I have two models which will be used for prediction. The predictor variable zq65 has very different summary results for Pr(>|t|), depending on the inclusion of an interaction term in the model.
Is this unusual to have Pr(>|t|) change so much (0.969479 vs. 0.06721), with the addition of another predictor (i.e., I(zmean*zpcum5))?
zpcum5 has a negative (-0.552) correlation with the data, so maybe I shouldn't be combining this with zmean which has a positive (0.800) correlation? As you can see, I'm not sure how to proceed.
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fmla_sqrtf <- as.formula("plotVol_sqrt ~ zmean + zq65 + zpcum5 + I(zmean*zpcum5) + mc20210624 * mc20210425")
fmla_sqrtj <- as.formula("plotVol_sqrt ~ zmean + zq65 + zpcum5 + mc20210624 * mc20210425")
Lm2_sqrtf <- lm(fmla_sqrtf, data = lidarDataSubset_B)
Lm2_sqrtj <- lm(fmla_sqrtj, data = lidarDataSubset_B)
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# -----------------------------------------------------------------------------------------------
# summary result for Lm2_sqrtf
# -----------------------------------------------------------------------------------------------
> summary(Lm2_sqrtf)
Call:
lm(formula = fmla_sqrtf, data = lidarDataSubset_B)
Residuals:
Min 1Q Median 3Q Max
-4.7570 -1.1558 0.1995 1.2259 4.9739
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -29.373843 18.350664 -1.601 0.110469
zmean 0.992839 0.278693 3.562 0.000426 ***
zq65 0.009464 0.247136 0.038 0.969479
zpcum5 -0.054502 0.015291 -3.564 0.000423 ***
I(zmean * zpcum5) 0.006974 0.002002 3.483 0.000568 ***
mc20210624 27.012833 20.034941 1.348 0.178557
mc20210425 64.169975 27.869473 2.303 0.021972 *
mc20210624:mc20210425 -59.130015 30.287534 -1.952 0.051810 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.807 on 308 degrees of freedom
Multiple R-squared: 0.7886, Adjusted R-squared: 0.7838
F-statistic: 164.2 on 7 and 308 DF, p-value: < 2.2e-16
# -----------------------------------------------------------------------------------------------
# -----------------------------------------------------------------------------------------------
# summary result for Lm2_sqrtj
# -----------------------------------------------------------------------------------------------
> summary(Lm2_sqrtj)
Call:
lm(formula = fmla_sqrtj, data = lidarDataSubset_B)
Residuals:
Min 1Q Median 3Q Max
-4.6753 -1.1852 0.1644 1.2388 5.2397
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -24.777054 18.629852 -1.330 0.18451
zmean 0.772571 0.276266 2.796 0.00549 **
zq65 0.409188 0.222777 1.837 0.06721 .
zpcum5 -0.007396 0.007260 -1.019 0.30912
mc20210624 19.686892 20.279864 0.971 0.33243
mc20210425 53.435600 28.192944 1.895 0.05898 .
mc20210624:mc20210425 -46.913907 30.620744 -1.532 0.12652
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.839 on 309 degrees of freedom
Multiple R-squared: 0.7803, Adjusted R-squared: 0.776
F-statistic: 182.9 on 6 and 309 DF, p-value: < 2.2e-16
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