Dear all,
I hope to be the clearest I can.
Let's say I have a dataset with 10 variables, where 4 of them represent for
me a certain phenomenon that I call Y.
The other 6 represent for me another phenomenon that I call X.
Each one of those variables (10) contains 37 units. Those units are just
the respondents of my analysis (a survey).
Since all the questions are based on a Likert scale, they are qualitative
variables. The scale is from 0 to 7 for all of
them, but there are "-1" and "-2" values where the answer is missing. Hence
the scale goes actually from -2 to 7.
What I want to do is to calculate the regression between my Y (which
contains 4 variables in this case and 37 answers
for each variable) and my X (which contains 6 variables instead and the
same number of respondents). I know that for
qualitative analyses I should use Anova instead of the regression, although
I have read somewhere that it is even possible
to make the regression.
Until now I have tried to act this way:
__________________________________________________________________________________________________________
> apply(Y, 1, function(Y) mean(Y[Y>0])) #calculate the average per rows
(respondents) without considering the negative values
> Y.reg<- c(apply(Y, 1, function(Y) mean(Y[Y>0]))) #create the vector Y,
thus it results like 1 variable with 37 numbers
> apply(X, 1, function(X) mean(X[X>0]))
> X.reg<- c(apply(X, 1, function(X) mean(X[X>0]))) #create the vector
X, thus it results like 1 variable with 37 numbers
> reg1<- lm(Y.reg~ X.reg) #make the first regression
> summary(reg1) #see the results
Call:
lm(formula = Y.reg ~ X.reg)
Residuals:
Min 1Q Median 3Q Max
-2.26183 -0.49434 -0.02658 0.37260 2.08899
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.2577 0.4986 8.539 4.46e-10 ***
X.reg 0.1008 0.1282 0.786 0.437
---
Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1
Residual standard error: 0.7827 on 35 degrees of freedom
Multiple R-squared: 0.01736, Adjusted R-squared: -0.01072
F-statistic: 0.6182 on 1 and 35 DF, p-value: 0.437
> layout(matrix(1:4,2,2)) #graphical approach
> plot(reg1)
please see the pfd() function attached.
________________________________________________________________________________________________________
But as you can see, although I do not use Y as composed by 4 variables and
X by 6, and I do not consider the negative values
too, I get a very low score as my R^2.
If I act with anova instead I have this problem:
________________________________________________________________________________________________________
> Ymatrix<- as.matrix(Y)
> Xmatrix<- as.matrix(X) #where both this Y and X are in their first form,
thus composed by more variables (4 and 6) and with
#negative values as well.
> Errore in UseMethod("anova") :
no applicable method for 'anova' applied to an object of class
"c('matrix', 'integer', 'numeric')"
________________________________________________________________________________________________________
To be honest, a few days ago I succeeded in using anova, but unfortunately
I do not remember how and I did not save the
command anywhere.
What I would like to know is:
- First of all, am I wrong in how I approach to my problem?
- What do you think about the regression output?
- Finally, how can I do to make the anova? If I have to do it.
I really hope I have been clear. Thank you all for any kind of help.
Best,
Andrea
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