Re: [R] lme: reproducing example
Thanks! I think the minor differences taking the values with rnorm result of the homogen distribution without an effect. But the results of aov and lme should be similiar for data with effects, too (at least for simple and balanced designs). Karl --- Pascal A. Niklaus [EMAIL PROTECTED] schrieb: Karl Knoblick wrote: Dear R-community! I still have the problem reproducing the following example using lme. id-factor(rep(rep(1:5,rep(3,5)),3)) factA - factor(rep(c(a1,a2,a3),rep(15,3))) factB - factor(rep(c(B1,B2,B3),15)) Y-numeric(length=45) Y[ 1: 9]-c(56,52,48,57,54,46,55,51,51) Y[10:18]-c(58,51,50,54,53,46,54,50,49) Y[19:27]-c(53,49,48,56,48,52,52,52,50) Y[28:36]-c(55,51,46,57,49,50,55,51,47) Y[37:45]-c(56,48,51,58,50,48,58,46,52) df-data.frame(id, factA, factB, Y) df.aov - aov(Y ~ factA*factB + Error(factA:id), data=df) summary(df.aov) Is there a way to get the same results with lme as with aov with Error()? HOW??? One idea was the following: df$factAid=factor(paste(as.character(df$factA),:,as.character(df$id),sep=)) df.lme - lme(Y~factA*factB,df,random=~1|factAid,method=REML) The degrees of freedom look right, but the F values don't match aov. Hope somebody can help! Thanks!! Karl Hmmm, strange, it works if I use factB:id as plot... it also works when I use factA:id as plot and replace your Y's by random numbers... is this a problem with convergence? Pascal df$Y=rnorm(45) summary(aov(Y ~ factB*factA + Error(id:factA),data=df)) Error: id:factA Df Sum Sq Mean Sq F value Pr(F) factA 2 2.9398 1.4699 0.9014 0.4318 Residuals 12 19.5675 1.6306 Error: Within Df Sum Sq Mean Sq F value Pr(F) factB2 7.1431 3.5716 7.4964 0.002956 ** factB:factA 4 4.2411 1.0603 2.2254 0.096377 . Residuals 24 11.4345 0.4764 --- Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1 anova(lme(Y ~ factB*factA ,data=df, random = ~ 1 | plot)) numDF denDF F-value p-value (Intercept) 124 0.014294 0.9058 factB 224 7.496097 0.0030 factA 212 0.901489 0.4318 factB:factA 424 2.225317 0.0964 Pascal summary(aov(Y ~ factA*factB + Error(factB:id))) Error: factB:id Df Sum Sq Mean Sq F valuePr(F) factB 2 370.71 185.36 51.488 1.293e-06 *** Residuals 12 43.203.60 --- Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1 Error: Within Df Sum Sq Mean Sq F value Pr(F) factA2 9.911 4.956 1.6248 0.21788 factA:factB 4 45.556 11.389 3.7341 0.01686 * Residuals 24 73.200 3.050 df$plot - factor(paste(df$factB,df$id)) anova(lme(Y ~ factB*factA , data=df, random = ~1 | plot)) numDF denDF F-value p-value (Intercept) 124 33296.02 .0001 factB 21251.47 .0001 factA 224 1.63 0.2178 factB:factA 424 3.73 0.0168 __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
[R] lme: reproducing example
Dear R-community! I still have the problem reproducing the following example using lme. id-factor(rep(rep(1:5,rep(3,5)),3)) factA - factor(rep(c(a1,a2,a3),rep(15,3))) factB - factor(rep(c(B1,B2,B3),15)) Y-numeric(length=45) Y[ 1: 9]-c(56,52,48,57,54,46,55,51,51) Y[10:18]-c(58,51,50,54,53,46,54,50,49) Y[19:27]-c(53,49,48,56,48,52,52,52,50) Y[28:36]-c(55,51,46,57,49,50,55,51,47) Y[37:45]-c(56,48,51,58,50,48,58,46,52) df-data.frame(id, factA, factB, Y) df.aov - aov(Y ~ factA*factB + Error(factA:id), data=df) summary(df.aov) Is there a way to get the same results with lme as with aov with Error()? HOW??? One idea was the following: df$factAid=factor(paste(as.character(df$factA),:,as.character(df$id),sep=)) df.lme - lme(Y~factA*factB,df,random=~1|factAid,method=REML) The degrees of freedom look right, but the F values don't match aov. Hope somebody can help! Thanks!! Karl __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
Re: [R] lme: reproducing example
Karl Knoblick wrote: Dear R-community! I still have the problem reproducing the following example using lme. id-factor(rep(rep(1:5,rep(3,5)),3)) factA - factor(rep(c(a1,a2,a3),rep(15,3))) factB - factor(rep(c(B1,B2,B3),15)) Y-numeric(length=45) Y[ 1: 9]-c(56,52,48,57,54,46,55,51,51) Y[10:18]-c(58,51,50,54,53,46,54,50,49) Y[19:27]-c(53,49,48,56,48,52,52,52,50) Y[28:36]-c(55,51,46,57,49,50,55,51,47) Y[37:45]-c(56,48,51,58,50,48,58,46,52) df-data.frame(id, factA, factB, Y) df.aov - aov(Y ~ factA*factB + Error(factA:id), data=df) summary(df.aov) Is there a way to get the same results with lme as with aov with Error()? HOW??? One idea was the following: df$factAid=factor(paste(as.character(df$factA),:,as.character(df$id),sep=)) df.lme - lme(Y~factA*factB,df,random=~1|factAid,method=REML) The degrees of freedom look right, but the F values don't match aov. Hope somebody can help! Thanks!! Karl Hmmm, strange, it works if I use factB:id as plot... it also works when I use factA:id as plot and replace your Y's by random numbers... is this a problem with convergence? Pascal df$Y=rnorm(45) summary(aov(Y ~ factB*factA + Error(id:factA),data=df)) Error: id:factA Df Sum Sq Mean Sq F value Pr(F) factA 2 2.9398 1.4699 0.9014 0.4318 Residuals 12 19.5675 1.6306 Error: Within Df Sum Sq Mean Sq F value Pr(F) factB2 7.1431 3.5716 7.4964 0.002956 ** factB:factA 4 4.2411 1.0603 2.2254 0.096377 . Residuals 24 11.4345 0.4764 --- Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1 anova(lme(Y ~ factB*factA ,data=df, random = ~ 1 | plot)) numDF denDF F-value p-value (Intercept) 124 0.014294 0.9058 factB 224 7.496097 0.0030 factA 212 0.901489 0.4318 factB:factA 424 2.225317 0.0964 Pascal summary(aov(Y ~ factA*factB + Error(factB:id))) Error: factB:id Df Sum Sq Mean Sq F valuePr(F) factB 2 370.71 185.36 51.488 1.293e-06 *** Residuals 12 43.203.60 --- Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1 Error: Within Df Sum Sq Mean Sq F value Pr(F) factA2 9.911 4.956 1.6248 0.21788 factA:factB 4 45.556 11.389 3.7341 0.01686 * Residuals 24 73.200 3.050 df$plot - factor(paste(df$factB,df$id)) anova(lme(Y ~ factB*factA , data=df, random = ~1 | plot)) numDF denDF F-value p-value (Intercept) 124 33296.02 .0001 factB 21251.47 .0001 factA 224 1.63 0.2178 factB:factA 424 3.73 0.0168 __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
