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Fixes issue#48 added check for possible use of poly() #49

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Fixes issue#48 added check for possible use of poly() #49

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16 changes: 8 additions & 8 deletions R/calibrate.R
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Original file line number Diff line number Diff line change
Expand Up @@ -276,8 +276,8 @@ calibrate.lm <- function(object, y0, interval = c("inversion", "Wald", "none"),
adjust = c("none", "Bonferroni", "Scheffe"), k, ...) {

# Check model formula for correctness
xname <- all.vars(stats::formula(object)[[3L]])
yname <- all.vars(stats::formula(object)[[2L]])
xname <- all.vars(object$terms[[3L]])
yname <- all.vars(object$terms[[2L]])
if (length(xname) != 1L) {
stop("Only one independent variable allowed.")
}
Expand All @@ -288,19 +288,19 @@ calibrate.lm <- function(object, y0, interval = c("inversion", "Wald", "none"),
# Check for intercept using terms object from model fit. Alternatively, this
# can also be checked by testing if the first column name in model.matrix is
# equal to "(Intercept)".
if (!attr(stats::terms(object), "intercept")) {
if (!attr(object$terms, "intercept")) {
stop(paste(deparse(substitute(object)), "must contain an intercept."))
}

# Extract x values and y values from model frame
mf <- stats::model.frame(object)
if (ncol(mf) != 2) {
mf <- object$model
if (ncol(mf) != 2 || !is.vector(mf[,2]) ) {
stop("calibrate only works for the simple linear regression model.")
}
x <- stats::model.matrix(object)[, 2]
y <- stats::model.response(mf)
x <- mf[,2]
y <- mf[,1]

# Eta - mean response or mean of observed respone values
# Eta - mean response or mean of observed response values
eta <- mean(y0) # mean of new observations
m <- length(y0) # number of new observations
if (mean.response && m > 1) stop("Only one mean response value allowed.")
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5 changes: 3 additions & 2 deletions tests/testthat/test-calibrate.R
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Original file line number Diff line number Diff line change
Expand Up @@ -172,12 +172,13 @@ test_that("errors get handled apprropriately", {
# Multiple linear regression
mlr.fit1 <- lm(weight ~ time + I(time ^ 2), data = crystal)
mlr.fit2 <- lm(cbind(weight, weight ^ 2) ~ time, data = crystal)
mlr.poly <- lm(weight ~ poly(time,2), data = crystal)

# Expectations
expect_error(calibrate(nls.fit, y0 = c(309, 296, 419)))
expect_error(calibrate(mlr.fit1, y0 = c(309, 296, 419)))
expect_error(calibrate(mlr.fit1, y0 = c(309, 296, 419)))

expect_error(calibrate(mlr.fit2, y0 = c(309, 296, 419)))
expect_error(calibrate(mlr.poly, y0 = c(309, 296, 419)))
})


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