search for: hous

I would like to use grep and gsub to manipulate a vector to make the names used consistent, i.e. reduce a level or two. However, here is what I found when I attempted to use grep and gsub: > tmp_test<-c("House 1 Plot Plus +100","House 2 Plot Plus +100","House 3 Plot Plus -100","House 4 Plot Plus -100","House 1 Plus +100","House 2 Plus +100","House 3 Plus -100","House 4 Plus -100") > gsub(tmp_test[2], tmp_test[1], tmp_test) [1...

Guys, Say I have the following: ----- class Person < ActiveRecord::Base has_one :house end class House < ActiveRecord::Base belongs_to :person attr_accessor :color end ----- Then I have the following code: ----- john = Person.new john.house.color = "Blue" john.save ----- What I would like to have happen, is that on first call to john.house, if house hadn''t...

Hi there, i have a model house and a model owner A house can have many owners. Now i want to find all owners of a specific street and i want to list them So i do: houses = House.find(:all, :conditions => ''street LIKE "%foo%"'') So now i got an array of all houses in that street @owners = ho...

Hello, Let''s say I have a model for a "house". Each house has_a "city", which in turn has_a "state" which in turn has_a "country". The objective is to retrieve all houses in a given state, say "Massachusetts". Being new to Ruby on Rails what I like is that things that should be simple usually are...

...here is only one independent variable, I would expect the Wald test and the likelihood ratio test to give similar p-values. However the p-values obtained from anova(fit1,fit3) (refer to example below) are very different (0.0002622986 vs. 1). Why is this so? > library(MASS) > fit1 <- polr(housing$Sat~1) > fit2<- polr(housing$Sat~housing$Infl) > fit3<- polr(housing$Sat~housing$Cont) > summary(fit1) Re-fitting to get Hessian Call: polr(formula = housing$Sat ~ 1) No coefficients Intercepts: Value Std. Error t value Low|Medium -0.6931 0.2500 -2.7726 Medi...

Hi I have read the howto on qdisc''s a few times but I cant figure out how to use the shaping capabilities to serve my needs. In the village whera I live we have created a wireless local network consisting of 10 houses. One of these houses has an ADSL connection and services this connection to the other houses. To gain access to the network a member must have an accesspoint in client mode, the idea is that if each house has to use a certain accesspoint (with a specific known ip address) to access the router the...

...rks as expected for regular main effects models, however when the model includes an interaction effect it seems to have problems with matching the parameters to the predictor terms. An example: library("MASS"); options(contrasts = c("contr.treatment", "contr.poly")); house.plr1 <- polr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing); drop1(house.plr1,attributes(house.plr1$terms)$term.labels,test="Chisq"); house.plr2 <- polr(Sat ~ Infl * Type + Cont, weights = Freq, data = housing); drop1(house.plr2,attributes(house.plr2$terms)$term.labels...

...but polr apparently thinks there is a minus in front of eta, as is apprent below. Is this a bug og a feature I have overlooked? Here is the naked code for reproduction, below the results. ------------------------------------------------------------------------ --- version library( MASS ) data( housing ) hnames <- lapply( housing[,-5], levels ) house.plr <- polr( Sat ~ Infl + Type + Cont, data=housing, weights=Freq ) summary( house.plr ) newdat <- expand.grid( hnames[-1] )[1:5,] cbind( newdat, predict( house.plr, newdat, type="probs" ) ) # Baseline probs: diff( c(0,tigol( c(...

Suppose I have one table: states id statename And I have two other tables that contains states: houses id color state_id places id place_name state_id How would my model relationships look like? class State < ActiveRecord::Base belongs_to house belongs_to place end class House < ActiveRecord::Base has_one state end class Place < ActiveRecord::Base...

...ou have been unable to find the information from the links below please contact us again telling us you have not been able to find the information and restate as specifically as possible your question. Most general questions can be answered by referring to the A-Z section of our website: http://housing.lon.ac.uk/site7/AZ.htm Some specific areas of common enquiry: CAN WE HELP YOU? http://housing.lon.ac.uk/site7/College.htm I START NEXT SEPTEMBER CAN YOU HELP? As no suitable offers are published prior to MAY you should re-apply for a password at the end of APRIL. You will not be sent a passwo...

Given a set of data: > names(data) [1] "city" "house" "visit" "value" I am looking for a way to compute the variance components of the nested model (ie, visit 1 at house 2 at city 3 isn't related to visit 1 and house 2 at city 4), but different houses in the same city may be related, and different visits to the s...

Hi all, Suppose I had the below example where a survey was carried out recording the number of each type of pet in each house count<-c(2,1,2,1,2,3,4) house<-c("house1","house1","house2","house3","house4","house4","house4") pet<-c("dogs","cats","dogs","dogs","budgie","cat","hamst...

Hi, I am trying to test the proportional-odds model using the "polr" function in the MASS library with the dataset of "housing" contained in the MASS book ("Sat" (factor: low, medium, high) is the dependent variable, "Infl" (low, medium, high), "Type" (tower, apartment, atrium, terrace) and "Cont" (low, high) are the predictor variables (factors). And I have some questions, h...

Hi all, Is there a way to create pagination with a complex SQL, more complex than the :conditions option will support? I have two databases, houses and images. Houses has_many images, and each Image belongs_to house. I''m creating a search engine for the house records and I''d like to be able to filter out all the houses without any associated images. So far I''m doing this by constructing a SQL query that goes s...

Hi, I am renovating my house completely and installing new cabling for communication. I'm not to into this PBX thing but I would like to have a simple one for my house, to have different phone numbers for my family members, some kind of integration with my Broadband telephone, and possibly Skype.... Browsing the digi...

Hi all, The nobs method of (MASS:::polr class) takes into account of weight, but nobs method of glm does not. I wonder what is the rationale of such design behind nobs.glm. Thanks in advance. Best Regards. > library(MASS) > house.plr <- polr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing) > house.logit <- glm(I(Sat=='High') ~ Infl + Type + Cont, binomial,weights = Freq, data = housing) > nobs(house.plr) [1] 1681 > nobs(house.logit) [1] 72 -- Wincent Ronggui HUANG Sociology Department of...

Hello: My dataset set contains several thousand rows of data, with each row containing information for a house. The variables include the sale price of the house, the quarter and year of sale, the attributes of the house, and the attributes of the neighborhood and the city in which the house is located. The data is for a 10-year period. No ho...

Hi all, I''m looking for some insight on why my text_field_with_auto_complete might not work in Internet Explorer. I''m using it in a fairly simple way, without much customization. I''ve broken it down and made a test page at http://realty.colemanation.org/houses/test which shows the behavior I''m having problems with. For me on Windows XP this page works fine in Firefox, with the autocomplete fields finding their values from the database and doing what they should. In Internet Explorer there''s no autocomplete behavior at all. (Not...

Dear All, I have been struggling to understand why for the housing data in MASS library R and stata give coef. estimates that are really different. I also tried to come up with many many examples myself (see below, of course I did not have the set.seed command included) and all of my `random' examples seem to give verry similar output. For the housing data,...

Hi all, I have data collected from a survey administered on a subset of the population. I also have the population proportions of variables such as gender, race and housing type. I would like to combine the weights from each separate cross tab (of gender, race and housing type) such that the weighted proportions of my survey data matches that of the population. I have tried the following: library(survey) gender.population <- read.table("http://dl.dropbo...