The function performs analysis of polynomial regression in simple designs with quantitative treatments. This function performs analysis the lack of fit
data is a data.frame
data frame with two columns, treatments and response (completely randomized design)
data frame with three columns, treatments, blocks and response (randomized block design)
data frame with four columns, treatments, rows, cols and response (latin square design)
data frame with five columns, treatments, square, rows, cols and response (several latin squares)
1 = completely randomized design
2 = randomized block design
3 = latin square design
4 = several latin squares
FALSE = a single response variable
TRUE = multivariable response
type is form of obtain sum of squares
1 = a sequential sum of squares
2 = a partial sum of squares
The response and the treatments must be numeric. Other variables can be numeric or factors.
Returns analysis of variance, models, t test for coefficients and R squared and adjusted R squared.
Emmanuel Arnhold <[email protected]>
KAPS, M. and LAMBERSON, W. R. Biostatistics for Animal Science: an introductory text. 2nd Edition. CABI Publishing, Wallingford, Oxfordshire, UK, 2009. 504p.
SAMPAIO, I. B. M. Estatistica aplicada a experimentacao animal. 3nd Edition. Belo Horizonte: Editora FEPMVZ, Fundacao de Ensino e Pesquisa em Medicina Veterinaria e Zootecnia, 2010. 264p.
lm, lme(package nlme), ea1(package easyanova), er1
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# analysis in completely randomized design data(data1) r1=er2(data1) names(r1) r1 r1 # analysis in randomized block design data(data2) r2=er2(data2, design=2) r2 # analysis in latin square design data(data3) r3=er2(data3, design=3) r3 # analysis in several latin squares data(data4) r4=er2(data4, design=4) r4 # data treatments=rep(c(0.5,1,1.5,2,2.5,3), c(3,3,3,3,3,3)) r1=rnorm(18,60,3) r2=r1*1:18 r3=r1*18:1 r4=r1*c(c(1:10),10,10,10,10,10,10,10,10) data6=data.frame(treatments,r1,r2,r3, r4) # use the argument list = TRUE er2(data6, design=1, list=TRUE)
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