calculates synthetic scores of multiple deprivation from unidimensional indicators and/or basic items of deprivation. The scores are weighted sums of the individual indicators / items taking values on the [0,1] range. Several alternative weighting methods are available, notably Betti & Verma's (1998) double-weighting method.

mdepriv(
  data,
  items,
  sampling_weights = NA,
  method = c("cz", "ds", "bv", "equal"),
  bv_corr_type = c("mixed", "pearson"),
  rhoH = NA,
  wa = NA,
  wb = NA,
  user_def_weights = NA,
  score_i_heading = "score_i",
  output = c("view", "all", "weighting_scheme", "aggregate_deprivation_level",
    "summary_by_dimension", "summary_by_item", "summary_scores", "score_i",
    "sum_sampling_weights", "data", "items", "sampling_weights", "wa", "wb", "rhoH",
    "user_def_weights", "score_i_heading")
)

Arguments

data

a data.frame or a matrix with columns containing variables representing deprivation items measured on the [0,1] range. Optionally a column with user defined sampling weights (s. argument sampling_weights) can be included.

items

a character string vector or list of such vectors specifying the indicators / items within the argument data, from which the deprivation scores are derived. By using a list with more than one vector, the items are grouped in dimensions. By naming the list elements, the user defines the dimensions' names. Else, dimensions are named by default "Dimension 1" to "Dimension K" where K is the number of dimensions.

sampling_weights

a character string corresponding to the column heading of a numeric variable within the argument data which is specified as sampling weights. By default set to NA, which means no specification of sampling weights.

method

a character string selecting the weighting scheme. Available choices are "cz" (default), "ds", "bv" and "equal" (s. Details).

bv_corr_type

a character string selecting the correlation type if method = "bv". Available choices are "mixed" (default) and "pearson". The option "mixed" automatically detects the appropriate correlation type "pearson", "polyserial" or "polychoric" for each pair of items (s. Details).

rhoH

numeric. Permits setting rhoH if the argument method is set to "bv", or the argument wb is set to either "mixed" or "pearson". rhoH distributes high and low coefficients of the triangular item correlation table to two factors. By default rhoH is set to NA, which causes its automatic calculation according to Betti & Verma's (1998) suggestion to divide the ordered set of correlation coefficients at the point of their largest gap. Alternatively, the user can set a value for rhoH in the interval [-1, +1]. When rhoH is automatically calculated, the weights of items that overall are more weakly correlated with the other items turn out higher, compared to their weights when the user chooses a rhoH value far from the automatic version. If the user chooses more than one dimension, rhoH is common for all.

wa, wb

two single character strings providing alternative, more flexible ways to select the weighting schemes. Weights are computed as the product of two terms as in the Betti-Verma scheme. wa selects the form of the first factor and is one of "cz", "ds", "bv" or "equal" (s. Details). wb selects the form of the second factor and is one of "mixed", "pearson" or "diagonal", where the latter sets all off-diagonal correlations to zero (s. Details). wa and wb are set both by default to NA, which means no specification. Specify either wa and wb, or method, not both.

user_def_weights

a numeric vector or list of such vectors to pass user-defined weights. To pass these weights correctly to the corresponding items, the structure of the vector, respectively of the list, must match the argument items. By creating a list with more than one vector, the user groups the items in dimensions. The elements of the vector, respectively of each vector within the list, must sum to 1. User-defined names of dimensions are inherited from the argument items. user_def_weights is set by default to NA, which means unspecified.

score_i_heading

a character string (default: "score_i") giving a heading to the score_i column in the output "data".

output

a character string vector selecting the output. Available multiple choices are "view" (default), "all", "weighting_scheme", "aggregate_deprivation_level", "summary_by_dimension", "summary_by_item", "summary_scores", "score_i", "sum_sampling_weights", "data", "items", "sampling_weights", "wa", "wb", "rhoH", "user_def_weights" and "score_i_heading" (s. Value).

Value

a list or a single object according to the argument output. Possible output elements:

  • "view" (default) composes a list including "weighting_scheme", "aggregate_deprivation_level", "summary_by_dimension", "summary_by_item" and "summary_scores".

  • "all" delivers a list with all possible output elements.

  • "weighting_scheme" a character string returning the weighting scheme chosen by the argument method, the arguments wa and wb, or the argument user_def_weights, respectively.

  • "aggregate_deprivation_level" a single numeric value in the [0,1] range displaying the aggregate deprivation level.

  • "summary_by_dimension" a data.frame containing the variables: Dimension (dimension names), N_Item (number of items per dimension), Index (within-dimension index), Weight (dimension weights), Contri (dimension contribution), Share (dimension relative contribution). If the user did not specify two or more dimensions (groups of items) "summary_by_dimension" is dropped from the output, unless it is explicitly requested as an element of the argument output.

  • "summary_by_item" a data.frame containing variables: Dimension (dimension names), Item (item names), Index (item means), Weight (item weights), Contri (item contributions), Share (relative item contributions). The column Dimension is dropped unless at least two dimensions (groups of items) are specified or if "summary_by_dimension" is explicitly requested as an element of the argument output.

  • "summary_scores" a data.frame containing these statistics of "score_i": N_Obs. (number of observations), Mean (mean), Std._Dev. (standard deviation), Min (minimum), Max (maximum).

  • "score_i" a numeric vector returning the score for each observation.

  • "sum_sampling_weights" a numeric value equal to the sum of sampling weights. If the argument sampling_weights is unspecified, NA is returned.

  • "data" a data.frame including the argument data as well as a merged column containing the scores (default heading "score_i", which can be alter by the argument score_i_heading).

  • "items" a named list of one or more character vectors returning the argument items grouped as dimensions. If no dimensions were specified a list with only one vector is returned. The list can be re-used as a template for the argument "items" in the functions mdepriv and corr_mat without needing to prior unlist.

  • "sampling_weights" single character strings returning the specification of the argument sampling_weights, if unspecified NA.

  • "wa", "wb" two single character strings giving the weighting scheme for the 1st, respectively the 2nd weighting factor. If the argument user_def_weights is specified, NA's are returned.

  • "rhoH" a numeric value giving the effective rhoH. For weighting schemes not relying on rhoH, NA is returned.

  • "user_def_weights" a named list of one or more numeric vectors returning the argument user_def_weights grouped as dimensions. The names of the list's elements are identical with those of the output "items". If the argument user_def_weights is unspecified, NA is returned.

  • "score_i_heanding" single character strings returning the specification of the argumnent score_i_heanding.

Details

mdepriv is an adaptation for R of a homonymous community-contributed Stata command developed by Pi Alperin & Van Kerm (2009) for computing synthetic scores of multiple deprivation from unidimensional indicators and/or basic items of deprivation. The underlying literature and algebra are not recapitulated here. They are documented in Pi Alperin & Van Kerm (2009). There are minor differences vis-a-vis the predecessor in Stata, pointed out in the vignette("what_is_different_from_stata.html").

The scores are weighted sums of the individual indicators / items. Both the items and the scores are limited to the [0, 1] range, and the item weights automatically sum to 1. As customary in deprivation research, all indicators / items require negative orientation (i.e., higher values are less desirable). In preparation, any item with values on [0, max], where max > 1, has to be transformed, using a suitable transformation. The choice of transformation function is dictated by substantive concerns. With \(i\) indexing the \(i-th\) observation and \(j\) indexing the \(j-th\) item, and all \(xij\) >= 0, plausible functions are:

  • \(yij = xij / (theoretical maximum of xj)\), particularly if all \(xj\) to be transformed have natural scales.

  • \(yij = xij / (c * mean(xj))\) in the absence of natural scales or theoretical maxima, with \(c >\) 1 a constant identical for all \(xj\) and chosen large enough so that all \(max(yj) <=\) 1.

  • the asymptotic \(yij = (xij / mean(xj)) / (1 + (xij / mean(xj)))\), which implies \(mean(yj) =\) 0.5 and \(max(yj)\) < 1 for all \(yj\)

and many others.

The multiplicative re-scaling (first two examples) preserves the coefficient of variation, an important aspect of item discrimination; in the third example, \(yj\) = 0.5 at the point where \(xj = mean(xj)\) for all \(xj\). This ensures the same neutral location for all items thus transformed. Dichotomous items must be coded 0/1, where 1 represents the undesirable state.

The transformation of ordinal indicators requires special care. Multiplicative re-scaling, which turns ordinal variables into pseudo-interval level ones in the [0, 1] range, is acceptable if it is unlikely to create significant distortions. Else, a data-driven transformation, such as the ridit, may be appropriate.

The available weighting schemes with the argument method, respectively wa are:

  • "cz" for Cerioli & Zani (1990) weighting.

  • "ds" for Desai & Shah (1988) weighting.

  • "bv" for Betti & Verma (1998) weighting.

  • "equal" for equi-proportionate weighting of items.

The differences among the four methods are visualized in this graph, as far as the weighting of dichotomous items is concerned. From \(equal\) to \(Betti-Verma\), the weighting schemes are increasingly sensitive to items with low prevalence in the sample. Thus, in all schemes except "equal", assets that most households own, and which only the most destitute lack (e.g., minimal cooking equipment), are given higher weights than items that most lack, and which only the better-off may own (e.g., a motorcycle) (Items are negatively coded, e.g., 1 = "household lacks the asset").

For continuous items (e.g., a food insecurity score), higher values denote less desirable states. Item weights are proportionate to (1 - \(mean(yj)\)) for the Desai-Shah scheme, to \(log\)(1 / \(mean(yj)\)) for Cerioli-Zani, and to the coefficients of variation [i.e., \(std.dev(xj)\) / \(mean(xj)\)] for Betti-Verma.

Differently from the other three methods, Betti-Verma also controls for redundancy among items by lowering the weights of items that are highly correlated with many items.

Formulas and literature references are provided in Pi Alperin & Van Kerm (2009). "cz" and "ds" are built on the function w.mean. Whereas "bv" relies for its 1st factor (wa) on w.cv. "bv"'s 2nd factor (wb) as well as any specification of wb but "diagonal" rely on weightedCorr.

When setting the argument bv_corr_type, respectively wb to "mixed", the appropriate correlation type "pearson", "polyserial" or "polychoric" is automatically detected for each pair of items by the following rules:

  • "pearson": both items have > 10 distinct values.

  • "polyserial": one item has \(\le\) 10, the other > 10 distinct values.

  • "polychoric": both items have \(\le\) 10 distinct values.

When bv_corr_type respectively wb is set to "pearson" this correlation type is forced on all item pairs.

References

Betti, G. & Verma, V. K. (1998), 'Measuring the degree of poverty in a dynamic and comparative context: a multi-dimensional approach using fuzzy set theory', Working Paper 22, Dipartimento di Metodi Quantitativi, Universit`a di Siena.

Cerioli, A. & Zani, S. (1990), 'A fuzzy approach to the measurement of poverty', in C. Dagum & M. Zenga (eds.), Income and Wealth Distribution, Inequality and Poverty, Springer Verlag, Berlin, 272-284.

Desai, M. & Shah, A. (1988), 'An econometric approach to the measurement of poverty', Oxford Economic Papers, 40(3):505-522.

Pi Alperin, M. N. & Van Kerm, P. (2009), 'mdepriv - Synthetic indicators of multiple deprivation', v2.0 (revised March 2014), CEPS/INSTEAD, Esch/Alzette, Luxembourg. http://medim.ceps.lu/stata/mdepriv_v3.pdf (2019-MM-DD).

See also

Examples

head(simul_data, 3) # data used for demonstration
#> id y1 y2 y3 y4 y5 y6 y7 sampl_weights #> 1 1 0 0 0 0.0 0.369 0.174 0.196 0.556 #> 2 2 1 0 1 0.2 0.762 0.832 1.000 1.500 #> 3 3 0 1 1 0.4 0.708 0.775 0.833 0.973
# least possible specification: data & items: mdepriv(simul_data, c("y1", "y2", "y3", "y4", "y5", "y6", "y7"))
#> $weighting_scheme #> [1] "Cerioli & Zani (1990) weighting scheme" #> #> $aggregate_deprivation_level #> [1] 0.3719576 #> #> $summary_by_item #> Item Index Weight Contri Share #> 1 y1 0.16000 0.31261773 0.05001884 0.1344746 #> 2 y2 0.70000 0.06084472 0.04259131 0.1145058 #> 3 y3 0.53000 0.10830307 0.05740063 0.1543204 #> 4 y4 0.27600 0.21960815 0.06061185 0.1629537 #> 5 y5 0.69579 0.06187379 0.04305116 0.1157421 #> 6 y6 0.50004 0.11822945 0.05911945 0.1589414 #> 7 y7 0.49918 0.11852309 0.05916435 0.1590621 #> 8 Total NA 1.00000000 0.37195759 1.0000000 #> #> $summary_scores #> N_Obs. Mean Std._Dev. Min Max #> 1 100 0.3719576 0.2120671 0.0654181 0.9521171 #>
# group items in dimensions: mdepriv(simul_data, list(c("y1", "y2", "y3", "y4"), c("y5", "y6", "y7")))
#> $weighting_scheme #> [1] "Cerioli & Zani (1990) weighting scheme" #> #> $aggregate_deprivation_level #> [1] 0.4202786 #> #> $summary_by_dimension #> Dimension N_Item Index Weight Contri Share #> 1 Dimension 1 4 0.3003002 0.5 0.1501501 0.3572632 #> 2 Dimension 2 3 0.5402570 0.5 0.2701285 0.6427368 #> 3 Total 7 NA 1.0 0.4202786 1.0000000 #> #> $summary_by_item #> Dimension Item Index Weight Contri Share #> 1 Dimension 1 y1 0.16000 0.22286104 0.03565777 0.08484317 #> 2 Dimension 1 y2 0.70000 0.04337540 0.03036278 0.07224441 #> 3 Dimension 1 y3 0.53000 0.07720783 0.04092015 0.09736434 #> 4 Dimension 1 y4 0.27600 0.15655574 0.04320938 0.10281129 #> 5 Dimension 2 y5 0.69579 0.10359735 0.07208200 0.17151004 #> 6 Dimension 2 y6 0.50004 0.19795550 0.09898567 0.23552393 #> 7 Dimension 2 y7 0.49918 0.19844715 0.09906085 0.23570282 #> 8 Total <NA> NA 1.00000000 0.42027859 1.00000000 #> #> $summary_scores #> N_Obs. Mean Std._Dev. Min Max #> 1 100 0.4202786 0.1815342 0.1095317 0.919828 #>
# customized labelling of dimensions: mdepriv(simul_data, list('Group A' = c("y1", "y2", "y3", "y4"), 'Group B' = c("y5", "y6", "y7")))
#> $weighting_scheme #> [1] "Cerioli & Zani (1990) weighting scheme" #> #> $aggregate_deprivation_level #> [1] 0.4202786 #> #> $summary_by_dimension #> Dimension N_Item Index Weight Contri Share #> 1 Group A 4 0.3003002 0.5 0.1501501 0.3572632 #> 2 Group B 3 0.5402570 0.5 0.2701285 0.6427368 #> 3 Total 7 NA 1.0 0.4202786 1.0000000 #> #> $summary_by_item #> Dimension Item Index Weight Contri Share #> 1 Group A y1 0.16000 0.22286104 0.03565777 0.08484317 #> 2 Group A y2 0.70000 0.04337540 0.03036278 0.07224441 #> 3 Group A y3 0.53000 0.07720783 0.04092015 0.09736434 #> 4 Group A y4 0.27600 0.15655574 0.04320938 0.10281129 #> 5 Group B y5 0.69579 0.10359735 0.07208200 0.17151004 #> 6 Group B y6 0.50004 0.19795550 0.09898567 0.23552393 #> 7 Group B y7 0.49918 0.19844715 0.09906085 0.23570282 #> 8 Total <NA> NA 1.00000000 0.42027859 1.00000000 #> #> $summary_scores #> N_Obs. Mean Std._Dev. Min Max #> 1 100 0.4202786 0.1815342 0.1095317 0.919828 #>
# available outputs no_dim_specified <- mdepriv(simul_data, c("y1", "y2", "y3", "y4", "y5", "y6", "y7"), output = "all") two_dim <- mdepriv(simul_data, list(c("y1", "y2", "y3", "y4"), c("y5", "y6", "y7")), output = "all") length(no_dim_specified)
#> [1] 14
length(two_dim)
#> [1] 15
data.frame(row.names = names(two_dim), no_or_1_dim_specified = ifelse(names(two_dim) %in% names(no_dim_specified), "X", ""), at_least_2_dim_speicified = "X")
#> no_or_1_dim_specified at_least_2_dim_speicified #> weighting_scheme X X #> aggregate_deprivation_level X X #> summary_by_dimension X #> summary_by_item X X #> summary_scores X X #> score_i X X #> sum_sampling_weights X X #> data X X #> items X X #> sampling_weights X X #> wa X X #> wb X X #> rhoH X X #> user_def_weights X X #> score_i_heading X X
setdiff(names(two_dim), names(no_dim_specified))
#> [1] "summary_by_dimension"
# if no dimensions are specified, "summary_by_dimension" is dropped from the two output wrappers # (output = "view" (default), output = "all") # however, even if no dimension is specified "summary_by_dimension" is accessible mdepriv(simul_data, c("y1", "y2", "y3", "y4", "y5", "y6", "y7"), output = "summary_by_dimension")
#> Dimension N_Item Index Weight Contri Share #> 1 Dimension 1 7 0.3719576 1 0.3719576 1 #> 2 Total 7 NA 1 0.3719576 1
# apply sampling weights (3rd argument) with_s_w <- mdepriv(simul_data, c("y1", "y4", "y5", "y6"), "sampl_weights", output = "all") without_s_w <- mdepriv(simul_data, c("y1", "y4", "y5", "y6"), output = "all") # return sum and specification of sampling weights if applied ... with_s_w[c("sum_sampling_weights", "sampling_weights")]
#> $sum_sampling_weights #> [1] 100.237 #> #> $sampling_weights #> [1] "sampl_weights" #>
# if not, NA's are returned: without_s_w[c("sum_sampling_weights", "sampling_weights")]
#> $sum_sampling_weights #> [1] NA #> #> $sampling_weights #> [1] NA #>
# weighting schemes # the default weighting scheme is "Cerioli & Zani": method = "cz" mdepriv(simul_data, c("y1", "y2", "y3"), output = "weighting_scheme")
#> [1] "Cerioli & Zani (1990) weighting scheme"
methods <- c("cz", "ds", "bv", "equal") # 4 standard weighting schemes availble sapply(X = methods, function(X) mdepriv(simul_data, c("y1", "y2", "y3"), method = X, output = "weighting_scheme"))
#> cz #> "Cerioli & Zani (1990) weighting scheme" #> ds #> "Desai & Shah (1988) weighting scheme" #> bv #> "Betti & Verma (1998) weighting scheme" #> equal #> "Equi-proportionate weighting scheme"
# alternative, more flexible ways to select (double) weighting schemes mdepriv(simul_data, c("y1", "y2", "y3"), wa = "cz", wb = "mixed", output = "weighting_scheme")
#> [1] "User-defined weighting scheme: wa = “cz”, wb = “mixed”."
# some of the double weighting specification are almost lookalikes of the standard weight methods method_bv_pearson <- mdepriv(simul_data, c("y1", "y2", "y3"), method = "bv", bv_corr_type = "pearson", output = "all") method_bv_pearson$weighting_scheme
#> [1] "Betti & Verma (1998) weighting scheme"
wa_wb_bv_pearson <- mdepriv(simul_data, c("y1", "y2", "y3"), wa = "bv", wb = "pearson", output = "all") wa_wb_bv_pearson$weighting_scheme
#> [1] "User-defined weighting scheme: wa = “bv”, wb = “pearson”."
all.equal(method_bv_pearson[-1], wa_wb_bv_pearson[-1])
#> [1] TRUE
# either a fixed or a data driven rhoH is involved in any true double weighting scheme # (effective single weighting schemes: method: "cs", "ds", "equal" or wb = "diagonal") items_sel <- c("y1", "y2", "y3", "y4", "y5", "y6", "y7") # a selection of items # data driven: mdepriv(simul_data, items_sel, method = "bv", output = "rhoH")
#> [1] 0.6563016
mdepriv(simul_data, items_sel, wa = "cz", wb = "pearson", output = "rhoH")
#> [1] 0.3445555
# fixed: mdepriv(simul_data, items_sel, method = "bv", rhoH = 0.3, output = "rhoH")
#> [1] 0.3
mdepriv(simul_data, items_sel, wa = "cz", wb = "pearson", rhoH = 0.3, output = "rhoH")
#> [1] 0.3
# check how weighting settings are applied: bv <- mdepriv(simul_data, items_sel, method = "bv", output = "all") bv[c("weighting_scheme", "wa", "wb", "rhoH")]
#> $weighting_scheme #> [1] "Betti & Verma (1998) weighting scheme" #> #> $wa #> [1] "bv" #> #> $wb #> [1] "mixed" #> #> $rhoH #> [1] 0.6563016 #>
ds <- mdepriv(simul_data, items_sel, method = "ds", output = "all") ds[c("weighting_scheme", "wa", "wb", "rhoH")]
#> $weighting_scheme #> [1] "Desai & Shah (1988) weighting scheme" #> #> $wa #> [1] "ds" #> #> $wb #> [1] "diagonal" #> #> $rhoH #> [1] NA #>
equal_pearson <- mdepriv(simul_data, items_sel, wa = "equal", wb = "pearson", output = "all") equal_pearson[c("weighting_scheme", "wa", "wb", "rhoH")]
#> $weighting_scheme #> [1] "User-defined weighting scheme: wa = “equal”, wb = “pearson”." #> #> $wa #> [1] "equal" #> #> $wb #> [1] "pearson" #> #> $rhoH #> [1] 0.3445555 #>
equal_pearson_rhoH_fixed <- mdepriv(simul_data, items_sel, wa = "equal", wb = "pearson", rhoH = 0.3 , output = "all") equal_pearson_rhoH_fixed[c("weighting_scheme", "wa", "wb", "rhoH")]
#> $weighting_scheme #> [1] "User-defined weighting scheme: wa = “equal”, wb = “pearson”." #> #> $wa #> [1] "equal" #> #> $wb #> [1] "pearson" #> #> $rhoH #> [1] 0.3 #>
# pass expertise-base weights to the items dim <- list('Group A' = c("y1", "y2", "y3"), 'Group B' = c("y4", "y5", "y6")) # 'expertise weights' structured as dimensions w_expertise <- list(c(0.5, 0.25, 0.25), c(0.4, 0.45, 0.15)) model_expertise <- mdepriv(simul_data, items = dim, user_def_weights = w_expertise, output = "all") # check weighting settings ... model_expertise[c("weighting_scheme", "wa", "wb", "rhoH", "user_def_weights")]
#> $weighting_scheme #> [1] "Item-wise user-defined weighting scheme" #> #> $wa #> [1] NA #> #> $wb #> [1] NA #> #> $rhoH #> [1] NA #> #> $user_def_weights #> $user_def_weights$`Group A` #> [1] 0.50 0.25 0.25 #> #> $user_def_weights$`Group B` #> [1] 0.40 0.45 0.15 #> #>
# ... wa, wb and rhoH are not involved, when expertise weights are applied, # and therefore returned as NA's. # user-defined names of dimensions are inherited from the argument items. # use outputs elements dim <- list(c("y1", "y2", "y3"), c("y4", "y5", "y6", "y7")) model_1 <- mdepriv(simul_data, items = dim, method = "bv", output = "all") model_1$summary_by_item
#> Dimension Item Index Weight Contri Share #> 1 Dimension 1 y1 0.16000 0.26712437 0.04273990 0.10578109 #> 2 Dimension 1 y2 0.70000 0.08075457 0.05652820 0.13990708 #> 3 Dimension 1 y3 0.53000 0.15212106 0.08062416 0.19954450 #> 4 Dimension 2 y4 0.27600 0.15553907 0.04292878 0.10624858 #> 5 Dimension 2 y5 0.69579 0.04661465 0.03243401 0.08027406 #> 6 Dimension 2 y6 0.50004 0.12448765 0.06224880 0.15406555 #> 7 Dimension 2 y7 0.49918 0.17335863 0.08653716 0.21417915 #> 8 Total <NA> NA 1.00000000 0.40404102 1.00000000
by_item_no_total <- subset(model_1$summary_by_item, Weight != 1) barplot(Weight ~ Item, data = by_item_no_total)
model_1$summary_scores
#> N_Obs. Mean Std._Dev. Min Max #> 1 100 0.404041 0.2030587 0.0606887 0.9495825
hist(model_1$score_i, main = 'model: method = "bv", bv_corr_type = "mixed" (default)', xlab = "scores")
# output data ... head(model_1$data, 3)
#> id y1 y2 y3 y4 y5 y6 y7 sampl_weights score_i #> 1 1 0 0 0 0.0 0.369 0.174 0.196 0.556 0.07283995 #> 2 2 1 0 1 0.2 0.762 0.832 1.000 1.500 0.76280596 #> 3 3 0 1 1 0.4 0.708 0.775 0.833 0.973 0.56898009
# ... compare to input data ... head(simul_data, 3)
#> id y1 y2 y3 y4 y5 y6 y7 sampl_weights #> 1 1 0 0 0 0.0 0.369 0.174 0.196 0.556 #> 2 2 1 0 1 0.2 0.762 0.832 1.000 1.500 #> 3 3 0 1 1 0.4 0.708 0.775 0.833 0.973
# ... only the scores have been merged to the (input) data all.equal(model_1$data[ ,names(model_1$data) != "score_i"], simul_data)
#> [1] TRUE
# scores are twofold accessible all.equal(model_1$score_i, model_1$data$score_i)
#> [1] TRUE
# re-use output of a model as arguments in another model: dim <- list(c("y1", "y2", "y3"), c("y4", "y5", "y6", "y7")) model_1 <- mdepriv(simul_data, items = dim, method = "bv", output = "all") model_2 <- mdepriv(simul_data, items = model_1$items, method = "ds", output = "all") all.equal(model_1$items, model_2$items)
#> [1] TRUE
# how do the scores of the 2 models differ? plot(model_1$score_i, model_2$score_i, xlab = model_1$weighting_scheme, ylab = model_2$weighting_scheme, xlim = c(0,1), ylim = c(0,1), asp = 1, main = "same item grouping")
abline(0, 1, col = "red", lty = 2, lwd = 2)
# accumulating scores from different models in the output data if (FALSE) { # this code will throw an error message with a hint on how to handle the re-use ... # ... of 'data' output as agrument. so run it and read! model_3 <- mdepriv(model_1$data, items = model_1$items, wa = "cz", wb = "mixed", output = "all")} model_3 <- mdepriv(model_1$data, items = model_1$items, wa = "cz", wb = "mixed", output = "all", score_i_heading = "score_i_model_3") head(model_3$data, 3)
#> id y1 y2 y3 y4 y5 y6 y7 sampl_weights score_i score_i_model_3 #> 1 1 0 0 0 0.0 0.369 0.174 0.196 0.556 0.07283995 0.07444566 #> 2 2 1 0 1 0.2 0.762 0.832 1.000 1.500 0.76280596 0.78444558 #> 3 3 0 1 1 0.4 0.708 0.775 0.833 0.973 0.56898009 0.54205364
# if gathering scores from iteratered runs is the purpose it's expedient to avoid confusion ... # ... by naming already the 1st scores column with reference to its model model_1 <- mdepriv(simul_data, dim, method = "bv", score_i_heading = "score_i_1", output = "all") model_2 <- mdepriv(model_1$data, model_1$items, method = "ds", score_i_heading = "score_i_2", output = "all") model_3 <- mdepriv(model_2$data, model_1$items, wa = "cz", wb = "mixed", score_i_heading = "score_i_3", output = "all") head(model_3$data, 3)
#> id y1 y2 y3 y4 y5 y6 y7 sampl_weights score_i_1 score_i_2 #> 1 1 0 0 0 0.0 0.369 0.174 0.196 0.556 0.07283995 0.07328948 #> 2 2 1 0 1 0.2 0.762 0.832 1.000 1.500 0.76280596 0.72556101 #> 3 3 0 1 1 0.4 0.708 0.775 0.833 0.973 0.56898009 0.56186065 #> score_i_3 #> 1 0.07444566 #> 2 0.78444558 #> 3 0.54205364