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Rev 64891 Rev 75334
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(IM <- influence.measures(lm.SR))
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(IM <- influence.measures(lm.SR))
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summary(IM)
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summary(IM)
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## colnames will differ in the next line
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## colnames will differ in the next line
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all.equal(dfbetas(lm.SR), IM$infmat[, 1:5], check.attributes = FALSE,
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all.equal(dfbetas(lm.SR), IM$infmat[, 1:5], check.attributes = FALSE,
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          tolerance = 1e-12)
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          tolerance = 1e-12)
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signif(dfbeta(lm.SR), 3)
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signif(dfbeta(lm.SR), 3)
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covratio (lm.SR)
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covratio (lm.SR)
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## Multivariate lm ("mlm") --- Example from  ?SSD
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reacttime <- matrix(c(
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420, 420, 480, 480, 600, 780,
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420, 480, 480, 360, 480, 600,
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480, 480, 540, 660, 780, 780,
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420, 540, 540, 480, 780, 900,
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540, 660, 540, 480, 660, 720,
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360, 420, 360, 360, 480, 540,
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480, 480, 600, 540, 720, 840,
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480, 600, 660, 540, 720, 900,
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540, 600, 540, 480, 720, 780,
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480, 420, 540, 540, 660, 780),
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ncol = 6, byrow = TRUE,
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dimnames = list(subj = 1:10,
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              cond = c("deg0NA", "deg4NA", "deg8NA",
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                       "deg0NP", "deg4NP", "deg8NP")))
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mlmfit <- lm(reacttime ~ 1)
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ImMLM <- influence.measures(mlmfit)## fails in R <= 3.5.1
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## and the print() and summary() methods had failed additionally:
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oo <- capture.output(ImMLM) # now ok
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summary(ImMLM) # "ok"
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## predict.lm(.)
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## predict.lm(.)
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all.equal(predict(roller.lm,                 se.fit=TRUE)$se.fit,
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all.equal(predict(roller.lm,                 se.fit=TRUE)$se.fit,
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          predict(roller.lm, newdata=roller, se.fit=TRUE)$se.fit, tolerance =  1e-14)
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          predict(roller.lm, newdata=roller, se.fit=TRUE)$se.fit, tolerance =  1e-14)