Hi all,
Consider the data set where there are a continuous response variable, a
continuous predictor "weeks" and a categorical variable "region" with five
levels "a", "b", "c",
"d", "e".
I fit the ANCOVA model as follows. Here the reference level is region "a"
and there are 4 dummy variables. The interaction terms (in red below)
represent the slope
difference between each region and the baseline region "a" and the
corresponding p-value is for testing whether this slope difference is zero.
Is there a way to directly test whether the slope corresponding to each
individual factor level is 0 or not, instead of testing the slope
difference from the baseline level?
Thanks very much.
Hanna
> mod <- lm(response ~ weeks*region,data)> summary(mod)
Call:
lm(formula = response ~ weeks * region, data = data)
Residuals:
Min 1Q Median 3Q Max
-0.19228 -0.07433 -0.01283 0.04439 0.24544
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.2105556 0.0954567 12.682 1.2e-14 ***
weeks -0.0213333 0.0147293 -1.448 0.156
regionb -0.0257778 0.1349962 -0.191 0.850
regionc -0.0344444 0.1349962 -0.255 0.800
regiond -0.0754444 0.1349962 -0.559 0.580
regione -0.1482222 0.1349962 -1.098 0.280 weeks:regionb
-0.0007222 0.0208304 -0.035 0.973
weeks:regionc -0.0017778 0.0208304 -0.085 0.932
weeks:regiond 0.0030000 0.0208304 0.144 0.886
weeks:regione 0.0301667 0.0208304 1.448 0.156 ---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1082 on 35 degrees of freedom
Multiple R-squared: 0.2678, Adjusted R-squared: 0.07946
F-statistic: 1.422 on 9 and 35 DF, p-value: 0.2165
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