foulley.calving {agridat} | R Documentation

## Calving difficulty by calf sex and age of dam

### Description

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.

### Source

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

Used with permission of Jean-Louis Foulley.

### Examples

    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')))
    if(require(reshape2)){
    d2 <- acast(dat, sex+age~score, value.var='count')
    d2 <- prop.table(d2, margin=1)
    require(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
    ## Not run:
      require(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
      detach("package:ordinal") # to avoid VarCorr clash with lme4

    ## End(Not run)

[Package agridat version 1.16 Index]