Dear list, I am relatively new to ordinal models and have been working through the example given by Frank Harrell in the predict.lrm {Design} help
All of this makes sense to me, except for the responses, i,e how do i interpret them? i would be extremely grateful if someone could explain the results? First i establish the date and model - > y <- factor(sample(1:3, 400, TRUE), 1:3, c('good','better','best')) > x1 <- runif(400) > x2 <- runif(400) > f <- lrm(y ~ rcs(x1,4)*x2, x=TRUE) Get 0.95 confidence limits for Prob[better or best # How do i interpret this on the y scale i.e good,better,best? > > L <- predict(f, se.fit=TRUE) #omitted kint= so use 1st intercept > plogis(with(L, linear.predictors + 1.96*cbind(-se.fit,se.fit))) > se.fit > 1 0.6430994 0.8305201 > 2 0.5812662 0.7919122 > 3 0.5692593 0.7976906 > 4 0.5600308 0.7278637 > 5 0.6845250 0.8819143 > 6 0.5518848 0.7228657 > 7 0.5876031 0.7717215 > 8 0.6291766 0.8354423 > 9 0.5839353 0.8333790 > 10 0.5631326 0.8314051 Get Prob(better) than all others - # Does this mean that for data point 1, y= best as it has the higher probability? > predict(f, type="fitted.ind")[1:10,] y=good y=better y=best 1 0.2517915 0.3469692 0.4012392 2 0.3031733 0.3554471 0.3413796 3 0.3046236 0.3555365 0.3398398 4 0.3514780 0.3546880 0.2938340 5 0.1989827 0.3251784 0.4758390 6 0.3581265 0.3540297 0.2878438 7 0.3130150 0.3559091 0.3310759 8 0.2541324 0.3476007 0.3982669 9 0.2740127 0.3519713 0.3740160 10 0.2839907 0.3535331 0.3624763 Establish data frame to use as newdata > d <- data.frame(x1=c(.1,.5),x2=c(.5,.15)) Predict newdata - Prob(Y>=j) for new observation > predict(f, d, type="fitted") # Does this mean that for data point 1, y= better as it has the higher probability? y>=better y>=best 1 0.6800290 0.3239935 2 0.5846743 0.2409657 # Prob(Y=j) # Again - Does this mean that for data point 1, y= better as it has the higher probability? predict(f, d, type="fitted.ind") y=good y=better y=best 1 0.3199710 0.3560355 0.3239935 2 0.4153257 0.3437086 0.2409657 predict mean(y) using codes 1,2,3 # How do i interpret this on the y scale i.e good,better,best? > predict(f, d, type='mean', codes=TRUE) 1 2 2.004022 1.825640 Thanks for any advice it is greatly appreciated John [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.