Fix typos

NEWS.md

@@ -741,7 +741,7 @@ plm()): Between, between, Sum, Within.
         before (all default to FALSE).
   * warning issued if an index variable is to be constructed that subsequently 
         overwrites an already present column of the same name ('id' and/or 'time').
-  * pacified warning in subsetting with with non-existent rows and columns due
+  * pacified warning in subsetting with non-existent rows and columns due
         to deprecation of 'structure(NULL, *)' in R >= 3.4.0.
   * \$<-.pdata.frame: preserves storage mode and sets correct class if propagation
                          to higher class occurred for a pseries prior to assignment

R/deprecated.R

@@ -87,7 +87,7 @@ plm.data <- function(x, indexes = NULL) {
   class(x) <- setdiff(class(x), "pdata.frame")
 
   # class "plm.dim" always has indexes in first two columns (id, time)
-  # while "pdata.frame" leaves the index variables at it's place (if not dropped at all with drop.index = T)
+  # while "pdata.frame" leaves the index variables at its place (if not dropped at all with drop.index = T)
   x <- x[ , c(names_indexes, setdiff(orig_col_order, names_indexes))]
 
   # set class

R/detect_lin_dep_alias.R

@@ -101,7 +101,7 @@
 #' form <- price ~ 0 + cpi + fact1 + fact2
 #' mf <- model.frame(Cigar.p, form)
 #' # no linear dependence in the pooling model's model matrix
-#' # (with intercept in the formula, there would be linear depedence)
+#' # (with intercept in the formula, there would be linear dependence)
 #' detect.lindep(model.matrix(mf, model = "pooling"))
 #' # linear dependence present in the FE transformed model matrix
 #' modmat_FE <- model.matrix(mf, model = "within")

R/est_pi.R

@@ -54,8 +54,8 @@ aneweytest <- function(formula, data, subset, na.action, index = NULL,  ...){
     if(!balanced) stop("'aneweytest' not implemented for unbalanced data")
 
     ht <- match.call(expand.dots = FALSE)
-    m <- match(c("formula", "data", "subset", "na.action",
-                 "effect", "model", "inst.method", "restict.matrix",
+    ~ <- match(c("formula", "data", "subset", "na.action",
+                 "effect", "model", "inst.method", "restrict.matrix",
                  "restrict.rhs", "index"), names(ht), 0)
     ht <- ht[c(1L, m)]
     ht[[1L]] <- as.name("plm")

R/tool_ranfixef.R

@@ -448,7 +448,7 @@ ranef.plm <- function(object, effect = NULL, ...) {
 #' Overall Intercept for Within Models Along its Standard Error
 #' 
 #' This function gives an overall intercept for within models and its
-#' accompanying standard error or an within model with the overall intercept
+#' accompanying standard error or a within model with the overall intercept
 #' 
 #' The (somewhat artificial) intercept for within models (fixed
 #' effects models) was made popular by Stata of StataCorp

R/tool_transformations_collapse.R

@@ -666,7 +666,7 @@ pdiff.collapse <- function(x, effect = c("individual", "time"), has.intercept =
     }
   }
 
-  ## last data preperation before return
+  ## last data preparation before return
   res <- na.omit(res)
   if(is.matrix(x)) {
     # original pdiff (coded in base R) removes constant columns in matrix, 

inst/removed/test_model.matrix_pmodel.response_NA.R

@@ -1,7 +1,7 @@
 ## Tests for correct construction in case of NAs of model.matrix[.pFormula|.plm] and pmodel.response.[pFormula|.plm]
 
 # see, if NA dropping in construction of model.matrix and pmodel.response is done correctly.
-# Some special NA patterns were not handeled correctly pre rev. 192 if pmodel.repsonse or model.matrix were called directly
+# Some special NA patterns were not handeled correctly pre rev. 192 if pmodel.response or model.matrix were called directly
 
 # 1) model.matrix[.pFormula|.plm] 
 # 2) pmodel.response.[pFormula|.plm]
@@ -190,7 +190,7 @@ resp_pFormula_NA_depvar_fe   <- plm:::pmodel.response.formula(form, data = pGrun
 #Error in model.matrix.pFormula(pFormula(formula), data = data, model = model,  : 
 #  dims [product 199] do not match the length of object [0] 
 
-# pmodel.repsonse.plm
+# pmodel.response.plm
 resp_plm_NA_depvar_pool  <- plm:::pmodel.response.plm(plm(form, data = pGrunfeld_NA_dep_var, model = "pooling"))
 resp_plm_NA_depvar_fe    <- plm:::pmodel.response.plm(plm(form, data = pGrunfeld_NA_dep_var, model = "within"))
 resp_plm_NA_depvar_fe_tw <- plm:::pmodel.response.plm(plm(form, data = pGrunfeld_NA_dep_var, model = "within", effect = "twoways"))
@@ -202,17 +202,17 @@ resp_plm_NA_indepvar_fe_tw <- plm:::pmodel.response.plm(plm(form, data = pGrunfe
 resp_plm_NA_indepvar_re    <- plm:::pmodel.response.plm(plm(form, data = pGrunfeld_NA_indep_var, model = "random"))
 
 
-# pmodel.repsonse.pFormula with NA in dependent variable
+# pmodel.response.pFormula with NA in dependent variable
 resp_pFormula_NA_depvar_pool  <- plm:::pmodel.response.formula(form, data = pGrunfeld_NA_dep_var, model = "pooling")
 resp_pFormula_NA_depvar_fe    <- plm:::pmodel.response.formula(form, data = pGrunfeld_NA_dep_var, model = "within")
 resp_pFormula_NA_depvar_fe_tw <- plm:::pmodel.response.formula(form, data = pGrunfeld_NA_dep_var, model = "within", effect = "twoways")
 # NOT OK: error
 #resp_pFormula_NA_depvar_re    <- plm:::pmodel.response.pFormula(form, data = pGrunfeld_NA_dep_var, model = "random")
 
-# pmodel.repsonse.pFormula with NA in _in_dependent variable
+# pmodel.response.pFormula with NA in _in_dependent variable
 # NA in independent variable is detected and vector of dependent variable (response) adjusted according (drop the observation)
 # -> resulting response has 199 entries, albeit there are 200 obs for the response but NA in independent variable
-# -> thus, the results of pmodel.repsonse and model.matrix match
+# -> thus, the results of pmodel.response and model.matrix match
 resp_pFormula_NA_indepvar_pool  <- plm:::pmodel.response.formula(form, data = pGrunfeld_NA_indep_var, model = "pooling")
 resp_pFormula_NA_indepvar_fe    <- plm:::pmodel.response.formula(form, data = pGrunfeld_NA_indep_var, model = "within")
 resp_pFormula_NA_indepvar_fe_tw <- plm:::pmodel.response.formula(form, data = pGrunfeld_NA_indep_var, model = "within", effect = "twoways")

inst/tests/test_detect_lin_dep_alias.R

@@ -1,7 +1,7 @@
 # Tests for functions:
 #  * detect.lindep
 #  * alias
-# YC 2017/10/09 :  RE model par defaut pb because the between model is empty
+# YC 2017/10/09 :  RE model par default pb because the between model is empty
 
 
 library(plm)

inst/tests/test_groupGenerics_pseries.R

@@ -67,7 +67,7 @@ print(summary(zz2))
 if (!isTRUE(all.equal(zz$coefficients, zz2$coefficients))) stop("estimation results not equal")
 
 
-############# (3) assinging to a pdata.frame #############
+############# (3) assigning to a pdata.frame #############
 ## test for assigning by $<- to a pdata.frame
 ## pre rev. 634: decimals which had been integers previously were converted to integers with typeof == integer
 ##               and gave wrong results.
@@ -188,7 +188,7 @@ mode(x2)
 typeof(x2)
 
 y2 <- y / 10
-class(y2) # c("myclass", "interger") - not propagated to c("myclass", "numeric")
+class(y2) # c("myclass", "integer") - not propagated to c("myclass", "numeric")
 mode(y2)
 typeof(y2)
 y2 # 0.1 0.2 0.3 - class is c("myclass", "integer") but result is decimals!

inst/tests/test_lag_lead.R

@@ -35,13 +35,13 @@ plm:::lagr.pseries(Grunfeld$firm2)
 # from the sample's unique factor levels, but it should stay in the levels
 plm:::lagr.pseries(Grunfeld$fac)
 if (!(length(unique(Grunfeld$fac)) == 200)) stop("wrong factor values") # 200
-if (!(length(unique(plm:::lagr.pseries(Grunfeld$fac))) == 191)) stop("plm:::lagr.pseries: wrong actually uniquely occuring factor values")  # 191
+if (!(length(unique(plm:::lagr.pseries(Grunfeld$fac))) == 191)) stop("plm:::lagr.pseries: wrong actually uniquely occurring factor values")  # 191
 if (!(length(levels(plm:::lagr.pseries(Grunfeld$fac))) == 200)) stop("wrong factor levels")  # 200
 
 # plm::lead eliminates e.g., level "200"
 plm:::leadr.pseries(Grunfeld$fac)
 if (!(length(unique(Grunfeld$fac)) == 200)) stop("wrong factor levels") # 200
-if (!(length(unique(plm:::leadr.pseries(Grunfeld$fac))) == 191)) stop("plm:::leadr.pseries: wrong actually uniquely occuring factor values") # 191
+if (!(length(unique(plm:::leadr.pseries(Grunfeld$fac))) == 191)) stop("plm:::leadr.pseries: wrong actually uniquely occurring factor values") # 191
 if (!(length(levels(plm:::leadr.pseries(Grunfeld$fac))) == 200)) stop("plm:::leadr.pseries: wrong factor levels")  # 200
 
 

inst/tests/test_lagt_leadt.R

@@ -36,7 +36,7 @@ lag(Grunfeld$firm2)
 # from the sample's unique factor levels, but it should stay in the levels
 lag(Grunfeld$fac)
 if (!(length(unique(Grunfeld$fac)) == 200)) stop("wrong factor levels") # 200
-if (!(length(unique(lag(Grunfeld$fac))) == 191)) stop("wrong actually uniquely occuring factor levels")  # 191
+if (!(length(unique(lag(Grunfeld$fac))) == 191)) stop("wrong actually uniquely occurring factor levels")  # 191
 if (!(length(levels(lag(Grunfeld$fac))) == 200)) stop("wrong factor levels")  # 200
 
 # lead eliminates e.g., level "200"

inst/tests/test_pbgtest.R

@@ -33,7 +33,7 @@ g_fe_lm <- lm(inv ~ factor(firm) + value + capital, data = Grunfeld)
 
   # compare plm::pbgtest to lmtest::bgtest
   # Hint: for lm::bgtest(), if no order argument is supplied, order=1 is default,
-  #      while plm::pbgtest() assues mininum number of obs over time (typically != 1)
+  #      while plm::pbgtest() assues minimum number of obs over time (typically != 1)
 
   # panelmodel interface
   plm::pbgtest(g_pool, order = 1)

inst/tests/test_pdata.frame_print_duplicated_rownames.R

@@ -1,6 +1,6 @@
 # Currently, duplicated row names are allowed for pdata frames.
 # This leads to an error when printing pdata frames with duplicate
-# row names, becase print.pdata.frame uses print.data.frame
+# row names, because print.pdata.frame uses print.data.frame
 #
 # This is a testfile to check if the workaround works
 library(plm)

inst/tests/test_pdata.frame_unused_levels.R

@@ -28,7 +28,7 @@ if (!isTRUE(all.equal(levels(iind), levels(droplevels(iind))))) stop("unused lev
 ######## test of dropping unused level in factor (non index variable)
 df <- data.frame(id = c(1,1,2), time = c(1,2,1),
                  f = factor(c("a", "a", "b")),
-                 f2 = factor(c(1,2,3), levels = c(1,2,3,4)), # level 4 is unsed
+                 f2 = factor(c(1,2,3), levels = c(1,2,3,4)), # level 4 is unused
                  n = c(1:3))
 pdf <- pdata.frame(df, drop.unused.levels = TRUE)
 
@@ -41,7 +41,7 @@ if (!isTRUE(all.equal(levels(pdf$f2), c("1", "2", "3")))) stop("used levels in n
 dfindex <- data.frame(id = c(1,1,2), 
                       time = factor(c(1,2,1), levels = c(1,2,9)), # level 9 is unused
                       f = factor(c("a", "a", "b")),
-                      f2 = factor(c(1,2,3), levels = c(1,2,3,4)), # level 4 is unsed
+                      f2 = factor(c(1,2,3), levels = c(1,2,3,4)), # level 4 is unused
                       n = c(1:3))
 pdfindex <- pdata.frame(dfindex, drop.unused.levels = FALSE)
 

inst/tests/test_plm.data.R

@@ -1,13 +1,13 @@
 # testfile to check if the deprecated function plm.data() is correctly
 # reworked by using pdata.frame()
 # 
-# Usefull especially if future changes to pdata.frame() affect the "plm.dim"
+# Useful especially if future changes to pdata.frame() affect the "plm.dim"
 # object onces created by plm.data()
 
 library(plm)
 
 # use a data set that has index variables not in first two columns, because
-# plm.dim objects always have them in the fist two colums (id, time)
+# plm.dim objects always have them in the first two columns (id, time)
 data("Hedonic", package = "plm")
 pHed <- pdata.frame(Hedonic, index = "townid")
 plm:::pos.index(pHed) # gives position of indexes

inst/tests/test_plmtest_unbalanced.R

@@ -27,7 +27,7 @@
 # [...]
 
 ##### Grunfeld data set - balanced ####
-# Table 4.2 [Output from EViews], similiar to above table but with p-values
+# Table 4.2 [Output from EViews], similar to above table but with p-values
 ##### EViews add-in BPTest for some older version of EViews needed:
 ##### http://www.eviews.com/Addins/addins.shtml#addins
 ##### http://forums.eviews.com/viewtopic.php?f=23&t=2228
@@ -280,7 +280,7 @@ p.val_2 <- (1/2)*pchisq(crit_2, df=0, lower.tail = F) + (1/2) * pchisq(crit_2, d
 #                       , data = pnlswork, model = "pooling")
 # 
 # 
-# # Reassembles Exmaple 1 in http://www.stata.com/manuals14/xtxtregpostestimation.pdf
+# # Reassembles Example 1 in http://www.stata.com/manuals14/xtxtregpostestimation.pdf
 # # use modified plmtest() as a wrapper
 # options(digits = 10)
 # plmtest(plm_pool_nlswork, type="bp")

inst/tests/test_preserve_rownames.R

@@ -14,7 +14,7 @@ if(!isTRUE(all.equal(names(residuals(gr)),       row.names(Grunfeld)))) stop("or
 
 
 # make a pdata.frame with "fancy" row.names (default)
-# [i.e., combination of individual index an time index]
+# [i.e., combination of individual index and time index]
 pGrunfeld <- pdata.frame(Grunfeld, index = c("firm", "year"))
 row.names(pGrunfeld) # fancy row.names
 gr_fancy_rownames <- plm(inv ~ value + capital, data=pGrunfeld, model="pooling")

inst/tests/test_vcovG_lin_dep.R

@@ -61,7 +61,7 @@ if (!identical(vcovBK(mod_fe_lin_dep), vcovBK(mod_fe_no_lin_dep))) {
   vcovBK(cr2)
 
 
-# just run test for for pgmm models (as vcovXX.pgmm methods use vcovXX.plm)
+# just run test for pgmm models (as vcovXX.pgmm methods use vcovXX.plm)
 # (no linear dependence involved here)
 data("EmplUK", package="plm")
 ar <- pgmm(dynformula(log(emp) ~ log(wage) + log(capital) + log(output),