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() type 3 and Anova() type 3 summary() and anova() type 3 I have tried options(contrasts = c("contr.sum", "contr.poly")) and it does not change any results. … I have run my pre-determined contrasts of interest using emmeans, which had high significance, so I'm having a hard time understanding why the Anova() type 3 and summary() aren't showing any significance …

I have found that the summary() function and the anova() function from lmertest yield different results. … However, I am finding that the anova function does not return a significant interaction effect Origin:Fert, whereas the summary function reports that OriginCO:FertUnfertilized is significant. …

Here is the problem: when I run the anova command 'Religion' is significant, but it is not if I then run the summary of the saturated model. … . + Group:Religion) anova(baseline, GroupM, ReligionM, Group_Rel) summary(Group_Rel) and here is the output: # Model df AIC BIC logLik Test L.Ratio p-value # baseline 1 5 1822.944 …

I have created multiple linear mixed effect models to determine the effects of these variables on shannon diversity value. summary(lmer_model_precip) Linear mixed model fit by REML ['lmerMod'] Formula: … Error t value (Intercept) 0.06001 0.01213 4.945 Flower_Quantity -0.02689 0.02739 -0.982 Correlation of Fixed Effects: (Intr) Flowr_Qntty -0.778 anova(lmer_model_precip) Analysis …

As can be seen from the summary output below, the t.tests do not show a significant congruity effect (p = 0.12), while the anova output shows a very significant congruity effect (p = 2.8e-10). … I am therefore unsure what causes the very significant result in the anova output. …

I would like to understand why an anova is made on a lme (or lmer) and how to interpret the anova output. lm1 <- lme(EWL..mg.h. ~ Condition*Session + Condition*Sex + Condition*Mass, random = ~1|Individu … , data = data_respiro_acoustic) summary(m1) Anova(m1, type = c("III")) Summary Output : Linear mixed-effects model fit by REML Data: data_respiro_acoustic AIC BIC logLik 1232.332 …

However, if I use both anova and summary, I obtain results that I am not sure on how to interpret. … As you can see, the t value from summary is 2.04 and the F value from anova is 0.0416. …

In fact, it seems that the anova(model, type = 3) function is actually using type 2 SS, which we can verify by running anova(model, type = 2). … Here I am running only a single model using lmer and trying to understand why lmerTest::anova(model) and summary(model) are producing different P-values, despite the fact that they should be computed in …

I'm doing a two-factor ANOVA using the lmerTest package. Each factor has multiple levels. … But, I'm unsure how to interpret the summary table produced by summary() and how the numbers compare to the table produced by glht(). …

From my understanding, the addition of a -1 in the fixed effects of a lmer() model would avoid comparisons of factor levels to a baseline (e.g. the (intercept) in the model summary). … Prep library(lmerTest) mtcars$am<-as.factor(mtcars$am) Traditional model M1<-lmer(mpg~am*hp+(1|carb),data=mtcars) summary(M1) Estimate Std. …

(m1) instead of summary(m1) I get: > anova(m1) Analysis of Variance Table Df Sum Sq Mean Sq F value trt 1 0.024 0.024 0.0238 test 2 89.561 44.780 44.7805 trt:test 2 54.305 27.153 … 27.1525 > How to get a report like anova(m1) but with associated p-values? …

My model is lme(var ~ treatment*period+side, method="ML", random=list(IDlog=~1), na.action=na.omit, data=changes) This is the anova of the model numDF denDF F-value p-value … .0001 treatment 2 473 3.49473 0.0311 period 4 473 12.51296 <.0001 side 1 473 12.16210 0.0005 treatment:period 8 473 2.02865 0.0416 and the summary …

Error t value Pr(>|t|) Gender2 -0.2443 0.1791 -1.364 0.1731 Information2 0.1605 0.3582 0.448 0.6543 Anova(fm1, type = 3) Anova Table (Type III tests) … This seems to solve the problem -- presumably lmerTest somehow does this automatically when calling a type 3 ANOVA. With contrast coding, the summary now matches the ANOVA for variables with 1 df. …

Since I have an unbalanced design (16, 19, 14 and 17 obs per group) after removing outliers, I wanted to use the lmer function to run a mixed anova (or mixed model now) in R. … (anova(lmer_mixed_ANOVA,type=3)) print(summary(lmer_mixed_ANOVA)) and I get this as output: [1] "data_RT_control" Analysis of Deviance Table (Type III Wald chisquare tests) Response: control_out …

I started with a maximal model and used LRTs (anova()) to remove non-significant predictors. … However, how do I know if the levels that are not shown in the summary (i.e. e.g. L23:INSTRUCTION1) are significant? …