# NOT RUN { # Example 1. See help(glm) print(d.AD <- data.frame(treatment = gl(3, 3), outcome = gl(3, 1, 9), counts = c(18,17,15,20,10,20,25,13,12))) vglm.D93 <- vglm(counts ~ outcome + treatment, family = poissonff, data = d.AD, trace = TRUE) summary(vglm.D93) # Example 2. Multinomial logit model pneumo <- transform(pneumo, let = log(exposure.time)) vglm(cbind(normal, mild, severe) ~ let, multinomial, data = pneumo) # Example 3. Proportional odds model fit3 <- vglm(cbind(normal, mild, severe) ~ let, propodds, data = pneumo) coef(fit3, matrix = TRUE) constraints(fit3) model.matrix(fit3, type = "lm") # LM model matrix model.matrix(fit3) # Larger VGLM (or VLM) model matrix # Example 4. Bivariate logistic model fit4 <- vglm(cbind(nBnW, nBW, BnW, BW) ~ age, binom2.or, coalminers) coef(fit4, matrix = TRUE) depvar(fit4) # Response are proportions weights(fit4, type = "prior") # Example 5. The use of the xij argument (simple case). # The constraint matrix for 'op' has one column. nn <- 1000 eyesdat <- round(data.frame(lop = runif(nn), rop = runif(nn), op = runif(nn)), digits = 2) eyesdat <- transform(eyesdat, eta1 = -1 + 2 * lop, eta2 = -1 + 2 * lop) eyesdat <- transform(eyesdat, leye = rbinom(nn, size = 1, prob = logitlink(eta1, inverse = TRUE)), reye = rbinom(nn, size = 1, prob = logitlink(eta2, inverse = TRUE))) head(eyesdat) fit5 <- vglm(cbind(leye, reye) ~ op, binom2.or(exchangeable = TRUE, zero = 3), data = eyesdat, trace = TRUE, xij = list(op ~ lop + rop + fill(lop)), form2 = ~ op + lop + rop + fill(lop)) coef(fit5) coef(fit5, matrix = TRUE) constraints(fit5) fit5@control$xij head(model.matrix(fit5)) # Example 6. The use of the 'constraints' argument. as.character(~ bs(year,df=3)) # Get the white spaces right clist <- list("(Intercept)" = diag(3), "bs(year, df = 3)" = rbind(1, 0, 0)) fit1 <- vglm(r1 ~ bs(year,df=3), gev(zero = NULL), data = venice, constraints = clist, trace = TRUE) coef(fit1, matrix = TRUE) # Check # }
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