*
*
Hi ,
Can anyone help me please with this problem?*
*
*CASE-I*
all_raw_data_NAomitted is my data frame.It has columns with names i1 ,i2,
i3,i4
, till i15.It has 291 rows actually ,couldnt show here.
The data frame looks like this:--
i1 i2 i3 i4 i5 i6 i7 i8 i9 i10 i11 i12 i13 i14 i15
2 2 2 2 2 2 2 2 2 2 2 1 2 2 3 2
3 2 2 2 2 3 2 2 3 3 3 2 3 3 3 3
4 2 2 2 2 2 2 2 1 1 1 2 1 2 2 2
6 2 2 1 2 1 1 2 2 1 1 1 1 2 2 2
8 3 2 2 2 3 3 3 2 3 2 3 2 3 3 2
9 2 2 2 2 2 2 3 3 3 2 3 3 3 2 2
10 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
12 2 2 2 3 2 2 2 1 3 2 1 2 2 3 3
While doing regression i1 being the dependent variable and i2 as the
predictor the outputs produced are not correct.The o/ps are as shown
below:---
*all_raw_data_NAomitted$i1<-as.vector(as.matrix(all_raw_data_NAomitted$i1))
all_raw_data_NAomitted$i2<-as.vector(as.matrix(all_raw_data_NAomitted$i2))
*
*
*
*fit<-lrm(i1 ~ i2 + NULL,all_raw_data_NAomitted)*
> source("regression.R")
[1] "Printing regression value........................."
Call:
lm(formula = i1 ~ i2, data = all_raw_data_NAomitted)
Residuals:
Min 1Q Median 3Q Max
-1.46154 -0.19277 -0.03529 -0.03529 1.96471
*Coefficients:*
* Estimate Std. Error t value Pr(>|t|)*
*(Intercept) 1.19277 0.05302 22.50 <2e-16 ****
*i22 0.84252 0.06469 13.03 <2e-16 ****
*i23 1.52723 0.11021 13.86 <2e-16 ****
*i24 2.26877 0.14409 15.74 <2e-16 ****
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4831 on 287 degrees of freedom
Multiple R-squared: 0.5815, Adjusted R-squared: 0.5771
F-statistic: 132.9 on 3 and 287 DF, p-value: < 2.2e-16
Error in main() :
In addition: Warning messages:
1: In model.matrix.default(mt, mf, contrasts) :
variable 'i1' converted to a factor
2: In model.matrix.default(mt, mf, contrasts) :
variable 'i2' converted to a factor
*The results produced are incorrect and do not match with SPSS results ,you
can find it out having a look at the coefficients sections of the result.my
variables were i1 and i2.*
*CASE-II*
Whereas if I do this the results produced are correct:--
> d1<-c(1,2,3,NA,6,7,8)
> d2<-c(2,3,4,3,1,2,2)
> d3<-c(2,1,2,1,2,1,3)
> d4<-c(5,6,2,1,1,2,2)
> d<-data.frame(d1,d2,d3,d4)
> d
d1 d2 d3 d4
1 1 2 2 5
2 2 3 1 6
3 3 4 2 2
4 NA 3 1 1
5 6 1 2 1
6 7 2 1 2
7 8 2 3 2
> fit<-lm(d1 ~ d2+d3+d4)
> summary(fit)
Call:
lm(formula = d1 ~ d2 + d3 + d4)
Residuals:
1 2 3 5 6 7
-1.7865 0.9698 -1.2250 -1.4802 1.2761 2.2459
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.1912 5.1807 1.774 0.218
d2 -0.7570 1.2208 -0.620 0.598
d3 0.0151 1.7474 0.009 0.994
d4 -0.9842 0.6772 -1.453 0.283
Residual standard error: 2.692 on 2 degrees of freedom
(1 observation deleted due to missingness)
Multiple R-squared: 0.6507, Adjusted R-squared: 0.1267
F-statistic: 1.242 on 3 and 2 DF, p-value: 0.4751
In case (I) if I make the individual columns as vectors also ,I do not get
correct results.what could be the cause of the incorrect results produced.
--
Thanks
Moumita
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