Displaying 5 results from an estimated 5 matches for "no_middlewar".
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2011 Dec 26
2
glm predict issue
...ce? Below I include all details.
Thanks in advance,
Best regards,
Giovanni
> # define the extreme (factors and levels)
> experiments <- expand.grid(No_databases = seq(1000,100,by=-200),
+ Partitioning = c("sharding", "replication"),
+ No_middlewares = seq(500,100,by=-100),
+ Queue_size = c(100))
> experiments$No_databases <- as.factor(experiments$No_databases)
> experiments$Partitioning <- as.factor(experiments$Partitioning)
> experiments$No_middlewares <- as.factor(experiments$No_middlewares)
> experi...
2011 Nov 14
1
2^k*r (with replications) experimental design question
...: int 6 7 8 9 10 11 12 13 14 15 ...
$ Throughput : int 42 44 33 41 43 40 37 40 42 37 ...
$ No_databases : Factor w/ 2 levels "1","4": 1 1 1 1 1 1 1 1 1 1 ...
$ Partitioning : Factor w/ 2 levels "sharding","replication": 1 1 1 1 1 1 1 1 1 1 ...
$ No_middlewares: Factor w/ 2 levels "2","4": 1 1 1 1 1 1 1 1 1 1 ...
$ Queue_size : Factor w/ 2 levels "40","100": 1 1 1 1 1 1 1 1 1 1 ...
$ No_clients : Factor w/ 1 level "128": 1 1 1 1 1 1 1 1 1 1 ...
> head(throughput)
Time Throughput No_databases...
2011 Nov 14
0
aov output question
...more meaningful way e.g. show the percentage of variation of each factor towards the response I believe the formula would be something like:
for factor X: (Sum_Sq_X / Sum_Sq_Total)*100
Thanks in advance,
Best regards,
Giovanni
> #throughput.aov <- aov(Throughput~No_databases*Partitioning*No_middlewares*Queue_size,data=throughput)
> throughput.aov
Call:
aov(formula = Throughput ~ No_databases + Partitioning + No_middlewares +
Queue_size, data = throughput)
Terms:
No_databases Partitioning No_middlewares Queue_size Residuals
Sum of Squares 43146975 7394...
2011 Nov 14
1
issue plotting TukeyHSD
Hello,
When I try to use TukeyHSD in the following way it shows the confidence interval corresponding to the last factor only.
> throughput.aov <- aov(Throughput~No_databases+Partitioning+No_middlewares+Queue_size,data=throughput)
plot(TukeyHSD(throughput.aov)) # I expected here to see the confidence intervals for all factors but see only the last.
OTOH this one works but then it is unreadable due to the long labels of combined effects in the Y-axis ticks.
> throughput.aov <- aov(Throug...
2011 Nov 17
0
aov how to get the SST?
Hello,
I currently run aov in the following way:
> throughput.aov <- aov(log(Throughput)~No_databases+Partitioning+No_middlewares+Queue_size,data=throughput)
> summary(throughput.aov)
Df Sum Sq Mean Sq F value Pr(>F)
No_databases 1 184.68 184.675 136.6945 < 2.2e-16 ***
Partitioning 1 70.16 70.161 51.9321 2.516e-12 ***
No_middlewares 2 44.22 22.110 16.3654 1.395e-07 ***
Queue...