ridout.appleshoots {agridat} | R Documentation |

Root counts for propagated columnar apple shoots.

data("ridout.appleshoots")

A data frame with 270 observations on the following 4 variables.

`roots`

number of roots per shoot

`trtn`

number of shoots per treatment combination

`photo`

photoperiod, 8 or 16

`bap`

BAP concentration, numeric

There were 270 micropropagated shoots from the columnar apple cultivar Trajan. During the rooting period, shoot tips of length 1.0-1.5 cm were cultured on media with different concentrations of the cytokinin BAP in two growth chambers with 8 or 16 hour photoperiod.

The response variable is the number of roots after 4 weeks at 22 degrees C.

Almost all of the shoots in the 8 hour photoperiod rooted. Under the 16 hour photoperiod only about half rooted.

High BAP concentrations often inhibit root formation of apples, but perhaps not for columnar varieties.

Used with permission of Martin Ridout.

Ridout, M. S., Hinde, J. P., and Demetrio, C. G. B. (1998). Models for Count Data with Many Zeros. Proceedings of the 19th International Biometric Conference, 179-192.

SAS. Fitting Zero-Inflated Count Data Models by Using PROC GENMOD. support.sas.com/rnd/app/examples/stat/GENMODZIP/roots.pdf

## Not run: library(agridat) data(ridout.appleshoots) dat <- ridout.appleshoots # Change photo and bap to factors dat <- transform(dat, photo=factor(photo), bap=factor(bap)) libs(lattice) # histogram(~roots, dat, breaks=0:18-0.5) # For photo=8, Poisson distribution looks reasonable. # For photo=16, half of the shoots had no roots # Also, photo=8 has very roughly 1/45 as many zeros as photo=8, # so we anticipate prob(zero) is about 1/45=0.22 for photo=8. histogram(~roots|photo, dat, breaks=0:18-0.5, main="ridout.appleshoots") libs(latticeExtra) foo.obs <- histogram(~roots|photo*bap, dat, breaks=0:18-0.5, type="density", xlab="Number of roots for photoperiod 8, 16", ylab="Density for BAP levels", main="ridout.appleshoots") useOuterStrips(foo.obs) # Ordinary (non-ZIP) Poisson GLM m1 <- glm(roots ~ bap + photo + bap:photo, data=dat, family="poisson") summary(m1) # Appears to have overdispersion # ----- Fit a Zero-Inflated Poisson model ----- libs(pscl) # Use SAS contrasts to match SAS output oo <- options(contrasts=c('contr.SAS','contr.poly')) # There are unequal counts for each trt combination, which obviously affects # the distribution of counts, so use log(trtn) as an offset. dat$ltrtn <- log(dat$trtn) # Ordinary Poisson GLM: 1 + bap*photo. # Zero inflated probability depends only on photoperiod: 1 + photo m2 <- zeroinfl(roots ~ 1 + bap*photo | 1 + photo, data=dat, dist="poisson", offset=ltrtn) logLik(m2) # -622.2283 matches SAS Output 1 -2 * logLik(m2) # 1244.457 Matches Ridout Table 2, ZIP, H*P, P summary(m2) # Coefficients match SAS Output 3. exp(coef(m2, "zero")) # Photo=8 has .015 times as many zeros as photo=16 # Get predicted _probabilities_ # Prediction data newdat <- expand.grid(photo=c(8,16), bap=c(2.2, 4.4, 8.8, 17.6)) newdat <- aggregate(trtn~bap+photo, dat, FUN=mean) newdat$ltrtn <- log(newdat$trtn) # The predicted (Poisson + Zero) probabilities d2 <- cbind(newdat[,c('bap','photo')], predict(m2, newdata=newdat, type="prob")) libs(reshape2) d2 <- melt(d2, id.var = c('bap','photo')) # wide to tall d2$xpos <- as.numeric(as.character(d2$variable)) foo.poi <- xyplot(value~xpos|photo*bap, d2, col="black", pch=20, cex=1.5) # Plot data and model foo.obs <- update(foo.obs, main="ridout.appleshoots: observed (bars) & predicted (dots)") useOuterStrips(foo.obs + foo.poi) # Restore contrasts options(oo) ## End(Not run)

[Package *agridat* version 1.18 Index]