Output from redwing3.r

Transcription

1 Output from redwing3.r # redwing3.r library(doby) library(nlme) library(lsmeans) #library(lme4) # not used #library(lmertest) # not used #library(multcomp) # get the data # you may want to change the path to where you put the data set redwing<-read.table(file="redwing3.dat",header=t) head(redwing) treat block oil 1 a a a a b b is.factor(redwing$block) [1] FALSE redwing$blockfac <- factor(redwing$block) # Method 1: compute mean response for each plot and analyze those means # with standard model for an unreplicated RCBD # to get the mean for each plot we can use the # summaryby function in the doby package (EUdata <- summaryby(oil ~ treat + blockfac, data = redwing, + FUN = function(x) { c(m = mean(x)) } ) ) treat blockfac oil.m 1 a a a a b b b b c c c c d d d d e e e

2 20 e f f f f # produces oil.m for each # combination of the levels of treat and blockfac redwing2 <- groupeddata(oil.m~treat blockfac, data=eudata) # fit the linear mixed model m1<-lme(oil.m~treat,data=redwing2, random= ~1 blockfac) summary(m1) Data: redwing StdDev: Fixed effects: oil.m ~ treat (Intercept) treatb treatc treatd treate treatf (Intr) treatb treatc treatd treate treatb treatc treatd treate treatf Number of Observations: 24 Number of Groups: 4 anova(m1,terms="treat",type="marginal") F-test for: treat # test the contrasts of interest # Here we use the lsmeans() function in package lsmeans c1<-c(1,1,1,1,1,-5) lsmeans(m1,specs=lsm~treat,contr=list(lsm=list(treat.v.control=c1))) $`treat lsmeans`

3 treat lsmean SE df asymp.lcl asymp.ucl a NA b NA c NA d NA e NA f NA $`treat lsm` estimate SE df z.ratio p.value treat.v.control NA p values are not adjusted # The lsmeans function call above gives a test statistic that agrees with the # F stats from PROC MIXED, but it treats these test statistics as Z tests, not # t-tests. They are contrasts tests of the form C/s.e.(C) where C is the # estimated contrast, but the lsmeans function does not implement these as # t tests. Their squares are still the F statistics produced by the CONTRAST # statement in SAS, but lsmeans in R treats these as Z tests and uses their # large sample distribution, which is standard normal under the null # hypothesis, rather than t (or F if squared). This is emphasized by the # fact that the denominator DF given by lsmeans is listed as NA. # Because the lsmeans function refers the statistics to their large sample # std normal distribution rather than to a t distribution, it gives # different p-values than does PROC MIXED. # The anova function can be used to test hypotheses on the treatment means # with F tests. It is easier, though, if we first refit the model without # an intercept (that is, in the parameterization y_ij=mu_i+b_j+e_ij) # Then the fitted mu_i's are the estimated treatment means and specifying # the contrasts become a bit easier: # refit with alternate parameterization: m1a <-lme(oil.m~treat-1,data=redwing2, random= ~1 blockfac) summary(m1a) Data: redwing StdDev: Fixed effects: oil.m ~ treat - 1 treata treatb treatc treatd treate treatf treata treatb treatc treatd treate treatb 0.027

4 treatc treatd treate treatf Number of Observations: 24 Number of Groups: 4 # Test average of inoculation treatment means vs control mean: anova(m1a,type="marginal", L=c1) # agrees with SAS's result from CONTRAST F-test for linear combination(s) treata treatb treatc treatd treate treatf # Method 2: analyze data at subsample level. redwing$eu <- factor(paste(redwing$treat,redwing$block, sep="")) head(redwing) treat block oil blockfac EU 1 a a1 2 a a2 3 a a3 4 a a4 5 b b1 6 b b2 redwing3 <- groupeddata(oil~treat blockfac/eu, data=redwing) # fit the linear mixed model m2 <- lme(oil~treat,data=redwing3, random=list(blockfac= ~1, EU= ~1)) summary(m2) Data: redwing (Intercept) StdDev: Formula: ~1 EU %in% blockfac StdDev: Fixed effects: oil ~ treat (Intercept) treatb treatc treatd

5 treate treatf (Intr) treatb treatc treatd treate treatb treatc treatd treate treatf Number of Observations: 48 Number of Groups: blockfac EU %in% blockfac 4 24 # Now test main effect of treat anova(m2,terms="treat",type="marginal") F-test for: treat # test the contrasts of interest # Here we use the lsmeans() function in package lsmeans lsmeans(m2,specs=lsm~treat,contr=list(lsm=list(treat.v.control=c1))) $`treat lsmeans` treat lsmean SE df asymp.lcl asymp.ucl a NA b NA c NA d NA e NA f NA $`treat lsm` estimate SE df z.ratio p.value treat.v.control NA p values are not adjusted # Again, lsmeans uses a large sample Z test rather than an F test. # The F test can be obtained in the same way we did previously: refit # the model without an intercept and use the anova() function as follows: # refit with alternate parameterization: m2a <- lme(oil~treat-1,data=redwing3, random=list(blockfac= ~1, EU= ~1)) summary(m2a) Data: redwing

6 (Intercept) StdDev: Formula: ~1 EU %in% blockfac StdDev: Fixed effects: oil ~ treat - 1 treata treatb treatc treatd treate treatf treata treatb treatc treatd treate treatb treatc treatd treate treatf Number of Observations: 48 Number of Groups: blockfac EU %in% blockfac 4 24 # Test average of inoculation treatment means vs control mean: anova(m2a,type="marginal", L=c1) # agrees with SAS's result from CONTRAST F-test for linear combination(s) treata treatb treatc treatd treate treatf