*
*

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 ,couldn’t 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

	[[alternative HTML version deleted]]


