Francesco, My guess would be collinearity of the predictors. The linear model gives you the best fit to all of the predictors at once; unless the predictors are orthogonal (which in a case like this is certainly not the case), there is no guarantee that the parameter estimates which give the best overall fit for the linear model will be similar to regression coefficients if you were to regress the response on each predictor individually.
There are various ways to check collinearity, such as variance inflation factors (VIF). You may want to look into them. It's very dangerous to try to interpret your parameter estimates in the presence of collinearity. Jonathan On Fri, Dec 3, 2010 at 7:42 AM, Francesco Nutini <nutini.france...@gmail.com> wrote: > > > > > Dear R-users, > I'm studing a DB, structured like this (just a little part of my dataset): > _____________________________________________________________________________________________________________ > > > > > > > > > > Site > Latitude > Longitude > Year > Tot-Prod > Total_Density > dmp > > > > Dendoudi-1 > 15.441964 > -13.540179 > 2005 > 3271.16 > 1007 > 16993.25 > > > Dendoudi-2 > 15.397321 > -13.611607 > 2005 > 1616.84 > 250 > 25376.67 > > > … > … > … > … > … > … > … > > _____________________________________________________________________________________________________________ > > If I made a scatterplotmatrix with the command show below I obtain a matrix > (visible in the image) that show which variables is more correlated with dmp > data (violet color). > But, if I made a linear model between the dependent variable (dmp) and many > independent variables > I get different information about the significativity of the variable. > I mean, variables that appear correlated with dependent variable in the > matrix result not correlated in the summary of linear model, and vice versa. > Have I made a mistake in the interpretation of the result, or not? > > Thank you in advance, > Francesco > > > > #command for matrix-plot > > >>dta <- > senegal5[c( 2,4,5,6,7,8,9,13,15,17,21, > 39,44,45)] > >>dta.r <- > abs(cor(dta)) > >>dta.col > <- dmat.color(dta.r) > >>dta.o <- > order.single(dta.r) > >>cpairs(dta, > dta.o, panel.colors=dta.col, gap=.5, > >>main="Variables Ordered and Colored by > Correlation") > #command for linear model and summary() > > >>a<- lm ( dmp ~ Latitude > + Longitude + Year + Tot.Prod + Herbaceous.Prod.kg.ha. + Leaf.Prod + > Tree.bio + Total_Density + X1st.SpecieDensity.trunk.ha.+ > X2nd.SpecieDensity.trunk.ha.+ Herb_Specie_Index1 + iNDVI.JASO. > + > RFE.Cum.JASO., data=senegal5 ) > > > > >>summary(a) > > > > Call: > > lm(formula = dmp ~ > Latitude + Longitude + Year + Tot.Prod + Herbaceous.Prod.kg.ha. + > > Leaf.Prod + Tree.bio + Total_Density + > X1st.SpecieDensity.trunk.ha. + > > X2nd.SpecieDensity.trunk.ha. + > Herb_Specie_Index1 + iNDVI.JASO. + > > RFE.Cum.JASO., > data = senegal5) > > Residuals: > > Min > 1Q Median 3Q > Max > > -676.49 -195.77 -33.06 > 113.34 816.17 > > > > Coefficients: > > Estimate Std. Error > t value Pr(>|t|) > > (Intercept) -3.283e+05 4.505e+04 > -7.288 4.41e-11 *** > > Latitude -6.100e+01 1.990e+02 > -0.307 0.7598 > > Longitude -3.617e+02 8.639e+01 > -4.187 5.60e-05 *** > > Year 1.604e+02 2.300e+01 > 6.973 2.15e-10 *** > > Tot.Prod -4.893e+00 1.565e+02 > -0.031 0.9751 > > Herbaceous.Prod.kg.ha. 4.905e+00 1.565e+02 > 0.031 0.9751 > > Leaf.Prod > 4.842e+00 1.565e+02 > 0.031 0.9754 > > Tree.bio -4.241e+01 2.771e+02 > -0.153 0.8786 > > Total_Density -1.930e+00 8.933e-01 > -2.160 0.0329 * > > X1st.SpecieDensity.trunk.ha. 1.992e+00 > 9.246e-01 2.154 > 0.0333 * > > X2nd.SpecieDensity.trunk.ha. 3.416e+00 > 1.642e+00 2.080 0.0398 * > > > Herb_Specie_Index1 -1.091e+00 1.844e+00 > -0.592 0.5552 > > iNDVI.JASO. 8.914e+02 6.076e+01 > 14.670 < 2e-16 *** > > RFE.Cum.JASO. 2.525e+00 4.529e-01 > 5.575 1.68e-07 *** > > --- > > Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ > 1 > > > > Residual standard > error: 295.3 on 114 degrees of freedom > > Multiple R-squared: > 0.9206, Adjusted R-squared: 0.9116 > > F-statistic: 101.7 on > 13 and 114 DF, p-value: < 2.2e-16 > > > > > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > > ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.