Symptoms of Patients Suffering from Alzheimer's Syndrome

Description

Presence or absence of 6 symptoms of Alzheimer's disease (AD) in 240 patients diagnosed with early onset AD conducted in the Mercer Institute in St. James's Hospital, Dublin.

Usage

data(Alzheimer)

Format

A binary matrix, consisting of 240 rows and 6 columns, with each row denoting an individual and each column denoting the presence/absence of one of the 6 symptoms: Hallucination, Activity, Aggression, Agitation, Diurnal and Affective. A 1 denotes the presence of a symptom, a 0 the absence.

Source

Moran M, Walsh C, Lynch A, Coen RF, Coakley D, Lawlor BA (2004) “Syndromes of behavioural and psychological symptoms in mild Alzheimer's disease.” International Journal of Geriatric Psychiatry, 19(4), 359–364. ISSN 1099-1166. doi:10.1002/gps.1091. URL http://dx.doi.org/10.1002/gps.1091.

Walsh C (2006) “Latent Class Analysis Identification of Syndromes in Alzheimer's Disease: A Bayesian Approach.” metodoloyski zvezki - Advances in Methodology and Statistics, 3(1), pp.147 – 162. URL mrvar.fdv.uni-lj.si/pub/mz/mz3.1/walsh.pdf

Examples

data(Alzheimer)
fit2 <- blca.em(Alzheimer, 2)
summary(fit2)

fit3<- blca.em(Alzheimer, 3, restarts=25)
summary(fit3)

Results

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> library(BayesLCA)
Loading required package: e1071
Loading required package: coda
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/BayesLCA/Alzheimer.Rd_%03d_medium.png", width=480, height=480)
> ### Name: Alzheimer
> ### Title: Symptoms of Patients Suffering from Alzheimer's Syndrome
> ### Aliases: Alzheimer
> ### Keywords: datasets Alzheimer Saint James Alzheimers Syndrome
> 
> ### ** Examples
> 
> data(Alzheimer)
> fit2 <- blca.em(Alzheimer, 2)
Restart number 1, logpost = -749.44... 
New maximum found... Restart number 2, logpost = -749.44... 
Restart number 3, logpost = -749.44... 
Restart number 4, logpost = -749.44... 
New maximum found... Restart number 5, logpost = -749.42... 
> summary(fit2)
__________________

Bayes-LCA
Diagnostic Summary
__________________

Hyper-Parameters: 

 Item Probabilities:

 alpha: 
        Hallucination Activity Aggression Agitation Diurnal Affective
Group 1             1        1          1         1       1         1
Group 2             1        1          1         1       1         1

 beta: 
        Hallucination Activity Aggression Agitation Diurnal Affective
Group 1             1        1          1         1       1         1
Group 2             1        1          1         1       1         1

 Class Probabilities:

 delta: 
Group 1 Group 2 
      1       1 
__________________

Method: EM algorithm  

 Number of iterations: 19 

 Log-Posterior Increase at Convergence: 3.358276e-05 

 Log-Posterior: -749.4185 

 AIC: -1524.837 

 BIC: -1570.085 
> 
> fit3<- blca.em(Alzheimer, 3, restarts=25)
Restart number 1, logpost = -742.8... 
Restart number 2, logpost = -745.03... 
Restart number 3, logpost = -745.04... 
New maximum found... Restart number 4, logpost = -742.8... 
Restart number 5, logpost = -742.8... 
Restart number 6, logpost = -744.29... 
Restart number 7, logpost = -744.28... 
Restart number 8, logpost = -744.29... 
Restart number 9, logpost = -744.28... 
Restart number 10, logpost = -744.28... 
Restart number 11, logpost = -745.04... 
Restart number 12, logpost = -745.22... 
Restart number 13, logpost = -745.03... 
Restart number 14, logpost = -744.28... 
Restart number 15, logpost = -745.03... 
Restart number 16, logpost = -745.04... 
Restart number 17, logpost = -745.03... 
Restart number 18, logpost = -745.04... 
Restart number 19, logpost = -745.04... 
Restart number 20, logpost = -745.04... 
Restart number 21, logpost = -744.29... 
Restart number 22, logpost = -745.03... 
Restart number 23, logpost = -745.04... 
Restart number 24, logpost = -744.45... 
Restart number 25, logpost = -747.54... 
> summary(fit3)
__________________

Bayes-LCA
Diagnostic Summary
__________________

Hyper-Parameters: 

 Item Probabilities:

 alpha: 
        Hallucination Activity Aggression Agitation Diurnal Affective
Group 1             1        1          1         1       1         1
Group 2             1        1          1         1       1         1
Group 3             1        1          1         1       1         1

 beta: 
        Hallucination Activity Aggression Agitation Diurnal Affective
Group 1             1        1          1         1       1         1
Group 2             1        1          1         1       1         1
Group 3             1        1          1         1       1         1

 Class Probabilities:

 delta: 
Group 1 Group 2 Group 3 
      1       1       1 
__________________

Method: EM algorithm  

 Number of iterations: 242 

 Log-Posterior Increase at Convergence: 0.0008294783 

 Log-Posterior: -742.795 

 AIC: -1524.204 

 BIC: -1593.817 
> 
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> 
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> 
> 
> dev.off()
null device 
          1 
>