Revisions to Two factor ANOVA with repeated measures by mixed model (lmer) [duplicate]

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duplicates list edited from Using lmer for repeated-measures linear mixed-effect model to Unbalanced mixed effect ANOVA for repeated measures, Using lmer for repeated-measures linear mixed-effect model

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edited Aug 11, 2018 at 16:25

gung - Reinstate Monica

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Post Closed as "Duplicate" by gung - Reinstate Monica

Using lmer for repeated-measures linear mixed-effect model

occurred Aug 11, 2018 at 16:25

Post Reopened by gung - Reinstate Monica

occurred Aug 11, 2018 at 16:24

added 45 characters in body

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edited Mar 31, 2017 at 0:42

Juanchi

389
5
14

I did a simple experiment with trees: 5 species (sp) and two management (trt). I assessed the diameter (diam) through the time (month=1:24) and I'm interesting in the factors interaction effect, I mean if the management is suitable for some species.

The dataset has some limitations since it is not balanced, so in 2 out of 10 factors combinations (sp*trt) I have 4 trees (subjects) and for the rest there are 5 trees:

       sp    trt trees
1       A      L     5
2       A      W     5
3       B      L     5
4       B      W     5
5       C      L     5
6       C      W     5
7       P      L     4
8       P      W     5
9       T      L     5
10      T      W     4

I fitted the following model:

m1 <- lmer(diam ~ trt * sp * month + (1|tree), data = dat1)

I'm not sure if I'm doing right, mainly if the model is right...

If the model is correct, and the anova is the following table:

Analysis of Variance Table
             Df  Sum Sq Mean Sq F value
trt           1 0.12110 0.12110 17.3756
sp            4 0.06761 0.01690  2.4251
month         1 0.33917 0.33917 48.6659
trt:sp        4 0.00436 0.00109  0.1564
trt:month     1 0.15814 0.15814 22.6900
sp:month      4 0.24445 0.06111  8.7687
trt:sp:month  4 0.19109 0.04777  6.8547

What is it meaning about the effects?

I did a simple experiment with trees: 5 species (sp) and two management (trt). I assessed the diameter (diam) through the time (month=1:24) and I'm interesting in the factors interaction effect, I mean if the management is suitable for some species.

The dataset has some limitations since it is not balanced, so in 2 out of 10 factors combinations (sp*trt) I have 4 trees (subjects) and for the rest there are 5 trees:

       sp    trt trees
1       A      L     5
2       A      W     5
3       B      L     5
4       B      W     5
5       C      L     5
6       C      W     5
7       P      L     4
8       P      W     5
9       T      L     5
10      T      W     4

I fitted the following model:

m1 <- lmer(diam ~ trt * sp * month + (1|tree), data = dat1)

I'm not sure if I'm doing right, mainly if the model is right...

If the model is correct, and the anova is the following table:

Analysis of Variance Table
             Df  Sum Sq Mean Sq F value
trt           1 0.12110 0.12110 17.3756
sp            4 0.06761 0.01690  2.4251
month         1 0.33917 0.33917 48.6659
trt:sp        4 0.00436 0.00109  0.1564
trt:month     1 0.15814 0.15814 22.6900
sp:month      4 0.24445 0.06111  8.7687
trt:sp:month  4 0.19109 0.04777  6.8547

What is it meaning about the effects?

I did a simple experiment with trees: 5 species (sp) and two management (trt). I assessed the diameter (diam) through the time (month=1:24) and I'm interesting in the factors interaction effect, I mean if the management is suitable for some species.

The dataset has some limitations since it is not balanced, so in 2 out of 10 factors combinations (sp*trt) I have 4 trees (subjects) and for the rest there are 5 trees:

       sp    trt trees
1       A      L     5
2       A      W     5
3       B      L     5
4       B      W     5
5       C      L     5
6       C      W     5
7       P      L     4
8       P      W     5
9       T      L     5
10      T      W     4

I fitted the following model:

m1 <- lmer(diam ~ trt * sp * month + (1|tree), data = dat1)

I'm not sure if I'm doing right, mainly if the model is right...

If the model is correct, and the anova is the following table:

Analysis of Variance Table
             Df  Sum Sq Mean Sq F value
trt           1 0.12110 0.12110 17.3756
sp            4 0.06761 0.01690  2.4251
month         1 0.33917 0.33917 48.6659
trt:sp        4 0.00436 0.00109  0.1564
trt:month     1 0.15814 0.15814 22.6900
sp:month      4 0.24445 0.06111  8.7687
trt:sp:month  4 0.19109 0.04777  6.8547

anova table added

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edited Mar 31, 2017 at 0:20

Juanchi

389
5
14

I did a simple experiment with trees: 5 species (sp) and two management (trt). I assessed the diameter (diam) through the time (month=1:24) and I'm interesting in the factors interaction effect, I mean if the management is suitable for some species.

The dataset has some limitations since it is not balanced, so in 2 out of 10 factors combinations (sp*trt) I have 4 trees (subjects) and for the rest there are 5 trees:

       sp    trt trees
1       A      L     5
2       A      W     5
3       B      L     5
4       B      W     5
5       C      L     5
6       C      W     5
7       P      L     4
8       P      W     5
9       T      L     5
10      T      W     4

I fitted the following model:

m1 <- lmer(diam ~ trt * sp * month + (1|tree), data = dat1)

I'm not sure if I'm doing right, mainly if the model is right...

If the model is correct, and the anova is the following table:

Analysis of Variance Table
             Df  Sum Sq Mean Sq F value
trt           1 0.12110 0.12110 17.3756
sp            4 0.06761 0.01690  2.4251
month         1 0.33917 0.33917 48.6659
trt:sp        4 0.00436 0.00109  0.1564
trt:month     1 0.15814 0.15814 22.6900
sp:month      4 0.24445 0.06111  8.7687
trt:sp:month  4 0.19109 0.04777  6.8547

What is it meaning about the effects?

I did a simple experiment with trees: 5 species (sp) and two management (trt). I assessed the diameter (diam) through the time (month=1:24) and I'm interesting in the factors interaction effect, I mean if the management is suitable for some species.

The dataset has some limitations since it is not balanced, so in 2 out of 10 factors combinations (sp*trt) I have 4 trees (subjects) and for the rest there are 5 trees:

       sp    trt trees
1       A      L     5
2       A      W     5
3       B      L     5
4       B      W     5
5       C      L     5
6       C      W     5
7       P      L     4
8       P      W     5
9       T      L     5
10      T      W     4

I fitted the following model:

m1 <- lmer(diam ~ trt * sp * month + (1|tree), data = dat1)

I'm not sure if I'm doing right, mainly if the model is right...

I did a simple experiment with trees: 5 species (sp) and two management (trt). I assessed the diameter (diam) through the time (month=1:24) and I'm interesting in the factors interaction effect, I mean if the management is suitable for some species.

The dataset has some limitations since it is not balanced, so in 2 out of 10 factors combinations (sp*trt) I have 4 trees (subjects) and for the rest there are 5 trees:

       sp    trt trees
1       A      L     5
2       A      W     5
3       B      L     5
4       B      W     5
5       C      L     5
6       C      W     5
7       P      L     4
8       P      W     5
9       T      L     5
10      T      W     4

I fitted the following model:

m1 <- lmer(diam ~ trt * sp * month + (1|tree), data = dat1)

I'm not sure if I'm doing right, mainly if the model is right...

If the model is correct, and the anova is the following table:

Analysis of Variance Table
             Df  Sum Sq Mean Sq F value
trt           1 0.12110 0.12110 17.3756
sp            4 0.06761 0.01690  2.4251
month         1 0.33917 0.33917 48.6659
trt:sp        4 0.00436 0.00109  0.1564
trt:month     1 0.15814 0.15814 22.6900
sp:month      4 0.24445 0.06111  8.7687
trt:sp:month  4 0.19109 0.04777  6.8547

What is it meaning about the effects?

added residual plots

Source Link

edited Mar 31, 2017 at 0:02

Juanchi

389
5
14

I did a simple experiment with trees: 5 species (sp) and two management (trt). I assessed the diameter (diam) through the time (month=1:24) and I'm interesting in the factors interaction effect, I mean if the management is suitable for some species.

The dataset has some limitations since it is not balanced, so in 2 out of 10 factors combinations (sp*trt) I have 4 trees (subjects) and for the rest there are 5 trees:

       sp    trt trees
1       A      L     5
2       A      W     5
3       B      L     5
4       B      W     5
5       C      L     5
6       C      W     5
7       P      L     4
8       P      W     5
9       T      L     5
10      T      W     4

I fitted the following model:

m1 <- lmer(diam ~ trt * sp * month + (1|tree), data = dat1)

I'm not sure if I'm doing right, mainly if the model is right...

I did a simple experiment with trees: 5 species (sp) and two management (trt). I assessed the diameter (diam) through the time (month=1:24) and I'm interesting in the factors interaction effect, I mean if the management is suitable for some species.

The dataset has some limitations since it is not balanced, so in 2 out of 10 factors combinations (sp*trt) I have 4 trees (subjects) and for the rest there are 5 trees:

       sp    trt trees
1       A      L     5
2       A      W     5
3       B      L     5
4       B      W     5
5       C      L     5
6       C      W     5
7       P      L     4
8       P      W     5
9       T      L     5
10      T      W     4

I fitted the following model:

m1 <- lmer(diam ~ trt * sp * month + (1|tree), data = dat1)

I'm not sure if I'm doing right, mainly if the model is right...

I did a simple experiment with trees: 5 species (sp) and two management (trt). I assessed the diameter (diam) through the time (month=1:24) and I'm interesting in the factors interaction effect, I mean if the management is suitable for some species.

The dataset has some limitations since it is not balanced, so in 2 out of 10 factors combinations (sp*trt) I have 4 trees (subjects) and for the rest there are 5 trees:

       sp    trt trees
1       A      L     5
2       A      W     5
3       B      L     5
4       B      W     5
5       C      L     5
6       C      W     5
7       P      L     4
8       P      W     5
9       T      L     5
10      T      W     4

I fitted the following model:

m1 <- lmer(diam ~ trt * sp * month + (1|tree), data = dat1)

I'm not sure if I'm doing right, mainly if the model is right...

edited tags

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edited Mar 30, 2017 at 20:13

amoeba

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added 8 characters in body

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edited Mar 30, 2017 at 19:57

Juanchi

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Post Closed as "Not suitable for this site" by Michael R. Chernick, Peter Flom

occurred Mar 30, 2017 at 10:56

edited title

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edited Mar 30, 2017 at 6:07

Michael R. Chernick

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Updated formatting.

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edit approved Mar 30, 2017 at 5:44

Tavrock

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added 6 characters in body

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edited Mar 30, 2017 at 2:57

Juanchi

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asked Mar 30, 2017 at 2:51

Juanchi

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