search for: probt

..., 0.44, 0.35) lab8.dat<-data.frame(locn, n, corr.r) lab8.dat attach(lab8.dat) #write the function calc.prob.t<-function(n, r) #given a sample size (n) and correlation coefficient (r), returns the probability for that test { df<-n-2 t<-(r-0)/(sqrt((1-r2)/df)) probt<-2*(pt(t, df, lower.tail=FALSE)) probt } #try out the function... calc.prob.t(lab8.dat$n[1], lab8.dat$corr.r[1]) #it works. #write a loop to implement that function for every correlation in your dataset... p_unadj<-numeric(length(lab8.dat[,1])) p_unadj<-NULL p_unadj #all th...

...ghts) #Now the pooled G obs = tapply(obscounts,cov,sum) exp = expproppooled*sum(obs) Gp = 2*sum(obs*log(obs/exp)) dfp = max(as.integer(levels(cov)))-1 probp = pchisq(Gp, dfp, lower.tail=F) #total G Gt = sum(G) dft = sum(dfs) probt = pchisq(Gt, dft, lower.tail=F) #heterogeneity G Gh = Gt-Gp dfh = dft - dfp probh = pchisq(Gh, dfh, lower.tail=F) #F ratio Fratio = (Gp/dfp)/(Gh/dfh) probf = pf(Fratio, dfp, dfh, lower.tail=F) res = list(TotG=c(Gt, dft, probt), PooledG=c...

...eat year block; model yield=block year treat treat*year; lsmeans year treat /pdiff; lsmeans treat*year /slice=year pdiff; ods output diffs=dos; run; data tres; set dos; if year=_year; proc print data=tres; var year _year treat _treat estimate stderr df tvalue probt; run; Data are attached as a file: data.csv. In fact, I do not know if this is the best approach to analyze the data: - Should block be considered as random? We use the same file and randomization every year. We are interested in the long term effect of the treatment. - Data should be considered...

In the model: lm.1 <- lm(variable ~ BLOC + TIL * YEAR , data=selvanera) I found TIL*YEAR interaction significant. Then I am trying to compare means of the different levels of TIL inside every YEAR using: mc.2 <- glht(lm.1, linfct = mcp(TIL*YEAR="Tukey")) summary(mc.2, test = univariate()) but it does not work. There is any way of doing this, like the SLICE option in