Problem
Chapter 9 and 11 are failing to knit due to changes in dplyr(1.0) or broom(0.7)
I consulted this StackOverflow post for guidance (and provided my own solution) in order to solve the error which occurs in the below locations.
Example Solution
Looking at the documentation for do(), it appears to have been superseded with a recommendation to use nest_by(). Conveniently, the documentation examples cover almost this exact use case (see details)
# do() with named arguments becomes nest_by() + mutate() & list()
models <- by_cyl %>% do(mod = lm(mpg ~ disp, data = .))
# ->
models <- mtcars %>%
nest_by(cyl) %>%
mutate(mod = list(lm(mpg ~ disp, data = data)))
models %>% summarise(rsq = summary(mod)$r.squared)
# use broom to turn models into data
models %>% do(data.frame(
var = names(coef(.$mod)),
coef(summary(.$mod)))
)
# ->
if (requireNamespace("broom")) {
models %>% summarise(broom::tidy(mod))
} For the chunk containing errors on lines 400-414:
regressions <- smallchart.long %>%
nest_by(schoolid) %>%
mutate(fit = list(lm(MathAvgScore ~ year08, data=data)))
sd_filter <- smallchart.long %>%
group_by(schoolid) %>%
summarise(sds = sd(MathAvgScore))
regressions <- regressions %>%
right_join(sd_filter, by="schoolid") %>%
filter(!is.na(sds))
lm_info1 <- regressions %>%
summarise(tidy(fit)) %>%
ungroup() %>%
select(schoolid, term, estimate) %>%
spread(key = term, value = estimate) %>%
rename(rate = year08, int = `(Intercept)`)
lm_info2 <- regressions %>%
summarise(tidy(fit)) %>%
ungroup() %>%
select(schoolid, term, std.error) %>%
spread(key = term, value = std.error) %>%
rename(se_rate = year08, se_int = `(Intercept)`)
lm_info <- regressions %>%
summarise(glance(fit)) %>%
ungroup() %>%
select(schoolid, r.squared, df.residual) %>%
inner_join(lm_info1, by = "schoolid") %>%
inner_join(lm_info2, by = "schoolid") %>%
mutate(tstar = qt(.975, df.residual),
intlb = int - tstar * se_int,
intub = int + tstar * se_int,
ratelb = rate - tstar * se_rate,
rateub = rate + tstar * se_rate)This solution can nearly be line-for-lined copy for the errors occurring on lines 461-475.
Chapter 9 also has an issue here knitting due to failure to converge. Using 500 iterations seemed to do the trick:
hcs.lme=lme(MathAvgScore ~ year08 * charter, chart.long,
random = ~ 1 | schoolid, na.action=na.exclude,
correlation=corCompSymm(form = ~ 1 |schoolid),
weights=varIdent(form = ~1|year08), control = lmeControl(msMaxIter=500))
summary(hcs.lme)
# Linear mixed-effects model fit by REML
# Data: chart.long
# AIC BIC logLik
# 10299.2 10348.3 -5140.6
#
# Random effects:
# Formula: ~1 | schoolid
# (Intercept) Residual
# StdDev: 0.002264717 6.534915
#
# Correlation Structure: Compound symmetry
# Formula: ~1 | schoolid
# Parameter estimate(s):
# Rho
# 0.8209145
# Variance function:
# Structure: Different standard deviations per stratum
# Formula: ~1 | year08
# Parameter estimates:
# 0 1 2
# 1.000000 1.127902 1.079423
# Fixed effects: MathAvgScore ~ year08 * charter
# Value Std.Error DF t-value p-value
# (Intercept) 652.3347 0.2828597 1113 2306.2126 0.0000
# year08 1.1831 0.0907869 1113 13.0320 0.0000
# charter -5.9106 0.8611940 616 -6.8633 0.0000
# year08:charter 0.8316 0.3032040 1113 2.7426 0.0062
# Correlation:
# (Intr) year08 chartr
# year08 -0.208
# charter -0.328 0.068
# year08:charter 0.062 -0.299 -0.308
#
# Standardized Within-Group Residuals:
# Min Q1 Med Q3 Max
# -4.9760770 -0.4490767 0.0865079 0.5669240 3.0970658
#
# Number of Observations: 1733
# Number of Groups: 618
hcs.lme$modelStruct
# reStruct parameters:
# schoolid
# -7.967465
# corStruct parameters:
# [1] 1.998216
# varStruct parameters:
# [1] 0.1203593 0.0764270
anova(hcs.lme,cs.lme) # hcs not converging here
# Model df AIC BIC logLik Test L.Ratio p-value
# hcs.lme 1 9 10299.20 10348.30 -5140.600
# cs.lme 2 7 10315.94 10354.13 -5150.973 1 vs 2 20.74528 <.0001
Finally, in Chapter 11, there's a missing library(broom) and a handful of unscoped select() calls needing dplyr:: prefixed.
-
|
select(.,4, 5, 6, 7, 9, 10, 15, 19) |
Hope this unsolicited help is, well, helpful!
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