Read the warning message! It has converted your variables into factors.
Figure out why...and you will have solved the problem.
Alain Zuur
moumita wrote:
>
> *
> *
>
> 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
>
> [[alternative HTML version deleted]]
>
>
> ______________________________________________
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> PLEASE do read the posting guide
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> and provide commented, minimal, self-contained, reproducible code.
>
>
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