search for: territory

...for several hours now and I have made no progress. Here''s the issue: I have a table of sales reps that is linked to a table of territories. There are around a thousand territories that are numbered and each sales reps has many territories. I will be storing the sales rep id in the territory table to associate the two tables. I have not been specifying a "belongs to" and "has_many" relationship in my models because I need to be able to take a range of territories (for example 34-79) and since there are so many territories, I need to do this with text fields and...

...g classes using has_many :through relationships... (I''ve changed the class names to make it easier to understand, so don''t beat me up about naming conventions ;) ) class Movie < ActiveRecord::Base has_many :localdetails has_many :territories, :through=>:rights end class Territory < ActiveRecord::Base has_many :localdetails has_many :movies, :through=>:rights end class LocalDetail < ActiveRecord::Base belongs_to :movie belongs_to :territory end The tables contain the usual *_id fields, and all display correctly, so no need to worry there. Localdetails contai...

...Violet Swakman Hello everyone, I'm having trouble understanding my output from a linear mixed effects model (nlme :: lme), I hope someone can help me. Say I'm interested in the effect of Tarsus length on the Bar length of feathers. I used an lme since some birds were living in the same territory, so territory was included as random effect. Both Bar length and Tarsus length are seen as numerical values, Territory is seen as a factor. ######################### m1 <- lme(Bar_length~Tarsus_length, random = ~ 1|Territory, data=data) > summary(m1) Linear mixed-effects model fit by REML...

I am using package ks() to build 3D representations of bird territories and calculate territory volume from spatial data (simply x, y, and z coordinates). What I want to do is determine at what sample size (# locations collected) does the territory volume stop increasing. This should give me an idea of the number of points needed for future seasons. So I have a couple of birds each with 200...

...ch: hatching date (julian calendar) I have some NAs in the data. I want to test the model with all the variable then i want to remove some, but the ideal model: GLM.1 <-lmer(fledgesucc ~ +lay +hatch +elev +seadist +nnd +npd +meanterranova +minpengS1 +minpengS2 +wchillpengS1 +wchillpengS2 +(1|territory), family=binomial(logit), data=fledge) doesn't work because of these errors: 'Warning message: In mer_finalize(ans) : gr cannot be computed at initial par (65)'. "matrix is not symmetric [1,2]" If i delete one or more of the T records (i.e. minpengS2 +wchillpengS2) the mod...

...+\\)\\)?\\(\\.\\([A-Za-z0-9-]+\\)\\)?\\(@\\([A-Za-z]+\\)\\)?$" in + let l = ref [] in + if Str.string_match regex loc 0 then ( + let match_or_empty n = + try Str.matched_group n loc with + | Not_found -> "" + in + let lang = Str.matched_group 1 loc in + let territory = match_or_empty 3 in + (match territory with + | "" -> () + | territory -> l := (lang ^ "_" ^ territory) :: !l); + l := lang :: !l; + ); + l := "" :: !l; + List.rev !l + +let languages () = + match Setlocale.setlocale Setlocale.LC_MESSAGES None w...

...) numberProvs <- length(Pcode) # This next step reverses the order of Pcode to make # the names match the sorted Area names(Area) <- rev(Pcode) Lines <- 1:numberProvs plot(sort(Area), Lines, pch=20, col="red", xlab = " Area", ylab= "Province or Territory", axes=F) axis( 2, at=Lines, labels=names(Area), las=1) axis (1, labels= T) xstart <- 0 xend <- Area ystart <- rev(Lines) yend <- rev(Lines) arrows(xstart, ystart, xend, yend, col="red", length = 0) # --------------End of Crude working program----------------------------...

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Hello R users, I am trying to run a model with a binary response variable (nesting success: 0 failure, 1 success) and 8 fixed terms. Nesting success was examined in 72 cases in 34 territories (TER) during a 6 study years. Territories are nested within 14 patches (PATCH). I want to run a model taking into account these nested factors and repeated observation. To do this, I assume that the best

...s trickier. IME it's best > to avoid doing that as much as possible. If we have automation that looks at public topic branches (possibly because of a github pull request), then people will start making branches public sooner rather than later. That's when we start getting into fixup territory. If we don't put any of that automation in place, and we only run tests post-commit, then yeah, I think that keeping changes private as long as possible is probably best. -- Employee of Qualcomm Innovation Center, Inc. Qualcomm Innovation Center, Inc. is a member of Code Aurora Forum, a Li...

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Folks, I've entered into an R programming territory I'm not very familiar with, thus this probably very elementary question concerning the mechanic of a function call. I want to know from within a function the name of the variables I pass down. The function makes use of the "..." to allow for multiple unknown arguments, myfun = funct...

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...t;Jan.04","Feb.04","Mar.04", "Apr.04","May.04","Jun.04","Jul.04","Aug.04","Sep.04", "Oct.04")),direction = "long") The second dataset contains the variables es(Ccalls) [1] "TERRITORY" "REGION" "ACTIVITY_TYPE" [4] "CONTACT_TYPE" "TARGETED_FLG" "CONTACT_GRADE" [7] "CONTACT_PR_SPECIALITY" "UBM" "DEC02"...

Can anyone point me to a map file for New Zealand Territorial Local Authorities? Apologies if this is obvious, but I'm rather new to mapping things, and after an R search and a general Google search I can't see anything which seems quite right. I'm hoping that there is a publicly available file which will do it...

...ntities" for which I have ~500 measurements each. So, I have a matrix of 20 rows by ~500 columns. The 20 entities fall into two classes: "good" and "bad." I eventually would like to derive a model that would then be able to classify new entities as being in "good territory" or "bad territory" based upon my existing data set. I know that not all ~500 measurements are meaningful, so I thought the best place to begin would be to do a PCA in order to reduce the amount of data with which I have to work. I did this using the prcomp function and found...

...ily taken to be 1) Null deviance: 323.58 on 661 degrees of freedom Residual deviance: 285.65 on 659 degrees of freedom AIC: 291.65 Number of Fisher Scoring iterations: 6 Next, here's the results of a glmmPQL run: > apfa.glmm.1<-glmmPQL(Success~MeanAge+I(MeanAge^2), random=~1|Territory, family=logexposure(link="logexp", ExposureDays=apfa4$Days), data=apfa4) iteration 1 > summary(apfa.glmm.1) Linear mixed-effects model fit by maximum likelihood Data: apfa4 AIC BIC logLik NA NA NA Random effects: Formula: ~1 | Territory (Intercept) Residual StdDev...

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...quot;10"&gt;Prince Edward Island&lt;/option&gt;<br /> &lt;option value="3"&gt;Qu?bec&lt;/option&gt;<br /> &lt;option value="4"&gt;Saskatchewan&lt;/option&gt;<br /> &lt;option value="11"&gt;Yukon Territory&lt;/option&gt;&lt;select id="province_id" name="province_id"&gt;&lt;option value="2"&gt;Alberta&lt;/option&gt;<br /> &lt;option value="1"&gt;British Columbia&lt;/option&gt;<br /> &lt;option val...