Dear Prof. Thomas Yee



I$B!G(Bm very interested in your R program VGAM.

I tried below your data:

# Nonparametric proportional odds model

data(pneumo)pneumo = transform(pneumo, 
let=log(exposure.time))vgam(cbind(normal,mild,severe) ~ s(let), 
cumulative(par=TRUE), pneumo)

However, the results by Version of VGAM are different;

----------The result by Version 0.7-7 -----------------------------

Call:

vgam(formula = cbind(V1, V2, V3) ~ s(V4), family = cumulative(par = TRUE),

    data = train)

Number of linear predictors:    2

Names of linear predictors: logit(P[Y<=1]), logit(P[Y<=2])

Dispersion Parameter for cumulative family:   1

Residual Deviance:  2.9184 on NaN degrees of freedom

Log-likelihood: -203.2200 on NaN degrees of freedom

Number of Iterations:  6

DF for Terms and Approximate Chi-squares for Nonparametric Effects



              Df Npar Df Npar Chisq P(Chi)

(Intercept):1  1

(Intercept):2  1

s(V4)          1



----------The result by Version 0.7-3 -----------------------------

Call:

vgam(formula = cbind(V1, V2, V3) ~ s(V4), family = cumulative(par = TRUE),

    data = train)



Number of linear predictors:    2



Names of linear predictors: logit(P[Y<=1]), logit(P[Y<=2])

Dispersion Parameter for cumulative family:   1

Residual Deviance:  2.37107 on 10.368 degrees of freedom

Log-likelihood: -202.9463 on 10.368 degrees of freedom

Number of Iterations:  7

DF for Terms and Approximate Chi-squares for Nonparametric Effects

              Df Npar Df Npar Chisq  P(Chi)

(Intercept):1  1

(Intercept):2  1

s(V4)          1     2.6    1.95553 0.50986



I think that the result by Version 0.7-3 is right.



Please teach me if my result is right.



Yours sincerely,



Prof. M.Tsujitani

Osaka Electro-Communication University

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