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FME Models Lineals Generalitzats> womenlf$ones > summary(womenlf)id work income sons regionMin. : 1.0 not_work:155 Min. : 1.00 absent : 79 Atlantic: 301st Qu.: 66.5 parttime: 42 1st Qu.:10.00 present:184 BC : 29Median :132.0 fulltime: 66 Median :14.00 Ontario :108Mean :132.0 Mean :14.76 Prairie : 313rd Qu.:197.5 3rd Qu.:19.00 Quebec : 65Max. :263.0 Max. :45.00onesMin. :11st Qu.:1Median :1Mean :13rd Qu.:1Max. :1> attach(womenlf)> library(MASS)> library(nnet)> womenlf.mult summary(womenlf.mult)Call:multinom(formula = work ~ income + sons, data = womenlf, weights = ones)Coefficients:(Intercept) income sonspresentparttime -1.432321 0.006893838 0.02145558fulltime 1.982842 -0.097232073 -2.55860537Std. Errors:(Intercept) income sonspresentparttime 0.5924627 0.02345484 0.4690352Prof. Lídia Montero Pàg. 5-28 Curs 2.006-2.007
FME Models Lineals Generalitzatsfulltime 0.4841789 0.02809599 0.3621999Residual Deviance: 422.8819AIC: 434.8819Correlation of Coefficients:parttime:(Intercept) parttime:income parttime:sonspresentparttime:income -0.6951337parttime:sonspresent -0.7232561 0.1013946fulltime:(Intercept) 0.2484200 -0.1763135 -0.1779455fulltime:income -0.1147567 0.1757442 0.0097564fulltime:sonspresent -0.1935253 0.0265197 0.2689145fulltime:(Intercept) fulltime:incomeparttime:incomeparttime:sonspresentfulltime:(Intercept)fulltime:income -0.8462090fulltime:sonspresent -0.5465813 0.1989128>> # on hem obtingut els coeficients dels models:> # logit 1: parttime vs not_work> # logit 2: fulltime vs not_work>> # Model de resposta ordinal:> # situacio laboral = ingressos + fills>> library(MASS)> womenlf.polr summary(womenlf.polr)Re-fitting to get HessianCall:polr(formula = work ~ income + sons, data = womenlf, weights = ones)Coefficients:Value Std. Error t valueincome -0.05390064 0.01949011 -2.765538Prof. Lídia Montero Pàg. 5-29 Curs 2.006-2.007
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