| summ.glm | R Documentation |
Generalized linear regression summaries with options
Description
summ() prints output for a regression model in a fashion similar tosummary(), but formatted differently with more options.
Usage
## S3 method for class 'glm'summ( model, scale = FALSE, confint = getOption("summ-confint", FALSE), ci.width = getOption("summ-ci.width", 0.95), robust = getOption("summ-robust", FALSE), cluster = NULL, vifs = getOption("summ-vifs", FALSE), digits = getOption("jtools-digits", default = 2), exp = FALSE, pvals = getOption("summ-pvals", TRUE), n.sd = 1, center = FALSE, transform.response = FALSE, scale.only = FALSE, data = NULL, model.info = getOption("summ-model.info", TRUE), model.fit = getOption("summ-model.fit", TRUE), model.coefs = getOption("summ-model.coefs", TRUE), which.cols = NULL, vcov = NULL, ...)Arguments
model | A |
scale | If |
confint | Show confidence intervals instead of standard errors? Defaultis |
ci.width | A number between 0 and 1 that signifies the width of thedesired confidence interval. Default is |
robust | If not Default is This requires the |
cluster | For clustered standard errors, provide the column name ofthe cluster variable in the input data frame (as a string). Alternately,provide a vector of clusters. Note that you must set |
vifs | If |
digits | An integer specifying the number of digits past the decimal toreport in the output. Default is 2. You can change the default number ofdigits for all jtools functions with |
exp | If |
pvals | Show p values? If |
n.sd | If |
center | If you want coefficients for mean-centered variables but don'twant to standardize, set this to |
transform.response | Should scaling/centering apply to responsevariable? Default is |
scale.only | If you want to scale but not center, set this to |
data | If you provide the data used to fit the model here, that dataframe is used to re-fit the model (if |
model.info | Toggles printing of basic information on sample size,name of DV, and number of predictors. |
model.fit | Toggles printing of model fit statistics. |
model.coefs | Toggles printing of model coefficents. |
which.cols | Developmental feature. By providing columns by name,you can add/remove/reorder requested columns in the output. Not fullysupported, for now. |
vcov | You may provide your own variance-covariance matrix for theregression coefficients if you want to calculate standard errors insome way not accommodated by the |
... | Among other things, arguments are passed to |
Details
By default, this function will print the following items to theconsole:
The sample size
The name of the outcome variable
The chi-squared test, (Pseudo-)R-squared value and AIC/BIC.
A table with regression coefficients, standard errors, z values, andp values.
There are several options available for robust. The heavylifting is done by sandwich::vcovHC(), where those are betterdescribed.Put simply, you may choose from "HC0" to "HC5". Based on therecommendation of the developers of sandwich, the default is set to"HC3". Stata's default is "HC1", so that choice may be betterif the goal is to replicate Stata's output. Any option that is understood byvcovHC() will be accepted. Cluster-robust standard errors arecomputedif cluster is set to the name of the input data's cluster variableor is a vector of clusters.
The scale and center options are performed viarefittingthe model with scale_mod() and center_mod(),respectively. Each of those in turn uses gscale() for themean-centering and scaling.
Value
If saved, users can access most of the items that are returned inthe output (and without rounding).
coeftable | The outputted table of variables and coefficients |
model | The model for which statistics are displayed. This would bemost useful in cases in which |
Much other information can be accessed as attributes.
Author(s)
Jacob Long jacob.long@sc.edu
References
King, G., & Roberts, M. E. (2015). How robust standard errors exposemethodological problems they do not fix, and what to do about it.Political Analysis, 23(2), 159–179.\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1093/pan/mpu015")}
Lumley, T., Diehr, P., Emerson, S., & Chen, L. (2002). The Importance of theNormality Assumption in Large Public Health Data Sets. Annual ReviewofPublic Health, 23, 151–169.\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1146/annurev.publhealth.23.100901.140546")}
See Also
scale_mod() can simply perform the standardization ifpreferred.
gscale() does the heavy lifting for mean-centering and scalingbehind the scenes.
Other summ: summ.lm(),summ.merMod(),summ.rq(),summ.svyglm()
Examples
## Dobson (1990) Page 93: Randomized Controlled Trial : counts <- c(18,17,15,20,10,20,25,13,12) outcome <- gl(3,1,9) treatment <- gl(3,3) print(d.AD <- data.frame(treatment, outcome, counts)) glm.D93 <- glm(counts ~ outcome + treatment, family = poisson) # Summarize with standardized coefficients summ(glm.D93, scale = TRUE)