agridat (version 1.23)

johnson.blight: Potato blight due to weather in Prosser, Washington

Description

Potato blight due to weather in Prosser, Washington

Format

A data frame with 25 observations on the following 6 variables.

year

year

area

area affected, hectares

blight

blight detected, 0/1 numeric

rain.am

number of rainy days in April and May

rain.ja

number of rainy days in July and August

precip.m

precipitation in May when temp > 5C, milimeters

Details

The variable 'blight detected' is 1 if 'area' > 0.

References

Vinayanand Kandala, Logistic Regression

Examples

if (FALSE) {

library(agridat)
data(johnson.blight)
dat <- johnson.blight

# Define indicator for blight in previous year
dat$blight.prev[2:25] <- dat$blight[1:24]
dat$blight.prev[1] <- 0 # Need this to match the results of Johnson
dat$blight.prev <- factor(dat$blight.prev)
dat$blight <- factor(dat$blight)

# Johnson et al developed two logistic models to predict outbreak of blight

m1 <- glm(blight ~ blight.prev + rain.am + rain.ja, data=dat, family=binomial)
summary(m1)
##              Estimate Std. Error z value Pr(>|z|)
## (Intercept)  -11.4699     5.5976  -2.049   0.0405 *
## blight.prev1   3.8796     1.8066   2.148   0.0318 *
## rain.am        0.7162     0.3665   1.954   0.0507 .
## rain.ja        0.2587     0.2468   1.048   0.2945
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## (Dispersion parameter for binomial family taken to be 1)

##     Null deviance: 34.617  on 24  degrees of freedom
## Residual deviance: 13.703  on 21  degrees of freedom
## AIC: 21.703



m2 <- glm(blight ~ blight.prev + rain.am + precip.m, data=dat, family=binomial)
summary(m2)
##              Estimate Std. Error z value Pr(>|z|)
## (Intercept)   -7.5483     3.8070  -1.983   0.0474 *
## blight.prev1   3.5526     1.6061   2.212   0.0270 *
## rain.am        0.6290     0.2763   2.276   0.0228 *
## precip.m      -0.0904     0.1144  -0.790   0.4295
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## (Dispersion parameter for binomial family taken to be 1)

##     Null deviance: 34.617  on 24  degrees of freedom
## Residual deviance: 14.078  on 21  degrees of freedom
## AIC: 22.078

libs(lattice)
splom(dat[,c('blight','rain.am','rain.ja','precip.m')],
      main="johnson.blight - indicator of blight")

}