summary {ScottKnott} | R Documentation |
Returns (and prints) a summary list for SK
and SK.nest
objects.
## S3 method for class 'SK': summary(object, ...) ## S3 method for class 'SK.nest': summary(object, ...)
object |
A given object of the class SK or SK.nest . |
... |
Potential further arguments (require by generic). |
Enio Jelihovschi (eniojelihovs@gmail.com)
Jose Claudio Faria (joseclaudio.faria@gmail.com)
Sergio Oliveira (solive@uesc.br)
Chambers, J. M. and Hastie, T. J. (1992) Statistical Models in S. Wadsworth & Brooks/Cole.
## ## Example: Split-plot Experiment (SPE) ## ## Experimental structure: ## +------------------------------------+ ## | Block | ## +---------+------+------------------------------------+ ## | Variety | Man. | I II III IV V VI | ## +---------+------+------------------------------------+ ## | Ladak | A | 2.17 1.88 1.62 2.34 1.58 1.66 | ## | | B | 1.58 1.26 1.22 1.59 1.25 0.94 | ## | | C | 2.29 1.60 1.67 1.91 1.39 1.12 | ## | | D | 2.23 2.01 1.82 2.10 1.66 1.10 | ## +---------+------+------------------------------------+ ## | Cossack | A | 2.33 2.01 1.70 1.78 1.42 1.35 | ## | | B | 1.38 1.30 1.85 1.09 1.13 1.06 | ## | | C | 1.86 1.70 1.81 1.54 1.67 0.88 | ## | | D | 2.27 1.81 2.01 1.40 1.31 1.06 | ## +---------+------+------------------------------------+ ## | Ranger | A | 1.75 1.95 2.13 1.78 1.31 1.30 | ## | | B | 1.52 1.47 1.80 1.37 1.01 1.31 | ## | | C | 1.55 1.61 1.82 1.56 1.23 1.13 | ## | | D | 1.56 1.72 1.99 1.55 1.51 1.33 | ## +---------+-------------------------------------------+ ## Generating data y <- c(2.17, 1.88, 1.62, 2.34, 1.58, 1.66, 1.58, 1.26, 1.22, 1.59, 1.25, 0.94, 2.29, 1.60, 1.67, 1.91, 1.39, 1.12, 2.23, 2.01, 1.82, 2.10, 1.66, 1.10, 2.33, 2.01, 1.70, 1.78, 1.42, 1.35, 1.38, 1.30, 1.85, 1.09, 1.13, 1.06, 1.86, 1.70, 1.81, 1.54, 1.67, 0.88, 2.27, 1.81, 2.01, 1.40, 1.31, 1.06, 1.75, 1.95, 2.13, 1.78, 1.31, 1.30, 1.52, 1.47, 1.80, 1.37, 1.01, 1.31, 1.55, 1.61, 1.82, 1.56, 1.23, 1.13, 1.56, 1.72, 1.99, 1.55, 1.51, 1.33) var <- sort(gl(3, 24, lab=c('Ladak', 'Cossack', 'Ranger'))) man <- rep(gl(4, 6, lab=LETTERS[1:4]), 3) sub <- rep(gl(4, 6), 3) blo <- factor(rep(1:6, 12)) dm <- data.frame(var, man, sub, blo) # Design matrix (a data.frame object) dfm <- data.frame(var, sub, man, blo, y) ## PARAMETERS ARE THE DESIGN MATRIX AND THE RESPONSE VARIABLE ## MAIN FACTOR ANALYSIS ## Main factor = var sk1 <- SK(x=dm, y=y, model='y ~ blo + man*var + Error(blo/var)', which='var', error ='blo:var') summary(sk1) plot(sk1) ## Main factor = man sk2 <- SK(x=dm, y=y, model='y ~ blo + man*var + Error(blo/var)', which='man', error ='Within', sig.level=0.025 ) summary(sk2) plot(sk2, title='man', xlab='Groups', ylab='Group means') ## NESTED ANALYSIS ## Nested man/var=1 -> SK.nest sk3 <- SK.nest(x=dm, y=y, model='y ~ blo + man*var + Error(blo/var)', which='man:var', error ='Within', fl2=1 ) summary(sk3) plot(sk3, title='man/var=1')