mfx: Marginal Effects, Odds Ratios and Incidence Rate Ratios for GLMs
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1 mfx: Marginal Effects, Odds Ratios and Incidence Rate Ratios for GLMs Fernihough, A. mfx: Marginal Effects, Odds Ratios and Incidence Rate Ratios for GLMs Document Version: Publisher's PDF, also known as Version of record Queen's University Belfast - Research Portal: Link to publication record in Queen's University Belfast Research Portal General rights Copyright for the publications made accessible via the Queen's University Belfast Research Portal is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policy The Research Portal is Queen's institutional repository that provides access to Queen's research output. Every effort has been made to ensure that content in the Research Portal does not infringe any person's rights, or applicable UK laws. If you discover content in the Research Portal that you believe breaches copyright or violates any law, please contact openaccess@qub.ac.uk. Download date:03. Sep. 2018
2 Package mfx July 2, 2014 Type Package Title Marginal Effects, Odds Ratios and Incidence Rate Ratios for GLMs Version 1.1 Date Author Alan Fernihough Maintainer Alan Fernihough Estimates probit, logit, Poisson, negative binomial, and beta regression models, returning their marginal effects, odds ratios, or incidence rate ratios as an output. License GPL-2 GPL-3 Depends stats, sandwich, lmtest, MASS, betareg NeedsCompilation no Repository CRAN Date/Publication :45:05 R topics documented: betamfx betaor logitmfx logitor negbinirr negbinmfx poissonirr poissonmfx probitmfx Index 14 1
3 2 betamfx betamfx Marginal effects for a beta regression. This function estimates a beta regression model and calculates the corresponding marginal effects. betamfx(,, atmean = TRUE, = FALSE, = NULL, = NULL, = betareg.(), link.phi = NULL, type = "ML") atmean link.phi type default marginal effects represent the partial effects for the average observation. If atmean = FALSE the function calculates average partial effects. a list of arguments specified via betareg.. as in the betareg function. as in the betareg function. The underlying link function in the mean model (mu) is logit. If both =TRUE and!is.null() the function overrides the command and computes clustered standard errors. mfxest dcvar the ted betareg object. a character vector containing the variable names where the marginal effect refers to the impact of a discrete change on the outcome. For example, a factor variable. the matched.
4 betaor 3 References Francisco Cribari-Neto, Achim Zeileis (2010). Beta Regression in R. Journal of Statistical Software 34(2), Bettina Gruen, Ioannis Kosmidis, Achim Zeileis (2012). Extended Beta Regression in R: Shaken, Stirred, Mixed, and Partitioned. Journal of Statistical Software, 48(11), betaor, betareg # simulate some # beta outcome y = rbeta(n, shape1 = plogis( * x), shape2 = (abs(0.2*x))) # use Smithson and Verkuilen correction y = (y*(n-1)+0.5)/n =.frame(y,x) betamfx(y~x x, =) betaor Odds ratios for a beta regression. This function estimates a beta regression model and calculates the corresponding odds ratios. betaor(,, = FALSE, = NULL, = NULL, = betareg.(), link.phi = NULL, type = "ML") link.phi type a list of arguments specified via betareg.. as in the betareg function. as in the betareg function.
5 4 logitmfx The underlying link function in the mean model (mu) is "logit". If both =TRUE and!is.null() the function overrides the command and computes clustered standard errors. oddsratio the ted betareg object. the matched. References Francisco Cribari-Neto, Achim Zeileis (2010). Beta Regression in R. Journal of Statistical Software 34(2), Bettina Gruen, Ioannis Kosmidis, Achim Zeileis (2012). Extended Beta Regression in R: Shaken, Stirred, Mixed, and Partitioned. Journal of Statistical Software, 48(11), betamfx, betareg # simulate some # beta outcome y = rbeta(n, shape1 = plogis( * x), shape2 = (abs(0.2*x))) # use Smithson and Verkuilen correction y = (y*(n-1)+0.5)/n =.frame(y,x) betaor(y~x x, =) logitmfx Marginal effects for a logit regression. This function estimates a binary logistic regression model and calculates the corresponding marginal effects.
6 logitmfx 5 logitmfx(,, atmean = TRUE, = FALSE, = NULL, = NULL, start = NULL, = list()) atmean start default marginal effects represent the partial effects for the average observation. If atmean = FALSE the function calculates average partial effects. starting values for the parameters in the glm model. see glm.. If both =TRUE and!is.null() the function overrides the command and computes clustered standard errors. mfxest dcvar the ted glm object. a character vector containing the variable names where the marginal effect refers to the impact of a discrete change on the outcome. For example, a factor variable. the matched. References William H. Greene (2008). Econometric Analysis (6th ed.). Prentice Hall, N.Y. pp logitor, glm
7 6 logitor # simulate some # binary outcome y = ifelse(pnorm( *x + rnorm(n))>0.5, 1, 0) =.frame(y,x) logitmfx(=y~x, =) logitor Odds ratios for a logit regression. This function estimates a binary logistic regression model and calculates the corresponding odds ratios. logitor(,, = FALSE, = NULL, = NULL, start = NULL, = list()) start starting values for the parameters in the glm model. see glm.. If both =TRUE and!is.null() the function overrides the command and computes clustered standard errors. oddsratio the ted glm object. the matched.
8 negbinirr 7 logitmfx, glm # simulate some # binary outcome y = ifelse(pnorm( *x + rnorm(n))>0.5, 1, 0) =.frame(y,x) logitor(=y~x, =) negbinirr Incidence rate ratios for a negative binomial regression. This function estimates a negative binomial regression model and calculates the corresponding incidence rate ratios. negbinirr(,, = FALSE, = NULL, = NULL, start = NULL, = glm.()) start starting values for the parameters in the glm.nb model. see glm.. If both =TRUE and!is.null() the function overrides the command and computes clustered standard errors.
9 8 negbinmfx irr the ted glm.nb object. the matched. negbinmfx, glm.nb # simulate some y = rnegbin(n, mu = exp( * x), theta = 0.5) =.frame(y,x) negbinirr(=y~x,=) negbinmfx Marginal effects for a negative binomial regression. This function estimates a negative binomial regression model and calculates the corresponding marginal effects. negbinmfx(,, atmean = TRUE, = FALSE, = NULL, = NULL, start = NULL, = glm.()) atmean start default marginal effects represent the partial effects for the average observation. If atmean = FALSE the function calculates average partial effects. starting values for the parameters in the glm.nb model. see glm..
10 poissonirr 9 If both =TRUE and!is.null() the function overrides the command and computes clustered standard errors. mfxest dcvar the ted glm.nb object. a character vector containing the variable names where the marginal effect refers to the impact of a discrete change on the outcome. For example, a factor variable. the matched. negbinirr, glm.nb # simulate some y = rnegbin(n, mu = exp( * x), theta = 0.5) =.frame(y,x) negbinmfx(=y~x,=) poissonirr Incidence rate ratios for a Poisson regression. This function estimates a negative binomial regression model and calculates the corresponding incidence rate ratios. poissonirr(,, = FALSE, = NULL, = NULL, start = NULL, = list())
11 10 poissonirr start starting values for the parameters in the glm model. see glm.. If both =TRUE and!is.null() the function overrides the command and computes clustered standard errors. irr the ted glm object. the matched. poissonmfx, glm # simulate some y = rnegbin(n, mu = exp( * x), theta = 0.5) =.frame(y,x) poissonirr(=y~x,=)
12 poissonmfx 11 poissonmfx Marginal effects for a Poisson regression. This function estimates a Poisson regression model and calculates the corresponding marginal effects. poissonmfx(,, atmean = TRUE, = FALSE, = NULL, = NULL, start = NULL, = list()) atmean start default marginal effects represent the partial effects for the average observation. If atmean = FALSE the function calculates average partial effects. starting values for the parameters in the glm model. see glm.. If both =TRUE and!is.null() the function overrides the command and computes clustered standard errors. mfxest dcvar the ted glm object. a character vector containing the variable names where the marginal effect refers to the impact of a discrete change on the outcome. For example, a factor variable. the matched. poissonirr, glm
13 12 probitmfx # simulate some y = rnegbin(n, mu = exp( * x), theta = 0.5) =.frame(y,x) poissonmfx(=y~x,=) probitmfx Marginal effects for a probit regression. This function estimates a probit regression model and calculates the corresponding marginal effects. probitmfx(,, atmean = TRUE, = FALSE, = NULL, = NULL, start = NULL, = list()) atmean start default marginal effects represent the partial effects for the average observation. If atmean = FALSE the function calculates average partial effects. starting values for the parameters in the glm model. see glm.. If both =TRUE and!is.null() the function overrides the command and computes clustered standard errors.
14 probitmfx 13 mfxest dcvar the ted glm object. a character vector containing the variable names where the marginal effect refers to the impact of a discrete change on the outcome. For example, a factor variable. the matched. References William H. Greene (2008). Econometric Analysis (6th ed.). Prentice Hall, N.Y. pp glm # simulate some # binary outcome y = ifelse(pnorm( *x + rnorm(n))>0.5, 1, 0) =.frame(y,x) probitmfx(=y~x, =)
15 Index betamfx, 2, 4 betaor, 3, 3 betareg, 2 4 betareg., 2, 3 glm, 5 7, glm., 5 8, glm.nb, 7 9 logitmfx, 4, 7 logitor, 5, 6 negbinirr, 7, 9 negbinmfx, 8, 8 poissonirr, 9, 11 poissonmfx, 10, 11 print.betamfx (betamfx), 2 print.betaor (betaor), 3 print.logitmfx (logitmfx), 4 print.logitor (logitor), 6 print.negbinirr (negbinirr), 7 print.negbinmfx (negbinmfx), 8 print.poissonirr (poissonirr), 9 print.poissonmfx (poissonmfx), 11 print.probitmfx (probitmfx), 12 probitmfx, 12 14
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