R help - Jul 2009 - setting up LMER for repeated measures and how do I get a p value for my fixed effect, group?

R 2.8.1
Windows XP

I am trying to analyze repeated measures data (the data are listed at the end of
this Email message) and I need help to make sure that I have properly specified
my model, and would like to know why lmer does not return a p value for Group,
my fixed effect.

My subjects are divided into two groups (variable GROUP), individual subjects
are indicated by the variable SS, Value is the outcome measure, each subject has
Value measured three times.

I have used the following code:

fit1<-lmer(Value~Group+(1|SS),data=smallDS)
print(fit1)

Linear mixed model fit by REML 
Formula: Value ~ Group + (1 | SS) 
   Data: Dataset 
   AIC   BIC logLik deviance REMLdev
 284.8 290.4 -138.4    291.4   276.8
Random effects:
 Groups   Name        Variance Std.Dev.
 SS       (Intercept) 1038.0   32.218  
 Residual              552.7   23.510  
Number of obs: 30, groups: SS, 10

Fixed effects:
                        Estimate Std. Error t value
(Intercept)               152.87      15.63   9.778
Group[T.ContSIRNAnoEGF]   -15.87      22.11  -0.718

Correlation of Fixed Effects:
            (Intr)
G[T.CSIRNAE -0.707


Questions:
(1) Have I set up lmer properly to analyze the repeated measures data?
(2) How can I get a p value for Group, my fixed effect?


 
      Group SS Value
 [1,]     2  1   127
 [2,]     2  1   179
 [3,]     2  1   159
 [4,]     2  2   186
 [5,]     2  2   173
 [6,]     2  2   178
 [7,]     2  3   117
 [8,]     2  3   116
 [9,]     2  3    70
[10,]     2  4   176
[11,]     2  4   149
[12,]     2  4   138
[13,]     2  5   100
[14,]     2  5   105
[15,]     2  5    82
[16,]     1  6   142
[17,]     1  6   195
[18,]     1  6   222
[19,]     1  7   218
[20,]     1  7   178
[21,]     1  7   147
[22,]     1  8   135
[23,]     1  8   162
[24,]     1  8   177
[25,]     1  9   104
[26,]     1  9   102
[27,]     1  9   121
[28,]     1 10   135
[29,]     1 10   121
[30,]     1 10   134


John David Sorkin M.D., Ph.D.
Chief, Biostatistics and Informatics
University of Maryland School of Medicine Division of Gerontology
Baltimore VA Medical Center
10 North Greene Street
GRECC (BT/18/GR)
Baltimore, MD 21201-1524
(Phone) 410-605-7119
(Fax) 410-605-7913 (Please call phone number above prior to faxing)

Confidentiality Statement:
This email message, including any attachments, is for th...{{dropped:6}}

On Thu, Jul 23, 2009 at 9:02 AM, John Sorkin<jsorkin at
grecc.umaryland.edu> wrote:
> R 2.8.1
> Windows XP
>
> I am trying to analyze repeated measures data (the data are listed at the
end of this Email message) and I need help to make sure that I have properly
specified my model, and would like to know why lmer does not return a p value
for Group, my fixed effect.
>
The model looks appropriate to me.  If you had more repeated measures
within subjects you might want to model the temporal correlation by
structuring the within-subject error covariance; the correlation
argument to nlme::lme accepts a variety of corClasses for this purpose
(e.g. AR1).

FAQ 7.35 provides a link to one explanation for why lmer omits p-values:

https://stat.ethz.ch/pipermail/r-help/2006-May/094765.html

However in your balanced, normal case you can get (in this case
identical) p-values using eith aov or lme:

fit2 <- aov(Value ~ Group + Error(SS), data = smallDS)
#or
library(nlme)
fit3 <- lme(Value ~ Group, data = smallDS, random = ~1|SS)

As you might have suspected from the lmer t-value close to 0, the
associated p-value is about .5.

hth,

Kingsford Jones
> My subjects are divided into two groups (variable GROUP), individual
subjects are indicated by the variable SS, Value is the outcome measure, each
subject has Value measured three times.
>
> I have used the following code:
>
> fit1<-lmer(Value~Group+(1|SS),data=smallDS)
> print(fit1)
>
> Linear mixed model fit by REML
> Formula: Value ~ Group + (1 | SS)
> ? Data: Dataset
> ? AIC ? BIC logLik deviance REMLdev
> ?284.8 290.4 -138.4 ? ?291.4 ? 276.8
> Random effects:
> ?Groups ? Name ? ? ? ?Variance Std.Dev.
> ?SS ? ? ? (Intercept) 1038.0 ? 32.218
> ?Residual ? ? ? ? ? ? ?552.7 ? 23.510
> Number of obs: 30, groups: SS, 10
>
> Fixed effects:
> ? ? ? ? ? ? ? ? ? ? ? ?Estimate Std. Error t value
> (Intercept) ? ? ? ? ? ? ? 152.87 ? ? ?15.63 ? 9.778
> Group[T.ContSIRNAnoEGF] ? -15.87 ? ? ?22.11 ?-0.718
>
> Correlation of Fixed Effects:
> ? ? ? ? ? ?(Intr)
> G[T.CSIRNAE -0.707
>
>
> Questions:
> (1) Have I set up lmer properly to analyze the repeated measures data?
> (2) How can I get a p value for Group, my fixed effect?
>
>
>
> ? ? ?Group SS Value
> ?[1,] ? ? 2 ?1 ? 127
> ?[2,] ? ? 2 ?1 ? 179
> ?[3,] ? ? 2 ?1 ? 159
> ?[4,] ? ? 2 ?2 ? 186
> ?[5,] ? ? 2 ?2 ? 173
> ?[6,] ? ? 2 ?2 ? 178
> ?[7,] ? ? 2 ?3 ? 117
> ?[8,] ? ? 2 ?3 ? 116
> ?[9,] ? ? 2 ?3 ? ?70
> [10,] ? ? 2 ?4 ? 176
> [11,] ? ? 2 ?4 ? 149
> [12,] ? ? 2 ?4 ? 138
> [13,] ? ? 2 ?5 ? 100
> [14,] ? ? 2 ?5 ? 105
> [15,] ? ? 2 ?5 ? ?82
> [16,] ? ? 1 ?6 ? 142
> [17,] ? ? 1 ?6 ? 195
> [18,] ? ? 1 ?6 ? 222
> [19,] ? ? 1 ?7 ? 218
> [20,] ? ? 1 ?7 ? 178
> [21,] ? ? 1 ?7 ? 147
> [22,] ? ? 1 ?8 ? 135
> [23,] ? ? 1 ?8 ? 162
> [24,] ? ? 1 ?8 ? 177
> [25,] ? ? 1 ?9 ? 104
> [26,] ? ? 1 ?9 ? 102
> [27,] ? ? 1 ?9 ? 121
> [28,] ? ? 1 10 ? 135
> [29,] ? ? 1 10 ? 121
> [30,] ? ? 1 10 ? 134
>
>
> John David Sorkin M.D., Ph.D.
> Chief, Biostatistics and Informatics
> University of Maryland School of Medicine Division of Gerontology
> Baltimore VA Medical Center
> 10 North Greene Street
> GRECC (BT/18/GR)
> Baltimore, MD 21201-1524
> (Phone) 410-605-7119
> (Fax) 410-605-7913 (Please call phone number above prior to faxing)
>
> Confidentiality Statement:
> This email message, including any attachments, is for th...{{dropped:6}}
>
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