, 19 de Junio de 2018 13:07:27 Marcelino de la Cruz Rot escribi?: > Hola Jaume: > > Si miras el c?digo de biovars() ver?s que la

I don't know if timeboxing is the correct term to use to accomplish what I'm attempting, so allow me to explain. I have a set n of tagged observations in time series t. What I'm interested in is taking i seconds before and after every n. My code is below: # observations.xts is an xts time series and arg is the number of seconds to for the timebox timeboxes <-

My data: > dput(mydata) structure(list(V1 = c(1328565067, 1328565067.05, 1328565067.1, 1328565067.15, 1328565067.2, 1328565067.25), V2 = c(0.0963890795246276, 0.227296347215609, 0.240972698811569, 0.221208948983498, 0.230898231782485, 0.203282153087549), V3 = c(0.0245045248243853, 0.0835679411703579, 0.0612613120609633, 0.058568910563872, 0.0511868450318788, 0.0557714205674231 )), .Names =

I'd like to get the sum of every other row in a data.frame. When I actually set about doing this, I get the error in the subject line of this message. A sample of my data is below, followed by the function call that should give me the results I want: > dput(head(sens2)) structure(list(Time = c(1328565067, 1328565067.05, 1328565067.1, 1328565067.15, 1328565067.2, 1328565067.25), Y =

I have approximately 2.5 million rows from a number of sensor readings. Having plotted these, I can see a given pattern (say a spike in the amplitude away from the mean). I would now like to automate this procedure as we're expecting a great deal more data in the near future. Is there any package or function that will make this possible? Many thanks! I suppose, I could do something like:

I have a list of numbers corresponding to timestamps, a sample of which follows: c(1327211358, 1327221999, 1327527296, 1327555433, 1327701042, 1327761389, 1327780993, 1327815670, 1327822964, 1327897497, 1327897527, 1327937072, 1327938300, 1327957589, 1328044466, 1328127921, 1328157588, 1328213951, 1328236836, 1328300276, 1328335936, 1328429102) I would like to group these into hours. In other

> dput(sensor.sample) structure(c(1328565718.65, 1328566608.9, 1328566162.65, 1328566571.1, 1328566598.85, 1328565634.3, 1328566513.95, 1328565123.65, 1328565827.1, 1328566719.9, 1328565527.55, 1328565118.05, 1328565556.85, 1328565623.85, 1328565230.75, 1328566083.85, 1328566012.45, 1328566795.75, 1328565262.85, 1328566191.35, 1328565827.8, 1328566384.25, 1328565376.95, 1328566006.8,

I have two xts objects, call them "a" and "b", and am trying to merge them... > class(a) [1] "xts" "zoo" > class(b) [1] "xts" "zoo" > head(a) 2010-04-01 7.6343 2010-04-02 7.6343 2010-04-03 7.5458 2010-04-04 7.4532 2010-04-05 7.4040 2010-04-06 7.3317 > head(b) 2010-04-01 568.80 2010-04-05 571.01 2010-04-06

I have a list of bounds for a series of polygons. I do understand the formula to determine whether point i is within polygon X (X[x1] < i[x] & X[x2] > i[x] & X[y1] < i[y] & X[y2] > i[y]), and I can apply this throughout the dataset. However, this naive algorithm doesn't scale very well. The data set contains 10,000 points consisting of (n,e) pairs where I'm

Not sure if the question is appropos, but I have multiple csv's which are read into an xts object, corresponding to telemetry data (accelerometer, magnetometer/compass, and gyroscope). For examination, it would be ?ber useful if plot.zoo (or something similar) allowed me to zoom in and out of a subset of the time axes. -- Sent from my mobile device Envoyait de mon portable

When I run the code below on Macintosh and Windows, the plot comes out fine. However, on Linux, the png generated is invalid from R console, and loading strucchange crashes rkward. Is this a known issue on Linux and, if so, is there a workaround? Many thanks! require(strucchange) data("RealInt") bp.ri <- breakpoints(RealInt~1, h=15) summary(bp.ri) fac.ri <- breakfactor(bp.ri,

I'm trying to read in a CSV, with lines looking like: HEADER, Latitude DecDeg, Latitude Hemisphere, Longitude DecDeg, Longitude Hemisphere, Speed knots, Bearing Degrees, fixQualityGga, noOfSatGga, altGga, heightGga, selectionGsa, fixGsa, pdopGsa, hdopGsa, vdopGsa, noOfSatGsv, Time, *checkSum $GPS, 3747.0224, N, 12223.4522, W, 0.36, 348.21, 1, 3, 4.01, 175.5, -25.2, A, 2, 4.14,

I was on vacation the last week and wrote some code to run a 500-day correlation between the Nasdaq tracking stock (QQQ) and 191 currency pairs for 500 days. The initial run took 9 hours(!) and I'd like to make it faster. So, I'm including my code below, in hopes that somebody will be able to figure out how to make it faster, either through parallelisation, or by making changes. I've

Hi, after spending hours on getting geokit and ym4r running I don''t know how to do or how to getting a test running. I have try the tutorial from http://www.railslodge.com/plugins/179-ym4r but I don''t get it :(. Has sone one other tutorials he can recommend? Maybe someone knows were I did my mistake (I only want to show a fixed address, no database connection so far, pure