CRAN Package Check Results for Package pder

Last updated on 2021-07-01 01:49:21 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 1.0-1 2.32 114.82 117.14 OK
r-devel-linux-x86_64-debian-gcc 1.0-1 1.85 82.12 83.97 OK
r-devel-linux-x86_64-fedora-clang 1.0-1 138.79 OK
r-devel-linux-x86_64-fedora-gcc 1.0-1 133.84 OK
r-devel-windows-x86_64 1.0-1 4.00 147.00 151.00 OK
r-devel-windows-x86_64-gcc10-UCRT 1.0-1 OK
r-patched-linux-x86_64 1.0-1 2.88 107.93 110.81 OK
r-patched-solaris-x86 1.0-1 195.30 OK
r-release-linux-x86_64 1.0-1 1.95 107.99 109.94 OK
r-release-macos-arm64 1.0-1 ERROR
r-release-macos-x86_64 1.0-1 OK
r-release-windows-ix86+x86_64 1.0-1 5.00 141.00 146.00 OK
r-oldrel-macos-x86_64 1.0-1 OK
r-oldrel-windows-ix86+x86_64 1.0-1 5.00 120.00 125.00 OK

Check Details

Version: 1.0-1
Check: package dependencies
Result: NOTE
    Package suggested but not available for checking: ‘splm’
Flavor: r-release-macos-arm64

Version: 1.0-1
Check: examples
Result: ERROR
    Running examples in ‘pder-Ex.R’ failed
    The error most likely occurred in:
    
    > ### Name: HousePricesUS
    > ### Title: House Prices Data
    > ### Aliases: HousePricesUS
    > ### Keywords: datasets
    >
    > ### ** Examples
    >
    > #### Example 4-11
    >
    > ## ------------------------------------------------------------------------
    > data("HousePricesUS", package = "pder")
    > library("plm")
    > php <- pdata.frame(HousePricesUS)
    >
    > ## ------------------------------------------------------------------------
    > cbind("rho" = pcdtest(diff(log(php$price)), test = "rho")$statistic,
    + "|rho|" = pcdtest(diff(log(php$price)), test = "absrho")$statistic)
    Warning in pcdtest.pseries(diff(log(php$price)), test = "rho") :
     NA values encountered in input and removed
    Warning in pcdtest.pseries(diff(log(php$price)), test = "absrho") :
     NA values encountered in input and removed
     rho |rho|
    rho 0.3942212 0.4246933
    >
    > ## ------------------------------------------------------------------------
    > regions.names <- c("New Engl", "Mideast", "Southeast", "Great Lks",
    + "Plains", "Southwest", "Rocky Mnt", "Far West")
    > corr.table.hp <- cortab(diff(log(php$price)), grouping = php$region,
    + groupnames = regions.names)
    Warning in pcdtest.pseries(x, test = "rho", w = myw) :
     NA values encountered in input and removed
    Warning in pcdtest.pseries(x, test = "rho", w = myw) :
     NA values encountered in input and removed
    Warning in pcdtest.pseries(x, test = "rho", w = myw) :
     NA values encountered in input and removed
    Warning in pcdtest.pseries(x, test = "rho", w = myw) :
     NA values encountered in input and removed
    Warning in pcdtest.pseries(x, test = "rho", w = myw) :
     NA values encountered in input and removed
    Warning in pcdtest.pseries(x, test = "rho", w = myw) :
     NA values encountered in input and removed
    Warning in pcdtest.pseries(x, test = "rho", w = myw) :
     NA values encountered in input and removed
    Warning in pcdtest.pseries(x, test = "rho", w = myw) :
     NA values encountered in input and removed
    Warning in pcdtest.pseries(x, test = "rho", w = myw) :
     NA values encountered in input and removed
    Warning in pcdtest.pseries(x, test = "rho", w = myw) :
     NA values encountered in input and removed
    Warning in pcdtest.pseries(x, test = "rho", w = myw) :
     NA values encountered in input and removed
    Warning in pcdtest.pseries(x, test = "rho", w = myw) :
     NA values encountered in input and removed
    Warning in pcdtest.pseries(x, test = "rho", w = myw) :
     NA values encountered in input and removed
    Warning in pcdtest.pseries(x, test = "rho", w = myw) :
     NA values encountered in input and removed
    Warning in pcdtest.pseries(x, test = "rho", w = myw) :
     NA values encountered in input and removed
    Warning in pcdtest.pseries(x, test = "rho", w = myw) :
     NA values encountered in input and removed
    Warning in pcdtest.pseries(x, test = "rho", w = myw) :
     NA values encountered in input and removed
    Warning in pcdtest.pseries(x, test = "rho", w = myw) :
     NA values encountered in input and removed
    Warning in pcdtest.pseries(x, test = "rho", w = myw) :
     NA values encountered in input and removed
    Warning in pcdtest.pseries(x, test = "rho", w = myw) :
     NA values encountered in input and removed
    Warning in pcdtest.pseries(x, test = "rho", w = myw) :
     NA values encountered in input and removed
    Warning in pcdtest.pseries(x, test = "rho", w = myw) :
     NA values encountered in input and removed
    Warning in pcdtest.pseries(x, test = "rho", w = myw) :
     NA values encountered in input and removed
    Warning in pcdtest.pseries(x, test = "rho", w = myw) :
     NA values encountered in input and removed
    Warning in pcdtest.pseries(x, test = "rho", w = myw) :
     NA values encountered in input and removed
    Warning in pcdtest.pseries(x, test = "rho", w = myw) :
     NA values encountered in input and removed
    Warning in pcdtest.pseries(x, test = "rho", w = myw) :
     NA values encountered in input and removed
    Warning in pcdtest.pseries(x, test = "rho", w = myw) :
     NA values encountered in input and removed
    Warning in pcdtest.pseries(x, test = "rho", w = myw) :
     NA values encountered in input and removed
    Warning in pcdtest.pseries(x, test = "rho", w = myw) :
     NA values encountered in input and removed
    Warning in pcdtest.pseries(x, test = "rho", w = myw) :
     NA values encountered in input and removed
    Warning in pcdtest.pseries(x, test = "rho", w = myw) :
     NA values encountered in input and removed
    Warning in pcdtest.pseries(x, test = "rho", w = myw) :
     NA values encountered in input and removed
    Warning in pcdtest.pseries(x, test = "rho", w = myw) :
     NA values encountered in input and removed
    Warning in pcdtest.pseries(x, test = "rho", w = myw) :
     NA values encountered in input and removed
    Warning in pcdtest.pseries(x, test = "rho", w = myw) :
     NA values encountered in input and removed
    > colnames(corr.table.hp) <- substr(rownames(corr.table.hp), 1, 5)
    > round(corr.table.hp, 2)
     New E Midea South Great Plain South Rocky Far W
    New Engl 0.80 NA NA NA NA NA NA NA
    Mideast 0.68 0.66 NA NA NA NA NA NA
    Southeast 0.40 0.35 0.81 NA NA NA NA NA
    Great Lks 0.27 0.20 0.62 0.61 NA NA NA NA
    Plains 0.40 0.32 0.57 0.53 0.52 NA NA NA
    Southwest 0.07 -0.05 0.28 0.39 0.35 0.52 NA NA
    Rocky Mnt -0.03 -0.11 0.52 0.53 0.40 0.57 0.70 NA
    Far West 0.13 0.17 0.52 0.42 0.29 0.31 0.46 0.57
    >
    > ## ------------------------------------------------------------------------
    > pcdtest(diff(log(price)) ~ diff(lag(log(price))) + diff(lag(log(price), 2)),
    + data = php)
    
     Pesaran CD test for cross-sectional dependence in panels
    
    data: diff(log(price)) ~ diff(lag(log(price))) + diff(lag(log(price), 2))
    z = 59.009, p-value < 2.2e-16
    alternative hypothesis: cross-sectional dependence
    
    >
    > #### Example 9-2
    >
    > ## ------------------------------------------------------------------------
    > data("HousePricesUS", package = "pder")
    > swmod <- pvcm(log(price) ~ log(income), data = HousePricesUS, model= "random")
    > mgmod <- pmg(log(price) ~ log(income), data = HousePricesUS, model = "mg")
    > coefs <- cbind(coef(swmod), coef(mgmod))
    > dimnames(coefs)[[2]] <- c("Swamy", "MG")
    > coefs
     Swamy MG
    (Intercept) 3.8914242 3.8498054
    log(income) 0.2867014 0.3018117
    >
    > #### Example 9-3
    >
    > ## ------------------------------------------------------------------------
    > library("texreg")
    Version: 1.37.5
    Date: 2020-06-17
    Author: Philip Leifeld (University of Essex)
    
    Consider submitting praise using the praise or praise_interactive functions.
    Please cite the JSS article in your publications -- see citation("texreg").
    > data("RDSpillovers", package = "pder")
    > fm.rds <- lny ~ lnl + lnk + lnrd
    > mg.rds <- pmg(fm.rds, RDSpillovers, trend = TRUE)
    > dmg.rds <- update(mg.rds, . ~ lag(lny) + .)
    > screenreg(list('Static MG' = mg.rds, 'Dynamic MG'= dmg.rds), digits = 3)
    
    =======================================
     Static MG Dynamic MG
    ---------------------------------------
    (Intercept) 4.550 *** 4.038 ***
     (0.841) (0.778)
    lnl 0.568 *** 0.507 ***
     (0.086) (0.059)
    lnk 0.117 0.020
     (0.122) (0.085)
    lnrd -0.058 -0.092
     (0.079) (0.071)
    trend 0.022 ** 0.023 ***
     (0.008) (0.004)
    lag(lny) 0.223 ***
     (0.034)
    ---------------------------------------
    Num. obs. 2637 2518
    =======================================
    *** p < 0.001; ** p < 0.01; * p < 0.05
    >
    > ## ------------------------------------------------------------------------
    > library("msm")
    > b.lr <- coef(dmg.rds)["lnrd"]/(1 - coef(dmg.rds)["lag(lny)"])
    > SEb.lr <- deltamethod(~ x5 / (1 - x2),
    + mean = coef(dmg.rds), cov = vcov(dmg.rds))
    > z.lr <- b.lr / SEb.lr
    > pval.lr <- 2 * pnorm(abs(z.lr), lower.tail = FALSE)
    > lr.lnrd <- matrix(c(b.lr, SEb.lr, z.lr, pval.lr), nrow=1)
    > dimnames(lr.lnrd) <- list("lnrd (long run)", c("Est.", "SE", "z", "p.val"))
    > round(lr.lnrd, 3)
     Est. SE z p.val
    lnrd (long run) -0.118 0.091 -1.301 0.193
    >
    >
    > #### Example 9-4
    >
    > ## ------------------------------------------------------------------------
    > housep.np <- pvcm(log(price) ~ log(income), data = HousePricesUS, model = "within")
    > housep.pool <- plm(log(price) ~ log(income), data = HousePricesUS, model = "pooling")
    > housep.within <- plm(log(price) ~ log(income), data = HousePricesUS, model = "within")
    >
    > d <- data.frame(x = c(coef(housep.np)[[1]], coef(housep.np)[[2]]),
    + coef = rep(c("intercept", "log(income)"),
    + each = nrow(coef(housep.np))))
    > library("ggplot2")
    > ggplot(d, aes(x)) + geom_histogram(col = "black", fill = "white", bins = 8) +
    + facet_wrap(~ coef, scales = "free") + xlab("") + ylab("")
    >
    >
    > ## ------------------------------------------------------------------------
    > summary(housep.np)
    Oneway (individual) effect No-pooling model
    
    Call:
    pvcm(formula = log(price) ~ log(income), data = HousePricesUS,
     model = "within")
    
    Balanced Panel: n = 49, T = 29, N = 1421
    
    Residuals:
     Min. 1st Qu. Median 3rd Qu. Max.
    -0.279006789 -0.069921886 -0.005819077 0.064749895 0.352409710
    
    Coefficients:
     (Intercept) log(income)
     Min. :-0.2951 Min. :-1.1409
     1st Qu.: 3.1519 1st Qu.:-0.1378
     Median : 4.1457 Median : 0.2283
     Mean : 3.8498 Mean : 0.3018
     3rd Qu.: 4.7773 3rd Qu.: 0.6614
     Max. : 6.9108 Max. : 2.0369
    
    Total Sum of Squares: 3870.1
    Residual Sum of Squares: 13.739
    Multiple R-Squared: 0.99645
    >
    > ## ------------------------------------------------------------------------
    > pooltest(housep.pool, housep.np)
    
     F statistic
    
    data: log(price) ~ log(income)
    F = 25.778, df1 = 96, df2 = 1323, p-value < 2.2e-16
    alternative hypothesis: unstability
    
    > pooltest(housep.within, housep.np)
    
     F statistic
    
    data: log(price) ~ log(income)
    F = 16.074, df1 = 48, df2 = 1323, p-value < 2.2e-16
    alternative hypothesis: unstability
    
    >
    >
    > #### Example 9-5
    >
    > ## ------------------------------------------------------------------------
    > library("texreg")
    > cmgmod <- pmg(log(price) ~ log(income), data = HousePricesUS, model = "cmg")
    > screenreg(list(mg = mgmod, ccemg = cmgmod), digits = 3)
    
    ===========================================
     mg ccemg
    -------------------------------------------
    (Intercept) 3.850 *** -0.115
     (0.204) (0.256)
    log(income) 0.302 ** 1.135 ***
     (0.093) (0.195)
    y.bar 1.047 ***
     (0.058)
    log(income).bar -1.195 ***
     (0.199)
    -------------------------------------------
    Num. obs. 1421 1421
    ===========================================
    *** p < 0.001; ** p < 0.01; * p < 0.05
    >
    > #### Example 9-6
    >
    > ## ------------------------------------------------------------------------
    > ccemgmod <- pcce(log(price) ~ log(income), data=HousePricesUS, model="mg")
    > summary(ccemgmod)
    Common Correlated Effects Mean Groups model
    
    Call:
    pcce(formula = log(price) ~ log(income), data = HousePricesUS,
     model = "mg")
    
    Balanced Panel: n = 49, T = 29, N = 1421
    
    Residuals:
     Min. 1st Qu. Median Mean 3rd Qu. Max.
    -0.2374376 -0.0354899 0.0002718 0.0000000 0.0363912 0.2242333
    
    Coefficients:
     Estimate Std. Error z-value Pr(>|z|)
    log(income) 1.13540 0.19546 5.809 6.285e-09 ***
    ---
    Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
    Total Sum of Squares: 47.234
    Residual Sum of Squares: 5.6567
    HPY R-squared: 0.74027
    >
    > ## ------------------------------------------------------------------------
    > ccepmod <- pcce(log(price) ~ log(income), data=HousePricesUS, model="p")
    > summary(ccepmod)
    Common Correlated Effects Pooled model
    
    Call:
    pcce(formula = log(price) ~ log(income), data = HousePricesUS,
     model = "p")
    
    Balanced Panel: n = 49, T = 29, N = 1421
    
    Residuals:
     Min. 1st Qu. Median Mean 3rd Qu. Max.
    -0.278833 -0.039281 -0.002089 0.000000 0.039268 0.299930
    
    Coefficients:
     Estimate Std. Error z-value Pr(>|z|)
    log(income) 1.19941 0.20728 5.7864 7.193e-09 ***
    ---
    Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
    Total Sum of Squares: 47.234
    Residual Sum of Squares: 6.8851
    HPY R-squared: 0.69579
    >
    >
    >
    > #### Example 9-8
    >
    > ## ------------------------------------------------------------------------
    > data("HousePricesUS", package = "pder")
    > price <- pdata.frame(HousePricesUS)$price
    > purtest(log(price), test = "levinlin", lags = 2, exo = "trend")
    
     Levin-Lin-Chu Unit-Root Test (ex. var.: Individual Intercepts and
     Trend)
    
    data: log(price)
    z = -1.2573, p-value = 0.1043
    alternative hypothesis: stationarity
    
    > purtest(log(price), test = "madwu", lags = 2, exo = "trend")
    
     Maddala-Wu Unit-Root Test (ex. var.: Individual Intercepts and Trend)
    
    data: log(price)
    chisq = 99.843, df = 98, p-value = 0.4292
    alternative hypothesis: stationarity
    
    > purtest(log(price), test = "ips", lags = 2, exo = "trend")
    
     Im-Pesaran-Shin Unit-Root Test (ex. var.: Individual Intercepts and
     Trend)
    
    data: log(price)
    Wtbar = 0.76622, p-value = 0.7782
    alternative hypothesis: stationarity
    
    >
    >
    > #### Example 9-9
    >
    > ## ------------------------------------------------------------------------
    > tab5a <- matrix(NA, ncol = 4, nrow = 2)
    > tab5b <- matrix(NA, ncol = 4, nrow = 2)
    >
    > for(i in 1:4) {
    + mymod <- pmg(diff(log(income)) ~ lag(log(income)) +
    + lag(diff(log(income)), 1:i),
    + data = HousePricesUS,
    + model = "mg", trend = TRUE)
    + tab5a[1, i] <- pcdtest(mymod, test = "rho")$statistic
    + tab5b[1, i] <- pcdtest(mymod, test = "cd")$statistic
    + }
    >
    > for(i in 1:4) {
    + mymod <- pmg(diff(log(price)) ~ lag(log(price)) +
    + lag(diff(log(price)), 1:i),
    + data=HousePricesUS,
    + model="mg", trend = TRUE)
    + tab5a[2, i] <- pcdtest(mymod, test = "rho")$statistic
    + tab5b[2, i] <- pcdtest(mymod, test = "cd")$statistic
    + }
    >
    > tab5a <- round(tab5a, 3)
    > tab5b <- round(tab5b, 2)
    > dimnames(tab5a) <- list(c("income", "price"),
    + paste("ADF(", 1:4, ")", sep=""))
    > dimnames(tab5b) <- dimnames(tab5a)
    >
    > tab5a
     ADF(1) ADF(2) ADF(3) ADF(4)
    income 0.465 0.443 0.338 0.317
    price 0.346 0.326 0.252 0.194
    > tab5b
     ADF(1) ADF(2) ADF(3) ADF(4)
    income 82.84 77.40 57.96 53.21
    price 61.73 57.02 43.21 32.52
    >
    > ## ------------------------------------------------------------------------
    > php <- pdata.frame(HousePricesUS)
    > cipstest(log(php$price), type = "drift")
    Warning in cipstest(log(php$price), type = "drift") :
     p-value greater than printed p-value
    
     Pesaran's CIPS test for unit roots
    
    data: log(php$price)
    CIPS test = -2.0342, lag order = 2, p-value = 0.1
    alternative hypothesis: Stationarity
    
    > cipstest(diff(log(php$price)), type = "none")
    Warning in cipstest(diff(log(php$price)), type = "none") :
     p-value smaller than printed p-value
    
     Pesaran's CIPS test for unit roots
    
    data: diff(log(php$price))
    CIPS test = -1.8199, lag order = 2, p-value = 0.01
    alternative hypothesis: Stationarity
    
    >
    > ## ------------------------------------------------------------------------
    > cipstest(resid(ccemgmod), type="none")
    Warning in cipstest(resid(ccemgmod), type = "none") :
     p-value smaller than printed p-value
    
     Pesaran's CIPS test for unit roots
    
    data: resid(ccemgmod)
    CIPS test = -2.6588, lag order = 2, p-value = 0.01
    alternative hypothesis: Stationarity
    
    > cipstest(resid(ccepmod), type="none")
    Warning in cipstest(resid(ccepmod), type = "none") :
     p-value smaller than printed p-value
    
     Pesaran's CIPS test for unit roots
    
    data: resid(ccepmod)
    CIPS test = -2.1813, lag order = 2, p-value = 0.01
    alternative hypothesis: Stationarity
    
    >
    >
    > #### Example 10-2
    >
    > ## ------------------------------------------------------------------------
    > data("usaw49", package="pder")
    > library("plm")
    > php <- pdata.frame(HousePricesUS)
    > pcdtest(php$price, w = usaw49)
    
     Pesaran CD test for local cross-sectional dependence in panels
    
    data: php$price
    z = 37.288, p-value < 2.2e-16
    alternative hypothesis: cross-sectional dependence
    
    >
    > ## ------------------------------------------------------------------------
    > library("splm")
    Error in library("splm") : there is no package called ‘splm’
    Execution halted
Flavor: r-release-macos-arm64