case1301(Sleuth2)
case1301()所属R语言包：Sleuth2
Seaweed Grazers
海藻农牧
译者：生物统计家园网 机器人LoveR
描述----------Description----------
To study the influence of ocean grazers on regeneration rates of seaweed in the intertidal zone, a researcher scraped rock plots free of seaweed and observed the degree of regeneration when certain types of seaweed-grazing animals were denied access. The grazers were limpets (L), small fishes (f) and large fishes (F). Each plot received one of six treatments named by which grazers were allowed access. In addition, the researcher applied the treatments in eight blocks of 12 plots each. Within each block she randomly assigned treatments to plots. The blocks covered a wide range of tidal conditions.
研究海洋食草动物在潮间带海藻的再生率的影响，研究人员刮岩图的海藻和观察再生的程度时，某些类型的海藻放牧动物被拒绝访问。草食动物帽贝（L），小鱼（f）和大型鱼类（F）。每个小区处理命名的食草动物被允许访问。此外，研究者的治疗应用，在8块的每个12个图。在每个区块内，，她随机分为治疗方法，以图。这些模块涵盖了广泛的潮汐情况而定。
用法----------Usage----------
case1301
格式----------Format----------
A data frame with 96 observations on the following 3 variables.
一个数据框，96以下3个变量的观察。
Coverpercent of regenerated seaweed cover
Cover％的再生海藻盖
Blocka factor with levels "B1", "B2", "B3", "B4", "B5", "B6", "B7" and "B8"
Block同级别的因素"B1"，"B2"，"B3"，"B4"，"B5"，"B6"，"B7"和"B8"
Treata factor indicating treatment, with levels "C", "f", "fF", "L", "Lf" and "LfF"
Treat的一个因素处理，同级别"C"，"f"，"fF"，"L"，"Lf"和"LfF"
源----------Source----------
Ramsey, F.L. and Schafer, D.W. (2002). The Statistical Sleuth: A Course in Methods of Data Analysis (2nd ed), Duxbury.
拉姆齐，F.L.和Schafer，D.W. （2002年）。的统计的猎犬：A课程方法的数据分析（第二版），达克斯伯里。
参考文献----------References----------
Olson, A. (1993). Evolutionary and Ecological Interactions Affecting Seaweeds, Ph.D. Thesis. Oregon State University.
实例----------Examples----------
str(case1301)
# full two-way model with interactions[完整的双向互动模型]
fitfull <- aov(Cover ~ Treat*Block, case1301)
# Residual plot indicates a transformation might help[残差图表示的转变可能帮助]
plot(fitfull)
# Log of seaweed "regeneration ratio" [海藻“再生率”]
y <- with(case1301, log(Cover/(100-Cover)))
# Full two-way model with interactions[双向交互模型]
fitfull <- aov(y~Treat*Block, case1301)
# No problems indicated by residual plot[没有所指出的问题残差图]
plot(fitfull)
# Note that interactions are not statistically significant[请注意，没有统计学显着的相互作用是]
anova(fitfull)
# Additive model (no interactions)[加性模型（无相互作用）]
fitadditive <- aov(y ~ Treat + Block, case1301)
# Make indicator variables for presence of limpets, small fish, and large fish [指标变量的存在，帽贝，小鱼，大的鱼]
lmp <- with(case1301, ifelse(Treat %in% c("L", "Lf", "LfF"), 1, 0))
sml <- with(case1301, ifelse(Treat %in% c("f", "fF", "Lf", "LfF"), 1, 0))
big <- with(case1301, ifelse(Treat %in% c("fF", "LfF"), 1, 0))
fitsimple <- lm(y ~ Block + lmp + sml + big, case1301)
# Model with main effects of 3 "presence" factors seems ok.[型号与主效应3“存在”的因素，似乎确定。]
anova(fitsimple, fitadditive)
summary(fitsimple, cor=FALSE)
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-9-30 09:56:59
