Skip to main content
Cross Validated

Return to Question

added 1054 characters in body
Source Link
Alan
Alan
  • 71
  • 2

` 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

Scaled residuals: 
Min       1Q   Median       3Q      Max 
-2.65967 -0.54776 -0.07734  0.56748  2.79995 

Random effects:
Groups   Name        Variance Std.Dev.
river    (Intercept) 0.001965 0.04432 
Residual             0.012381 0.11127 
Number of obs: 151, groups:  river, 6

Fixed effects:
                                 Estimate Std. Error t value
(Intercept)                      -0.12808    0.04154  -3.084
category.of.urbanizationslightly  0.15972    0.05376   2.971
category.of.urbanizationvery      0.17063    0.05432   3.141

Correlation of Fixed Effects:
              (Intr) ctgr.d.rbnzcp
ctgr.d.rbnzcp -0.773              
ctgr.d.rbnzcm -0.765  0.591

`

` 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

Scaled residuals: 
Min       1Q   Median       3Q      Max 
-2.65967 -0.54776 -0.07734  0.56748  2.79995 

Random effects:
Groups   Name        Variance Std.Dev.
river    (Intercept) 0.001965 0.04432 
Residual             0.012381 0.11127 
Number of obs: 151, groups:  river, 6

Fixed effects:
                                 Estimate Std. Error t value
(Intercept)                      -0.12808    0.04154  -3.084
category.of.urbanizationslightly  0.15972    0.05376   2.971
category.of.urbanizationvery      0.17063    0.05432   3.141

Correlation of Fixed Effects:
              (Intr) ctgr.d.rbnzcp
ctgr.d.rbnzcp -0.773              
ctgr.d.rbnzcm -0.765  0.591
Became Hot Network Question
Post Migrated Here from stackoverflow.com (revisions)
Source Link
Alan
Alan

If the categorical variable is retained in my final model in R, then why does the post hoc analysis say the levels do not differ?

I am performing model selection in R with the anova() function, and my categorical variable was maintained in my final model, but when I did a post hoc analysis with the emmeans() function, it told me the levels did not differ. What does it mean?

I use R software, and I am studying how the body condition of a species of fish varies in 3 kinds of rivers: preserved, slightly urban and very urban. Each category has one replicate, so that means I have 2 preserved rivers, 2 slightly urbanized rivers and 2 very urbanized rivers, which means that "river" is a random factor, and "category of urbanization" is my fixed factor and predictor variable with 3 levels. While performing model selection in R with the anova() function, the categorical variable "category" is maintained:

`#it is a linear mixed model because condition is normally distributed
> lmm.1 <- lmer(condition ~ category.of.urbanization + (1|river), data = fish) 
> lmm.null <- lmer(condition ~ 1 + (1|river), data = fish) 
> anova(lmm.null, lmm.1)
refitting model(s) with ML (instead of REML)
Data: fish
Models:
lmm.null: condition ~ 1 + (1 | river)
lmm.1: condition ~ category.of.urbanization + (1 | river)
            npar     AIC     BIC logLik deviance  Chisq Df Pr(>Chisq)   
lmm.null       3 -214.42 -205.37 110.21  -220.42                        
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`

`

My p-value is 0.009184, meaning category of urbanization is an important predictor, and I expected that at least one level of the categorical variable would be different from the others. However, when trying to do a post hoc analysis, I called the emmeans() function, and R says that none of the levels differ, because the p-values are all above 0.05:

`> emmeans(lmm.1, pairwise ~ category.of.urbanization)
Registered S3 methods overwritten by 'broom':
method            from  
tidy.glht         jtools
tidy.summary.glht jtools
$emmeans
category.of.urbanization  emmean     SE   df lower.CL upper.CL
preserved                -0.1281 0.0441 3.42  -0.2592  0.00304
slightly urban            0.0316 0.0341 2.20  -0.1030  0.16632
very urban                0.0425 0.0350 2.43  -0.0852  0.17032

Degrees-of-freedom method: kenward-roger 
Confidence level used: 0.95 

$contrasts
contrast                    estimate     SE   df t.ratio p.value
preserved - slightly urban   -0.1597 0.0558 2.83  -2.863  0.1324
preserved - very urban       -0.1706 0.0563 2.95  -3.028  0.1123
slightly urban - very urban  -0.0109 0.0489 2.32  -0.223  0.9733

Degrees-of-freedom method: kenward-roger 
P value adjustment: tukey method for comparing a family of 3 estimates `

Please, what does this mean? How is the predictor variable significant, but with levels that aren't different? I have 151 fish, so my number of data and observations is not very low. I am sorry if I've made spelling mistakes, English is not my native language.