search for: inflhigh

...olr( Sat ~ Infl + Type + Cont, data=housing, weights=Freq ) > summary( house.plr ) Re-fitting to get Hessian Call: polr(formula = Sat ~ Infl + Type + Cont, data = housing, weights = Freq) Coefficients: Value Std. Error t value InflMedium 0.5663922 0.10465276 5.412109 InflHigh 1.2888137 0.12715609 10.135682 TypeApartment -0.5723552 0.11923800 -4.800107 TypeAtrium -0.3661908 0.15517331 -2.359882 TypeTerrace -1.0910073 0.15148595 -7.202036 ContHigh 0.3602803 0.09553577 3.771156 Intercepts: Value Std. Error t value Low|Medium -0.4961 0.124...

...0.6931 0.2500 2.7726 Residual Deviance: 158.2002 AIC: 162.2002 > summary(fit2) Re-fitting to get Hessian Call: polr(formula = housing$Sat ~ housing$Infl) Coefficients: Value Std. Error t value housing$InflMedium 6.347464e-06 0.5303301 1.196889e-05 housing$InflHigh 6.347464e-06 0.5303301 1.196889e-05 Intercepts: Value Std. Error t value Low|Medium -0.6931 0.3953 -1.7535 Medium|High 0.6932 0.3953 1.7536 Residual Deviance: 158.2002 AIC: 166.2002 > summary(fit3) Re-fitting to get Hessian Call: polr(formula = housing$Sat ~ housi...

...t; house.plr<-polr(Sat~Infl+Type+Cont,data=housing,weights=Freq) > summary(house.plr)Re-fitting to get HessianCall: polr(formula = Sat ~ Infl + Type + Cont, data = housing, weights = Freq)Coefficients: Value Std. Error t value InflMedium 0.5663922 0.10465276 5.412109 InflHigh 1.2888137 0.12715609 10.135682 TypeApartment -0.5723552 0.11923800 -4.800107 TypeAtrium -0.3661907 0.15517331 -2.359882 TypeTerrace -1.0910073 0.15148595 -7.202036 ContHigh 0.3602803 0.09553577 3.771156 Intercepts: Value Std. Error t value Low|Medium -0.4961 0.1248...

Hi all, I'm trying to discover the options available to me for logistic and linear regression. I'm doing some tests on a dataset and want to see how different flavours of the algorithms cope. So far for logistic regression I've tried glm(MASS) and lrm (Design) and found there is a big difference. Is there a list anywhere detailing the options available which details the specific

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, I have changed the data into numeric

...t;) summary(house.probit) ------------------------- Re-fitting to get Hessian Call: polr(formula = Sat ~ Infl + Type + Cont, data = housing, weights = Freq, method = "probit") Coefficients: Value Std. Error t value InflMedium 0.3464233 0.06413706 5.401297 InflHigh 0.7829149 0.07642620 10.244063 TypeApartment -0.3475372 0.07229093 -4.807480 TypeAtrium -0.2178874 0.09476607 -2.299213 TypeTerrace -0.6641737 0.09180004 -7.235005 ContHigh 0.2223862 0.05812267 3.826153 Intercepts: Value Std. Error t value Low|Medium -0.2998 0.07...