manifestoR (version 1.0-4)

scale_weighted: Scaling functions

Description

Scaling functions take a data.frame of variables with information about political parties/text and position the cases on a scale, i.e. output a vector of values. For applying scaling functions directly to text documents, refer to mp_scale.

scale_logit scales the data on a logit scale as described by Lowe et al. (2011).

scale_bipolar scales the data by adding up the variable values in pos and substracting the variable values in neg.

scale_ratio scales the data taking the ratio of the sum of the variable values in pos and the sum of the variable values in neg as suggested by Kim and Fording (1998) and by Laver & Garry (2000).

Usage

scale_weighted(data, vars = grep("per((\\d{3}(_\\d)?)|\\d{4}|(uncod))$",
  names(data), value = TRUE), weights = 1)
scale_logit(data, pos, neg, N = data[, "total"], zero_offset = 0.5, ...)
scale_bipolar(data, pos, neg, ...)
scale_ratio(data, pos, neg, ...)

Arguments

data

A data.frame with cases to be scaled

vars

variable names that should contribute to the linear combination; defaults to all CMP category percentage variables in the Manifesto Project's Main Dataset

weights

weights of the linear combination in the same order as `vars`.

pos

variable names that should contribute to the numerator ("positively")

neg

variable names that should contribute to the denominator ("negatively")

N

vector of numbers of quasi sentences to convert percentages to counts

zero_offset

Constant to be added to prevent 0/0 and log(0); defaults to 0.5 (smaller than any possible non-zero count)

...

further parameters passed on to scale_weighted

Details

scale_weighted scales the data as a weighted sum of the variable values

If variable names used for the definition of the scale are not present in the data frame they are assumed to be 0. scale_weighted scales the data as a weighted sum of the category percentages

References

Lowe, W., Benoit, K., Mikhaylov, S., & Laver, M. (2011). Scaling Policy Preferences from Coded Political Texts. Legislative Studies Quarterly, 36(1), 123-155.

Kim, H., & Fording, R. C. (1998). Voter ideology in western democracies, 1946-1989. European Journal of Political Research, 33(1), 73-97.

Laver, M., & Garry, J. (2000). Estimating Policy Positions from Political Texts. American Journal of Political Science, 44(3), 619-634.

See Also

mp_scale