Request for functions to calculate correlated factors influencing an outcome.

Hi
I used the MASS library
library(MASS)  (by reading about examples at
http://www.statmethods.net/stats/regression.html
<http://s.bl-1.com/h/ofLlK27?url=http://www.statmethods.net/stats/regression.html>
)
fit <- lm(Mileage~Disp+HP+Weight+Reliability,data=newx)
step <- stepAIC(fit, direction="both")
step$anova # display results

It showed the most relevant variables affecting Mileage.
While that is a start, I am looking for a model that fits the entire data
(including Mileage), not factors that influence Mileage.

Multi model inference / selection.

I was reading about glmulti.
Are there any other packages I could look at, for infering models that best
fit the data.

To use nlm / nls, I need a formula, as one of the parameters to best fit
the data and I am looking for functions that will help infer that formula
from the data.

Thanks
lalitha

On Sun, May 3, 2015 at 11:33 PM, Prashant Sethi <theseth.prashant at gmail.com>
wrote:

Hi,

I'm not an expert in data analysis (a beginner still learning tricks of
the trade) but I believe in your case since you're trying to determine the
correlation of a dependent variable with a number of factor variables, you
should try doing the regression analysis of your model. The function you'll
use for that is the lm() function. You can use the forward building or the
backward elimination method to build your model with the most critical
factors included.

Maybe you can give it a try.

Thanks and regards,
Prashant Sethi
On 3 May 2015 23:18, "Lalitha Viswanathan" <
lalitha.viswanathan79 at gmail.com> wrote:

Hi
I am sorry, I saved the file removing the dot after the Disp (as I was
going wrong on a read.delim which threw an error about !header, etc...The
dot was not the culprit, but I continued to leave it out.
Let me paste the full code here.
x<-read.table("/Users/Documents/StatsTest/fuelEfficiency.txt",
header=TRUE,
sep="\t")
x<-data.frame(x)
for (i in unique(x$Country)) { print (i); y <- subset(x, x$Country == i);
print(y); }
newx <- subset (x, select = c(Price, Reliability, Mileage, Weight, Disp,
HP))
cor(newx, method="pearson")
my.cor <-cor.test(newx$Weight, newx$Price, method="spearman")
my.cor <-cor.test(newx$Weight, newx$HP, method="spearman")
my.cor <-cor.test(newx$Disp, newx$HP, method="spearman")
Putting exact=NULL still doesn't remove the warning
my.cor <-cor.test(newx$Disp, newx$HP, method="kendall", exact=NULL)
I tried to find the correlation coeff for a various combination of
variables, but am unable to interpet the results. (Results pasted below in
an earlier post)

Followed it up with a normality test
shapiro.test(newx$Disp)
shapiro.test(newx$HP)

Then decided to do a kruskal.test(newx)
with the result
Kruskal-Wallis chi-squared = 328.94, df = 5, p-value < 2.2e-16

Question is : I am trying to find factors influencing efficiency (in this
case mileage)

What are the range of functions / examples I should be looking at, to find
a factor or combination of factors influencing efficiency?

Any pointers will be helpful

Thanks
Lalitha

On Sun, May 3, 2015 at 2:49 PM, Lalitha Viswanathan <
lalitha.viswanathan79 at gmail.com> wrote:

Hi
I have a dataset of the type attached.
Here's my code thus far.
dataset <-data.frame(read.delim("data", sep="\t", header=TRUE));
newData<-subset(dataset, select = c(Price, Reliability, Mileage, Weight,
Disp, HP));
cor(newData, method="pearson");
Results are
                 Price Reliability    Mileage     Weight       Disp
  HP
Price        1.0000000          NA -0.6537541  0.7017999  0.4856769
 0.6536433
Reliability         NA           1         NA         NA         NA
  NA
Mileage     -0.6537541          NA  1.0000000 -0.8478541 -0.6931928
-0.6667146
Weight       0.7017999          NA -0.8478541  1.0000000  0.8032804
 0.7629322
Disp         0.4856769          NA -0.6931928  0.8032804  1.0000000
 0.8181881
HP           0.6536433          NA -0.6667146  0.7629322  0.8181881
 1.0000000

It appears that Wt and Price, Wt and Disp, Wt and HP, Disp and HP, HP

and

Price are strongly correlated.
To find the statistical significance,
I am trying  sample.correln<-cor.test(newData$Disp, newData$HP,
method="kendall", exact=NULL)
Kendall's rank correlation tau

data:  newx$Disp and newx$HP
z = 7.2192, p-value = 5.229e-13
alternative hypothesis: true tau is not equal to 0
sample estimates:
      tau
0.6563871

If I try the same with
sample.correln<-cor.test(newData$Disp, newData$HP, method="pearson",
exact=NULL)
I get Warning message:
In cor.test.default(newx$Disp, newx$HP, method = "spearman", exact =

NULL)

:
  Cannot compute exact p-value with ties

sample.correln

Spearman's rank correlation rho

data:  newx$Disp and newx$HP
S = 5716.8, p-value < 2.2e-16
alternative hypothesis: true rho is not equal to 0
sample estimates:
      rho
0.8411566

I am not sure how to interpret these values.
Basically, I am trying to figure out which combination of factors
influences efficiency.

Thanks
Lalitha

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