View source: R/confint.mpitb.R
| confint.cotframe | R Documentation |
Extract the confidence intervals from the the estimated changes over time measures
## S3 method for class 'cotframe'
confint(object, parm = "coefficient", level = 0.95, ...)
object |
a "cotframe"-class object |
parm |
"coefficient". Confidence intervals are only available for AF measure point estimates. |
level |
the confidence level required. |
... |
additional argument(s) for methods. |
The confint method for "cotframe"-class objects find the confidence
intervals from the changes over time estimates data frame. This method work for
only one measure c("M0","H","A","hd","hdk"). Then, user should subset the
data frame with the estimates by the chosen measure (including other preferred categories, i.e.,
poverty cut-off, subgroup, etc.)
Confidence intervals extracted from the model cotframe object.
Ignacio Girela
coef, and summary methods, and mpitb.est function.
library(mpitbR)
data <- subset(syn_cdta)
data <- na.omit(data)
svydata <- survey::svydesign(id=~psu, weights = ~weight, strata = ~stratum, data = data)
indicators <- list(d1 = c("d_nutr","d_cm"),
d2 = c("d_satt","d_educ"),
d3 = c("d_elct","d_sani","d_wtr","d_hsg","d_ckfl","d_asst"))
# Specify mpitb project
set <- mpitb.set(svydata, indicators = indicators, name = "myname", desc = "pref. desc")
# Estimate the cross-sectional MPI and compare non-annualized changes over time
est <- mpitb.est(set, klist = c(33), measures = "M0", indmeasures = NULL,
tvar = "t", cotmeasures = "M0",
weights = "equal", over = c("area"))
coef(subset(est$lframe, measure == "M0" & t == 1))
confint(subset(est$lframe, measure == "M0" & t == 1))
summary(subset(est$lframe, measure == "M0" & t == 1))
coef(subset(est$cotframe, measure == "M0"))
confint(subset(est$cotframe, measure == "M0"))
summary(subset(est$cotframe, measure == "M0" & ctype == "abs" & ann == 0 & k == 33))
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