yates.missing: Factorial experiment with missing values In agridat: Agricultural Datasets

Description

Potato factorial experiment with missing values

Format

A data frame with 80 observations on the following 3 variables.

trt

treatment factor with levels 0 K N P NK KP NP NKP

block

block, 10 levels

y

infection intensity

Details

The response variable y is the intensity of infection of potato tubers innoculated with Phytophthora Erythroseptica.

Yates (1933) presents an iterative algorithm to estimate missing values in a matrix, using this data as an example.

Source

F. Yates, 1933. The analysis of replicated experiments when the field results are incomplete. Emp. J. Exp. Agric., 1, 129–142.

References

Steel & Torrie, 1980, Principles and Procedures of Statistics, 2nd Edition, page 212.

Examples

 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 ## Not run: library(agridat) data(yates.missing) dat <- yates.missing libs(lattice) bwplot(y ~ trt, data=dat, xlab="Treatment", ylab="Infection intensity", main="yates.missing") libs(reshape2) mat0 <- acast(dat[, c('trt','block','y')], trt~block, id.var=c('trt','block'), value.var='y') # Use lm to estimate missing values. The estimated missing values # are the same as in Yates (1933) m1 <- lm(y~trt+block, dat) dat\$pred <- predict(m1, new=dat[, c('trt','block')]) dat\$filled <- ifelse(is.na(dat\$y), dat\$pred, dat\$y) mat1 <- acast(dat[, c('trt','block','pred')], trt~block, id.var=c('trt','block'), value.var='pred') # Another method to estimate missing values via PCA libs("nipals") m2 <- nipals(mat0, center=FALSE, ncomp=3, fitted=TRUE) # mat2 <- m2\$scores mat2 <- m2\$fitted # Compare ord <- c("0","N","K","P","NK","NP","KP","NKP") print(mat0[ord,], na.print=".") round(mat1[ord,] ,2) round(mat2[ord,] ,2) # SVD with 3 components recovers original data better sum((mat0-mat1)^2, na.rm=TRUE) sum((mat0-mat2)^2, na.rm=TRUE) # Smaller SS => better fit ## End(Not run)

agridat documentation built on Dec. 20, 2021, 9:07 a.m.