Search Results

In the output from summary(model) I get estimates for each of the fixed effects. … of a categorical variable with >2 levels (the equivalent to Cohen's d when conducting a normal ANOVA). …

lmm.1 5 -219.80 -204.71 114.90 -229.80 9.3806 2 0.009184 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1` ` I had forgotten to include my model's summary … in my my question, so here it is: > summary(lmm.1) Linear mixed model fit by REML ['lmerMod'] Formula: condition ~ category.of.urbanization + (1 | river) Data: fish REML criterion at convergence: -214.3 …

> lm <- lme(weight~time*cond, random=~time|miRs, data=testDose) > anova(lm) numDF denDF F-value p-value (Intercept) 1 38 233748.85 <.0001 time 1 38 398.12 <.0001 … cond 3 38 7.14 0.0006 time:cond 3 38 2.34 0.0887 > summary(lm) Linear mixed-effects model fit by REML Data: testDose AIC BIC logLik 51.94759 72.21414 …

estimate a model with only a fixed effect of my predictor, as such: fit1 <- lme(fixed = stress ~ 1 + predictor_centred + predictor_mean, random = ~ 1 |ID, data = data, method = "REML", na.action=na.exclude) summary … fit2 <- lme(fixed = stress ~ 1 + predictor_centred + predictor_mean, random = ~ 1 + predictor_centred|ID, data = data, method = "REML", na.action=na.exclude) anova(fit1, fit2) Model df AIC …

See: Can ANOVA be significant when none of the pairwise t-tests is? Discrepancy between the results of the ANOVA and the post-hoc test: How should be such results interpreted and presented? … d,d, -d,d, -d,d) x = as.factor(c(0,0,1,1,2,2)) mm = lm(y~x) ### LR test 0.01058 lmtest::lrtest(mm) ### ANOVA test 0.103 mod = aov(mm) summary(mod) ### Tukey test 0.128822 TukeyHSD(mod) …

When I use car::Anova , there is no time * group interaction. Is it wrong to do the following analysis? What's the difference between them? … Anova(model, type = 3) Response: score Chisq Df Pr(>Chisq) (Intercept) 13.9981 1 0.000183 *** time 83.3778 5 < 2.2e-16 *** group 0.6852 1 0.407803 time:group …

tbl", "data.frame")) EDIT2: Updated model and output mixed.lmer.all <- lmer(Intertegular.Width ~ Urban.Intensity*Sociality*Nesting + Urban.Intensity*Diet + Genus + Sex + (1|SiteID), data=df) summary … (mixed.lmer.all); Anova(mixed.lmer.all) Output: REML criterion at convergence: 2893.8 Scaled residuals: Min 1Q Median 3Q Max -8.8063 -0.5070 0.0109 0.5188 7.0897 Random effects …

mod <- glmer(value~xval+(0+xval|zone),family=gaussian(link="log"),data = dat) #Null model mod0 <- glmer(value~(0+xval|zone),family=gaussian(link="log"),data = dat); anova(mod,mod0) summary(mod) # dispersion … and interrogation summary(mod) anova(mod) lme4::VarCorr(mod) car::Anova(mod) Based on the diagnostics the models look good right? …

0.0996 2 0.951421 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 I settle on m7, and proceed to fit the model: model <- lmer(Score ~ 1 + Groups*Time + (1 | ID), data = df) summary … I also pull the ANOVA table for this model: anova(model) Type III Analysis of Variance Table with Satterthwaite's method Sum Sq Mean Sq NumDF DenDF F value Pr(>F) Groups 122.848 …

(Model1,type=3) summary(Model1) Results: Factor Pr(>F) Group 7.643e-05 Diff 1.274e-09 Group:Diff 3.346e-16 Model2 <- lmer(RT~Group*Diff + (1|Item) + (1|Subject),data=data, lmerControl(optimizer = "bobyqa … "), REML=F, na.action=na.omit) anova(Model2,type=3) summary(Model2) Results: Factor Pr(>F) Group 8.300e-05 Diff 1.350e-09 Group:Diff 5.308e-06 As you can see, the two models produce different results …

library(lmerTest) fm1 <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy) fm0 <- update(fm1, . ~ . - Days) coef(summary(fm1))["Days", "Pr(>|t|)"] ## 3.623824e-06 coef(summary(fm1, ddf = "Kenward-Roger … "))["Days", "Pr(>|t|)"] ## 3.263808e-06 anova(fm1, fm0) ## p-value: 1.226e-06 pbkrtest::KRmodcomp(fm1, fm0) ## 3.263808e-06 The odd one out here is anova(), which does a likelihood ratio test (i.e. …

This is the thought process I have been working through so far: My first attempt to model the data was: library(nlme) m1 <- lme(MR ~ Condition * Run, random = ~1|ID, data = df) summary(m1) anova(m1) … (m2) anova(m2) Is this a correct model specification and does it capture the effect of Group? …

(and shown under Random Effects by your summary(empty_model)). … This entails that the "common" variance estimate is downwardly biased, in case the true data are generated by a random-effects Anova model $Y_{ij}=\gamma_{00}+u_{0j}+e_{ij}$. …

I'm able to do it for a simple main effect (e.g., Sediment): summary(glht(mod1,linfct=mcp(Sediment="Tukey"))) But the glht() function doesn't work for interaction terms. … Hydrology) mod2 <- lme(Variable ~ -1 + SH, data=mydata, random=~1|Site/Hydrology) summary(glht(mod2, linfct=mcp(SH="Tukey"))) Is it possible to use the same approach in the case of a 3-way anova? …

To get a nice summary of AIC and log-likelihood models, you can use the anova() function, which has been overloaded to accept mer objects. … For looking at the individual effects (i.e. the stuff you would see in a traditional ANOVA), you should look at the $t$-values for the fixed effects in the models (which are part of the summary() if I'm …