Calving difficulty by calf sex and age of dam

Usage

data("foulley.calving")

Format

A data frame with 54 observations on the following 4 variables.

sex: calf gender
age: dam age factor, 9 levels
score: score for birthing difficulty, S1 < S2 < S3
count: count of births for each category

Details

These data are calving difficulty scores for purebred US Simmental cows.

The raw data show that the greatest calving difficulty is for young dams with male calves. Differences between male/female calves decreased with age of the dam.

The goodness of fit can be improved by using a scaling effect for age of dam.

Note: The paper by Foulley and Gianola has '21943' as the count for score 1, F, >8. This data uses '20943' so that the marginal totals from this data match the marginal totals given in the paper.

Used with permission of Jean-Louis Foulley.

Source

JL Foulley, D Gianola (1996). Statistical Analysis of Ordered Categorical Data via a Structured Heteroskedastic Threshold Model. Genet Sel Evol, 28, 249–273. https://doi.org/10.1051/gse:19960304

Examples

if (FALSE) { # \dontrun{

library(agridat)
data(foulley.calving)
dat <- foulley.calving

## Plot
d2 <- transform(dat,
                age=ordered(age, levels=c("0.0-2.0","2.0-2.5","2.5-3.0",
                                          "3.0-3.5","3.5-4.0",
                                          "4.0-4.5","4.5-5.0","5.0-8.0","8.0+")),
                score=ordered(score, levels=c('S1','S2','S3')))
libs(reshape2)
d2 <- acast(dat, sex+age~score, value.var='count')
d2 <- prop.table(d2, margin=1)
libs(lattice)
thm <- simpleTheme(col=c('skyblue','gray','pink'))
barchart(d2, par.settings=thm, main="foulley.calving",
         xlab="Frequency of calving difficulty", ylab="Calf gender and dam age",
         auto.key=list(columns=3, text=c("Easy","Assited","Difficult")))


## Ordinal multinomial model

libs(ordinal)
m2 <- clm(score ~ sex*age, data=dat, weights=count, link='probit')
summary(m2)

##   Coefficients:
##                  Estimate Std. Error z value Pr(>|z|)    
## sexM             0.500605   0.015178  32.982  < 2e-16 ***
## age2.0-2.5      -0.237643   0.013846 -17.163  < 2e-16 ***
## age2.5-3.0      -0.681648   0.018894 -36.077  < 2e-16 ***
## age3.0-3.5      -0.957138   0.018322 -52.241  < 2e-16 ***
## age3.5-4.0      -1.082520   0.024356 -44.446  < 2e-16 ***
## age4.0-4.5      -1.146834   0.022496 -50.981  < 2e-16 ***
## age4.5-5.0      -1.175312   0.028257 -41.594  < 2e-16 ***
## age5.0-8.0      -1.280587   0.016948 -75.559  < 2e-16 ***
## age8.0+         -1.323749   0.024079 -54.974  < 2e-16 ***
## sexM:age2.0-2.5  0.003035   0.019333   0.157  0.87527    
## sexM:age2.5-3.0 -0.076677   0.026106  -2.937  0.00331 ** 
## sexM:age3.0-3.5 -0.080657   0.024635  -3.274  0.00106 ** 
## sexM:age3.5-4.0 -0.135774   0.032927  -4.124 3.73e-05 ***
## sexM:age4.0-4.5 -0.124303   0.029819  -4.169 3.07e-05 ***
## sexM:age4.5-5.0 -0.198897   0.038309  -5.192 2.08e-07 ***
## sexM:age5.0-8.0 -0.135524   0.022804  -5.943 2.80e-09 ***
## sexM:age8.0+    -0.131033   0.031852  -4.114 3.89e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Threshold coefficients:
##       Estimate Std. Error z value
## S1|S2  0.82504    0.01083   76.15
## S2|S3  1.52017    0.01138  133.62

## Note 1.52017 - 0.82504 = 0.695 matches Foulley's '2-3' threshold estimate

predict(m2) # probability of each category



} # }